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    <title>NF1 regulates mesenchymal glioblastoma plasticity and aggressiveness through the AP-1
      transcription factor FOSL1</title>
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      <article itemscope="" itemtype="http://schema.org/Article" data-itemscope="root">
        <h1 itemprop="headline"
          content="NF1 regulates mesenchymal glioblastoma plasticity and aggressiveness through the AP-1 transcription factor FO…">
          NF1 regulates mesenchymal glioblastoma plasticity and aggressiveness through the AP-1
          transcription factor FOSL1</h1>
        <meta itemprop="image"
          content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=NF1%20regulates%20mesenchymal%20glioblastoma%20plasticity%20and%20aggressiveness%20through%20the%20AP-1%20transcription%20factor%20FO%E2%80%A6">
        <ol data-itemprop="authors">
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Carolina Marques"><span data-itemprop="givenNames"><span
                itemprop="givenName">Carolina</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Marques</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Thomas Unterkircher"><span
              data-itemprop="givenNames"><span itemprop="givenName">Thomas</span></span><span
              data-itemprop="familyNames"><span
                itemprop="familyName">Unterkircher</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Paula Kroon"><span data-itemprop="givenNames"><span
                itemprop="givenName">Paula</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Kroon</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Barbara Oldrini"><span data-itemprop="givenNames"><span
                itemprop="givenName">Barbara</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Oldrini</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Annalisa Izzo"><span data-itemprop="givenNames"><span
                itemprop="givenName">Annalisa</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Izzo</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Yuliia Dramaretska"><span
              data-itemprop="givenNames"><span itemprop="givenName">Yuliia</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Dramaretska</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-3">3</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Roberto Ferrarese"><span data-itemprop="givenNames"><span
                itemprop="givenName">Roberto</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Ferrarese</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Eva Kling"><span data-itemprop="givenNames"><span
                itemprop="givenName">Eva</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Kling</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Oliver Schnell"><span data-itemprop="givenNames"><span
                itemprop="givenName">Oliver</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Schnell</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Sven Nelander"><span data-itemprop="givenNames"><span
                itemprop="givenName">Sven</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Nelander</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-4">4</a><a itemprop="affiliation"
                href="#author-organization-5">5</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Erwin F Wagner"><span data-itemprop="givenNames"><span
                itemprop="givenName">Erwin</span><span itemprop="givenName">F</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Wagner</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-6">6</a><a itemprop="affiliation"
                href="#author-organization-7">7</a><a itemprop="affiliation"
                href="#author-organization-8">8</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Latifa Bakiri"><span data-itemprop="givenNames"><span
                itemprop="givenName">Latifa</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Bakiri</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-6">6</a><a itemprop="affiliation"
                href="#author-organization-7">7</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Gaetano Gargiulo"><span data-itemprop="givenNames"><span
                itemprop="givenName">Gaetano</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Gargiulo</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-3">3</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Maria Stella Carro"><span
              data-itemprop="givenNames"><span itemprop="givenName">Maria</span><span
                itemprop="givenName">Stella</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Carro</span></span><span data-itemprop="emails"><a
                itemprop="email"
                href="mailto:maria.carro@uniklinik-freiburg.de">maria.carro@uniklinik-freiburg.de</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Massimo Squatrito"><span data-itemprop="givenNames"><span
                itemprop="givenName">Massimo</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Squatrito</span></span><span data-itemprop="emails"><a
                itemprop="email" href="mailto:msquatrito@cnio.es">msquatrito@cnio.es</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
        </ol>
        <ol data-itemprop="affiliations">
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-1"
            id="author-organization-1"><span itemprop="name">Seve Ballesteros Foundation Brain Tumor
              Group, Spanish National Cancer Research Centre</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Madrid</span><span
                itemprop="addressCountry">Spain</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-2"
            id="author-organization-2"><span itemprop="name">Department of Neurosurgery, Faculty of
              Medicine Freiburg</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Freiburg</span><span
                itemprop="addressCountry">Germany</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-3"
            id="author-organization-3"><span itemprop="name">Max-Delbrück-Center for Molecular
              Medicine in the Helmholtz Association (MDC)</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Berlin</span><span
                itemprop="addressCountry">Germany</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-4"
            id="author-organization-4"><span itemprop="name">Dept of Immunology, Genetics and
              Pathology and Science for Life Laboratory, Uppsala University,
              Rudbecklaboratoriet</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Uppsala</span><span
                itemprop="addressCountry">Sweden</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-5"
            id="author-organization-5"><span itemprop="name">Science for Life Laboratory, Uppsala
              University, Rudbecklaboratoriet</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Uppsala</span><span
                itemprop="addressCountry">Sweden</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-6"
            id="author-organization-6"><span itemprop="name">Genes, Development, and Disease Group,
              Spanish National Cancer Research Centre</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Madrid</span><span
                itemprop="addressCountry">Spain</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-7"
            id="author-organization-7"><span itemprop="name">Laboratory Medicine Department, Medical
              University of Vienna</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Vienna</span><span
                itemprop="addressCountry">Austria</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-8"
            id="author-organization-8"><span itemprop="name">Dermatology Department, Medical
              University of Vienna</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Vienna</span><span
                itemprop="addressCountry">Austria</span></address></li>
        </ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher">
          <meta itemprop="name" content="Unknown"><span itemscope=""
            itemtype="http://schema.org/ImageObject" itemprop="logo">
            <meta itemprop="url"
              content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown">
          </span>
        </span><time itemprop="datePublished" datetime="2021-08-17">2021-08-17</time>
        <ul data-itemprop="genre">
          <li itemprop="genre">Research Article</li>
        </ul>
        <ul data-itemprop="about">
          <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span
              itemprop="name">Cancer Biology</span></li>
        </ul>
        <ul data-itemprop="keywords">
          <li itemprop="keywords">GBM</li>
          <li itemprop="keywords">mesenchymal</li>
          <li itemprop="keywords">NF1</li>
          <li itemprop="keywords">FOSL1</li>
          <li itemprop="keywords">FRA-1</li>
          <li itemprop="keywords">Human</li>
          <li itemprop="keywords">Mouse</li>
        </ul>
        <ul data-itemprop="identifiers">
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/publisher-id"><span
              itemprop="name">publisher-id</span><span itemprop="value"
              data-itemtype="http://schema.org/Number">64846</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi">
            <span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.64846</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/elocation-id"><span
              itemprop="name">elocation-id</span><span itemprop="value">e64846</span>
          </li>
        </ul>
        <section data-itemprop="description">
          <h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2>
          <meta itemprop="description"
            content="The molecular basis underlying glioblastoma (GBM) heterogeneity and plasticity is not fully understood. Using transcriptomic data of human patient-derived brain tumor stem cell lines (BTSCs), classified based on GBM-intrinsic signatures, we identify the AP-1 transcription factor   as a key regulator of the mesenchymal (MES) subtype. We provide a mechanistic basis to the role of the neurofibromatosis type 1 gene (NF1 ), a negative regulator of the RAS/MAPK pathway, in GBM mesenchymal transformation through the modulation of   expression. Depletion of   in  -mutant human BTSCs and  -mutant mouse neural stem cells results in loss of the mesenchymal gene signature and reduction in stem cell properties and in vivo tumorigenic potential. Our data demonstrate that   controls GBM plasticity and aggressiveness in response to   alterations.">
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The molecular basis
            underlying glioblastoma (GBM) heterogeneity and plasticity is not fully understood.
            Using transcriptomic data of human patient-derived brain tumor stem cell lines (BTSCs),
            classified based on GBM-intrinsic signatures, we identify the AP-1 transcription factor
            as a key regulator of the mesenchymal (MES) subtype. We provide a mechanistic basis to
            the role of the neurofibromatosis type 1 gene (NF1), a negative regulator of the
            RAS/MAPK pathway, in GBM mesenchymal transformation through the modulation of
            expression. Depletion of in -mutant human BTSCs and -mutant mouse neural stem cells
            results in loss of the mesenchymal gene signature and reduction in stem cell properties
            and in vivo tumorigenic potential. Our data demonstrate that controls GBM plasticity and
            aggressiveness in response to alterations.</p>
        </section>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction
        </h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Gliomas are the most common
          primary brain tumor in adults. Given the strong association of the <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">isocitrate dehydrogenase 1</em> and <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis"><span
              data-itemtype="http://schema.org/Number">2</span></em> (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">IDH1/2</em>) genes mutations with glioma
          patients survival, the 2016 WHO classification, which integrates both histological and
          molecular features, has introduced the distinction of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">IDH</em>-wildtype (IDH-wt) or <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">IDH</em>-mutant (IDH-mut) in
          diffuse gliomas <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib51"><span>51</span><span>Louis et al.</span><span>2016</span></a></cite>.
          IDH-wt glioblastoma (GBM) represents the most frequent and aggressive form of gliomas,
          characterized by high molecular and cellular inter- and intra-tumoral heterogeneity.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Large-scale sequencing
          approaches have evidenced how concurrent perturbations of cell cycle regulators, growth
          and survival pathways, mediated by RAS/MAPK and PI3K/AKT signaling, play a significant
          role in driving adult GBMs <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Brennan
                  et al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib14"><span>14</span><span>Cancer
                  Genome Atlas Research Network</span><span>2008</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Verhaak
                  et al.</span><span>2010</span></a></cite></span>. Moreover, various studies have
          classified GBM in different subtypes, using transcriptional profiling, being now the
          proneural (PN), classical (CL), and mesenchymal (MES) the most widely accepted <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib64"><span>64</span><span>Phillips
                  et al.</span><span>2006</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Verhaak
                  et al.</span><span>2010</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                  al.</span><span>2017</span></a></cite></span>. Patients with the MES subtype tend
          to have worse survival rates compared to other subtypes, both in the primary and recurrent
          tumor settings <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib87"><span>87</span><span>Wang et al.</span><span>2017</span></a></cite>. The
          most frequent genetic alterations – neurofibromatosis type 1 gene (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em>) copy number loss or mutation – and
          important regulators of the MES subtype, such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">STAT3</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CEBPB,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">TAZ</em>, have been identified <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Bhat et
                  al.</span><span>2011</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Carro et
                  al.</span><span>2010</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Verhaak
                  et al.</span><span>2010</span></a></cite></span>. Nevertheless, the mechanisms of
          regulation of MES GBMs are still not fully understood. For example, whether the MES
          transcriptional signature is controlled through tumor cell-intrinsic mechanisms or
          influenced by the tumor microenvironment (TME) is still an unsolved question. In fact, the
          critical contribution of the TME adds another layer of complexity to MES GBMs. Tumors from
          this subtype are highly infiltrated by non-neoplastic cells as compared to PN and CL
          subtypes <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib87"><span>87</span><span>Wang et al.</span><span>2017</span></a></cite>.
          Additionally, MES tumors express high levels of angiogenic markers and exhibit high levels
          of necrosis <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib21"><span>21</span><span>Cooper et al.</span><span>2012</span></a></cite>.
        </p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Even though each subtype is
          associated with specific genetic alterations, there is a considerable plasticity among
          them: different subtypes coexist in the same tumors and shifts in subtypes can occur over
          time <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib63"><span>63</span><span>Patel et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib73"><span>73</span><span>Sottoriva et
                  al.</span><span>2013</span></a></cite></span>. This plasticity may be explained by
          acquisition of new genetic and epigenetic abnormalities, stem-like reprogramming, or
          clonal variation <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib29"><span>29</span><span>Fedele et al.</span><span>2019</span></a></cite>.
          It is also not fully understood whether the distinct subtypes evolve from a common glioma
          precursor <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib62"><span>62</span><span>Ozawa et al.</span><span>2014</span></a></cite>.
          For instance, PN and CL tumors often switch phenotype to MES upon recurrence, and
          treatment also increases the mesenchymal gene signature, suggesting that MES transition,
          or epithelial to mesenchymal (EMT)-like, in GBM is associated with tumor progression and
          therapy resistance <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib10"><span>10</span><span>Bhat et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib39"><span>39</span><span>Halliday
                  et al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib64"><span>64</span><span>Phillips
                  et al.</span><span>2006</span></a></cite></span>. Yet, the frequency and relevance
          of this EMT-like phenomenon in glioma progression remains unclear. EMT has also been
          associated with stemness in other cancers <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib54"><span>54</span><span>Mani et
                  al.</span><span>2008</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib78"><span>78</span><span>Tam and
                  Weinberg</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib92"><span>92</span><span>Ye et
                  al.</span><span>2015</span></a></cite></span>. Glioma stem cells (GSCs) share
          features with normal neural stem cells (NSCs) such as self-renewal and ability to
          differentiate into distinct cellular lineages (astrocytes, oligodendrocytes, and neurons)
          but are thought to be responsible for tumor relapse, given their ability to repopulate
          tumors and their resistance to treatment <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib5"><span>5</span><span>Bao et
                  al.</span><span>2006</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Chen et
                  al.</span><span>2012</span></a></cite></span>. GSCs heterogeneity is also being
          increasingly observed <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Bhat et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Mack et
                  al.</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                  et al.</span><span>2021</span></a></cite></span>, but whether
          genotype-to-phenotype connections exist remain to be clarified.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, which encodes FRA-1, is an AP-1
          transcription factor (TF) with prognostic value in different epithelial tumors, where its
          overexpression correlates with tumor progression or worse patient survival <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib20"><span>20</span><span>Chiappetta et
                  al.</span><span>2007</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib33"><span>33</span><span>Gao et
                  al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib81"><span>81</span><span>Usui et
                  al.</span><span>2012</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib82"><span>82</span><span>Vallejo
                  et al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib89"><span>89</span><span>Wu et
                  al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib91"><span>91</span><span>Xu et
                  al.</span><span>2017</span></a></cite></span>. Moreover, the role of <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> in EMT has been
          documented in breast and colorectal cancers <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib2"><span>2</span><span>Andreolas
                  et al.</span><span>2008</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Bakiri et
                  al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib24"><span>24</span><span>Diesch
                  et al.</span><span>2014</span></a></cite></span>. In GBM, it has been shown that
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> modulates in vitro
          glioma cell malignancy <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib23"><span>23</span><span>Debinski and
                Gibo</span><span>2005</span></a></cite>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Here we report that <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> loss, by increasing
          RAS/MAPK activity, modulates <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression, which in turn plays a
          central function in the regulation of MES GBM. Using a surrogate mouse model of MES GBM
          and patient-derived MES brain tumor stem cells (BTSCs), we show that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> is responsible for sustaining
          cell growth in vitro and in vivo, and for the maintenance of stem-like properties. We
          propose that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> is an
          important regulator of GBM stemness, MES features and plasticity, controlling an EMT-like
          process with therapeutically relevant implications.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results">Results</h2>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="fosl1-is-a-key-regulator-of-the-mes-subtype"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> is a key regulator of the MES
          subtype</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To study the tumor
          cell-intrinsic signaling pathways that modulate the GBM expression subtypes, we assembled
          a collection of transcriptomic data (both expression arrays and RNA-sequencing) of 144
          samples derived from 116 independent BTSC lines (see Materials and methods for details).
          Samples were then classified according to the previously reported 50-gene glioma-intrinsic
          transcriptional subtype signatures and the single-sample gene set enrichment analysis
          (ssGSEA)-based equivalent distribution resampling classification strategy <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib87"><span>87</span><span>Wang et al.</span><span>2017</span></a></cite>.
          Principal component analysis (PCA) showed a large overlap of the transcription profile
          among BTSCs classified either as CL/PN while most of the MES appeared as separate groups
          (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1A</a> and <a
            href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file
            1</a>). This separation is consistent with early evidence in GSCs <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Bhat et
                al.</span><span>2013</span></a></cite> and holds 92% of concordance in the
          identification of a recent two transcriptional subgroups classification of single-GSCs
          defined as developmental (DEV) and injury response (INJ) <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                et al.</span><span>2021</span></a></cite>. Differential gene expression analysis
          comparing mesenchymal versus non-mesenchymal BTSCs confirmed the clear separation among
          the two groups, with only a minor fraction of cell lines showing a mixed expression
          profile (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            1B</a> and <a href="#supp2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 2</a>), further supporting
          that GSCs exist along a major transcriptional gradient between two cellular states <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Bhat et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                  et al.</span><span>2021</span></a></cite></span>.</p>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-message="FALSE" data-warning="FALSE" data-programminglanguage="r">
          <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code>library(tidyverse)
library(cowplot)

library(readxl)
library(statmod)
library(ggpubr)
library(ggrepel)
library(ggridges)
library(ggplotify)
library(reshape2)
library(survival)
library(survminer)
library(pheatmap)
library(ggraph)
library(grid)
library(devtools)
library(RColorBrewer)
library(weights)
library(hexbin)

library(Biobase)
library(GSVA)
library(limma)
library(chromVAR)
library(clusterProfiler)
library(SummarizedExperiment)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(karyoploteR)
library(org.Mm.eg.db)
library(org.Hs.eg.db)

source(&#39;Scripts/plotqPCR.R&#39;, echo=F)
source(&#39;Scripts/replotGSEA.R&#39;, echo=F)
source(&#39;Scripts/ggplotLimdil.R&#39;, echo=F)
source(&#39;Scripts/survPlot.R&#39;, echo=F)
source(&#39;Scripts/statePlot.R&#39;, echo=F)
source(&#39;Scripts/emaplot.R&#39;, echo=F)
source(&#39;Scripts/plotDeviationTsne2.R&#39;, echo=F)
source(&#39;Scripts/tracksPlot.R&#39;, echo=F)
set.seed(12345) #seed for reproducibility of the analysis
symnum.args &lt;-  list(cutpoints = c(0, 0.001, 0.01, 0.05, 1), 
                     symbols = c(&quot;***&quot;, &quot;**&quot;, &quot;*&quot;,&quot;ns&quot;)) # symbols for pvalues
black_red &lt;- c(&quot;#000000&quot;,&quot;#E41A1C&quot;)
black_red_green &lt;- c(&quot;#000000&quot;,&quot;#E41A1C&quot;,&quot;#4DAF4A&quot;)
gray_black &lt;- c(&quot;#808080&quot;,&quot;#000000&quot;)
paired_black_red &lt;- c(&quot;#000000&quot;,&quot;#666666&quot;,&quot;#E41A1C&quot;,&quot;#FB9A99&quot;)
font_size &lt;- font(&quot;xy.text&quot;, size = 8) + font(&quot;xlab&quot;, size = 10) + font(&quot;ylab&quot;, size = 10) + font(&quot;title&quot;,size = 10)

BTSCs_exprs &lt;- read.delim(&quot;Data/BTSCs_exprs.txt&quot;)
BTSCs_subtypes &lt;- read.delim(&quot;Data/BTSCs_subtypes.txt&quot;)
gsea_report_all_analysis &lt;- read.delim(&quot;Data/gsea_report_all_analysis.txt&quot;)
qPCR_data &lt;- read.delim(&quot;Data/qPCR_data_2021.txt&quot;,
                         stringsAsFactors=FALSE)
tcga_cgga_data &lt;- read.delim(&quot;Data/TCGA_CGGA_data_tableS4.txt&quot;)
figure_S1C_data &lt;- read.delim(&quot;Data/Figure_S1C.txt&quot;)
figure_S1D_data &lt;- read.delim(&quot;Data/Figure_S1D.txt&quot;)
figure_S1E_data &lt;- read.delim(&quot;Data/Figure_S1E.txt&quot;)
figure_S2B_data &lt;- read.delim(&quot;Data/Figure_S2B.txt&quot;)
figure_S2C_data &lt;- read.delim(&quot;Data/Figure_S2C.txt&quot;)
load(&quot;Data/Figure_4_data.RData&quot;)
figure_4C_data &lt;- read.delim(&quot;Data/Figure_4C.txt&quot;)
figure_4F_data &lt;- read.delim(&quot;Data/Figure_4F.txt&quot;,
                             comment.char=&quot;#&quot;, 
                             stringsAsFactors=FALSE)
gene_signatures &lt;- read.delim(&quot;Data/gene_signatures_2021.txt&quot;) %&gt;%
  .[-1,] %&gt;% # exclude 1st row
  as.list(.) %&gt;% # convert to a list
  lapply(., function(x) x[!is.na(x)]) # remove NA
figure_5A_data &lt;- read.delim(&quot;Data/Figure_5A.txt&quot;)
figure_5B_data &lt;- read.delim(&quot;Data/Figure_5B.txt&quot;) %&gt;% 
  mutate(Phase = factor(Phase, levels = c(&quot;G1&quot;,&quot;S&quot;,&quot;G2&quot;)))
figure_5C_data &lt;- read.delim(&quot;Data/Figure_5C.txt&quot;) 
figure_5D_expr &lt;- read.delim(&quot;Data/NSCs_Kras_sgFosl1_exprs.txt&quot;,
                             sep = &quot;\t&quot;, stringsAsFactors = F)
figure_5D_pdata &lt;- read.delim(&quot;Data/NSCs_Kras_sgFosl1_pdata.txt&quot;,
                              sep = &quot;\t&quot;, stringsAsFactors = F)
stem_diff_genes &lt;- read.delim(&quot;Data/stem_diff_genes.txt&quot;, sep = &quot;\t&quot;)
figure_5E_data &lt;- read.delim(&quot;Data/Figure_5E.txt&quot;)
figure_6D_data &lt;- read.delim(&quot;Data/Figure_6D.txt&quot;) %&gt;% 
  mutate(Area_scaled = Area/1e07)
figure_7C_data &lt;- read.delim(&quot;Data/Figure_7C.txt&quot;)
figure_7D_data &lt;- read.delim(&quot;Data/Figure_7D.txt&quot;)
figure_7E_data &lt;- read.delim(&quot;Data/Figure_7E.txt&quot;)
figure_7F_data &lt;- read.delim(&quot;Data/Figure_7F.txt&quot;, 
                             stringsAsFactors=FALSE)
figure_7F_annotation &lt;- read.delim(&quot;Data/Figure_7F_annotation.txt&quot;, 
                             stringsAsFactors=FALSE)
figure_7G_data &lt;- read.delim(&quot;Data/Figure_7G.txt&quot;, 
                             stringsAsFactors=FALSE)
figure_S7A_data &lt;- read.delim(&quot;Data/Figure_S7A.txt&quot;)
figure_S8B_data &lt;- read.delim(&quot;Data/Figure_S8B.txt&quot;)
figure_S8C_data &lt;- read.delim(&quot;Data/Figure_S8C.txt&quot;)
figure_S8D_data &lt;- read.delim(&quot;Data/Figure_S8D.txt&quot;)
figure_S8G_data &lt;- read.delim(&quot;Data/Figure_S8G.txt&quot;)
figure_S8I_data &lt;- read.delim(&quot;Data/Figure_S8I.txt&quot;)
figure_S8K_data &lt;- read.delim(&quot;Data/Figure_S8K.txt&quot;)

################################################################
# Data Processing:
# 1. Data were downloaded from GEO
# 2. Common probes among platform were selected
# 3. Subtypes were calculated using `runSsGSEAwithPermutation` with 1000 permutation (set.seed(12345))
# 4. Each dataset was normalized (mean = 0, sd = 1) 
# 5. Datasets were then combined in one single dataset 

print(&quot;Setup and load data&quot;)
</code></pre>
        </stencila-code-chunk>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1a"
          title="Figure 1A."><label data-itemprop="label">Figure 1A.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>BTSCs_subtypes  &lt;-  BTSCs_subtypes %&gt;%
  mutate(concordant_subtype = case_when(Richards_2021 == &quot;INJ&quot; &amp; Wang_2017 != &quot;MES&quot; ~ &quot;NO&quot;,
                                        TRUE ~ &quot;YES&quot;))
row.names(BTSCs_subtypes) &lt;- BTSCs_subtypes$accession_ID

BTSCs_eset &lt;- ExpressionSet(assayData = as.matrix(BTSCs_exprs),
                                 phenoData=as(BTSCs_subtypes, &quot;AnnotatedDataFrame&quot;))
BTSCs_eset$Group &lt;- ifelse(pData(BTSCs_eset)$Wang_2017 == &quot;MES&quot;, &quot;MES&quot;, &quot;Non-MES&quot;)

###############################################################
# PCA
pdata  &lt;-  pData(BTSCs_eset) 
edata &lt;-  exprs(BTSCs_eset)
pc  &lt;-  prcomp(t(edata))
pc_matrix &lt;- data.frame(pc$x)
percentage &lt;- round(pc$sdev^2/ sum(pc$sdev^2) * 100, 2)
percentage &lt;- paste(colnames(pc_matrix), &quot;(&quot;, 
                    paste(as.character(percentage), &quot;%&quot;, &quot;)&quot;, sep=&quot;&quot;),sep = &quot;&quot;) 

pc_matrix$Wang_2017 &lt;- pdata$Wang_2017
pc_matrix$Richards_2021 &lt;- pdata$Richards_2021
pc_matrix$Dataset &lt;- pdata$dataset
pc_matrix$Concordant &lt;- pdata$concordant_subtype

figure_1a_left &lt;- pc_matrix %&gt;% 
  ggscatter(x = &quot;PC2&quot;, y = &quot;PC1&quot;, size = 0.8,
            color = &quot;Wang_2017&quot;, shape = &quot;Dataset&quot;,
            palette = &quot;Set1&quot;, ellipse = TRUE,
            xlab = percentage[2],
            ylab = percentage[1],
            title = &quot;Wang_2017&quot;,
            legend = &quot;right&quot;) + 
  font_size + 
  scale_shape_manual(values=seq(0,7))

figure_1a_left &lt;- ggpar(figure_1a_left, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

figure_1a_right &lt;- pc_matrix %&gt;% 
  ggscatter(x = &quot;PC2&quot;, y = &quot;PC1&quot;, size = 0.8,
            color = &quot;Richards_2021&quot;, shape = &quot;Dataset&quot;,
            palette = brewer.pal(9, &quot;Set1&quot;)[4:5], ellipse = TRUE,
            xlab = percentage[2],
            ylab = percentage[1],
            title = &quot;Richards_2021&quot;,
            legend = &quot;right&quot;) + 
  font_size + rremove(&quot;ylab&quot;) +
  scale_shape_manual(values=seq(0,7))

figure_1a_right &lt;- ggpar(figure_1a_right, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

figure_1a &lt;- ggarrange(figure_1a_left,figure_1a_right, nrow = 1, common.legend = T)
figure_1a</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="principal-component-pc-analysis-of-the-brain-tumor-stem-cells-btscs-expression-dataset">
              Principal component (PC) analysis of the brain tumor stem cells (BTSCs) expression
              dataset.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1b"
          title="Figure 1B."><label data-itemprop="label">Figure 1B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>###############################################################
# DEG analysis
# limma
BTSCs_eset_sel &lt;- BTSCs_eset[, BTSCs_eset$concordant_subtype == &quot;YES&quot;]
sml &lt;- ifelse(pData(BTSCs_eset_sel)[,&quot;Group&quot;] == &quot;MES&quot;,&quot;G1&quot;,&quot;G0&quot;)
fl &lt;- as.factor(sml)
BTSCs_eset_sel$description &lt;- fl
design &lt;- model.matrix(~ description + 0 + dataset, BTSCs_eset_sel) # include dataset to correct for batch effect
fit &lt;- lmFit(BTSCs_eset_sel, design)
cont.matrix &lt;- makeContrasts(descriptionG1-descriptionG0, levels=design)
fit2 &lt;- contrasts.fit(fit, cont.matrix)
fit2 &lt;- eBayes(fit2, 0.01)

combo_eset_tT &lt;- topTable(fit2, adjust=&quot;fdr&quot;, 
                          sort.by=&quot;logFC&quot;,  
                          p.value = 0.05, 
                          number=100) %&gt;%
  rownames_to_column(var = &quot;Gene.Symbol&quot;) %&gt;%
  arrange(-logFC)

combo_eset_tT_all &lt;- topTable(fit2, adjust=&quot;fdr&quot;, 
                              sort.by=&quot;logFC&quot;, 
                              p.value = 0.05, 
                              n=Inf) %&gt;%
  rownames_to_column(var = &quot;Gene.Symbol&quot;) %&gt;%
  arrange(-logFC)

# heatmap
combo_eset_expr &lt;- exprs(BTSCs_eset_sel)[combo_eset_tT$Gene.Symbol,]
combo_eset_annotation &lt;- pData(BTSCs_eset_sel)[,c(&quot;dataset&quot;, &quot;Wang_2017&quot;, &quot;Richards_2021&quot;)]
names(combo_eset_annotation) &lt;- c(&quot;Dataset&quot;, &quot;Wang_2017&quot;, &quot;Richards_2021&quot;)
combo_colors &lt;- list(Dataset = brewer.pal(8, &quot;Set2&quot;)[1:6],
                     Wang_2017 = brewer.pal(9, &quot;Set1&quot;)[1:3],
                     Richards_2021 = brewer.pal(9, &quot;Set1&quot;)[4:5])
names(combo_colors$Dataset) &lt;- levels(factor(combo_eset_annotation$Dataset))
names(combo_colors$Wang_2017) &lt;- levels(factor(combo_eset_annotation$Wang_2017))
names(combo_colors$Richards_2021) &lt;- levels(factor(combo_eset_annotation$Richards_2021))

figure_1b &lt;- pheatmap(t(combo_eset_expr), 
                      annotation_row = combo_eset_annotation,
                      scale = &quot;column&quot;, 
                      clustering_distance_rows = &quot;correlation&quot;,
                      show_rownames = F,
                      show_colnames = T,
                      fontsize_col = 5, 
                      border_color = NA,
                      cluster_col = F, cluster_rows  = T,
                      annotation_colors = combo_colors,
                      # cutree_rows = 3,
                      color = colorRampPalette(c(&quot;steelblue&quot;,&quot;white&quot;,&quot;red&quot;))(100),
                      silent = T)
figure_1b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="heatmap-of-the-top-100-differentially-expressed-genes-between-mes-and-non-mes-btscs">
              Heatmap of the top 100 differentially expressed genes between MES and non-MES BTSCs.
            </h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1d"
          title="Figure 1D."><label data-itemprop="label">Figure 1D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#############################################
BTSCs_df &lt;- merge(pData(BTSCs_eset_sel),
                       t(exprs(BTSCs_eset_sel)), 
                       by = &quot;row.names&quot;)

sub_comparisons &lt;- list( c(&quot;MES&quot;, &quot;PN&quot;), 
                         c(&quot;MES&quot;, &quot;CL&quot;), 
                         c(&quot;CL&quot;, &quot;PN&quot;))

figure_1d_left &lt;- BTSCs_df %&gt;%
  ggboxplot(x = &quot;Wang_2017&quot;, y = &quot;FOSL1&quot;,
            color = &quot;Wang_2017&quot;, 
            palette = brewer.pal(9, &quot;Set1&quot;)[1:3],
            outlier.size = 0, outlier.stroke = 0, 
            add = &quot;jitter&quot;, add.params = list(shape = &quot;dataset&quot;),
            ylab = &quot;FOSL1 mRNA (A.U.)&quot;, 
            ylim = c(-2,4.5),
            legend = &quot;right&quot;) + font_size + 
  theme(legend.position = &quot;none&quot;) +
  rremove(&quot;xlab&quot;) +
  scale_shape_manual(values = seq(0,7)) +
  stat_compare_means(comparisons = sub_comparisons, 
                     symnum.args =  symnum.args, 
                     method = &quot;t.test&quot;)
figure_1d_left &lt;- ggpar(figure_1d_left, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

# #t-test
# BTSCs_df %&gt;%
#    compare_means(FOSL1 ~ Wang_2017,
#                  comparisons = sub_comparisons, 
#                  symnum.args =  symnum.args, 
#                  method = &quot;t.test&quot;,
#                  data = .)
# 
# #Anova with Tukey post-hoc
# BTSCs_df %&gt;%
#   aov(FOSL1 ~ Wang_2017, data = .) %&gt;%
#   TukeyHSD() %&gt;% .$Wang_2017

figure_1d_right &lt;- BTSCs_df %&gt;%
  ggboxplot(x = &quot;Richards_2021&quot;, y = &quot;FOSL1&quot;,
            color = &quot;Richards_2021&quot;, 
            palette = brewer.pal(9, &quot;Set1&quot;)[4:5],
            outlier.size = 0, outlier.stroke = 0,
            add = &quot;jitter&quot;, add.params = list(shape = &quot;dataset&quot;),
            ylab = &quot;FOSL1 mRNA (A.U.)&quot;, 
            ylim = c(-2,4.5),
            legend = &quot;right&quot;) + font_size + 
  rremove(&quot;xlab&quot;) + rremove(&quot;ylab&quot;) +
  theme(legend.position = &quot;none&quot;) +
  scale_shape_manual(values = seq(0,7)) +
  stat_compare_means(comparisons = list(c(&quot;DEV&quot;, &quot;INJ&quot;)),
                     symnum.args =  symnum.args, 
                     label.y = 4,
                     method = &quot;t.test&quot;)

figure_1d_right &lt;- ggpar(figure_1d_right, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

figure_1d &lt;- plot_grid(figure_1d_left, figure_1d_right, nrow = 1, rel_widths = c(1, 0.7))
figure_1d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-mrna-expression-in-the-btscs-dataset"><em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA expression in the BTSCs
              dataset.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">One-way ANOVA with Tukey
              multiple pairwise comparison, ***p≤0.001, ns = not significant.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1e"
          title="Figure 1E."><label data-itemprop="label">Figure 1E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mol_comparison &lt;- list(c(&quot;IDHmut-codel&quot;,&quot;IDHmut-non-codel&quot;),
                       c(&quot;IDHmut-codel&quot;,&quot;IDHwt&quot;),
                       c(&quot;IDHmut-non-codel&quot;,&quot;IDHwt&quot;))

figure_1e &lt;- tcga_cgga_data %&gt;%
  group_by(dataset) %&gt;%
  mutate(FOSL1 = scale(FOSL1),
         IDH_codel.subtype = factor(IDH_codel.subtype,
         levels = c(&quot;IDHmut-codel&quot;,&quot;IDHmut-non-codel&quot;,&quot;IDHwt&quot;))) %&gt;%
  filter(., IDH_codel.subtype!=is.na(IDH_codel.subtype)) %&gt;%
  ggboxplot(x = &quot;IDH_codel.subtype&quot;, y = &quot;FOSL1&quot;,
            outlier.size = 0, outlier.stroke = 0,
            add = &quot;jitter&quot;, 
            add.params = list(size = 0.6, alpha = 0.3),
            facet.by = &quot;dataset&quot;, ylim = c(-3,6),
            ylab = &quot;FOSL1 mRNA (A.U.)&quot;) + 
  font_size + rremove(&quot;xlab&quot;) +
  scale_x_discrete(labels=function(x){sub(&quot;\\-&quot;, &quot;\n&quot;, x)}) + 
  stat_compare_means(method = &quot;t.test&quot;, comparison = mol_comparison, 
                     label.y = c(3.25,4.25,5.5),label = &quot;p.signif&quot;,
                     symnum.args = symnum.args)

# # To get the statistic
# tcga_cgga_data %&gt;%
#   dplyr::filter(., IDH_codel.subtype!=is.na(IDH_codel.subtype)) %&gt;%
#   compare_means(FOSL1 ~ IDH_codel.subtype, method = &quot;t.test&quot;, data = .,symnum.args = symnum.args, group.by = &quot;dataset&quot;)
# 
# #Anova with Tukey post-hoc
# tcga_cgga_data %&gt;%
#   dplyr::filter(., dataset == &quot;CGGA&quot;, IDH_codel.subtype!=is.na(IDH_codel.subtype)) %&gt;%
#   mutate(FOSL1 = scale(FOSL1)) %&gt;% 
#   aov(FOSL1 ~ IDH_codel.subtype, data = .) %&gt;% 
#   TukeyHSD() %&gt;% .$IDH_codel.subtype
# 
# tcga_cgga_data %&gt;%
#   dplyr::filter(., dataset == &quot;TCGA&quot;, IDH_codel.subtype!=is.na(IDH_codel.subtype)) %&gt;%
#   mutate(FOSL1 = scale(FOSL1)) %&gt;% 
#   aov(FOSL1 ~ IDH_codel.subtype, data = .) %&gt;% 
#   TukeyHSD() %&gt;% .$IDH_codel.subtype

figure_1e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-mrna-expression-in-the-cgga-and-tcga-datasets-tumors-were-separated-according-to-their-molecular-subtype-classification-one-way-anova-with-tukey-multiple-pairwise-comparison-p≤0001">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA
              expression in the CGGA and TCGA datasets. Tumors were separated according to their
              molecular subtype classification. One-way ANOVA with Tukey multiple pairwise
              comparison, ***p≤0.001.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1f"
          title="Figure 1F."><label data-itemprop="label">Figure 1F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Panel 1f, survival
figure_1f_left &lt;- survPlot(subset(tcga_cgga_data, dataset == &quot;CGGA&quot;)) + 
  ggtitle(&quot;CGGA&quot;)
figure_1f_right &lt;- survPlot(subset(tcga_cgga_data, dataset == &quot;TCGA&quot;)) + 
  ggtitle(&quot;TCGA&quot;)

figure_1f &lt;- plot_grid(figure_1f_left$plot, figure_1f_right$plot)

figure_1f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="kaplanmeier-survival-curves-of-idh-wt-gliomas-in-the-cgga-and-tcga-datasets-stratified-based-on-fosl1-expression-see-materials-and-methods-for-details">
              Kaplan–Meier survival curves of IDH-wt gliomas in the CGGA and TCGA datasets
              stratified based on FOSL1 expression (see Materials and methods for details).</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup1a"
          title="Figure 1—figure supplement 1A."><label data-itemprop="label">Figure 1—figure
            supplement 1A.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Expression data of the top 10 TF
figure_s1_data &lt;- BTSCs_df %&gt;%
  .[,c(&quot;dataset&quot;,&quot;Group&quot;, c(&quot;FOSL1&quot;,&quot;VDR&quot;,&quot;SP100&quot;,&quot;ELF4&quot;, &quot;BNC2&quot;, 
                            &quot;OLIG2&quot;,&quot;SOX11&quot;,&quot;ASCL1&quot;,&quot;SALL2&quot;,&quot;POU3F3&quot;))] %&gt;% 
  reshape2::melt(.)

figure_s1a &lt;- figure_s1_data %&gt;%
  ggplot(aes(x = Group, y = value)) +
  geom_boxplot(outlier.size = 0, outlier.stroke = 0) +
  geom_jitter(position = position_jitter(width = .25), 
              size = 2, alpha = 0.75,
              aes(color=Group, shape = dataset)) +
  ylim(-3,5) +
  scale_color_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;)) + 
  labs(y = &quot;mRNA (A.U.)&quot;, x = &quot;Subtype&quot;, 
       color = &quot;Subtype&quot;, shape = &quot;Dataset&quot;) +
  scale_shape_manual(values=seq(0,7)) + theme_bw() + 
  stat_compare_means(symnum.args = symnum.args, label.y = 4, label.x = 1.4,
                     method = &quot;t.test&quot;, label = &quot;p.signif&quot;) + 
facet_wrap(~variable,nrow = 2,ncol = 5) 

figure_s1a</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-the-top-10-scoring-tfs-in-the-mra-of-the-brain-tumor-stem-cells-btscs-dataset-comparing-mesenchymal-mes-versus-non-mes-students-t-test-p-001">
              mRNA expression of the top 10 scoring TFs in the MRA of the brain tumor stem cells
              (BTSCs) dataset, comparing mesenchymal (MES) versus non-MES. Student’s t test, ***p
              .001.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup1c"
          title="Figure 1—figure supplement 1C."><label data-itemprop="label">Figure 1—figure
            supplement 1C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># figure s1c left  Richards GSCs ssGSEA score subtypes
row.names(figure_S1C_data) &lt;- figure_S1C_data$Sample
subtypes_annotation &lt;- figure_S1C_data[,c(&quot;Richards_2021&quot;,&quot;Wang_2017&quot;)]
subtypes_gsea_colors &lt;- list(Wang_2017 = brewer.pal(8, &quot;Set1&quot;)[1:3],
                    Richards_2021 = brewer.pal(9, &quot;Set1&quot;)[4:5])
names(subtypes_gsea_colors$Wang_2017) &lt;- levels(factor(subtypes_annotation$Wang_2017))
names(subtypes_gsea_colors$Richards_2021) &lt;- levels(factor(subtypes_annotation$Richards_2021))

figure_s1c_left  &lt;- pheatmap(t(figure_S1C_data[2:6]), 
         annotation_col = subtypes_annotation,
         scale = &quot;row&quot;, 
         clustering_distance_cols = &quot;correlation&quot;,
         show_colnames = F, fontsize_col = 5, 
         border_color = NA,
         cluster_col = T, cluster_rows  = F,
         annotation_colors = subtypes_gsea_colors,
         color = colorRampPalette(c(&quot;steelblue&quot;,&quot;white&quot;,&quot;red&quot;))(100),
         silent = T)

# figure s1c right FOSL1 expression Richards GSCs
figure_s1c_right_a &lt;- figure_S1C_data %&gt;%
  subset(., Wang_2017 != is.na(Wang_2017)) %&gt;% 
  ggboxplot(x = &quot;Wang_2017&quot;, y = &quot;FOSL1&quot;,
            color = &quot;Wang_2017&quot;, 
            palette = brewer.pal(9, &quot;Set1&quot;)[1:3],
            outlier.size = 0, outlier.stroke = 0, 
            add = &quot;jitter&quot;, ylim = c(5,18),
            ylab = &quot;FOSL1 mRNA (A.U.)&quot;, 
            legend = &quot;right&quot;) + 
  theme(legend.position = &quot;none&quot;) + font_size + ggtitle(&quot;&quot;) +
  rremove(&quot;xlab&quot;) +
  stat_compare_means(comparisons = list( c(&quot;MES&quot;, &quot;PN&quot;), 
                                         c(&quot;MES&quot;, &quot;CL&quot;), 
                                         c(&quot;CL&quot;, &quot;PN&quot;)), 
                     symnum.args =  symnum.args, 
                     method = &quot;t.test&quot;)
figure_s1c_right_a &lt;- ggpar(figure_s1c_right_a, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;)) 

figure_s1c_right_b &lt;- figure_S1C_data %&gt;%
  subset(., Wang_2017 != is.na(Wang_2017)) %&gt;% 
  ggboxplot(x = &quot;Richards_2021&quot;, y = &quot;FOSL1&quot;,
         color = &quot;Richards_2021&quot;, 
         palette = brewer.pal(9, &quot;Set1&quot;)[4:5],
         outlier.size = 0, outlier.stroke = 0, 
         add = &quot;jitter&quot;,ylim = c(5,18),
         ylab = &quot;FOSL1 mRNA (A.U.)&quot;, 
         legend = &quot;right&quot;) + font_size + ggtitle(&quot;&quot;) +
  rremove(&quot;xlab&quot;) + rremove(&quot;ylab&quot;) +
  theme(legend.position = &quot;none&quot;) +
  stat_compare_means( comparisons = list(c(&quot;DEV&quot;, &quot;INJ&quot;)), 
                     symnum.args =  symnum.args, 
                     method = &quot;t.test&quot;)
figure_s1c_right_b &lt;- ggpar(figure_s1c_right_b, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

figure_s1c &lt;- plot_grid(figure_s1c_left$gtable,
                        figure_s1c_right_a, 
                        figure_s1c_right_b, 
                        nrow = 1, rel_widths = c(3,1, 0.7))
figure_s1c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-mrna-expression-in-the-richards-glioma-stem-cells-gscs-bulk-rna-seq-dataset-n--72-right-panel">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA
              expression in the Richards glioma stem cells (GSCs) bulk RNA-seq dataset (n = 72;
              right panel).</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Single-sample gene set
              enrichment analys (ssGSEA) was performed to identify the GSCs subtypes (left panel).
              Tumors were separated according to their expression subtype classification. One-way
              ANOVA with Tukey multiple pairwise comparison, ***p≤0.001, **p≤0.01, ns = not
              significant.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup1d"
          title="Figure 1—figure supplement 1D."><label data-itemprop="label">Figure 1—figure
            supplement 1D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># figure s1d ssGSEA score Richards GSCs scRNAseq
figure_S1D_data_long &lt;-  pivot_longer(data = figure_S1D_data[,-1],
                                 cols = Richards_DEV_2021:Wang_CL_2017)

figure_s1d &lt;- figure_S1D_data_long %&gt;%
  mutate(name = factor(name, levels=c(&quot;Wang_CL_2017&quot;,&quot;Wang_PN_2017&quot;,&quot;Wang_MES_2017&quot;,
                                      &quot;Richards_DEV_2021&quot;, &quot;Richards_INJ_2021&quot;))) %&gt;%
  ggplot(aes(x = X, y = Y)) + 
  geom_point(aes(color = value), alpha = 0.75, size = 0.5) + 
  labs(x=&quot;PC1&quot;,y=&quot;PC2&quot;) + 
  scale_colour_gradient2(low=&quot;blue&quot;, midpoint = 0.35, high=&quot;red&quot;) + 
  theme_bw() +
  facet_wrap(~name, nrow = 1)

figure_s1d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="ssgsea-scores-of-the-wang_2017-and-richards_2021-transcriptional-subtypes-performed-on-the-scrna-seq-gscs-data-65655-cells-from-28-samples-from-narrative-bib67">
              ssGSEA scores of the Wang_2017 and Richards_2021 transcriptional subtypes performed on
              the scRNA-seq GSCs data (65,655 cells from 28 samples) from <cite itemscope=""
                itemtype="http://schema.stenci.la/Cite" data-citationmode="Narrative"><a
                  href="#bib67"><span>67</span><span>Richards et
                    al.</span><span>2021</span></a></cite>.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup1e"
          title="Figure 1—figure supplement 1E."><label data-itemprop="label">Figure 1—figure
            supplement 1E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># figure s1e Top 10 TFs Richards GSCs scRNAseq
figure_S1E_data_long &lt;- pivot_longer(data = figure_S1E_data[,-1],cols = FOSL1:POU3F3) %&gt;% 
  mutate(name = factor(name, levels = c(&quot;FOSL1&quot;,&quot;VDR&quot;,&quot;SP100&quot;,&quot;ELF4&quot;, &quot;BNC2&quot;, &quot;OLIG2&quot;,&quot;SOX11&quot;,&quot;ASCL1&quot;,&quot;SALL2&quot;,&quot;POU3F3&quot;)),
         group = case_when(name %in% c(&quot;FOSL1&quot;,&quot;VDR&quot;,&quot;SP100&quot;,&quot;ELF4&quot;, &quot;BNC2&quot;) ~ &#39;MES&#39;,
                           name %in% c(&quot;OLIG2&quot;,&quot;SOX11&quot;,&quot;ASCL1&quot;,&quot;SALL2&quot;,&quot;POU3F3&quot;) ~ &#39;Non-MES&#39;))

# two-dimensional state plot
figure_s1e &lt;- figure_S1E_data_long %&gt;%
  ggplot(aes(x = X, y = Y)) +
  geom_point(aes(color = value), alpha = 0.75, size = 0.5) + 
  labs(x=&quot;PC1&quot;,y=&quot;PC2&quot;) + 
  theme_bw() +
  facet_wrap(.~name, nrow = 2) + 
  scale_color_gradient2(low = &quot;white&quot;, mid = &quot;#FFFFCC&quot;, high = &quot;red&quot;) 

figure_s1e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-the-top-10-scoring-tfs-on-the-scrna-seq-gscs-data-from-narrative-bib67">
              mRNA expression of the top 10 scoring TFs on the scRNA-seq GSCs data from <cite
                itemscope="" itemtype="http://schema.stenci.la/Cite"
                data-citationmode="Narrative"><a href="#bib67"><span>67</span><span>Richards et
                    al.</span><span>2021</span></a></cite>.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup2a"
          title="Figure 1—figure supplement 2A."><label data-itemprop="label">Figure 1—figure
            supplement 2A.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># figure s2a FOSL1 expression CGGA and TCGA stratified by subtypes
sub_comparisons &lt;- list( c(&quot;MES&quot;, &quot;PN&quot;), 
                         c(&quot;MES&quot;, &quot;CL&quot;), 
                         c(&quot;CL&quot;, &quot;PN&quot;))

figure_s2a_left &lt;- tcga_cgga_data %&gt;%
  .[!is.na(.$Wang_2017),] %&gt;% 
  group_by(dataset) %&gt;%
  mutate(FOSL1 = scale(FOSL1),
         Wang_2017 = factor(Wang_2017, levels= c(&quot;CL&quot;,&quot;MES&quot;,&quot;PN&quot;))) %&gt;%
  filter(., IDH_codel.subtype == &quot;IDHwt&quot;) %&gt;%
  ggboxplot(x = &quot;Wang_2017&quot;, y = &quot;FOSL1&quot;,
            color = &quot;Wang_2017&quot;, 
            palette = brewer.pal(9, &quot;Set1&quot;)[1:3],
            outlier.size = 0, outlier.stroke = 0, 
            add = &quot;jitter&quot;,
            add.params = list(size = 0.8, alpha = 0.5),
            facet.by = &quot;dataset&quot;, ylim = c(-3,4),
            ylab = &quot;FOSL1 mRNA (A.U.)&quot;) + 
  font_size + rremove(&quot;xlab&quot;) +
  stat_compare_means(comparisons = sub_comparisons, 
                     symnum.args =  symnum.args, 
                     method = &quot;t.test&quot;)

figure_s2a_left &lt;- ggpar(figure_s2a_left, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

# #Anova with Tukey post-hoc
# tcga_cgga_data %&gt;%
#   .[!is.na(.$Wang_2017),] %&gt;% 
#   group_by(dataset) %&gt;%
#   mutate(FOSL1 = scale(FOSL1),
#          Wang_2017 = factor(Wang_2017, levels= c(&quot;CL&quot;,&quot;MES&quot;,&quot;PN&quot;))) %&gt;%
#   filter(., IDH_codel.subtype == &quot;IDHwt&quot;) %&gt;% 
#   aov(FOSL1 ~ Wang_2017, data = .) %&gt;%
#   TukeyHSD() %&gt;% .$Wang_2017

figure_s2a_right &lt;- tcga_cgga_data %&gt;%
  .[!is.na(.$Richards_2021),] %&gt;% 
  group_by(dataset) %&gt;%
  mutate(FOSL1 = scale(FOSL1),
         Richards_2021 = factor(Richards_2021, levels= c(&quot;DEV&quot;,&quot;INJ&quot;))) %&gt;%
  filter(., IDH_codel.subtype == &quot;IDHwt&quot;) %&gt;%
  ggboxplot(x = &quot;Richards_2021&quot;, y = &quot;FOSL1&quot;,
            color = &quot;Richards_2021&quot;, 
            palette = brewer.pal(9, &quot;Set1&quot;)[4:5],
            outlier.size = 0, outlier.stroke = 0, 
            add = &quot;jitter&quot;,
            add.params = list(size = 0.8, alpha = 0.5),
            facet.by = &quot;dataset&quot;, ylim = c(-3,4),
            ylab = &quot;FOSL1 mRNA (A.U.)&quot;) + 
  font_size + rremove(&quot;xlab&quot;) +
  stat_compare_means(comparisons = list(c(&quot;DEV&quot;, &quot;INJ&quot;)),
                     symnum.args =  symnum.args, 
                     method = &quot;t.test&quot;)

figure_s2a_right &lt;- ggpar(figure_s2a_right, font.legend = c(8,&quot;plain&quot;,&quot;black&quot;))

figure_s2a &lt;- plot_grid(figure_s2a_left, figure_s2a_right)
figure_s2a</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-mrna-expression-in-idh-wt-tumors-of-the-cgga-and-tcga-datasets"><em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA expression
              in IDH-wt tumors of the CGGA and TCGA datasets.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Tumors were separated
              according to their expression subtype classification. One-way ANOVA with Tukey
              multiple pairwise comparison, ***p≤0.001, **p≤0.01, ns = not significant.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup2b"
          title="Figure 1—figure supplement 2B."><label data-itemprop="label">Figure 1—figure
            supplement 2B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># figure s2b ssGSEA score Neftel tumor scRNAseq
figure_S2B_data_long &lt;- pivot_longer(data = figure_S2B_data[,-1],
                                    cols = Richards_DEV_2021:Wang_CL_2017)
figure_s2b &lt;- figure_S2B_data_long %&gt;%
  mutate(name = factor(name, levels=c(&quot;Wang_CL_2017&quot;,&quot;Wang_PN_2017&quot;,&quot;Wang_MES_2017&quot;,
                                      &quot;Richards_DEV_2021&quot;,&quot;Richards_INJ_2021&quot;))) %&gt;%
  ggplot(aes(x = X, y = Y)) + 
  geom_point(aes(color = value), alpha = 0.75, size = 0.5) + 
  ylim(-2.75,2.75) + xlim(-2.75,2.75)+
  geom_hline(yintercept = 0, size = 0.25) + 
  geom_vline(xintercept = 0, size = 0.25) +
  labs(x=&quot;&quot;,y=&quot;&quot;) + 
  scale_colour_gradient2(low=&quot;blue&quot;, midpoint = 0.5, high=&quot;red&quot;) + 
  theme_bw() +
  theme(axis.ticks.length=unit(0, &quot;cm&quot;), axis.text.x=element_blank(), 
        axis.text.y=element_blank()) +
  facet_wrap(.~name, nrow =1)

figure_s2b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="single-sample-gene-set-enrichment-analysis-ssgsea-scores-of-the-wang_2017-and-richards_2021-transcriptional-subtypes-performed-on-the-scrna-seq-data-6863-cells-from-narrative-bib60">
              Single-sample gene set enrichment analysis (ssGSEA) scores of the Wang_2017 and
              Richards_2021 transcriptional subtypes performed on the scRNA-seq data (6863 cells)
              from <cite itemscope="" itemtype="http://schema.stenci.la/Cite"
                data-citationmode="Narrative"><a href="#bib60"><span>60</span><span>Neftel et
                    al.</span><span>2019</span></a></cite>.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1sup2c"
          title="Figure 1—figure supplement 2C."><label data-itemprop="label">Figure 1—figure
            supplement 2C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># figure s2c Top 10 TFs Neftel tumor scRNAseq, hexbins on scaled expression
figure_S2C_data_long &lt;- pivot_longer(data = figure_S2C_data[,-1],cols = FOSL1:POU3F3) 

figure_s2c &lt;- figure_S2C_data_long %&gt;%
  mutate(name = factor(name, levels= c(&quot;FOSL1&quot;,&quot;VDR&quot;,&quot;SP100&quot;,&quot;ELF4&quot;, &quot;BNC2&quot;, &quot;OLIG2&quot;,&quot;SOX11&quot;,&quot;ASCL1&quot;,&quot;SALL2&quot;,&quot;POU3F3&quot;))) %&gt;% 
  ggplot(aes(x = X, y = Y, z = value)) +
  stat_summary_hex(bins=100, fun = &quot;median&quot;) + 
  ylim(-2.75,2.75) + xlim(-2.75,2.75)+
  geom_hline(yintercept = 0, size = 0.25) + 
  geom_vline(xintercept = 0, size = 0.25) +
  labs(x=&quot;&quot;,y=&quot;&quot;) + 
  scale_fill_gradientn(colours = c(brewer.pal(n = 8, name = &quot;YlOrRd&quot;))) + 
  theme_bw() +
  theme(axis.ticks.length=unit(-0.1, &quot;cm&quot;), axis.text.x=element_blank(), 
        axis.text.y=element_blank()) +
  facet_wrap(.~name,nrow = 2,ncol = 5)

figure_s2c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="normalized-mrna-expression-of-the-top-10-scoring-tfs-on-the-scrna-seq-tumor-data-from-narrative-bib60">
              Normalized mRNA expression of the top 10 scoring TFs on the scRNA-seq tumor data from
              <cite itemscope="" itemtype="http://schema.stenci.la/Cite"
                data-citationmode="Narrative"><a href="#bib60"><span>60</span><span>Neftel et
                    al.</span><span>2019</span></a></cite>.</h3>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To reveal the signaling
          pathways underlying the differences between MES and non-MES BTSCs, we then applied a
          network-based approach based on the Algorithm for the Reconstruction of Accurate Cellular
          Networks (ARACNe) <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib6"><span>6</span><span>Basso et
                  al.</span><span>2005</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Carro et
                  al.</span><span>2010</span></a></cite></span>, which identifies a list of TFs with
          their predicted targets, defined as regulons. The regulon for each TF is constituted by
          all the genes whose expression data exhibit significant mutual information with that of a
          given TF and are thus expected to be regulated by that TF <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib17"><span>17</span><span>Castro
                  et al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Fletcher
                  et al.</span><span>2013</span></a></cite></span>. Enrichment of a relevant gene
          signature in each of the regulons can point to the TFs acting as master regulators (MRs)
          of the response or phenotype <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Carro et
                  al.</span><span>2010</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Fletcher
                  et al.</span><span>2013</span></a></cite></span>. Master regulator analysis (MRA)
          identified a series of TFs, among which <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">VDR</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">OLIG2, SP100</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ELF4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SOX11</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BNC2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ASCL1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SALL2,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">POU3F3</em> were the top 10 most
          statistically significant (Benjamini–Hochberg p&lt;0.0001) (<a href="#fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1C</a> and <a href="#supp3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 3</a>). <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">VDR</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SP100</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ELF4,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BNC2</em> were significantly upregulated in
          the MES BTSCs, while <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">OLIG2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SOX11</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ASCL1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SALL2,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">POU3F3</em> were upregulated in the non-MES
          BTSCs (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1D</a>
          and <a href="#fig1s1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1—figure
            supplement 1A</a>). Gene set enrichment analysis (GSEA) evidenced how the regulons for
          the top 10 TFs are enriched for genes that are differentially expressed among the two
          classes (MES and non-MES) with <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> having the highest enrichment
          score (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1C</a>,
          <a href="#fig1s1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1—figure
            supplement 1B</a>, and <a href="#supp3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 3</a>). Lastly, an analysis
          of an independent BTSCs dataset <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                et al.</span><span>2021</span></a></cite> evidenced that the differential expression
          of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and the other
          TFs was maintained both at bulk (<a href="#fig1s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1—figure supplement 1C</a>) and at a
          single-cell level (<a href="#fig1s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1—figure supplement 1D, E</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We then analyzed the CGGA and
          TCGA pan-glioma datasets <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib18"><span>18</span><span>Ceccarelli et
                  al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib96"><span>96</span><span>Zhao et
                  al.</span><span>2017</span></a></cite></span> and observed that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression is elevated in the
          IDH-wt glioma molecular subgroup (<a href="#fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1E</a> and <a href="#supp4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 4</a>) with a significant
          upregulation in the MES subtype in bulk tumors, and it is also enriched in MES-like cells
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib60"><span>60</span><span>Neftel et al.</span><span>2019</span></a></cite> at
          the single-cell level (<a href="#fig1s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1—figure supplement 2A–C</a>).
          Importantly, high expression levels were associated with worse prognosis in IDH-wt tumors
          (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1F</a>), thus
          suggesting that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>
          could represent not only a key regulator of the glioma-intrinsic MES signature, but also a
          putative key player in MES glioma pathogenesis.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="nf1-modulates-the-mes-signature-and-fosl1-expression"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> modulates the MES signature and <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> alterations and activation of the
          RAS/MAPK signaling have been previously associated with the MES GBM subtype <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Brennan
                  et al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Verhaak
                  et al.</span><span>2010</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib86"><span>86</span><span>Wang et
                  al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                  al.</span><span>2017</span></a></cite></span>. However, whether <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> plays a broader functional role in
          the regulation of the MES gene signature (MGS) in IDH-wt gliomas still remains to be
          established.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We initially grouped, according
          to the previously described GBM subtype-specific gene signatures, a subset of IDH-wt
          glioma samples of the TCGA dataset for which RNA-seq data were available (n = 229) (see
          Materials and methods for details). By analyzing the frequency of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> alterations (either point mutations
          or biallelic gene loss), we confirmed a significant enrichment of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> alterations in MES versus non-MES
          tumors (Fisher’s exact test p=0.0106) (<a href="#fig2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2A, B</a>). Importantly, we detected
          higher level of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>
          mRNA in the cohort of IDH-wt gliomas with <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> alterations (Student’s t test
          p=0.018) (<a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            2C</a>), as well as a significant negative correlation between <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> mRNA levels (Pearson R = −0.44,
          p=7.8e-12) (<a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            2D</a> and <a href="#supp4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 4</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2a"
          title="Figure 2A."><label data-itemprop="label">Figure 2A.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># select only TCGA IDH-wt samples
tcga_idwt_samples &lt;- as.character(
  filter(tcga_cgga_data, dataset == &quot;TCGA&quot; &amp; 
           IDH_codel.subtype == &quot;IDHwt&quot;)$Sample)

# ssGSEA values
tumor_gsva_annotation &lt;- tcga_cgga_data %&gt;% 
  filter(dataset == &quot;TCGA&quot; &amp; IDH_codel.subtype == &quot;IDHwt&quot;) %&gt;% 
  .[ ,c(&quot;Sample&quot;, &quot;Wang_2017&quot;,&quot;Richards_2021&quot;,&quot;NF1_status&quot;)] %&gt;% 
  mutate(NF1_status = factor(NF1_status),
         Wang_2017 = factor(Wang_2017),
         Richards_2021 = factor(Richards_2021)) %&gt;% 
  arrange(Richards_2021, Wang_2017, NF1_status) %&gt;% 
  column_to_rownames(&quot;Sample&quot;)

tumor_gsva_colors &lt;- list(Wang_2017 = brewer.pal(9, &quot;Set1&quot;)[1:3],
                          Richards_2021 = brewer.pal(9, &quot;Set1&quot;)[4:5],
                          NF1_status = c(&quot;#FC8D62&quot;,&quot;#66C2A5&quot;))
names(tumor_gsva_colors$Wang_2017) &lt;- levels(tumor_gsva_annotation$Wang_2017)
names(tumor_gsva_colors$Richards_2021) &lt;- levels(tumor_gsva_annotation$Richards_2021)
names(tumor_gsva_colors$NF1_status) &lt;- levels(tumor_gsva_annotation$NF1_status)

tumor_data &lt;- tcga_cgga_data %&gt;% 
  filter(dataset == &quot;TCGA&quot; &amp; IDH_codel.subtype == &quot;IDHwt&quot;) %&gt;% 
  .[,c(&quot;Sample&quot;,&quot;Developmental_ssGSEA&quot;,&quot;Injury_ssGSEA&quot;,
       &quot;Classical_ssGSEA&quot;,&quot;Mesenchymal_ssGSEA&quot;,&quot;Proneural_ssGSEA&quot;)] %&gt;% 
  column_to_rownames(&quot;Sample&quot;)

tumor_data &lt;- tumor_data[row.names(tumor_gsva_annotation),]
figure_2a &lt;- pheatmap(t(tumor_data),
                      scale = &quot;row&quot;,
                      cluster_cols = F,
                      cluster_rows = F,
                      annotation_col = tumor_gsva_annotation,
                      show_colnames = F, fontsize_row = 8,
                      border_color = NA,
                      annotation_colors = tumor_gsva_colors,
                      clustering_distance_cols = &quot;correlation&quot;,
                      color = colorRampPalette(c(&quot;steelblue&quot;,&quot;white&quot;,&quot;red&quot;))(100),
                      silent = T)

figure_2a</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="heatmap-of-the-subtypes-single-sample-gene-set-enrichment-analysis-ssgsea-scores-and-nf1-genetic-alterations-of-the-idh-wt-gliomas-in-the-tcga-dataset">
              Heatmap of the subtypes single-sample gene set enrichment analysis (ssGSEA) scores and
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> genetic
              alterations of the IDH-wt gliomas in the TCGA dataset.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2b"
          title="Figure 2B."><label data-itemprop="label">Figure 2B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>tumor_NF1_status &lt;- tumor_gsva_annotation %&gt;%
  mutate(concordant_subtype = case_when(Richards_2021 == &quot;INJ&quot; &amp; Wang_2017 != &quot;MES&quot; ~ &quot;NO&quot;,
                                        Wang_2017 == &quot;MES&quot; &amp; Richards_2021 != &quot;INJ&quot; ~ &quot;NO&quot;,
                                        TRUE ~ &quot;YES&quot;)) %&gt;% 
  filter(concordant_subtype == &quot;YES&quot;) %&gt;% 
  mutate(MES_status = ifelse(Richards_2021 == &quot;INJ&quot;,&quot;MES&quot;,&quot;Non-MES&quot;)) 

fisher &lt;- fisher.test(table(tumor_NF1_status$NF1_status,
                            tumor_NF1_status$MES_status),
                      alternative=&quot;two.sided&quot;)

figure_2b_data &lt;- table(tumor_NF1_status$MES_status, tumor_NF1_status$NF1_status) %&gt;% 
  reshape2::melt(., varnames = c(&quot;Subtype_group&quot;,&quot;NF1_status&quot;), id.vars = &quot;Subtype_group&quot;)

figure_2b &lt;- figure_2b_data %&gt;% 
  group_by(Subtype_group) %&gt;%
  mutate(perc = round(value/sum(value),2)*100) %&gt;% 
  ggplot(aes(x = Subtype_group, y = perc, 
             fill = NF1_status, cumulative = TRUE)) +
  geom_col(width = 0.75) +  ylab(&quot;percentage&quot;) +
  theme_pubr(legend = &quot;none&quot;) + font_size +
  scale_fill_manual(values = c(&quot;#FC8D62&quot;,&quot;#66C2A5&quot;)) +
  geom_text(aes(label = paste0(perc,&quot;%&quot;)),
            position = position_stack(vjust = 0.5), 
            color = &quot;white&quot;, size = 3.5) +
  ggtitle(sprintf(&quot;Fisher&#39;s exact test:\n P value = %s&quot;, 
                  signif(fisher$p.value,digits = 4)))

figure_2b &lt;- ggpar(figure_2b, font.main = c(8,&quot;black&quot;,&quot;plain&quot;))

figure_2b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="frequency-of-nf1-alterations-in-mes-and-non-mes-idh-wt-gliomas">Frequency of <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> alterations in MES
              and non-MES IDH-wt gliomas.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2c"
          title="Figure 2C."><label data-itemprop="label">Figure 2C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>idhwt_nf1_data &lt;- tcga_cgga_data %&gt;%  
  filter(.,dataset == &quot;TCGA&quot; &amp; 
           IDH_codel.subtype == &quot;IDHwt&quot; &amp; 
           NF1_status!=is.na(NF1_status)) %&gt;%
  mutate(NF1_status = factor(NF1_status, levels = c(&quot;NotAltered&quot;,&quot;Altered&quot;)))

# Panel 2c, NF1_status
figure_2c &lt;- idhwt_nf1_data %&gt;%
  ggboxplot(x = &quot;NF1_status&quot;, y = &quot;FOSL1&quot;,
            add = &quot;jitter&quot;,
            outlier.size = 0, outlier.stroke = 0,
            add.params = list(color = &quot;NF1_status&quot;,
                              size = 0.8, alpha = 0.5),
            palette = &quot;Set2&quot;, legend = &quot;none&quot;, title =&quot;&quot;,
            ylab = &quot;FOSL1 mRNA (log2)&quot;,
            xlab = &quot;NF1 status&quot;) + font_size +
  stat_compare_means(method = &quot;t.test&quot;, label = &quot;p.format&quot;) 
 
# idhwt_nf1_data %&gt;%
#   compare_means(FOSL1 ~ NF1_status, method = &quot;t.test&quot;, 
#                 data = .,symnum.args = symnum.args)

figure_2c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-mrna-expression-in-idh-wt-gliomas-stratified-according-to-nf1-alterations">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA
              expression in IDH-wt gliomas, stratified according to NF1 alterations.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2d"
          title="Figure 2D."><label data-itemprop="label">Figure 2D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Panel 2d, NF1mRNA correlation
figure_2d &lt;- idhwt_nf1_data %&gt;%
  ggscatter(x = &quot;NF1&quot;, y = &quot;FOSL1&quot;, color = &quot;NF1_status&quot;,
            palette = &quot;Set2&quot;, alpha = 0.5, size = 0.8,
            add = &quot;reg.line&quot;, # Add regression line
            add.params = list(color = &quot;black&quot;, fill = &quot;gray&quot;),
            conf.int = TRUE, # Add confidence interval
            cor.coef = TRUE,
            legend = &quot;none&quot;, title = &quot;&quot;,
            cor.coeff.args = list(method = &quot;pearson&quot;, 
                                  label.x = 8, label.y = 3.5, 
                                  label.sep = &quot;\n&quot;),
            ylab = &quot;FOSL1 mRNA (log2)&quot;,
            xlab = &quot;NF1 mRNA (log2)&quot;) + font_size

# idhwt_nf1_data %&gt;%  do(tidy(cor.test(.$NF1, .$FOSL1)))

figure_2d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="correlation-of-fosl1-and-nf1-mrna-expression-in-idh-wt-gliomas-pearson-correlation-r--−0044-p78e-12">
              Correlation of FOSL1 and NF1 mRNA expression in IDH-wt gliomas. Pearson correlation, R
              = −0.044, p=7.8e-12.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2e"
          title="Figure 2E."><label data-itemprop="label">Figure 2E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_2e &lt;- qPCR_data %&gt;% 
  plotqPCR(panel = &quot;2E&quot;,
           normalizer = &quot;18s&quot;,
           ref_group = &quot;Ctrl&quot;,
           levels =c(&quot;Ctrl&quot;,&quot;GRD&quot;),
           pvalue = T,
           facet_by = &quot;Cells&quot;,
           legend = &quot;none&quot;, palette = black_red,
           ylim = c(0,1.25),
           ylab = &quot;FOSL1 mRNA (A.U.)&quot;)

figure_2e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-fosl1-expression-upon-nf1-grd-overexpression-in-btsc-232-and-btsc-233-cells">
              qRT-PCR analysis of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression upon NF1-GRD
              overexpression in BTSC 232 and BTSC 233 cells.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2g"
          title="Figure 2G."><label data-itemprop="label">Figure 2G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_2g &lt;- gsea_report_all_analysis %&gt;%
  filter(Analysis == &quot;BTSC233_NF1_GRD&quot;) %&gt;%
  ggplot(aes(x = reorder(NAME,NES), y = NES)) +
  geom_col(aes(fill = TYPE, color =`FDR.q.val`&lt;0.05), width = 0.75) +
  coord_flip() + xlab(&quot;&quot;) +
  theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
  scale_fill_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;)) +
  scale_color_manual(values = c(&quot;gray&quot;,&quot;black&quot;))

figure_2g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="gene-set-enrichment-analysis-gsea-results-of-btsc-233-cells-transduced-with-nf1-grd-expressing-lentivirus-versus-ctrl-nes-normalized-enrichment-score">
              Gene set enrichment analysis (GSEA) results of BTSC 233 cells transduced with NF1-GRD
              expressing lentivirus versus Ctrl. NES: normalized enrichment score.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2h"
          title="Figure 2H."><label data-itemprop="label">Figure 2H.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_2h &lt;- qPCR_data %&gt;%
  plot_normqPCR(panel = &quot;2H&quot;, 
                normalizer = &quot;18s&quot;,
                ref_group = &quot;shCtrl&quot;,
                levels = c(&quot;shCtrl&quot;,&quot;shNF1_1&quot;,&quot;shNF1_4&quot;,&quot;shNF1_5&quot;),
                pvalue = T, 
                facet_by = &quot;Cells&quot;,
                legend = &quot;none&quot;, 
                palette = &quot;Set1&quot;, 
                ylim = c(0,2.5),
                ylab = &quot;FOSL1 mRNA  (A.U.)&quot;)

figure_2h</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-fosl1-expression-upon-nf1-knockdown-in-btsc-3021-and-btsc-3047-cells">
              qRT-PCR analysis of FOSL1 expression upon <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> knockdown in BTSC 3021 and BTSC
              3047 cells.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2i"
          title="Figure 2I."><label data-itemprop="label">Figure 2I.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_2i &lt;- gsea_report_all_analysis %&gt;%
  filter(Analysis == &quot;BTSC3021_shNF1&quot;) %&gt;%
  ggplot(aes(x = reorder(NAME,NES), y = NES)) +
  geom_col(aes(fill = TYPE, color =`FDR.q.val`&lt;0.05), width = 0.75) +
  coord_flip() + xlab(&quot;&quot;) +
  theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
  scale_fill_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;)) +
  scale_color_manual(values = c(&quot;gray&quot;,&quot;black&quot;))

figure_2i</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-fosl1-expression-upon-nf1-knockdown-in-btsc-3021-and-btsc-3047-cells-1">
              qRT-PCR analysis of FOSL1 expression upon <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> knockdown in BTSC 3021 and BTSC
              3047 cells.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2j"
          title="Figure 2J."><label data-itemprop="label">Figure 2J.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_2j_left_a &lt;- qPCR_data %&gt;% 
  filter(Cells == &quot;BTSC 232&quot; &amp; Gene %in% c(&quot;18s&quot;,&quot;FOSL1&quot;)) %&gt;% 
  plotqPCR(panel = &quot;2J&quot;,
           normalizer = &quot;18s&quot;, 
           ref_group = &quot;Ctrl+Ctrl&quot;,
           levels = c(&quot;Ctrl+Ctrl&quot;, &quot;Ctrl+FOSL1&quot;,
                      &quot;GRD+Ctrl&quot;,&quot;GRD+FOSL1&quot;),
           pvalue = F, legend = &quot;none&quot;,
           palette = paired_black_red, 
           ylab = NULL)

figure_2j_right_a &lt;- qPCR_data %&gt;% 
  filter(Cells == &quot;BTSC 232&quot; &amp; Gene != &quot;FOSL1&quot;) %&gt;% 
  plotqPCR(panel = &quot;2J&quot;,
           normalizer = &quot;18s&quot;, 
           ref_group = &quot;Ctrl+Ctrl&quot;,
           levels = c(&quot;Ctrl+Ctrl&quot;, &quot;Ctrl+FOSL1&quot;,
                      &quot;GRD+Ctrl&quot;,&quot;GRD+FOSL1&quot;),
           pvalue = F, legend = &quot;right&quot;,
           palette = paired_black_red, ylab = NULL) +
  rremove(&quot;ylab&quot;)

figure_2j_left_b &lt;- qPCR_data %&gt;%
  filter(Cells == &quot;BTSC 233&quot; &amp; Gene %in% c(&quot;18s&quot;,&quot;FOSL1&quot;)) %&gt;%
  plotqPCR(panel = &quot;2J&quot;,
           normalizer = &quot;18s&quot;,
           ref_group = &quot;Ctrl+Ctrl&quot;,
           levels = c(&quot;Ctrl+Ctrl&quot;, &quot;Ctrl+FOSL1&quot;,
                      &quot;GRD+Ctrl&quot;,&quot;GRD+FOSL1&quot;),
           pvalue = F, legend = &quot;none&quot;,
           palette = paired_black_red, ylab = NULL)

figure_2j_right_b &lt;- qPCR_data %&gt;%
  filter(Cells == &quot;BTSC 233&quot; &amp; Gene != &quot;FOSL1&quot;) %&gt;%
  plotqPCR(panel = &quot;2J&quot;,
           normalizer = &quot;18s&quot;,
           ref_group = &quot;Ctrl+Ctrl&quot;,
           levels = c(&quot;Ctrl+Ctrl&quot;, &quot;Ctrl+FOSL1&quot;,
                      &quot;GRD+Ctrl&quot;,&quot;GRD+FOSL1&quot;),
           pvalue = F, legend = &quot;right&quot;,
           palette = paired_black_red, ylab = NULL) +
  rremove(&quot;ylab&quot;)

figure_2j &lt;- ggarrange(figure_2j_left_a, figure_2j_right_a,
                       figure_2j_left_b, figure_2j_right_b,
                       widths = c(0.35,1,0.35,1), nrow = 1,
                      legend = &#39;right&#39;,common.legend = T)
  
figure_2j</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-mes-genes-expression-upon-nf1-grd-and-fosl1-co-expression-in-btsc-232-and-btsc-233-cells">
              qRT-PCR analysis of MES genes expression upon NF1-GRD and FOSL1 co-expression in BTSC
              232 and BTSC 233 cells.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">qRT-PCR data in (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>), (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>), and (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">J</strong>) are presented as
              mean ± SD (n = 3, technical replicates), normalized to 18S rRNA expression; Student’s
              t test, *p≤0.05, **p≤0.01, ***p≤0.001, ns = not significant.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup1d"
          title="Figure 2—figure supplement 1D."><label data-itemprop="label">Figure 2—figure
            supplement 1D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S3d &lt;- as.ggplot(~ replotGSEA(path = &quot;Data/GSEA/Freiburg_BTSC233_NF1_GRD.Gsea.1557494919416&quot;, 
                                     gene.set = &quot;BILD_HRAS_ONCOGENIC_SIGNATURE&quot;, class.name = &quot;NF1-GRD positively correlated&quot;))

figure_S3d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="gene-set-enrichment-analysis-gsea-of-ras-induced-oncogenic-signature-in-btsc-233-cells-transduced-with-nf1-grd-expressing-lentivirus-versus-ctrl">
              Gene set enrichment analysis (GSEA) of Ras-induced oncogenic signature in BTSC 233
              cells transduced with NF1-GRD expressing lentivirus versus Ctrl.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s1"
          title="Figure 2—figure supplement 1 (static version)."><label data-itemprop="label">Figure
            2—figure supplement 1 (static version).</label><img
            src="index.html.media/fig2-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="nf1-grd-expression-leads-to-downregulation-of-ras-signaling">NF1-GRD expression
              leads to downregulation of RAS signaling.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot analysis of ERK
              and pERK expression in BTSC 233 cells transduced with NF1-GRD expressing lentivirus
              and stimulated with 10% FBS or 100 ng/ml EGF. α-Tubulin is included as loading
              control. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>)
              Densitometric analysis of western blot in (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) Western blot analysis of
              active Ras pull-down assay in BTSC 233 expressing NF1-GRD or control in the presence
              or absence of growth factors. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Gene set enrichment
              analysis (GSEA) of Ras-induced oncogenic signature in BTSC 233 cells transduced with
              NF1-GRD expressing lentivirus versus Ctrl. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">E</strong>) EdU staining of BTSC 233 cell
              line upon NF1-GRD overexpression, counterstained with DAPI. Quantification of the
              fluorescence intensity of EdU staining is shown in the <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">right panel</em>. Ctrl, n = 4; NF1-GRD,
              n = 4. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>)
              Micrographs showing representative BTSC 233 Ctrl and NF1-GRD grown for 2 weeks after
              cell transduction.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup2b"
          title="Figure 2—figure supplement 2B."><label data-itemprop="label">Figure 2—figure
            supplement 2B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S4b &lt;- as.ggplot(~ replotGSEA(path = &quot;Data/GSEA/Freiburg_BTSC233_NF1_GRD.Gsea.1612877445786&quot;, 
                                     gene.set = &quot;FOSL1_REGULON&quot;, 
                                     class.name = &quot;NF1-GRD positively correlated&quot;))

figure_S4b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="gene-set-enrichment-analysis-gsea-of-fosl1-targets-signature-in-btsc-233-cells-transduced-with-nf1-grd-or-ctrl-vector">
              Gene set enrichment analysis (GSEA) of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> targets signature in BTSC 233
              cells transduced with NF1-GRD or Ctrl vector.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup2c"
          title="Figure 2—figure supplement 2C."><label data-itemprop="label">Figure 2—figure
            supplement 2C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S4c_left &lt;-  qPCR_data %&gt;% 
  filter(Cells == &quot;BTSC 233&quot;) %&gt;%
  plot_normqPCR(panel = &quot;S4C&quot;, 
                ref_group = &quot;Ctrl&quot;, 
                pvalue = T, 
                facet_by = &quot;Cells&quot;,
                palette = black_red, 
                ylim = c(0,1.75), 
                pvalues_y = 1.6)

figure_S4c_right &lt;- qPCR_data %&gt;%
  filter(Cells == &quot;BTSC 232&quot;) %&gt;%
  plot_normqPCR(panel = &quot;S4C&quot;, 
                ref_group = &quot;Ctrl&quot;, 
                pvalue = T, 
                facet_by = &quot;Cells&quot;, 
                ylab = FALSE,
                palette = black_red, 
                ylim = c(0,1.75), 
                remove_y = T, 
                pvalues_y = 1.6)

figure_S4c &lt;- ggarrange(figure_S4c_left,
                        figure_S4c_right, 
                        ncol = 2, 
                        common.legend = T, 
                        widths = c(2.25,2),
                        legend = &quot;right&quot;)

figure_S4c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-mesenchymal-fosl1-targets-itga3-itga5-plau-serpine1-and-tnc-in-btsc-233-and-232-cells-transduced-with-nf1-grd-expressing-lentivirus">
              qRT-PCR analysis of mesenchymal FOSL1 targets (<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">ITGA3</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">ITGA5</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">PLAU</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">SERPINE1</em>, and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">TNC</em>) in BTSC 233 and 232 cells
              transduced with NF1-GRD expressing lentivirus.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data are normalized to 18S
              rRNA expression.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup2f"
          title="Figure 2—figure supplement 2F."><label data-itemprop="label">Figure 2—figure
            supplement 2F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S4f &lt;- as.ggplot(~ replotGSEA(path = &quot;Data/GSEA/Freiburg_BTSC3021_NF1_shNF1.Gsea.1612877131389&quot;, 
                                     gene.set = &quot;FOSL1_REGULON&quot;, 
                                     class.name = &quot;shNF1 positively correlated&quot;))
figure_S4f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="gsea-of-fosl1-targets-signature-in-btsc-3021-cells-transduced-with-shnf1-or-shctrl">
              GSEA of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>
              targets signature in BTSC 3021 cells transduced with sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> or shCtrl.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup2f"
          title="Figure 2—figure supplement 2G."><label data-itemprop="label">Figure 2—figure
            supplement 2G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S4g_left &lt;-  qPCR_data %&gt;% 
filter(Cells == &quot;BTSC 3021&quot;) %&gt;%
  plot_normqPCR(panel = &quot;S4G&quot;, 
                ref_group = &quot;shCtrl&quot;, 
                pvalue = F, 
                facet_by = &quot;Cells&quot;,
                legend = &quot;right&quot;, 
                palette = brewer.pal(4,&quot;Set1&quot;)[c(1,2,4)], 
                ylim = c(0,3), 
                pvalues_y = 2.75)

figure_S4g_right &lt;-  qPCR_data %&gt;% 
filter(Cells == &quot;BTSC 3047&quot;) %&gt;%
  plot_normqPCR(panel = &quot;S4G&quot;, 
                ref_group = &quot;shCtrl&quot;, 
                pvalue = F, 
                facet_by = &quot;Cells&quot;,
                ylab = FALSE, 
                legend = &quot;right&quot;,
                palette = brewer.pal(4,&quot;Set1&quot;)[c(1,3,4)], 
                ylim = c(0,3), 
                remove_y = T, 
                pvalues_y = 2.75)

figure_S4g &lt;- ggarrange(figure_S4g_left,
                        figure_S4g_right, 
                        ncol = 2, 
                        common.legend = T, 
                        widths = c(2.25,2), 
                        legend = &quot;right&quot;)

figure_S4g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-mesenchymal-fosl1-targets-btsc-3021-and-3047-cells-transduced-with-shnf1-expressing-lentiviruses">
              qRT-PCR analysis of mesenchymal <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> targets BTSC 3021 and 3047
              cells transduced with shNF1 expressing lentiviruses.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data are normalized to 18S
              rRNA expression.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup2h"
          title="Figure 2—figure supplement 2H."><label data-itemprop="label">Figure 2—figure
            supplement 2H.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S4h &lt;- qPCR_data %&gt;%
  plot_normqPCR(panel = &quot;S4H&quot;, 
                ref_group = &quot;Ctrl&quot;, 
                pvalue = T, 
                facet_by = &quot;Cells&quot;,
                legend = &quot;right&quot;, 
                palette = black_red, 
                ylim = c(0,1.75), 
                pvalues_y = 1.6)

figure_S4h</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-mes-genes-master-regulators-expression-bhlhb2-cebpb-fosl2-runx1-stat3-and-taz-upon-nf1-grd-overexpression-in-btsc-233">
              qRT-PCR analysis of MES genes master regulators expression (<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">BHLHB2</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">CEBPB</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL2</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">RUNX1</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">STAT3</em>, and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">TAZ</em>) upon NF1-GRD overexpression in
              BTSC 233.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup2h"
          title="Figure 2—figure supplement 2I."><label data-itemprop="label">Figure 2—figure
            supplement 2I.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S4i &lt;- qPCR_data %&gt;%
  plot_normqPCR(panel = &quot;S4I&quot;, 
                ref_group = &quot;shCtrl&quot;, 
                pvalue = T, 
                facet_by = &quot;Cells&quot;,
                legend = &quot;right&quot;, 
                palette = &quot;Set1&quot;, 
                pvalues_y = 5.5, 
                ylim = c(0,6))

figure_S4i</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-mes-genes-master-regulators-expression-bhlhb2-cebpb-fosl2-runx1-stat3-and-taz-upon-nf1-knockdown-in-btsc-3021-cells">
              qRT-PCR analysis of MES genes master regulators expression (<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">BHLHB2</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">CEBPB</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL2</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">RUNX1</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">STAT3</em>, and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">TAZ</em>) upon <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> knockdown in BTSC 3021 cells.
            </h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s2"
          title="Figure 2—figure supplement 2 (static version)."><label data-itemprop="label">Figure
            2—figure supplement 2 (static version).</label><img
            src="index.html.media/fig2-figsupp2.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="modulation-of-nf1-expression-regulates-fosl1-targets-and-mesenchymal-genes">
              Modulation of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>
              expression regulates <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> targets and mesenchymal
              genes.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot analysis of
              FLAG-NF1-GRD expression in mesenchymal (MES) cells (BTSC 233 and 232). (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) Gene set
              enrichment analysis (GSEA) of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> targets signature in BTSC 233
              cells transduced with NF1-GRD or Ctrl vector. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) qRT-PCR analysis of
              mesenchymal <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>
              targets (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ITGA3</em>, <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">ITGA5</em>, <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">PLAU</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">SERPINE1,</em> and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">TNC</em>) in BTSC 233 and 232 cells
              transduced with NF1-GRD expressing lentivirus. Data are normalized to 18S rRNA
              expression. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Osteogenesis differentiation
              assay of BTSC 233 transduced as indicated above. Alzarin Red staining indicates
              osteogenesis differentiation. Scale bar represents 200 µm. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">E</strong>) Western blot analysis of NF1
              expression upon <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>
              knockdown in non-MES cells (BTSC 3021 and 3047). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>) GSEA of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> targets signature in BTSC
              3021 cells transduced with sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> or shCtrl. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>) qRT-PCR analysis
              of mesenchymal <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>
              targets BTSC 3021 and 3047 cells transduced with sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> expressing lentiviruses. Data
              are normalized to 18S rRNA expression. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">H</strong>, <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong>) qRT-PCR analysis of MES genes
              master regulators expression (<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">BHLHB2</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">CEBPB</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL2</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">RUNX1</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">STAT3,</em> and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">TAZ</em>) upon NF1-GRD overexpression in
              BTSC 233 (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>)
              or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> knockdown in
              BTSC 3021 cells (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong>). Data are normalized to GAPDH
              or 18S rRNA expression, respectively. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>) Western blot analysis of
              FLAG-NF1-GRD and FLAG-FRA-1 expression in BTSC 233 cells. qRT-PCR data in (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>), (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>), (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>), and (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">I</strong>) are presented as
              mean ± SD (n = 3, technical replicates); Student’s t test, ns = not-significant,
              *p≤0.05, **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup3b"
          title="Figure 2—figure supplement 3B."><label data-itemprop="label">Figure 2—figure
            supplement 3B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S5b_left &lt;- qPCR_data %&gt;%
  filter(Cells == &quot;BTSC 3021&quot;) %&gt;% 
  plotqPCR(panel = &quot;S5B&quot;, 
           normalizer = &quot;18s&quot;, 
           ref_group = &quot;shCtrl_DMSO&quot;, 
           levels = c(&quot;shCtrl_DMSO&quot;,&quot;shCtrl_GDC-0623&quot;,
                      &quot;shNF1_5_DMSO&quot;,&quot;shNF1_5_GDC-0623&quot;),
           pvalue = F,
           legend = &quot;right&quot;, 
           palette = &quot;Set1&quot;,
           ylim = c(0,5),
           title = &quot;BTSC 3021&quot;)

figure_S5b_right &lt;- qPCR_data %&gt;%
  filter(Cells == &quot;BTSC 3047&quot;) %&gt;% 
  plotqPCR(panel = &quot;S5B&quot;, 
           normalizer = &quot;18s&quot;, 
           ref_group = &quot;shCtrl_DMSO&quot;, 
           levels = c(&quot;shCtrl_DMSO&quot;,&quot;shCtrl_GDC-0623&quot;,
                      &quot;shNF1_5_DMSO&quot;,&quot;shNF1_5_GDC-0623&quot;),
           pvalue = F,
           legend = &quot;right&quot;, 
           palette = &quot;Set1&quot;,
           ylim = c(0,3),
           title = &quot;BTSC 3047&quot;)

figure_S5b &lt;- ggarrange(figure_S5b_left,
                        figure_S5b_right,
                        common.legend = T, 
                        ncol = 1)

figure_S5b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="qrt-pcr-analysis-of-fosl1-and-the-mes-genes-itga3-and-serpine1-in-samples-treated-as-in-a">
              qRT-PCR analysis of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and the MES genes <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">ITGA3</em> and <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">SERPINE1</em> in samples
              treated as in (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">A</em>).
            </h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data are presented as mean
              ± SD (n = 3), normalized to 18S rRNA expression; Student’s t test of DMSO vs. GDC-0623
              (either shCtrl or shNF1_5), **p≤0.01, ***p≤0.001, ns = not significant.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup3f"
          title="Figure 2—figure supplement 3F."><label data-itemprop="label">Figure 2—figure
            supplement 3F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S5f &lt;- gsea_report_all_analysis %&gt;%
  filter(Analysis == &quot;NSCs_shNF1_sgFosl1&quot;) %&gt;%
  ggplot(aes(x = reorder(NAME,NES), y = NES)) +
  geom_col(aes(fill = TYPE, color =`FDR.q.val`&lt; 0.1), width = 0.75) +
  coord_flip() + xlab(&quot;&quot;) +
  theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
  scale_fill_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;)) +
  scale_color_manual(values = c(&quot;gray&quot;,&quot;black&quot;))

figure_S5f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="gene-set-enrichment-analysis-gsea-results-of-p53-null-shnf1-nscs-sgfosl1_1-and-sgfosl1_3-versus-sgctrl-neural-stem-cells-nscs-n--3-for-each-group">
              Gene set enrichment analysis (GSEA) results of p53-null sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Nf1</em> NSCs sgFosl1_1 and sgFosl1_3
              versus sgCtrl neural stem cells (NSCs); n = 3 for each group.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2sup3g"
          title="Figure 2—figure supplement 3G."><label data-itemprop="label">Figure 2—figure
            supplement 3G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S5g_left &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;S5G_left&quot;, normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;sgCtrl&quot;, 
           levels = c(&quot;sgCtrl&quot;,&quot;sgFosl1_1&quot;,&quot;sgFosl1_3&quot;),
           pvalue = F,
           ylim = c(0,1.5),
           legend = &quot;right&quot;, 
           palette = black_red_green,
           title = &quot;MES genes&quot;)

figure_S5g_right &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;S5G_right&quot;, normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;sgCtrl&quot;, 
           levels = c(&quot;sgCtrl&quot;,&quot;sgFosl1_1&quot;,&quot;sgFosl1_3&quot;),
           pvalue = F,
           ylim = c(0,4),
           legend = &quot;right&quot;, 
           palette = black_red_green,
           title = &quot;PN genes&quot;)

figure_S5g &lt;- ggarrange(figure_S5g_left,
                        figure_S5g_right,
                        common.legend = T, 
                        legend = &quot;right&quot;,
                        nrow = 1,widths = c(1.5,1))

figure_S5g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-mes-left-panel-and-pn-genes-right-panel-in-sgctrl-and-sgfosl1-in-p53-null-shnf1-nscs">
              mRNA expression of MES (left panel) and PN genes (right panel) in sgCtrl and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> in p53-null
              sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1</em> NSCs.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two experiments are presented as mean ± SD (n = 3, technical replicates),
              normalized to <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Gapdh</em>
              expression.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s3"
          title="Figure 2—figure supplement 3 (static version)."><label data-itemprop="label">Figure
            2—figure supplement 3 (static version).</label><img
            src="index.html.media/fig2-figsupp3.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mapk-inhibition-reverts-the-effects-of-nf1-silencing-on-fosl1-and-mesenchymal-genes-expression">
              MAPK inhibition reverts the effects of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> silencing on <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and mesenchymal genes
              expression.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot analysis of
              non-mesenchymal (non-MES) cells (BTSC 3021 and 3047) transduced with shCtrl or
              shNF1<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">5 and treated with
                the MEK inhibitor GDC-0623 (1 μM for 16 hr); α-tubulin was used as loading control.
                (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) qRT-PCR
                analysis of _FOSL1</em> and the MES genes <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">ITGA3</em> and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">SERPINE1</em> in samples treated as in
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>). Data are
              presented as mean ± SD (n = 3), normalized to 18S rRNA expression; Student’s t test of
              DMSO vs. GDC-0623 (either shCtrl or shNF1<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">5), **p≤0.01, ***p≤0.001, ns =
                not significant. (<strong itemscope=""
                  itemtype="http://schema.stenci.la/Strong">C</strong>) Western blot analysis using
                the specified antibodies of p53-null NSCs, parental and infected with sh_Nf1</em> or
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> and treated for 16
              hr with the MAPK inhibitors trametinib (200 nM) or U0126 (10 μM); vinculin was used as
              loading control. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) qRT-PCR analysis of <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and the MES
              genes (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Plau</em>, <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Plaur</em>, <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Timp1,</em> and <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cd44</em>), in samples
              treated as in (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>). Data are presented as mean ±
              SD (n = 3, technical replicates), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Actin</em> expression. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) FRA-1 expression
              detected by western blot in p53-null sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Nf1</em> NSCs upon transduction with
              sgRNAs targeting <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>; vinculin was used as loading
              control. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>)
              Gene set enrichment analysis (GSEA) results of p53-null sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Nf1</em> NSCs sgFosl1<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">1 and sgFosl1_3 versus sgCtrl neural
                stem cells (NSCs); n = 3 for each group. (<strong itemscope=""
                  itemtype="http://schema.stenci.la/Strong">G</strong>) mRNA expression of MES
                (_left panel</em>) and PN genes (<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">right panel</em>) in sgCtrl and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> in p53-null
              sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1</em> NSCs. Data
              from a representative of two experiments are presented as mean ± SD (n = 3, technical
              replicates), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To test whether a NF1-MAPK
          signaling is involved in the regulation of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and the MES subtype, we
          manipulated <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>
          expression in patient-derived GBM tumorspheres of either MES or non-MES subtypes. To
          recapitulate the activity of the full-length NF1 protein, we transduced the cells with the
          NF1 GTPase-activating domain (NF1-GRD), spanning the whole predicted Ras GTPase-activating
          (GAP) domain <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib58"><span>58</span><span>McCormick</span><span>1990</span></a></cite>.
          NF1-GRD expression in the MES cell line BTSC 233 led to (i) inhibition of RAS activity as
          confirmed by analysis of pERK expression upon EGF or serum stimulation (<a href="#fig2s1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement
            1A, B</a>) as well as by RAS pull-down assay (<a href="#fig2s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 1C</a>); (ii) strong
          reduction of a RAS-induced oncogenic signature expression (NES = −1.7; FDR q-value &lt;
          0.001) (<a href="#fig2s1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            2—figure supplement 1D</a>); and (iii) diminished cell proliferation (<a href="#fig2s1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement
            1E, F</a>). Consistent with the negative correlation of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> mRNA levels in IDH-wt gliomas (<a
            href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2D</a>),
          NF1-GRD overexpression in two independent MES GBM lines (BTSC 233 and BTSC 232) was
          associated with a significative downregulation of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>-regulated genes (<a href="#fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2E</a> and <a href="#fig2s2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement
            2A–C</a>). Concurrently, we also observed a significant decrease of two
          well-characterized mesenchymal features, namely CHI3L1 expression (<a href="#fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2F</a>) as well as the
          ability of MES GBM cells to differentiate into osteocytes, a feature shared with
          mesenchymal stem cells (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib66"><span>66</span><span>Ricci-Vitiani et
                al.</span><span>2008</span></a></cite>; <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib79"><span>79</span><span>Tso et
                al.</span><span>2006</span></a></cite>; <a href="#fig2s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2D</a>). Moreover,
          NF1-GRD expression led to a significant reduction of the <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> regulon and the MGSs, with a
          concurrent increase of the <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">OLIG2</em> regulon and the non-MES gene
          signatures (non-MGSs) (<a href="#fig2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2G</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Conversely, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> knockdown with three independent
          shRNAs (shNF1<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">1, shNF1_4, and
            shNF1_5) in two non-MES lines (BTSC 3021 and BTSC 3047) (<a href="#fig2s2" itemscope=""
              itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2E</a>) led to an
            upregulation of _FOSL1</em> (<a href="#fig2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2H</a>), with a concomitant significant
          increase in its targets (<a href="#fig2s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2F, G</a>), an
          upregulation of the MGSs, and downregulation of the N-MGSs (<a href="#fig2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2I</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The observed NF1-mediated gene
          expression changes might be potentially driven by an effect on <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> or other previously described
          mesenchymal TFs (such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BHLHB2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CEBPB</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">RUNX1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">STAT3</em>, and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">TAZ</em>;) <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Bhat et
                  al.</span><span>2011</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Carro et
                  al.</span><span>2010</span></a></cite></span>. Interestingly, only <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1,</em> and to some extent
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">CEBPB,</em> was consistently
          downregulated upon NF1-GRD expression (<a href="#fig2s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2H</a>) and
          upregulated following <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> knockdown (<a href="#fig2s2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2I</a>).
          To then test whether <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> was playing a direct role in the
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>-mediated regulation
          of mesenchymal genes expression, we overexpressed <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> in the MES GBM lines transduced
          with the NF1-GRD (<a href="#fig2s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2J</a>). qRT-PCR
          analysis showed that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> was able to rescue the
          NF1-GRD-mediated downregulation of mesenchymal genes, such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ITGA3, ITGA5, SERPINE1,</em> and <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">TNC</em> (<a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3J</a>). Lastly, exposure of
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> silenced cells to
          the MEK inhibitor GDC-0623, led to a reduction of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> upregulation, both at the protein
          and the mRNA levels, as well as to a downregulation of the mesenchymal genes <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">ITGA3</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SERPINE1</em> (<a href="#fig2s3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement
            3A, B</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3b"
          title="Figure 3B."><label data-itemprop="label">Figure 3B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_3b &lt;- qPCR_data %&gt;% 
  plotqPCR(panel = &quot;3B&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Parental&quot;,
           levels = c(&quot;Parental&quot;,&quot;shNf1&quot;,&quot;sgNf1&quot;,&quot;KrasG12V&quot;), 
           pvalue = T,
           legend = &quot;top&quot;, 
           palette = &quot;Set1&quot;, 
           ylim = c(0,8))

figure_3b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-fosl1-and-mes-genes-plau-plaur-timp1-and-cd44-in-infected-p53-null-nscs-compared-to-parental-cells-not-infected">
              mRNA expression of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and MES genes (<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Plau</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Plaur</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Timp1</em>, and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Cd44</em>) in infected p53-null NSCs
              compared to parental cells (not infected).</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two experiments are presented as mean ± SD (n = 3), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression. Student’s t test,
              relative to parental cells: ns = not significant, *p≤0.05, **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3b"
          title="Figure 3D."><label data-itemprop="label">Figure 3D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_3d &lt;- gsea_report_all_analysis %&gt;%
  filter(Analysis == &quot;NSCs_Kras_sgFosl1&quot;) %&gt;%
  ggplot(aes(x = reorder(NAME,NES), y = NES)) +
  geom_col(aes(fill = TYPE, color =`FDR.q.val`&lt;0.05), width = 0.75) +
  coord_flip() + xlab(&quot;&quot;) +
  theme_cowplot() + theme(axis.text.y=element_text(size = 7)) +
  scale_fill_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;)) +
  scale_color_manual(values = c(&quot;gray&quot;,&quot;black&quot;))

figure_3d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="gene-set-enrichment-analysis-gsea-results-of-p53-null-krasg12v-sgfosl1_1-versus-sgctrl-nscs">
              Gene set enrichment analysis (GSEA) results of p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">KrasG<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">12V</sup></em> sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1_</em>1 versus sgCtrl NSCs.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3e"
          title="Figure 3E."><label data-itemprop="label">Figure 3E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_3e &lt;-  qPCR_data %&gt;%
  plotqPCR(panel = &quot;3E&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;sgCtrl&quot;,
           levels = c(&quot;sgCtrl&quot;,&quot;sgFosl1&quot;), 
           pvalue = T,
           legend = &quot;none&quot;, 
           palette = black_red, 
           ylim = c(0,1.5),
           title = &quot;MES genes&quot;)

figure_3e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-mes-in-sgctrl-and-sgfosl1_1-p53-null-krasg12v-nscs">mRNA
              expression of MES in sgCtrl and sgFosl1_1 p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">KrasG<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">12V</sup></em> NSCs.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two experiments are presented as mean ± SD (n = 3, technical replicates),
              normalized to <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Gapdh</em>
              expression. Student’s t test, relative to sgCtrl: *p≤0.05; **p≤0.01; ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3f"
          title="Figure 3F."><label data-itemprop="label">Figure 3F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_3f &lt;- qPCR_data %&gt;% 
  filter(Gene != &quot;Dll3&quot;) %&gt;% 
  plotqPCR(panel = &quot;3F&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;sgCtrl&quot;,
           levels = c(&quot;sgCtrl&quot;,&quot;sgFosl1&quot;),
           pvalue = T,
           legend = &quot;right&quot;, 
           palette = black_red, ylim = c(0,10),
           title = &quot;PN genes&quot;)

figure_3f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-pn-genes-in-sgctrl-and-sgfosl1_1-p53-null-krasg12v-nscs">mRNA
              expression of PN genes in sgCtrl and sgFosl1_1 p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">KrasG<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">12V</sup></em> NSCs.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two experiments are presented as mean ± SD (n = 3, technical replicates),
              normalized to <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Gapdh</em>
              expression. Student’s t test, relative to sgCtrl: *p≤0.05; **p≤0.01; ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3"
          title="Figure 3 (static version)."><label data-itemprop="label">Figure 3 (static
            version).</label><img src="index.html.media/fig3.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-is-induced-by-mapk-kinase-activation-and-is-required-for-mesenchymal-mes-gene-expression">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> is induced by
              MAPK kinase activation and is required for mesenchymal (MES) gene expression.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot analysis using
              the specified antibodies of p53-null neural stem cells (NSCs), parental and infected
              with sg<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1</em>, sh<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1,</em> and <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em>; vinculin was used
              as loading control. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) mRNA expression of <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and MES genes
              (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Plau, Plaur, Timp1,</em>
              and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cd44</em>) in
              infected p53-null NSCs compared to parental cells (not infected). Data from a
              representative of two experiments are presented as mean ± SD (n = 3), normalized to
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression.
              Student’s t test, relative to parental cells: ns = not significant, *p≤0.05, **p≤0.01,
              ***p≤0.001. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) FRA-1 expression detected by
              western blot in p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs upon
              transduction with sgRNAs targeting <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>, after selection with 1 µg/mL
              puromycin; vinculin was used as loading control. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Gene set enrichment
              analysis (GSEA) results of p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 versus sgCtrl NSCs.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong><strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>) mRNA expression
              of MES (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) and
              PN genes (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>)
              in sgCtrl and sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs. Data from a
              representative of two experiments are presented as mean ± SD (n = 3, technical
              replicates), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression. Student’s t test,
              relative to sgCtrl: *p≤0.05; **p≤0.01; ***p≤0.001.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Overall these evidences
          implicate the NF1-MAPK signaling in the regulation of the MGSs through the modulation of
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="fosl1-deletion-induces-a-shift-from-a-mes-to-a-pn-gene-signature"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> deletion induces a shift from a
          MES to a PN gene signature</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To further explore the
          NF1-MAPK-FOSL1 axis in MES GBM, we used a combination of the RCAS-Tva system with the
          CRISPR/Cas9 technology, recently developed in our laboratory <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib61"><span>61</span><span>Oldrini et
                al.</span><span>2018</span></a></cite>, to induce <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Nf1</em> loss or <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras</em> mutation. Mouse NSCs from <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">hGFAP-Tva; hGFAP-Cre; Trp53<sup
              itemscope="" itemtype="http://schema.stenci.la/Superscript">lox</sup>;
            ROSA26-LSL-Cas9</em> pups were isolated and infected with viruses produced by DF1
          packaging cells transduced with RCAS vectors targeting the expression of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Nf1</em> through shRNA and sgRNA (sh<em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1</em> and sg<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Nf1</em>) or overexpressing a mutant form of
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras</em> (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em>). Loss of NF1
          expression was confirmed by western blot, and FRA-1 was upregulated in the two models of
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1</em> loss compared to
          parental cells and further upregulated in cells infected with <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> (<a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3A</a>). Consistent with
          activation of the Ras signaling, as a result of both <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Nf1</em> loss and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras</em> mutation, the MEK/ERK pathway was
          more active in infected cells compared to parental cells (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3A</a>). Higher levels of activation of
          the MEK/ERK pathway were more pronounced in the <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras</em> mutant cells and were associated
          with a stronger induction of mesenchymal genes such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Plau</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Plaur</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Timp1,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cd44</em> (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3B</a>). Moreover, the upregulation of
          both FRA-1 and the mesenchymal genes was blocked by exposing sh<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Nf1</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras</em> mutant cells to the MAPK
          inhibitors trametinib or U0126 (<a href="#fig2s3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 3C, D</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Taking advantage of the Cas9
          expression in the generated p53-null NSCs models, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> was knocked out through sgRNAs.
          Efficient downregulation of FRA-1 was achieved with two different sgRNAs (<a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3C</a> and <a href="#fig2s3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 3E</a>).
          Cells transduced with sg<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 and sg<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_3 were then subjected to further
          studies.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">As suggested by the data
          presented here on the human BTSCs datasets and cell lines, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> appears to be a key regulator of
          the MES subtype. Consistently, RNA-seq analysis followed by GSEA of p53-null <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> sg<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 versus sgCtrl revealed a
          significant loss of the MGSs and increase in the N-MGSs (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3D</a>). These findings were validated by
          qRT-PCR with a significant decrease in expression of a panel of MES genes (<em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Plau</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Itga7</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Timp1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Plaur</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fn1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cyr61</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Actn1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">S100a4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Vim</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cd44</em>) (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3E</a>) and increased expression of PN
          genes (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Olig2</em>, <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Ncam1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Bcan</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Lgr5</em>) in the <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> knock-out (KO) <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs (<a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3F</a>). A similar trend was
          observed in the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO
          sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nf1</em> NSCs (<a
            href="#fig2s3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure
            supplement 3F, G</a>), and the extent of MSG regulation appeared proportional to the
          extent of MAPK activation by individual perturbations (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3A</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Altogether, these data
          indicated that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup
              itemscope="" itemtype="http://schema.stenci.la/Superscript">G12V</sup></em>–transduced
          cells, which show the highest <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression and mesenchymal
          commitment, are a suitable model to functionally study the role of a MAPK-FOSL1 axis in
          MES GBM.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="fosl1-depletion-affects-the-chromatin-accessibility-of-the-mesenchymal-transcription-program-and-differentiation-genes">
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> depletion affects
          the chromatin accessibility of the mesenchymal transcription program and differentiation
          genes</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">FOSL1 is a member of the AP-1
          TF super family, which may be composed of a diverse set of homo- and heterodimers of the
          individual members of the JUN, FOS, ATF, and MAF protein families. In GBM, AP-1 can act as
          a pioneer factor for other transcriptional regulators, such as ATF3, to coordinate
          response to stress in GSCs <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib34"><span>34</span><span>Gargiulo et al.</span><span>2013</span></a></cite>.
          To test the effect of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> ablation on chromatin regulation,
          we performed open chromatin profiling using ATAC-seq in the p53-null <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs model (<a
            href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3C</a>). This
          analysis revealed that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss strongly affects chromatin
          accessibility of known cis-regulatory elements such as transcription start sites (TSS) and
          CpG islands (CGI), as gauged by unsupervised clustering of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> wild-type and KO cells (<a
            href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4A</a>).
          Consistent with a role for <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 in maintaining chromatin
          accessibility at direct target genes, deletion of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> caused the selective closing of
          chromatin associated with the major AP-1 TFs binding sites (<a href="#fig4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 4B</a>). Upon <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss, profiling of the motifs
          indicated that chromatin associated with AP-1/2 TFs binding were closed and – conversely –
          a diverse set of general and lineage-specific TFs, including MFZ1, NRF1, RREB1, and others
          (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4C</a>), were
          opened. The genes associated with changes in chromatin accessibility upon <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss are involved in several cell
          fate commitment, differentiation, and morphogenesis programs (<a href="#fig4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 4D, E</a>). Next, we investigated
          chromatin remodeling dynamics using <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">limma</em> and identified 9749 regions with
          significant differential accessibility (absolute log2 fold-change &gt;1, FDR &lt; 0.05).
          Importantly, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss
          induced opening of chromatin associated with lineage-specific markers, along with closing
          of chromatin at the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">loci</em>
          of genes, associated with mesenchymal GBM identity in human tumors and BTSC lines (<a
            href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4F–H</a>).
          Taken all together, this evidence further indicates that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 might modulate the
          mesenchymal transcriptional program by regulating the chromatin accessibility of MES
          genes.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4a"
          title="Figure 4A."><label data-itemprop="label">Figure 4A.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>ATACSeq_sample_cor &lt;- getSampleCorrelation(Figure_4_data) 
ATACSeq_cor_annotation &lt;- as.data.frame(colData(Figure_4_data))[,c(&quot;depth&quot;, &quot;gRNA&quot;)]
ATACSeq_cor_colors &lt;- list(gRNA = black_red_green)
names(ATACSeq_cor_colors$gRNA) &lt;- levels(factor(ATACSeq_cor_annotation$gRNA))

figure_4a &lt;- pheatmap(as.dist(ATACSeq_sample_cor), 
                      annotation_row = ATACSeq_cor_annotation,
                      annotation_colors = ATACSeq_cor_colors,
                      clustering_distance_rows = as.dist(1-ATACSeq_sample_cor),
                      clustering_distance_cols = as.dist(1-ATACSeq_sample_cor),
                      silent = T)

figure_4a</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="correlation-heatmap-of-the-atac-seq-samples-clustering-of-the-fosl1-wt-sgctrl-n--4-and-fosl1-depleted-sgfosl1_1-and-sgfosl1_3-n--8-samples-is-based-upon-the-bias-corrected-deviations-in-chromatin-accessibility-see-materials-and-methods">
              Correlation heatmap of the ATAC-seq samples. Clustering of the <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>-WT (sgCtrl, n = 4) and <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>-depleted (sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_3, n = 8)
              samples is based upon the bias corrected deviations in chromatin accessibility (see
              Materials and methods).</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4b"
          title="Figure 4B."><label data-itemprop="label">Figure 4B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Getting differential deviation between control and KO samples 
difdev &lt;- differentialDeviations(Figure_4_data, &quot;Cell_type&quot;)

difdev_sig &lt;- difdev[difdev$p_value_adjusted &lt; 0.05,]
difdev_sig &lt;- difdev_sig[order(difdev_sig$p_value_adjusted),]
difdev_sig$TFs &lt;- gsub(&quot;.*_&quot;, &quot;&quot;, rownames(difdev_sig))

#Plot tSNE maps based with samples clustered based on chromatin deviations
set.seed(1234)
tsne_results &lt;- deviationsTsne(Figure_4_data, threshold = 3)
tsne_plots &lt;- plotDeviationsTsne2(Figure_4_data, 
                                  tsne_results, 
                                  annotation = head(difdev_sig$TFs,6), 
                                  sample_column = &quot;gRNA&quot;)

#plot the top 5 differentially deviating motifs between Fosl1 WT and KO cells
figure_4b &lt;- ggarrange(tsne_plots[[1]]+ xlab(&quot;&quot;),
          tsne_plots[[2]]+ xlab(&quot;&quot;) + ylab(&quot;&quot;),
          tsne_plots[[3]]+ xlab(&quot;&quot;) + ylab(&quot;&quot;),
          tsne_plots[[4]],
          tsne_plots[[5]]+ ylab(&quot;&quot;),
          tsne_plots[[6]]+ ylab(&quot;&quot;),
          widths = 80, heights = 80, ncol = 3, nrow = 2)

figure_4b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="tsne-visualization-of-cellular-similarity-between-fosl1-depleted-and-control-cells-based-on-chromatin-accessibility">
              tSNE visualization of cellular similarity between <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>-depleted and control cells
              based on chromatin accessibility.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Samples are color-coded
              according to the cell type (black, red, and green for sgCtrl, sgFosl1_1, and sgFosl1_3
              cells, respectively), or by directional z-scores.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4c"
          title="Figure 4C."><label data-itemprop="label">Figure 4C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># motifs with decreased accessibility upon Fosl1 KO
KO_down &lt;- figure_4C_data[figure_4C_data$zmean_diff &lt; 0,]
KO_down &lt;- KO_down[order(KO_down$zmean_diff),]

# motifs with increased accessibility upon Fosl1 KO
KO_up &lt;- figure_4C_data[figure_4C_data$zmean_diff &gt; 0,]
KO_up &lt;- KO_up[order(-KO_up$zmean_diff),]

figure_4c &lt;-figure_4C_data %&gt;% 
  ggplot(aes(x = zmean_diff, y = -log10(p_value_adjusted),
             labels=TFs, color = zmean_diff)) + 
  geom_point(alpha = 0.5)+
  geom_vline(xintercept = 0, colour=&quot;grey&quot;, linetype=&quot;dashed&quot;) +
  geom_point(size = 1) +
  scale_color_gradient2(name = &quot;&quot;,
                        mid = &quot;lightgray&quot;, 
                        low = &quot;blue&quot;, 
                        high = &quot;red&quot;) +
  ylim(0,8) + xlim(-19,19) + 
  ggtitle(&quot;Fosl1 KO vs Ctrl&quot;) +
  geom_text_repel(data          = head(KO_down,5), aes(label=TFs),
                   size          = 3,
                   box.padding   = 0.35,
                   point.padding = 0.5,
                   segment.size  = 0.2,
                   force         = 60,
                   segment.color = &quot;grey50&quot;) +
  geom_text_repel(data         = head(KO_up,5), aes(label=TFs),
                   size          = 3,
                   box.padding   = 0.35,
                   point.padding = 0.5,
                   segment.size  = 0.2,
                   force         = 60,
                   segment.color = &quot;grey50&quot;) +
  labs(x = &quot;Bias corrected deviations&quot;, y = &quot;-log10(padj)&quot;) +
  theme_pubr(margin = T) + font_size +
  theme(legend.position = c(0.8,0.8),legend.key.size = unit(0.75,&quot;line&quot;),
        legend.direction = &quot;horizontal&quot;, legend.text = element_text(size = 8))

figure_4c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="volcano-plot-illustrating-the-mean-difference-in-bias-corrected-accessibility-deviations-between-fosl1-deficient-and-control-cells-against-the-fdr-corrected-p-value-for-that-difference">
              Volcano plot illustrating the mean difference in bias-corrected accessibility
              deviations between <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>-deficient and control cells
              against the FDR-corrected p-value for that difference.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The top differential motifs
              are highlighted in violet and red, indicating decreased and increased accessibility,
              respectively.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4d"
          title="Figure 4D."><label data-itemprop="label">Figure 4D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>ego_down &lt;- enrichGO(gene = as.vector(KO_down$TFs),
                     OrgDb = &#39;org.Hs.eg.db&#39;,
                     keyType = &#39;SYMBOL&#39;,
                     pvalueCutoff= 0.01,
                     maxGSSize = 500,
                     pAdjustMethod = &quot;BH&quot;,
                     ont = &quot;BP&quot;)

ego_up &lt;- enrichGO(gene = as.vector(KO_up$TFs),
                   OrgDb = &#39;org.Hs.eg.db&#39;,
                   keyType = &#39;SYMBOL&#39;,
                   pvalueCutoff= 0.01,
                   maxGSSize = 500,
                   pAdjustMethod = &quot;BH&quot;,
                   ont = &quot;BP&quot;)

figure_4d &lt;- emaplot(ego_down, showCategory=10, 
                      pie_scale = 0.5,
                      line_scale = 0.5,
                      color = &quot;p.adjust&quot;,
                     text_size = 25) +
  theme(text = element_text(size = 8)) +
  ggtitle(&quot;GO pathways enriched \nin Fosl1-KO closed regions&quot;)

figure_4d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="top-enriched-gene-ontology-go-biological-processes-pathways-for-the-regions-with-decreased-chromatin-accessibility-upon-fosl1-loss">
              Top enriched Gene Ontology (GO) biological processes pathways for the regions with
              decreased chromatin accessibility upon <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The nodes represent the
              functional categories from the respective databases, color-coded by the significance
              of enrichment (FDR &lt; 0.05). The node size indicates the number of query genes
              represented among the ontology term, and the edges highlight the relative
              relationships among these categories.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4e"
          title="Figure 4E."><label data-itemprop="label">Figure 4E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_4e &lt;-emaplot(ego_up, showCategory=10, 
                     pie_scale=0.5,
                     line_scale = 0.5,
                     color = &quot;p.adjust&quot;,
                     text_size = 25) +
  theme(text = element_text(size = 8)) +
  ggtitle(&quot;GO pathways enriched \nin Fosl1-KO open regions&quot;)

figure_4e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="top-enriched-gene-ontology-go-biological-processes-pathways-for-the-regions-with-increased-chromatin-accessibility-upon-fosl1-loss">
              Top enriched Gene Ontology (GO) biological processes pathways for the regions with
              increased chromatin accessibility upon <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The nodes represent the
              functional categories from the respective databases, color-coded by the significance
              of enrichment (FDR &lt; 0.05). The node size indicates the number of query genes
              represented among the ontology term, and the edges highlight the relative
              relationships among these categories.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4f"
          title="Figure 4F."><label data-itemprop="label">Figure 4F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>seqmonk_count &lt;- figure_4F_data

DE_probes &lt;- data.frame(name = seqmonk_count$Feature,
                        baseMean = rowMeans(seqmonk_count[16:27]),
                        log2FoldChange = -seqmonk_count$Log2.Fold.Change..LIMMA.stats.p.1.0.after.correction.,
                        padj = seqmonk_count$FDR..LIMMA.stats.p.1.0.after.correction.,
                        stringsAsFactors = F)

DE_probes &lt;- arrange(DE_probes,-log2FoldChange)

Wang_MES &lt;- c(gene_signatures[[&quot;Wang_MES_2017&quot;]])
Wang_PN &lt;- c(gene_signatures[[&quot;Wang_PN_2017&quot;]])
BTSC_MES &lt;- subset(combo_eset_tT, logFC&gt;0)$Gene.Symbol
BTSC_NonMES &lt;- subset(combo_eset_tT, logFC&lt;0)$Gene.Symbol

DE_probes &lt;- DE_probes %&gt;% 
  mutate(Wang_2017 =  case_when(toupper(name) %in% Wang_MES ~ &quot;MES&quot;,
                            toupper(name) %in% Wang_PN ~ &quot;PN&quot;,
                            TRUE ~ &quot;Other&quot;),
         Wang_2017 = factor(Wang_2017, 
                            levels = c(&quot;MES&quot;,&quot;PN&quot;,&quot;Other&quot;)),
         BTSCs_DE = case_when(toupper(name) %in% BTSC_MES ~ &quot;MES&quot;,
                            toupper(name) %in% BTSC_NonMES ~ &quot;NonMES&quot;,
                            TRUE ~ &quot;Other&quot;))

figure_4f &lt;- DE_probes %&gt;% 
  ggplot(aes(x=log2FoldChange, y = Wang_2017)) +
  geom_density_ridges(aes(fill = Wang_2017),
                      alpha = 0.7,
                      scale = 1) +
  xlim(-4,4) +
  geom_vline(xintercept = c(-1,0,1),
            linetype = &quot;dashed&quot;, 
            size = 0.25) +
  scale_fill_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;,&quot;#4DAF4A&quot;)) +
  theme_pubr(legend = &quot;none&quot;) + font_size +
  ggtitle(&quot;Wang_2017&quot;) +
  rremove(&quot;ylab&quot;)

figure_4f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="density-plots-showing-the-distributions-of-the-log2-fold-changes-in-chromatin-accessibility-of-the-indicated-probes-as-measured-with-limma-by-comparing-fosl1-ko-versus-control-cells">
              Density plots showing the distributions of the log2 fold-changes in chromatin
              accessibility of the indicated probes, as measured with <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">limma</em> by comparing Fosl1-KO versus
              control cells.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4g"
          title="Figure 4G."><label data-itemprop="label">Figure 4G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_4g &lt;- DE_probes %&gt;% 
  ggplot(aes(x=log2FoldChange, y = BTSCs_DE)) +
  geom_density_ridges(aes(fill = BTSCs_DE),
                      alpha = 0.7,
                      scale = 1) +
  xlim(-4,4) +
  geom_vline(xintercept = c(-1,0,1),
            linetype = &quot;dashed&quot;, 
            size = 0.25) +
  scale_fill_manual(values = c(&quot;#F5AE26&quot;, &quot;#EA549D&quot;,&quot;#4DAF4A&quot;)) +
  theme_pubr(legend = &quot;none&quot;) + font_size +
  ggtitle(&quot;BTSC_DE&quot;) +
  rremove(&quot;ylab&quot;) 

figure_4g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="density-plots-showing-the-distributions-of-the-log2-fold-changes-in-chromatin-accessibility-of-the-indicated-probes-as-measured-with-limma-by-comparing-fosl1-ko-versus-control-cells-1">
              Density plots showing the distributions of the log2 fold-changes in chromatin
              accessibility of the indicated probes, as measured with <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">limma</em> by comparing Fosl1-KO versus
              control cells.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4g"
          title="Figure 4H."><label data-itemprop="label">Figure 4H.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_4h_left &lt;- tracksPlot(figure_4F_data, 
                             gene = &quot;Bnc2&quot;,
                             region.min = -120000, 
                             region.max = 5000)

figure_4h_right &lt;- tracksPlot(figure_4F_data, 
                              gene = &quot;Sox11&quot;,
                              bigwig.ymax = 50)
</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="representative-atac-seq-tracks-of-two-technical-replicates-for-the-mes-bnc2-and-non-mes-sox11-markers-loci-tracks-are-color-coded-as-in-panels-a-and-b">
              Representative ATAC-seq tracks of two technical replicates for the MES <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Bnc2</em> and non-MES <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Sox11</em> markers loci.
              Tracks are color-coded as in panels (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong"><em itemscope=""
                  itemtype="http://schema.stenci.la/Emphasis">A</em></strong>) and (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong"><em itemscope=""
                  itemtype="http://schema.stenci.la/Emphasis">B</em></strong>).</h3>
          </figcaption>
        </figure>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="fosl1-deletion-reduces-stemness-and-tumor-growth"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> deletion reduces stemness and
          tumor growth</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Ras activating mutations have
          been widely used to study gliomagenesis, in combination with other alterations as <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Akt</em> mutation, loss of <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Ink4a/Arf</em> or <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Trp53</em><span
            data-itemtype="http://schema.org/Number">0</span><span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib32"><span>32</span><span>Friedmann-Morvinski et
                  al.</span><span>2012</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib44"><span>44</span><span>Holland
                  et al.</span><span>2000</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib45"><span>45</span><span>Koschmann et
                  al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib59"><span>59</span><span>Muñoz et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib80"><span>80</span><span>Uhrbom
                  et al.</span><span>2002</span></a></cite></span>. Thus, we then explored the
          possibility that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>
          could modulate the tumorigenic potential of the p53-null <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras</em> mutant cells.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Cell viability was
          significantly decreased in <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO cell lines as compared to
          sgCtrl (<a href="#fig5a" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            5A</a>). Concomitantly, we observed a significant decreased percentage of cells in
          S-phase (mean values: sgCtrl = 42.6%; sgFosl1_1 = 21.6%, Student’s t test p≤0.001;
          sgFosl1_3 = 20.4%, Student’s t test p=0.003), an increase in percentage of cells in G2/M
          (mean values: sgCtrl = 11.7%, sgFosl1_1 = 28.4%, Student’s t test p≤0.001; sgFosl1_3 =
          23.4%, Student’s t test p=0.012) (<a href="#fig5b" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 5B</a>), and a reduction of the
          expression of cell cycle regulator genes (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Ccnb1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Ccnd1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Ccne1,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cdk1</em>, among others) (<a href="#fig5s1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5—figure supplement 1A</a>).
        </p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5a"
          title="Figure 5A."><label data-itemprop="label">Figure 5A.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mean_d1 &lt;- figure_5A_data %&gt;%
  filter(day == &quot;d1&quot;) %&gt;% 
  group_by(Sample, day) %&gt;% 
  summarise_all(mean) %&gt;% 
  dplyr::select(-day) %&gt;% 
  dplyr::rename(d1_mean = value)

figure_5A_data &lt;- full_join(figure_5A_data, mean_d1,&quot;Sample&quot;) %&gt;% 
  mutate(value_norm = value/d1_mean)

figure_5a &lt;- figure_5A_data %&gt;% 
  ggline(x = &quot;day&quot;, y = &quot;value_norm&quot;, 
  add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
  ylab = &quot;Cell growth (A.U.)&quot;, 
  add.params =list(size = 0.5),
  color = &quot;Sample&quot;, 
  palette = black_red_green) + 
  ylim(0,30) + font_size

# # Two-way ANOVA
# figure_5A_data %&gt;%
#   filter(Sample != &quot;sgFosl1_1&quot;) %&gt;% 
#   aov(value_norm ~ Sample * day, data = .) %&gt;% 
#   summary()
# 
# figure_5A_data %&gt;%
#   filter(Sample != &quot;sgFosl1_3&quot;) %&gt;% 
#   aov(value_norm ~ Sample * day, data = .) %&gt;% 
#   summary()

figure_5a</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="cell-viability-of-control-and-fosl1-ko-p53-null-krasg12v-neural-stem-cells-nscs-measured-by-mtt-assay-absorbance-values-were-normalized-to-day-1">
              Cell viability of control and Fosl1 KO p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> neural stem cells
              (NSCs) measured by MTT assay; absorbance values were normalized to day 1.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of three independent experiments are presented as mean ± SD (n = 10, technical
              replicates). Two-way ANOVA, relative to sgCtrl for both sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 and sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_3: ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5b"
          title="Figure 5B."><label data-itemprop="label">Figure 5B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_5b &lt;- figure_5B_data %&gt;% 
  ggbarplot(x = &quot;Phase&quot;, y = &quot;Percentage&quot;,
  add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
  ylab = &quot;% cell population&quot;,
  position = position_dodge(0.8), 
  color = &quot;Sample&quot;, 
  palette = black_red_green) +
  scale_y_continuous(expand = c(0, 0), limits = c(0,50)) 

# figure_5B_data %&gt;%
#   compare_means(Percentage ~ Sample, ref.group = &quot;sgCtrl&quot;, method = &quot;t.test&quot;, 
#                 symnum.args = symnum.args,data = ., group.by = &quot;Phase&quot;)

figure_5b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="quantification-of-cell-cycle-populations-of-control-and-fosl1-ko-p53-null-krasg12v-nscs-by-flow-cytometry-analysis-of-pi-staining">
              Quantification of cell cycle populations of control and Fosl1 KO p53-null <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs by flow
              cytometry analysis of PI staining.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two independent experiments are presented as mean ± SD (n = 3, technical
              replicates). Student’s t test, relative to sgCtrl: *p≤0.05; **p≤0.01; ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5c"
          title="Figure 5C."><label data-itemprop="label">Figure 5C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>
elda_5C &lt;- elda(response = figure_5C_data$Response, dose = figure_5C_data$Dose, 
                tested = figure_5C_data$Tested,group = figure_5C_data$Group)

# elda_bar(elda_5C,c(&quot;Black&quot;,&quot;Red&quot;))

figure_5c &lt;- ggplotlimdil(elda_5C)
# elda_5C # Pvalue for the limited dilution assay

figure_5c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="representative-limiting-dilution-experiment-on-p53-null-krasg12v-sgctrl-and-sgfosl1_1-nscs-calculated-with-extreme-limiting-dilution-assay-elda-analysis-bar-plot-inlet-shows-the-estimated-stem-cell-frequency-with-the-confidence-interval-chi-square-p00001">
              Representative limiting dilution experiment on p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> sgCtrl and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 NSCs,
              calculated with extreme limiting dilution assay (ELDA) analysis; bar plot inlet shows
              the estimated stem cell frequency with the confidence interval; chi-square
              p&lt;0.0001.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5d"
          title="Figure 5D."><label data-itemprop="label">Figure 5D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>QuantSeq_gset &lt;- ExpressionSet(assayData = as.matrix(t(figure_5D_expr)),
                               phenoData=as(figure_5D_pdata, 
                                            &quot;AnnotatedDataFrame&quot;))
col_annotation &lt;- stem_diff_genes %&gt;% 
  dplyr::rename(Marker = Group) %&gt;%
  filter(., !Gene_symbol %in% c(&quot;Nanog&quot;,&quot;Sall4&quot;)) %&gt;% # filter out genes with very low expr
  mutate(Marker = as.factor(Marker)) # %&gt;% column_to_rownames(&quot;Gene_symbol&quot;)

QuantSeq_colors &lt;- list(Marker = brewer.pal(9, &quot;Set1&quot;)[3:6],
                 sgRNA = c(&quot;#808080&quot;,&quot;#000000&quot;))
names(QuantSeq_colors$Marker) &lt;- levels(col_annotation$Marker)
names(QuantSeq_colors$sgRNA) &lt;- levels(factor(pData(QuantSeq_gset)[,&quot;sgRNA&quot;]))

figure_5d &lt;- exprs(QuantSeq_gset[featureNames(QuantSeq_gset) %in% row.names(stem_diff_genes),]) %&gt;% 
  data.frame(.) %&gt;% # reorder based on the order of stem_diff_genes
  .[row.names(col_annotation),] %&gt;% # reorder based on the order of stem_diff_genes
  na.omit(.) %&gt;% # Pou5f1 (Oct4) it&#39;s not expressed
  pheatmap(., 
           annotation_col = pData(QuantSeq_gset)[,&quot;sgRNA&quot;,drop = F],
           annotation_row = col_annotation[,&quot;Marker&quot;, drop = F],
           gaps_row=c(13,18,24), 
           scale = &quot;row&quot;, 
           show_rownames = T, 
           show_colnames = F,
           fontsize_row = 8,
           annotation_colors = QuantSeq_colors,
           border_color = NA, 
           cluster_rows = F,
           cluster_cols = F,
           annotation_legend = F,
           color = colorRampPalette(c(&quot;steelblue&quot;,&quot;white&quot;,&quot;red&quot;))(100),
           silent = T) 

figure_5d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="heatmap-of-expression-of-stem-cell-yellow-and-lineage-specific-neuronal--purple-astrocytic--green-and-oligodendrocytic--orange-genes-comparing-sgctrl-and-sgfosl11-p53-null-_krasg12v-nscs-two-biological-replicates-are-shown">
              Heatmap of expression of stem cell (yellow) and lineage-specific (neuronal – purple,
              astrocytic – green, and oligodendrocytic – orange) genes, comparing sgCtrl and
              sgFosl1<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">1 p53-null
                _Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs. Two
              biological replicates are shown.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5e"
          title="Figure 5E."><label data-itemprop="label">Figure 5E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_5e &lt;- figure_5E_data %&gt;% 
  ggplot(aes(x = sgRNA, y = norm_value, color = sgRNA)) +
  geom_boxplot(outlier.size = 0, outlier.stroke = 0) +
  geom_jitter(position = position_jitter(width = .25), 
              size = 1, alpha = 0.5) + 
  ylab(&quot;Fold change (A.U.)&quot;) + xlab(&quot;&quot;) + theme_bw() + 
  facet_wrap(.~ Marker, scales = &quot;free&quot;) +
  theme(legend.position = &quot;none&quot;,
        axis.title.y = element_text(size = 10)) +
  scale_color_manual(values = black_red_green) +
  stat_compare_means(method = &quot;t.test&quot;, 
                     ref.group = &quot;sgCtrl&quot;, 
                     label = &quot;p.signif&quot;,
                     symnum.args = symnum.args)

figure_5e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="quantification-of-pixel-area-fold-change-relative-to-sgctrl-of-cd44-gfap-and-olig2-relative-to-dapi-pixel-area-per-field-of-view-in-control-and-fosl1-ko-p53-null-krasg12v-nscs">
              Quantification of pixel area (fold-change relative to sgCtrl) of CD44, GFAP, and OLIG2
              relative to DAPI pixel area per field of view in control and Fosl1 KO p53-null <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two independent experiments; Student’s t test, relative to sgCtrl: ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5i"
          title="Figure 5I."><label data-itemprop="label">Figure 5I.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_5i &lt;-  qPCR_data %&gt;%
  plotqPCR(panel = &quot;5I&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;sgCtrl-T4&quot;, 
           levels = c(&quot;sgCtrl-T4&quot;,&quot;sgFosl1-T3&quot;,&quot;sgFosl1-T4&quot;), 
           pvalue = T,  
           legend = &quot;none&quot;, 
           palette = black_red_green, 
           ylim = c(0,1.5),
           title = &quot;MES genes&quot;)

figure_5i</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-mes-genes-in-the-samples-sgctrlt4-higher-fra-1-expression-and-sgfosl1_1t3-and-t4-no-detectable-fra-1-expression">
              mRNA expression of MES genes in the samples sgCtrl–T4 (higher FRA-1 expression) and
              sg<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1–T3 and –T4
              (no detectable FRA-1 expression).</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5j"
          title="Figure 5J."><label data-itemprop="label">Figure 5J.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_5j &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;5J&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;sgCtrl-T4&quot;, 
           levels = c(&quot;sgCtrl-T4&quot;,&quot;sgFosl1-T3&quot;,&quot;sgFosl1-T4&quot;), 
           pvalue = T,
           legend = &quot;right&quot;, 
           palette = black_red_green, 
           ylim = c(0,50),
           title = &quot;PN genes&quot;)

figure_5j</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-pn-genes-in-samples-as-in-h">mRNA expression of PN genes in
              samples as in (<strong itemscope="" itemtype="http://schema.stenci.la/Strong"><em
                  itemscope="" itemtype="http://schema.stenci.la/Emphasis">H</em></strong>).</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5"
          title="Figure 5 (static version)."><label data-itemprop="label">Figure 5 (static
            version).</label><img src="index.html.media/fig5.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-knock-out-ko-impairs-cell-growth-and-stemness-in-vitro-and-increases-survival-in-a-orthotopic-glioma-model">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> knock-out
              (KO) impairs cell growth and stemness in vitro and increases survival in a orthotopic
              glioma model.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Cell viability of control and
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO p53-null
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> neural stem
              cells (NSCs) measured by MTT assay; absorbance values were normalized to day 1. Data
              from a representative of three independent experiments are presented as mean ± SD
              (n = 10, technical replicates). Two-way ANOVA, relative to sgCtrl for both sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_3: ***p≤0.001.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>)
              Quantification of cell cycle populations of control and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs by flow
              cytometry analysis of PI staining. Data from a representative of two independent
              experiments are presented as mean ± SD (n = 3, technical replicates). Student’s t
              test, relative to sgCtrl: *p≤0.05; **p≤0.01; ***p≤0.001. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) Representative limiting
              dilution experiment on p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> sgCtrl and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 NSCs,
              calculated with extreme limiting dilution assay (ELDA) analysis; bar plot inlet shows
              the estimated stem cell frequency with the confidence interval; chi-square
              p&lt;0.0001. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Heatmap of expression of stem
              cell (yellow) and lineage-specific (neuronal – purple, astrocytic – green, and
              oligodendrocytic – orange) genes, comparing sgCtrl and sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs. Two
              biological replicates are shown. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">E</strong>) Quantification of pixel area
              (fold-change relative to sgCtrl) of CD44, GFAP, and OLIG2 relative to DAPI pixel area
              per field of view in control and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs. Data from a
              representative of two independent experiments; Student’s t test, relative to sgCtrl:
              ***p≤0.001. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>) Kaplan–Meier survival curves
              of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">nu</em>/<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">nu</em> mice injected with
              p53-null <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup
                  itemscope="" itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> sgCtrl
              (n = 9) and sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 (n = 6) NSCs. Log-rank
              p=0.0263. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>)
              Western blot analysis using the indicated antibodies of four sgCtrl and four sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 tumors
              (showing low or no detectable expression of FRA-1); vinculin was used as loading
              control. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>)
              Representative images of IHCs using the indicated antibodies. Scale bars represent 100
              µm. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">I</strong>) mRNA
              expression of MES genes in the samples sgCtrl–T4 (higher FRA-1 expression) and sg<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1–T3 and –T4 (no
              detectable FRA-1 expression). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>) mRNA expression of PN genes in
              samples as in (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">H</strong>). Data from a representative of
              two experiments are presented as mean ± SD (n = 3, technical replicates), normalized
              to <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression.
              Student’s t test for sgFosl1_1 tumors, relative to sgCtrl–T4: ns = not significant,
              *p≤0.05, **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5s1"
          title="Figure 5—figure supplement 1."><label data-itemprop="label">Figure 5—figure
            supplement 1.</label><img src="index.html.media/fig5-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-loss-is-associated-with-the-reduction-of-proliferative-genes-and-increase-in-differentiation-genes">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> loss is
              associated with the reduction of proliferative genes and increase in differentiation
              genes.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Heatmap showing a reduction in
              expression of cell cycle regulators in sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 as compared to sgCtrl
              p53-null <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup
                  itemscope="" itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> neural
              stem cells (NSCs). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Representative images of
              immunofluorescence staining of the indicated markers in sgCtrl and sg<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1 p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs plated on
              laminin-coated coverslips. Scale bars represent 50 µm.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Another aspect that contributes
          to GBM aggressiveness is its heterogeneity, attributable in part to the presence of GSCs.
          By using limiting dilution assays, we found that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> is required for the maintenance
          of stem cell activity, with a stem cell frequency estimate of sgFosl1_1 = 28.6 compared to
          sgCtrl = 3 (chi-square p&lt;2.2e-16) (<a href="#fig5c" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 5C</a>). Moreover, RNA-seq analysis
          showed that sg<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1
          cells downregulated the expression of stem genes (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Elf4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Klf4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Itgb1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Nes</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Sall4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">L1cam</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Melk</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cd44</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Myc</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fut4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cxcr4</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Prom1</em>) while upregulating the
          expression of lineage-specific genes: neuronal (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Map2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Ncam1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Tubb3</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Slc1a2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Rbfox3</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Dcx</em>), astrocytic (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Aldh1l1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Gfap</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">S100b</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Slc1a3</em>), and oligodendrocytic (<em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Olig2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Sox10</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cnp</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Mbp</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Cspg4</em>) (<a href="#fig5d" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 5D</a>). The different expression of some
          of the stem/differentiation markers was confirmed also by immunofluorescence analysis.
          While <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO cells
          presented low expression of the stem cell marker CD44, differentiation markers as GFAP and
          OLIG2 were significantly higher when compared to sgCtrl cells (<a href="#fig5e"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5E</a> and <a href="#fig5s1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5—figure supplement 1B</a>).
        </p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We then sought to test whether
          (i) p53-null <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup
              itemscope="" itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs were
          tumorigenic and (ii) <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> played any role in their
          tumorigenic potential. Intracranial injections of p53-null <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> NSCs in <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">nu</em>/<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">nu</em> mice led to the development of
          high-grade tumors with a median survival of 37 days in control cells (n = 9). In contrast,
          sg<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>_1-injected mice
          (n = 6) had a significant increase in median survival (54.5 days, log-rank p=0.0263) (<a
            href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5F</a>).
          Consistent with our in vitro experiments (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3D–F</a>), <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em>-depleted tumors failed to support
          mesenchymal program (<a href="#fig5i" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 5G–I</a>). Western blot,
          immunohistochemical, and qPCR analysis show the reduction of MES markers (VIM, CD44, and
          S100A4) and the expression of the PN marker OLIG2 to depend on <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> expression (<a href="#fig5"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5G</a><a href="#fig5j"
            itemscope="" itemtype="http://schema.stenci.la/Link">–J</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Overall, our data in p53-null
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras</em> mutant NSCs support
          the conclusion that, besides controlling cell proliferation, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> plays a critical role in the
          maintenance of the stem cell activity and tumorigenicity.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="fosl1-amplifies-mesenchymal-gene-expression"><em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> amplifies mesenchymal gene
          expression</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To further assess the role of
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> as a key player in
          the control of the MGS, we used a mouse model of inducible <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpression containing the
          alleles <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">LSLG12V</sup>; Trp53<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">lox</sup>; ROSA26<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">LSLrtTA-IRES-EGFP</sup>; Col1a1<sup
              itemscope="" itemtype="http://schema.stenci.la/Superscript">TetO-Fosl1</sup></em>
          (here referred to as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">tetON</sup></em>). Similar to the
          loss-of-function approach here used, this allelic combination allows the expression of <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> and deletion of <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">p53</em> after Cre
          recombination. Moreover, the expression of the reverse tetracycline transactivator (rtTA)
          allows, upon induction with doxycycline (Dox), the ectopic expression of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> (Flag tagged), under the control
          of the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Col1a1</em> locus and
          a tetracycline‐responsive element (TRE or Tet-O) <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib8"><span>8</span><span>Belteki</span><span>2005</span></a></cite><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib42"><span>42</span><span>Hasenfuss et
                  al.</span><span>2014</span></a></cite></span>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">NSCs derived from <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">WT</sup></em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> mice were infected in
          vitro with a lentiviral vector expressing the Cre recombinase and efficient infection was
          confirmed by fluorescence microscopy as the cells expressing the rtTA should express GFP
          (data not shown). FRA-1 overexpression, as well as Flag-tag expression, was then tested by
          western blot after 72 hr of Dox induction (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6A</a>). When <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> NSCs were analyzed by
          qRT-PCR for the expression of MES/PN markers, a significant upregulation of most MES genes
          and downregulation of PN genes was found in the cells overexpressing <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> (<a href="#fig6b" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6B, C</a>), thereby complementing our
          findings in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> KO
          cells.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6b"
          title="Figure 6B."><label data-itemprop="label">Figure 6B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6b_left &lt;- qPCR_data %&gt;%
  filter(Gene %in% c(&quot;Gapdh&quot;,&quot;Fosl1&quot;)) %&gt;%
  plotqPCR(panel = &quot;6B&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Mock&quot;, 
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;), 
           pvalue = T,
           legend = &quot;none&quot;, 
           palette = gray_black, 
           ylim=c(0,200),
           title = &quot;MES genes&quot;)

figure_6b_right &lt;- qPCR_data %&gt;%
  filter(Gene!= &quot;Fosl1&quot;) %&gt;%
  plotqPCR(panel = &quot;6B&quot;,
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Mock&quot;, 
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;),
           pvalue = T, ylab = FALSE,
           legend = &quot;none&quot;, 
           palette = gray_black, 
           ylim=c(0,15),
           title = &quot; &quot;) 

figure_6b &lt;- plot_grid(figure_6b_left,
                       figure_6b_right,
                       rel_widths = c(0.3,1))

figure_6b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-fosl1-and-mesenchymal-mes-genes-in-fosl1teton-p53-null-krasg12v-cells-upon-72-hr-induction-with-1-µgml-dox">
              mRNA expression of Fosl1 and mesenchymal (MES) genes in <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> p53-null <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> cells upon 72 hr
              induction with 1 µg/mL Dox.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6c"
          title="Figure 6C."><label data-itemprop="label">Figure 6C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6c &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;6C&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Mock&quot;,
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;), 
           pvalue = T, 
           legend = &quot;right&quot;, 
           palette = gray_black, 
           ylim=c(0,1.5),
           title = &quot;PN genes&quot;) 

figure_6c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-pn-genes-in-fosl1teton-p53-null-krasg12v-cells-upon-72-hr-induction-with-1-µgml-dox">
              mRNA expression of PN genes in <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> p53-null <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> cells upon 72 hr
              induction with 1 µg/mL Dox.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6d"
          title="Figure 6D."><label data-itemprop="label">Figure 6D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6d &lt;- figure_6D_data %&gt;% 
  ggplot(aes(x = Group, y = Area_scaled, color = Group)) +
  geom_boxplot(outlier.size = 0, outlier.stroke = 0) +
  geom_jitter(position = position_jitter(width = .25), size = 1) + 
  ylab(&quot;Tumor Area&quot;) + xlab(&quot;&quot;) + theme_bw() + ylim(0,2.8) +
  theme(legend.position = &quot;none&quot;,  axis.title.y = element_text(size = 10)) +
  scale_color_manual(values = c(&quot;#A6CEE3&quot;, &quot;#1F78B4&quot;)) +
  stat_compare_means(method = &quot;t.test&quot;, ref.group = &quot;Mock&quot;, 
                     label = &quot;p.format&quot;, symnum.args = symnum.args)

figure_6d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="quantification-of-tumor-area-µm2-of-dox-and-dox-tumors-n--88-for-each-mouse-the-brain-section-on-the-hematoxylin-and-eosin-he-slide-with-a-larger-tumor-was-considered-and-quantified-using-the-zen-software-zeiss">
              Quantification of tumor area (µm<sup itemscope=""
                itemtype="http://schema.stenci.la/Superscript"><span
                  data-itemtype="http://schema.org/Number">2</span></sup>) of –Dox and +Dox tumors
              (n = 8/8). For each mouse, the brain section on the hematoxylin and eosin (H&amp;E)
              slide with a larger tumor was considered and quantified using the ZEN software
              (Zeiss).</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6f"
          title="Figure 6F."><label data-itemprop="label">Figure 6F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6f_left &lt;- qPCR_data %&gt;%
  filter(Gene %in% c(&quot;Gapdh&quot;,&quot;Fosl1&quot;)) %&gt;%
  plotqPCR(panel = &quot;6F&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Mock&quot;, 
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;), 
           pvalue = T,
           legend = &quot;none&quot;,
           palette = c(&quot;#A6CEE3&quot;, &quot;#1F78B4&quot;), 
           ylim=c(0,40),
           title = &quot;MES genes&quot;)

figure_6f_right &lt;- qPCR_data %&gt;%
  filter(Gene!= &quot;Fosl1&quot;) %&gt;%
  plotqPCR(panel = &quot;6F&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Mock&quot;, 
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;), 
           pvalue = T, ylab = FALSE,
           legend = &quot;none&quot;, 
           palette = c(&quot;#A6CEE3&quot;, &quot;#1F78B4&quot;), 
           ylim=c(0,6),
           title = &quot; &quot;) 

figure_6f &lt;- plot_grid(figure_6f_left,
                       figure_6f_right,
                       rel_widths = c(0.3,1))

figure_6f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-fosl1-and-mes-genes-in-tumorspheres-in-the-absence-or-presence-of-dox-for-19-days">
              mRNA expression of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and MES genes in tumorspheres
              in the absence or presence of Dox for 19 days.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6g"
          title="Figure 6G."><label data-itemprop="label">Figure 6G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6g &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;6G&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Mock&quot;,
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;), 
           pvalue = T, 
           legend = &quot;right&quot;, 
           palette = c(&quot;#A6CEE3&quot;, &quot;#1F78B4&quot;), 
           ylim=c(0,2),
           title = &quot;PN genes&quot;) 

figure_6g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-pn-genes-in-tumorspheres-in-the-absence-or-presence-of-dox-for-19-days">
              mRNA expression of PN genes in tumorspheres in the absence or presence of Dox for 19
              days.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6i"
          title="Figure 6I."><label data-itemprop="label">Figure 6I.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6i &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;6I&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Dox&quot;, 
           levels = c(&quot;Dox&quot;,&quot;Mock&quot;), 
           pvalue = T,
           legend = &quot;none&quot;, 
           palette = c(&quot;#E31A1C&quot;,&quot;#FB9A99&quot;), 
           ylim=c(0,2.5),
           title = &quot;MES genes&quot;)

figure_6i</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-fosl1-and-mes-genes-in-tumorspheres-in-the-presence-or-absence-of-dox-for-19-days">
              mRNA expression of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and MES genes in tumorspheres
              in the presence or absence of Dox for 19 days.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6j"
          title="Figure 6I."><label data-itemprop="label">Figure 6I.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_6j &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;6J&quot;, 
           normalizer = &quot;Gapdh&quot;, 
           ref_group = &quot;Dox&quot;,
           levels = c(&quot;Dox&quot;,&quot;Mock&quot;), 
           pvalue = T, 
           legend = &quot;right&quot;, 
           palette = c(&quot;#E31A1C&quot;,&quot;#FB9A99&quot;), 
           ylim=c(0,10),
           title = &quot;PN genes&quot;) 

figure_6j</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-pn-genes-in-tumorspheres-in-the-presence-or-absence-of-dox-for-19-days-qrt-pcr-data-from-a-representative-of-two-experiments-are-presented-as-mean-±-sd-n--3-technical-replicates-normalized-to-gapdh-expression">
              mRNA expression of PN genes in tumorspheres in the presence or absence of Dox for 19
              days. qRT-PCR data from a representative of two experiments are presented as mean ± SD
              (n = 3, technical replicates), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Student’s t test, relative
              to the respective control (−Dox in <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>, <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>, <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>, and <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">G</strong>; +Dox in <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong> and <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>): ns = not significant,
              *p≤0.05, **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6"
          title="Figure 6 (static version)."><label data-itemprop="label">Figure 6 (static
            version).</label><img src="index.html.media/fig6.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-overexpression-upregulates-the-mes-gene-signature-mgs-and-induces-larger-tumors-in-vivo">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpression
              upregulates the MES gene signature (MGS) and induces larger tumors in vivo.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot analysis of FRA-1
              and Flag expression on <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> and <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">WT</sup></em> neural stem
              cells (NSCs) derived from <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">LSLG12V</sup>; Trp53<sup
                  itemscope="" itemtype="http://schema.stenci.la/Superscript">lox</sup>; ROSA26<sup
                  itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">LSLrtTA-IRES-EGFP</sup>; Col1a1<sup
                  itemscope="" itemtype="http://schema.stenci.la/Superscript">TetO-Fosl1</sup></em>
              mice upon in vitro infection with Cre and induction of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpression with 1 µg/mL
              doxycycline (Dox) for 72 hr; vinculin was used as loading control. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) mRNA expression
              of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and
              mesenchymal (MES) genes in <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> p53-null <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> cells upon 72 hr
              induction with 1 µg/mL Dox. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) mRNA expression of PN genes in
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> p53-null <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> cells upon 72 hr
              induction with 1 µg/mL Dox. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Quantification of tumor area
              (µm<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span
                  data-itemtype="http://schema.org/Number">2</span></sup>) of –Dox and +Dox tumors
              (n = 8/8). For each mouse, the brain section on the hematoxylin and eosin (H&amp;E)
              slide with a larger tumor was considered and quantified using the ZEN software
              (Zeiss). (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>)
              Western blot detection of FRA-1 expression in tumorspheres derived from a control
              (−Dox) tumor. Tumorspheres were isolated and kept without Dox until first passage,
              when 1 µg/mL Dox was added and kept for 19 days (+Dox in vitro). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>) mRNA expression of <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and MES genes in
              tumorspheres in the absence or presence of Dox for 19 days. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">G</strong>) mRNA expression of PN genes in
              tumorspheres in the absence or presence of Dox for 19 days. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">H</strong>) Western blot detection of
              FRA-1 expression in tumorspheres derived from a <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpressing (+Dox) tumor.
              Tumorspheres were isolated and kept with 1 µg/mL Dox until first passage, when Dox was
              removed for 19 days (−Dox in vitro). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong>) mRNA expression of <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> and MES genes in
              tumorspheres in the presence or absence of Dox for 19 days. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>) mRNA expression of PN genes in
              tumorspheres in the presence or absence of Dox for 19 days. qRT-PCR data from a
              representative of two experiments are presented as mean ± SD (n = 3, technical
              replicates), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Gapdh</em> expression. Student’s t test,
              relative to the respective control (−Dox in <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>, <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>, <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>, and <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">G</strong>; +Dox in <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong> and <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>): ns = not significant,
              *p≤0.05, **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6s1"
          title="Figure 6—figure supplement 1."><label data-itemprop="label">Figure 6—figure
            supplement 1.</label><img src="index.html.media/fig6-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="characterization-of-fosl1-overexpressing-mouse-tumors">Characterization of <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpressing
              mouse tumors.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Kaplan–Meier survival curves
              of C57BL/6J wildtype mice injected with p53-null <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">G12V</sup> Fosl1<sup itemscope=""
                  itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> neural stem
              cells (NSCs) subjected to doxycycline (Dox) diet (n = 8) or kept as controls (n = 8);
              log-rank p-value=0.814. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Hematoxylin and eosin
              (H&amp;E) and immunohistochemical staining, using the indicated antibodies, of
              representative –Dox and +Dox tumors. Scale bars represent 100 µm.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To investigate if the MES
          phenotype induced with <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpression would have any
          effect in vivo, p53-null <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Kras<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">G12V</sup> Fosl1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">tetON</sup></em> NSCs were
          intracranially injected into syngeneic C57BL/6J wildtype mice. Injected mice were
          randomized and subjected to Dox diet (food pellets and drinking water) or kept as controls
          with regular food and drinking water with 1% sucrose. <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> overexpressing mice (+Dox)
          developed larger tumors that were more infiltrative and aggressive than controls (–Dox),
          which mostly grew as superficial tumor masses instead (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6D</a>). This phenotype appears to be
          independent of tumor cells proliferation as gauged by Ki-67 staining and does not affect
          overall survival (<a href="#fig6s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6—figure supplement 1A, B</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Tumorspheres were derived from
          –Dox and +Dox tumor-bearing mice, and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> expression was manipulated in
          vitro through addition or withdrawal of Dox from the culture medium. In the case of
          tumorspheres derived from a –Dox tumor, when Dox was added for 19 days, high levels of
          FRA-1 expression were detected by western blot (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6E</a>). At the mRNA level, Dox treatment
          also greatly increased <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> expression, as well as some of
          the MES genes (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            6F</a>), while the expression of PN genes was downregulated (<a href="#fig6"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6G</a>). Conversely, when
          Dox was removed from +Dox-derived tumorspheres for 19 days, the expression of FRA-1
          decreased (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            6H, I</a>), along with the expression of MES genes (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6I</a>), while PN genes were upregulated
          (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6J</a>).
          These results confirm the essential role of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Fosl1</em> in the regulation of the MGS in
          p53-null <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Kras<sup
              itemscope="" itemtype="http://schema.stenci.la/Superscript">G12V</sup></em> tumor
          cells and the plasticity between the PN and MES subtypes.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="fosl1-controls-growth-stemness-and-mesenchymal-gene-expression-in-patient-derived-btscs">
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> controls growth,
          stemness, and mesenchymal gene expression in patient-derived BTSCs</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To prove the relevance of our
          findings in the context of human tumors, we analyzed BTSC lines characterized as non-MES
          (h676, h543, and BTSC 268) or MES (BTSC 349, BTSC 380, and BTSC 233) (this study and <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib62"><span>62</span><span>Ozawa et al.</span><span>2014</span></a></cite>).
          By western blot, we found that consistent with what was observed either in human BTSCs (<a
            href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1D</a>) or
          mouse NSCs (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            3A</a>), MES cell lines expressed high levels of FRA-1 and activation of the MEK/ERK
          pathway (<a href="#fig7" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            7A</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7c"
          title="Figure 7C."><label data-itemprop="label">Figure 7C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mean_7C_d1 &lt;- figure_7C_data %&gt;%
  filter(day == &quot;d1&quot;) %&gt;% 
  group_by(Sample, Treatment, day) %&gt;% 
  summarise_all(mean) %&gt;% 
  dplyr::select(-day) %&gt;% 
  dplyr::rename(d1_mean = value)

figure_7C_data_norm &lt;- full_join(figure_7C_data, mean_7C_d1, 
                            by = c(&quot;Sample&quot;, &quot;Treatment&quot;)) %&gt;% 
  mutate(value_norm = value/d1_mean,
         Sample_treatment = paste(Sample, Treatment, sep = &quot;&quot;))

figure_7c &lt;- figure_7C_data_norm %&gt;% 
  ggline(x = &quot;day&quot;, y = &quot;value_norm&quot;, 
  add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
  ylab = &quot;Cell growth (A.U.)&quot;, 
  add.params =list(size = 1),
  color = &quot;Sample_treatment&quot;, 
  palette = &quot;Paired&quot;) +
  theme(legend.position = c(.05, .95), 
        legend.justification = c(&quot;left&quot;, &quot;top&quot;)) + 
  font_size

# figure_7C_data_norm %&gt;% 
#   filter(Sample == &quot;shFOSL1_10&quot;) %&gt;% 
#   aov(value_norm ~ Treatment + day, data = .) %&gt;%
#   summary()
# 
# figure_7C_data_norm %&gt;% 
#   filter(Sample == &quot;shFOSL1_3&quot;) %&gt;% 
#   aov(value_norm ~ Treatment + day, data = .) %&gt;%
#   summary()

# figure_7C_data_norm %&gt;%
#   compare_means(value_norm ~ Treatment,
#                 group1 = &quot;Mock&quot;,
#                 group2 = &quot;Dox&quot;,
#                 method = &quot;anova&quot;,
#                 symnum.args = symnum.args,data = .,
#                 group.by = &quot;Sample&quot;)

figure_7c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="cell-growth-of-btsc-380-shgfp-and-shfosl1-in-the-absence-or-presence-of-dox-measured-by-mtt-assay-absorbance-values-were-normalized-to-day-1-data-from-a-representative-of-three-independent-experiments-are-presented-as-mean-±-sd-n--15-technical-replicates">
              Cell growth of BTSC 380 shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, in the absence or presence
              of Dox, measured by MTT assay; absorbance values were normalized to day 1. Data from a
              representative of three independent experiments are presented as mean ± SD (n = 15,
              technical replicates).</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Two-way ANOVA, –Dox vs.
              +Dox: ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7d"
          title="Figure 7D."><label data-itemprop="label">Figure 7D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_7d &lt;- figure_7D_data %&gt;% 
  mutate(Sample = factor(Sample, 
                         levels = c(&quot;shGFP&quot;, &quot;shFOSL1_3&quot;,&quot;shFOSL1_10&quot;)),
         Sample_treatment = paste(Sample, Treatment, sep = &quot;&quot;)) %&gt;% 
  ggbarplot(x = &quot;Sample&quot;, 
            y = &quot;BrdU&quot;, 
            position = position_dodge(0.8), 
            add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
            ylab = &quot;% BrdU+ cells&quot;,
            color = &quot;Sample_treatment&quot;, 
            palette = &quot;Paired&quot;, legend = &quot;none&quot;) +
  scale_y_continuous(expand = c(0, 0), limits = c(0,40)) + font_size 

# figure_7D_data %&gt;%
#   compare_means(BrdU ~ Treatment,
#                 ref.group = &quot;-Dox&quot;,
#                 method = &quot;t.test&quot;,
#                 symnum.args = symnum.args,data = .,
#                 group.by = &quot;Sample&quot;, var.equal = T)

# figure_7D_data %&gt;%
#   compare_means(BrdU ~ Treatment, group1 = &quot;-Dox&quot;, group2 = &quot;+Dox&quot;, method = &quot;anova&quot;,
#                 symnum.args = symnum.args,data = ., group.by = &quot;Sample&quot;)

figure_7d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="brdu-of-btsc-380-shgfp-and-shfosl1-in-the-absence-or-presence-of-dox-analyzed-by-flow-cytometry-data-from-a-representative-of-two-independent-experiments-are-presented-as-mean-±-sd-n--3-technical-replicates-students-t-test-relative-to-the-respective-control-dox-p≤005">
              BrdU of BTSC 380 shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, in the absence or presence
              of Dox, analyzed by flow cytometry. Data from a representative of two independent
              experiments are presented as mean ± SD (n = 3, technical replicates). Student’s t
              test, relative to the respective control (–Dox): *p≤0.05.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7e"
          title="Figure 7E."><label data-itemprop="label">Figure 7E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>elda_7e_gfp &lt;- figure_7E_data %&gt;% 
  filter(shRNA ==&quot;shGFP&quot;) %&gt;% 
  with(.,  elda(response = Response, 
                dose = Dose, 
                tested = Tested, 
                group = Group))

elda_7e_FOSL1_3 &lt;- figure_7E_data %&gt;% 
  filter(shRNA ==&quot;shFOSL1_3&quot;) %&gt;% 
  with(.,  elda(response = Response, 
                dose = Dose, 
                tested = Tested, 
                group = Group))

elda_7e_FOSL1_10 &lt;- figure_7E_data %&gt;% 
  filter(shRNA ==&quot;shFOSL1_10&quot;) %&gt;% 
  with(.,  elda(response = Response, 
                dose = Dose,
                tested = Tested, 
                group = Group))

# elda_7e_gfp; elda_7e_FOSL1_3; elda_7e_FOSL1_10 # Pvalue for the limited dilution assay

# elda_bar(elda_7e_gfp, brewer.pal(6,&quot;Paired&quot;)[5:6])
# elda_bar(elda_7e_FOSL1_3, brewer.pal(6,&quot;Paired&quot;)[3:4])
# elda_bar(elda_7e_FOSL1_10, brewer.pal(6,&quot;Paired&quot;)[1:2])
#   

figure_7e &lt;- plot_grid(ggplotlimdil(elda_7e_gfp,
                                    col.group = brewer.pal(6,&quot;Paired&quot;)[5:6]) + 
                         font_size,
                       ggplotlimdil(elda_7e_FOSL1_3, 
                                    col.group = brewer.pal(6,&quot;Paired&quot;)[3:4]) + 
                         font_size,
                       ggplotlimdil(elda_7e_FOSL1_10, 
                                    col.group = brewer.pal(6,&quot;Paired&quot;)[1:2]) + 
                         font_size, 
                       nrow = 1)

figure_7e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="representative-limiting-dilution-analysis-on-btsc380-for-shgfp-and-shfosl1-in-the-presence-or-absence-of-dox-calculated-with-extreme-limiting-dilution-assay-elda-analysis-bar-plot-inlets-show-the-estimated-stem-cell-frequency-with-the-confidence-interval-chi-square-p-values-are-indicated">
              Representative limiting dilution analysis on BTSC380 for shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, in the presence or absence
              of Dox, calculated with extreme limiting dilution assay (ELDA) analysis; bar plot
              inlets show the estimated stem cell frequency with the confidence interval; chi-square
              p-values are indicated.</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7e"
          title="Figure 7F."><label data-itemprop="label">Figure 7F.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#PCA
pc  &lt;-  prcomp(t(figure_7F_data[,-c(1:12)]))
pc_matrix &lt;- data.frame(pc$x)
percentage &lt;- round(pc$sdev^2/ sum(pc$sdev^2) * 100, 2)
percentage &lt;- paste(colnames(pc_matrix), &quot;(&quot;,
                    paste(as.character(percentage), &quot;%&quot;, &quot;)&quot;, sep=&quot;&quot;),sep = &quot;&quot;)
pc_matrix$Group &lt;- figure_7F_annotation$Group

figure_7f &lt;- pc_matrix %&gt;%
    ggscatter(x = &quot;PC1&quot;, y = &quot;PC2&quot;, 
             size = 1.5,
             color = &quot;Group&quot;,
             palette = c(&quot;orange&quot;,&quot;maroon1&quot;), 
             ellipse = F,
             xlab = percentage[1],
             ylab = percentage[2],
             legend = c(0.8,0.25)) +
  font_size

figure_7f</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="principal-component-analysis-of-h3k27ac-signal-over-fosl1fra-1-binding-sites-calculated-using-macs-on-encode-samples-see-materials-and-methods-in-non-mes-n--10-and-mes-btsc-n--10-from-narrative-bib53">
              Principal component analysis of H3K27Ac signal over <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 binding sites,
              calculated using MACS on ENCODE samples (see Materials and methods), in non-MES
              (n = 10) and MES BTSC (n = 10) (from <cite itemscope=""
                itemtype="http://schema.stenci.la/Cite" data-citationmode="Narrative"><a
                  href="#bib53"><span>53</span><span>Mack et
                    al.</span><span>2019</span></a></cite>).</h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7g"
          title="Figure 7G."><label data-itemprop="label">Figure 7G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># BTSC_MES_probes &lt;- figure_7G_data %&gt;% 
#   filter(Feature %in% BTSC_MES) %&gt;% 
#   arrange(-Log2.Fold.Change)

# Wang_MES_probes &lt;- figure_7G_data %&gt;% 
#   filter(Feature %in% Wang_MES) %&gt;% 
#   arrange(-Log2.Fold.Change)

#Volcano plot
figure_7g &lt;- figure_7G_data %&gt;%
    mutate(Fold_group = case_when(Log2.Fold.Change &gt; 2 ~ &quot;Lfc&gt;&quot;,
                                Log2.Fold.Change &lt; -2 ~ &quot;Lfc&lt;-&quot;,
                                TRUE ~ &quot;Lfc&quot;)) %&gt;% 
    ggplot(aes(x = Log2.Fold.Change, y = -log10(P.value))) + 
    geom_point(aes(col = Fold_group),size = 0.01, alpha = 0.5) +
    scale_color_manual(values = c(&quot;gray&quot;,&quot;steelblue&quot;,&quot;red&quot;)) +
    xlim(-5.5,5.5) + ylim(0,45) +
    geom_vline(xintercept = 0, colour=&quot;grey&quot;, linetype=&quot;dashed&quot;) +
    theme_pubr(legend = &quot;none&quot;) + font_size +  
    ggtitle(&quot;MES vs Non-MES&quot;) 

figure_7g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="volcano-plot-illustrating-the-log2-fold-change-differences-in-h3k27ac-signal-between-non-mes-and-mes-btscs-against-the-p-value-for-that-difference">
              Volcano plot illustrating the log2 fold-change differences in H3K27Ac signal between
              non-MES and MES BTSCs against the p-value for that difference.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Blue and red probes
              represent statistically significant differences (FDR &lt; 0.005) in H3K27Ac signal
              between non-MES and MES BTSCs.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7j"
          title="Figure 7J."><label data-itemprop="label">Figure 7J.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_7j &lt;- qPCR_data %&gt;% 
    plotChIP(panel = &quot;7J&quot;, 
             ref_group = &quot;IgG&quot;, 
             levels = c(&quot;IgG&quot;,&quot;FRA1&quot;),
             palette = gray_black, 
             pvalue =T, 
             ylim=c(0,0.03), 
             pvalues_y = 0.028) 

figure_7j</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="representative-chip-experiment-in-btsc-349-cells">Representative ChIP experiment
              in BTSC 349 cells.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The panel shows FRA-1
              binding to the promoter of a subset of MES targets (n = 3, technical replicates)
              expressed as a percentage of the initial DNA amount in the immune-precipitated
              fraction. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NANOG</em> gene
              was used as a negative control. Student’s t test, relative to IgG: ns =
              not significant, **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7" title="Figure 7.">
          <label data-itemprop="label">Figure 7.</label><img src="index.html.media/fig7.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="fosl1-contributes-to-mesenchymal-mes-genes-activation-cell-growth-and-stemness-in-mes-brain-tumor-stem-cells-btscs">
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> contributes to
              mesenchymal (MES) genes activation, cell growth, and stemness in MES brain tumor stem
              cells (BTSCs).</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot analysis using
              the specified antibodies of human BTSC lines, characterized as non-MES (<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">left</em>) and MES (<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">right</em>). (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) Western blot
              detection of FRA-1 in MES BTSC 380 upon transduction with inducible shRNAs targeting
              GFP (control) and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, analyzed after 3 and 7 days
              of doxycycline (Dox) treatment; vinculin was used as loading control. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) Cell growth of
              BTSC 380 shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, in the absence or presence
              of Dox, measured by MTT assay; absorbance values were normalized to day 1. Data from a
              representative of three independent experiments are presented as mean ± SD (n = 15,
              technical replicates). Two-way ANOVA, –Dox vs. +Dox: ***p≤0.001. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) BrdU of BTSC 380 shGFP and
              sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, in
              the absence or presence of Dox, analyzed by flow cytometry. Data from a representative
              of two independent experiments are presented as mean ± SD (n = 3, technical
              replicates). Student’s t test, relative to the respective control (–Dox): *p≤0.05.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>)
              Representative limiting dilution analysis on BTSC380 for shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, in the presence or absence
              of Dox, calculated with extreme limiting dilution assay (ELDA) analysis; bar plot
              inlets show the estimated stem cell frequency with the confidence interval; chi-square
              p-values are indicated. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>) Principal component analysis
              of H3K27Ac signal over <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 binding sites,
              calculated using MACS on ENCODE samples (see Materials and methods), in non-MES
              (n = 10) and MES BTSC (n = 10) (from <cite itemscope=""
                itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Mack
                    et al.</span><span>2019</span></a></cite>). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">G</strong>) Volcano plot illustrating the
              log2 fold-change differences in H3K27Ac signal between non-MES and MES BTSCs against
              the p-value for that difference. Blue and red probes represent statistically
              significant differences (FDR &lt; 0.005) in H3K27Ac signal between non-MES and MES
              BTSCs. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>)
              Heatmap of ChIP-seq enrichment of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 or OLIG2 binding sites
              for the indicated profiles. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong>) View of the <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">PLAU</em>, <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">CD44,</em> and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">OLIG2</em> loci of selected profiles.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">J</strong>)
              Representative ChIP experiment in BTSC 349 cells. The panel shows FRA-1 binding to the
              promoter of a subset of MES targets (n = 3, technical replicates) expressed as a
              percentage of the initial DNA amount in the immune-precipitated fraction. <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">NANOG</em> gene was used as
              a negative control. Student’s t test, relative to IgG: ns = not significant, **p≤0.01,
              ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1b"
          title="Figure 7—figure supplement 1B."><label data-itemprop="label">Figure 7—figure
            supplement 1B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mean_S8B_d1 &lt;- figure_S8B_data %&gt;%
  filter(day == &quot;d1&quot;) %&gt;% 
  group_by(Sample, Treatment, day) %&gt;% 
  summarise_all(mean) %&gt;% 
  dplyr::select(-day) %&gt;% 
  dplyr::rename(d1_mean = value)

figure_S8B_data_norm &lt;- full_join(figure_S8B_data,mean_S8B_d1, 
                             by = c(&quot;Sample&quot;, &quot;Treatment&quot;)) %&gt;% 
  mutate(value_norm = value/d1_mean,
         Sample_treatment = paste(Sample, Treatment, sep = &quot; &quot;))

# figure_S8B_data_norm %&gt;% 
#   filter(Sample == &quot;shFOSL1_3&quot;) %&gt;% 
#   aov(value_norm ~ Treatment * day, data = .) %&gt;%
#   summary()

figure_S8b &lt;- figure_S8B_data_norm %&gt;% 
  ggline(x = &quot;day&quot;, y = &quot;value_norm&quot;,
         add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
         ylab = &quot;Cell growth (A.U.)&quot;,
         add.params =list(size = 1),
         color = &quot;Sample_treatment&quot;, 
         palette = &quot;Paired&quot;)

figure_S8b</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="cell-growth-of-btsc-349-shgfp-and-shfosl1_3-cells-in-the-absence-or-presence-of-dox-measured-by-mtt-assay-absorbance-values-were-normalized-to-day-1">
              Cell growth of BTSC 349 shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 cells, in the absence or
              presence of Dox, measured by MTT assay; absorbance values were normalized to day 1.
            </h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of three independent experiments are presented as mean ± SD (n = 15, technical
              replicates). Student’s t test on day 7, relative to sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 –Dox: ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1c"
          title="Figure 7—figure supplement 1C."><label data-itemprop="label">Figure 7—figure
            supplement 1C.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S8c &lt;- figure_S8C_data %&gt;% 
  mutate(Sample = factor(Sample, levels = c(&quot;shGFP&quot;, &quot;shFOSL1_3&quot;)),
         Sample_treatment = paste(Sample, Treatment, sep = &quot;&quot;)) %&gt;% 
  ggbarplot(x = &quot;Sample&quot;, y = &quot;BrdU&quot;, 
            position = position_dodge(0.8), 
            add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
            ylab = &quot;% BrdU+ cells&quot;,
            color = &quot;Sample_treatment&quot;, 
            palette = &quot;Paired&quot;, 
            legend = &quot;none&quot;) +
  scale_y_continuous(expand = c(0, 0), limits = c(0,25))

# figure_S8C_data %&gt;% 
#   compare_means(BrdU ~ Treatment, group1 = &quot;Mock&quot;, group2 = &quot;Dox&quot;, method = &quot;anova&quot;,
#                 symnum.args = symnum.args,data = ., group.by = &quot;Sample&quot;)

# figure_S8C_data %&gt;%
#   compare_means(BrdU ~ Treatment, ref.group = &quot;Mock&quot;, method = &quot;t.test&quot;,
#                 symnum.args = symnum.args,data = ., group.by = &quot;Sample&quot;, alternative = &quot;less&quot;, var.equal = T)

figure_S8c</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="brdu-incorporation-of-btsc-349-shgfp-and-shfosl1_3-in-the-absence-or-presence-of-dox-analyzed-by-flow-cytometry">
              BrdU incorporation of BTSC 349 shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3, in the absence or presence
              of Dox, analyzed by flow cytometry.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two independent experiments are presented as mean ± SD (n = 3). Student’s t test,
              relative to the respective control (–Dox): ns = not significant, **p≤0.01. </p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1d"
          title="Figure 7—figure supplement 1D."><label data-itemprop="label">Figure 7—figure
            supplement 1D.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>elda_S8d &lt;- elda(response = figure_S8D_data$Response, 
                 dose = figure_S8D_data$Dose,
                 tested = figure_S8D_data$Tested,
                 group = figure_S8D_data$Group)

figure_S8d &lt;- ggplotlimdil(elda_S8d, 
                           col.group = brewer.pal(6,&quot;Paired&quot;)[1:2]) +
  font_size

# elda_bar(elda_S8d, brewer.pal(6,&quot;Paired&quot;)[1:2])

# elda_S8d # Pvalue for the limited dilution assay

figure_S8d</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="representative-limiting-dilution-analysis-on-btsc-349-shfosl1_3-in-the-presence-or-absence-of-dox-calculated-with-extreme-limiting-dilution-assay-elda-analysis-p00001">
              Representative limiting dilution analysis on BTSC 349 sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 in the presence or absence
              of Dox, calculated with extreme limiting dilution assay (ELDA) analysis; p&lt;0.0001.
            </h3>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1e"
          title="Figure 7—figure supplement 1E."><label data-itemprop="label">Figure 7—figure
            supplement 1E.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>figure_S8e_left &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;S8E_left&quot;, 
           normalizer = &quot;GAPDH&quot;, 
           ref_group = &quot;Mock&quot;, 
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;),
           pvalue = T, 
           title = &quot;MES genes&quot;,
           legend = &quot;none&quot;, 
           palette = c(&quot;#A6CEE3&quot;, &quot;#1F78B4&quot;), 
           ylim=c(0,2)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

figure_S8e_right &lt;- qPCR_data %&gt;%
  plotqPCR(panel = &quot;S8E_right&quot;, 
           normalizer = &quot;GAPDH&quot;, 
           ref_group = &quot;Mock&quot;, 
           levels = c(&quot;Mock&quot;,&quot;Dox&quot;), 
           pvalue = T, 
           title = &quot;PN genes&quot;, 
           ylab = FALSE,
           legend = &quot;right&quot;, 
           palette = c(&quot;#A6CEE3&quot;, &quot;#1F78B4&quot;), 
           ylim=c(0,4), pvalues_y = 3.7) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

figure_S8e &lt;- plot_grid(figure_S8e_left,
                        figure_S8e_right, 
                        rel_widths = c(1.2,0.75), 
                        align = &quot;h&quot;)

figure_S8e</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="mrna-expression-of-fosl1-mes-and-pn-genes-in-btsc-349-shfosl1_3-cells-in-the-absence-or-presence-of-dox-for-3-days">
              mRNA expression of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>, MES, and PN genes in BTSC
              349 sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 cells
              in the absence or presence of Dox for 3 days.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of three experiments are presented as mean ± SD (n = 3, technical replicates),
              normalized to <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">GAPDH</em>
              expression. Student’s t test, relative to –Dox: ns = not significant, *p≤0.05,
              **p≤0.01, ***p≤0.001.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1g"
          title="Figure 7—figure supplement 1G."><label data-itemprop="label">Figure 7—figure
            supplement 1G.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mean_S8G_d1 &lt;- figure_S8G_data %&gt;%
  filter(day == &quot;d1&quot;) %&gt;% 
  group_by(Sample, Treatment, day) %&gt;% 
  summarise_all(mean) %&gt;% 
  dplyr::select(-day) %&gt;% 
  dplyr::rename(d1_mean = value)

figure_S8G_data_norm &lt;- full_join(figure_S8G_data,mean_S8G_d1, 
                             by = c(&quot;Sample&quot;, &quot;Treatment&quot;)) %&gt;% 
  mutate(value_norm = value/d1_mean,
         Sample_treatment = paste(Sample, Treatment, sep = &quot; &quot;))

# figure_S8G_data_norm %&gt;%
#   filter(Sample == &quot;shGFP&quot;) %&gt;%
#   aov(value_norm ~ Treatment * day, data = .) %&gt;%
#   summary()

figure_S8g &lt;- figure_S8G_data_norm %&gt;% 
  ggline(x = &quot;day&quot;, y = &quot;value_norm&quot;,
         add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
         ylab = &quot;Cell growth (A.U.)&quot;,
         add.params =list(size = 1),
         color = &quot;Sample_treatment&quot;, 
         palette = &quot;Paired&quot;,
         legend = &quot;right&quot;) 

figure_S8g</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="cell-growth-of-btsc-380-grown-in-differentiation-conditions-in-the-absence-or-presence-of-dox-measured-by-mtt-assay-absorbance-values-were-normalized-to-day-1">
              Cell growth of BTSC 380 grown in differentiation conditions, in the absence or
              presence of Dox, measured by MTT assay; absorbance values were normalized to day 1.
            </h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two independent experiments are presented as mean ± SD (n = 10, technical
              replicates).</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1i"
          title="Figure 7—figure supplement 1I."><label data-itemprop="label">Figure 7—figure
            supplement 1I.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mean_S8I_d1 &lt;- figure_S8I_data %&gt;%
  filter(day == &quot;d1&quot;) %&gt;%
  group_by(Cell_line, Group, day) %&gt;%
  summarise_all(mean) %&gt;%
  dplyr::select(-day) %&gt;%
  dplyr::rename(d1_mean = value)

figure_S8I_data_norm &lt;- full_join(figure_S8I_data,
                                  mean_S8I_d1, 
                                  by = c(&quot;Cell_line&quot;, &quot;Group&quot;)) %&gt;%
  mutate(value_norm = value/d1_mean)

figure_S8i &lt;- figure_S8I_data_norm %&gt;%
  ggline(x = &quot;day&quot;, y = &quot;value_norm&quot;, 
         facet.by = &quot;Cell_line&quot;,
         add = c(&quot;mean_sd&quot;, &quot;jitter&quot;), 
         ylab = &quot;Cell growth (A.U.)&quot;, 
         add.params =list(size = 1),
         color = &quot;Group&quot;, 
         palette = &quot;Set1&quot;, 
         legend = &quot;right&quot;)

figure_S8i</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="cell-growth-of-cells-as-in-h-measured-by-mtt-assay-absorbance-values-were-normalized-to-day-1">
              Cell growth of cells as in (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">H</strong>), measured by MTT assay;
              absorbance values were normalized to day 1.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of three independent experiments are presented as mean ± SD.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7sup1k"
          title="Figure 7—figure supplement 1K."><label data-itemprop="label">Figure 7—figure
            supplement 1K.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r,">
            <pre class="language-r," itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>mean_S8K_d1 &lt;- figure_S8K_data %&gt;%
  filter(day == &quot;d1&quot;) %&gt;% 
  group_by(Cell_line, Sample, Treatment, day) %&gt;% 
  summarise_all(mean) %&gt;% 
  dplyr::select(-day) %&gt;% 
  dplyr::rename(d1_mean = value)

figure_S8K_data_norm &lt;- full_join(figure_S8K_data, mean_S8K_d1, 
                             by = c(&quot;Cell_line&quot;,&quot;Sample&quot;, &quot;Treatment&quot;)) %&gt;% 
  mutate(value_norm = value/d1_mean,
         Sample_treatment = paste(Sample, Treatment, sep = &quot; &quot;))

figure_S8k &lt;- figure_S8K_data_norm %&gt;% 
  ggline(x = &quot;day&quot;, y = &quot;value_norm&quot;,
         add = c(&quot;mean_sd&quot;, &quot;jitter&quot;),
         facet.by = &quot;Cell_line&quot;,
         ylab = &quot;Cell growth (A.U.)&quot;,
         add.params =list(size = 1),
         color = &quot;Sample_treatment&quot;, 
         palette = &quot;Paired&quot;,
         legend = &quot;right&quot;) 

# figure_S8K_data_norm %&gt;%
#   filter(&quot;Cell_line&quot; == &quot;h543&quot; &amp; Sample == &quot;shGFP&quot;) %&gt;%
#   aov(value_norm ~ Treatment * day, data = .) %&gt;%
#   summary()

figure_S8k</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="cell-growth-of-cells-as-in-j-measured-by-mtt-assay-absorbance-values-were-normalized-to-day-1">
              Cell growth of cells as in (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>), measured by MTT assay;
              absorbance values were normalized to day 1.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data from a representative
              of two independent experiments are presented as mean ± SD (n = 10, technical
              replicates).</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7s1"
          title="Figure 7—figure supplement 1 (static version)."><label data-itemprop="label">Figure
            7—figure supplement 1 (static version).</label><img
            src="index.html.media/fig7-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="further-characterization-of-fosl1-role-in-human-brain-tumor-stem-cells-btscs">
              Further characterization of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> role in human brain tumor
              stem cells (BTSCs).</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Western blot detection of
              FRA-1 in BTSC 349 upon transduction with inducible shGFP (control) or sh<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3, analyzed
              after 3 and 7 days of doxycycline (Dox) treatment; vinculin was used as loading
              control. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>)
              Cell growth of BTSC 349 shGFP and sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 cells, in the absence or
              presence of Dox, measured by MTT assay; absorbance values were normalized to day 1.
              Data from a representative of three independent experiments are presented as mean ± SD
              (n = 15, technical replicates). Student’s t test on day 7, relative to sh<em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 –Dox:
              ***p≤0.001. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) BrdU incorporation of BTSC 349
              shGFP and sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3,
              in the absence or presence of Dox, analyzed by flow cytometry. Data from a
              representative of two independent experiments are presented as mean ± SD (n = 3).
              Student’s t test, relative to the respective control (–Dox): ns = not significant,
              **p≤0.01. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>)
              Representative limiting dilution analysis on BTSC 349 sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 in the presence or absence
              of Dox, calculated with extreme limiting dilution assay (ELDA) analysis; p&lt;0.0001.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) mRNA
              expression of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>,
              MES, and PN genes in BTSC 349 sh<em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 cells in the absence or
              presence of Dox for 3 days. Data from a representative of three experiments are
              presented as mean ± SD (n = 3, technical replicates), normalized to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">GAPDH</em> expression. Student’s t test,
              relative to –Dox: ns = not significant, *p≤0.05, **p≤0.01, ***p≤0.001. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>) Representative
              images of BTSC 380 grown in either neurosphere medium (NS) or in differentiation
              conditions (NS + 0.5% FBS + TNFalpha 5 ng/mL) for 5 days. Scale bar = 250 μm. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>) Cell growth of
              BTSC 380 grown in differentiation conditions, in the absence or presence of Dox,
              measured by MTT assay; absorbance values were normalized to day 1. Data from a
              representative of two independent experiments are presented as mean ± SD (n = 10,
              technical replicates). (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">H</strong>) Western blot detection of
              FRA-1 in h543 and h676 upon transduction with pBabe (control) or pBabe-FOSL1; vinculin
              was used as loading control. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">I</strong>) Cell growth of cells as in
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>), measured
              by MTT assay; absorbance values were normalized to day 1. Data from a representative
              of three independent experiments are presented as mean ± SD. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>) Western blot detection of
              FRA-1 in h543 and h676 upon transduction with the indicated shRNA. To note that FRA-1
              is barely detectable in h676 cells (see also panel <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">H</strong> and <a href="#fig1"
                itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1A</a>). (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">K</strong>) Cell growth of
              cells as in (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">J</strong>), measured by MTT assay;
              absorbance values were normalized to day 1. Data from a representative of two
              independent experiments are presented as mean ± SD (n = 10, technical replicates).</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To study the role of <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> in the context of
          human BTSCs, its expression was modulated in the MES BTSC 380 using two Dox-inducible
          shRNAs (sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 and
          sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_10). We
          confirmed by western blot FRA-1 downregulation after 3 and 7 days of Dox treatment (<a
            href="#fig7" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7B</a>). In
          line to what was observed in mouse glioma-initiating cells, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> silencing in MES BTSC 380
          resulted in reduced cell growth (<a href="#fig7" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7C</a>) with a significant reduction of
          the percentage of BrdU positive cells compared to Dox-untreated cells (<a href="#fig7"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7D</a>). Moreover, <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> silencing decreased
          the sphere-forming capacity of MES BTSC 380 with an estimated stem cell frequency of shGFP
          –Dox = 3.5, shGFP +Dox = 3.4, chi-square p=0.8457; sh<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 –Dox = 4.3, sh<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_3 +Dox = 7.6, chi-square
          p=0.0002195; sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_10
          –Dox = 5.4, sh<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>_10
          +Dox = 11.1, chi-square p=5.918e-06 (<a href="#fig7" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7E</a>). Comparable results were also
          obtained in the MES BTSC 349 cells (<a href="#fig7s1sa" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7—figure supplement 1A–D</a>). In line
          with our mouse experiments, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> silencing resulted in the
          significant downregulation of the MES genes (<a href="#fig7s1e" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7—figure supplement 1E</a>, <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">left panel</em>), whereas
          proneural gene expression was unchanged (<a href="#fig7s1e" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7—figure supplement 1E</a>, <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">right panel</em>). Of note, <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> silencing affected
          BTSCs fitness also when propagated in differentiation conditions (<a href="#fig7s1f"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7—figure supplement
            1F, G</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Similar to what was observed in
          mouse tumors (<a href="#fig6s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6—figure supplement 1B</a>), <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> overexpression in
          two non-MES lines (h543 and h676) did not lead to changes in their proliferation capacity
          (<a href="#fig7s1i" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7—figure
            supplement 1H, I</a>). Most importantly, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> silencing in these non-MES lines
          had no impact on cell growth (<a href="#fig7s1k" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7—figure supplement 1J, K</a>),
          underscoring a mesenchymal context-dependent role for <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> in glioma cells.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We then tested whether <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 modulates the
          MGS via direct target regulation. To this end, we first identified high-confidence <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 binding sites
          in chromatin immunoprecipitation-seq (ChIP-seq) previously generated in the KRAS mutant
          HCT116 colorectal cancer cell line (see Materials and methods), and then we determined the
          counts per million reads (CPM) of the enhancer histone mark H3K27Ac in a set of MES
          (n = 10) and non-MES BTSCs (n = 10) <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Mack et
                al.</span><span>2019</span></a></cite>, selected based on the highest and lowest <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression,
          respectively. PCA showed a marked separation of the two groups of BTSCs (<a href="#fig7"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7F</a>). Differential
          enrichment analysis by DESeq2 revealed 11748 regions statistically significant (FDR &lt;
          0.005) for H3K27Ac at <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 binding sites in either MES
          or non-MES BTSCs (<a href="#fig7" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7G</a>). Next, we compared H3K27Ac
          distribution over <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 binding sites to that of
          the non-MES MR <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">OLIG2</em>.
          This analysis showed that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 binding sites were
          systematically decorated with H3K27Ac in MES BTSCs, while the inverse trend was observed
          at <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">OLIG2</em> binding sites
          (<a href="#fig7" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7H, I</a>).
          Validation by ChIP-qPCR in an independent MES BTSC line (BTSC 349) confirmed FRA-1 direct
          binding at promoters of some MES genes including <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">PLAU</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">TNC</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ITGA5,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CD44</em> in GBM cells (<a href="#fig7"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7J</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Altogether, our data support
          that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1
          regulates MES gene expression and aggressiveness in human gliomas via direct
          transcriptional regulation, downstream of the NF1-MAPK-FOSL1 signaling.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="discussion">Discussion</h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The most broadly accepted
          transcriptional classification of GBM was originally based on gene expression profiles of
          bulk tumors <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib85"><span>85</span><span>Verhaak et al.</span><span>2010</span></a></cite>,
          which did not discriminate the contribution of tumor cells and TME to the transcriptional
          signatures. It is now becoming evident that both cell-intrinsic and -extrinsic cues can
          contribute to the specification of the MES subtype <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Bhat et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib41"><span>41</span><span>Hara et
                  al.</span><span>2021</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib60"><span>60</span><span>Neftel
                  et al.</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib71"><span>71</span><span>Schmitt
                  et al.</span><span>2021</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                  al.</span><span>2017</span></a></cite></span>. Bhat and colleagues had shown that
          while some of the MES GBMs maintained the mesenchymal characteristics when expanded in
          vitro as BTSCs, some others lost the MGS after few passages while exhibiting a higher
          non-MGSs <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib10"><span>10</span><span>Bhat et al.</span><span>2013</span></a></cite>.
          These data, together with the evidence that xenografts into immunocompromised mice of
          BTSCs derived from MES GBMs were also unable to fully restore the MES phenotype <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib10"><span>10</span><span>Bhat et al.</span><span>2013</span></a></cite>,
          suggested that the presence of an intact TME potentially contributed to the maintenance of
          a MGS. In support of this, Schmitt and colleagues have recently shown that innate immune
          cells divert GBM cells to a proneural-to-mesenchymal transition (PN-to-MES) that also
          contributes to therapeutic resistance <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib71"><span>71</span><span>Schmitt et
                al.</span><span>2021</span></a></cite>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The transcriptional GBM
          subtypes were lately redefined based on the expression of glioma-intrinsic genes, thus
          excluding the genes expressed by cells of the TME <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                  et al.</span><span>2021</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                  al.</span><span>2017</span></a></cite></span>. Our MRA on the BTSCs points to the
          AP-1 family member <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>
          as one pioneer TF contributing to the cell-intrinsic MGS. Previous tumor bulk analysis
          identified a related AP-1 family member <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL2</em>, together with <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CEBPB</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">STAT3,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">TAZ</em>, as important regulators of the MES
          GBM subtype <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib9"><span>9</span><span>Bhat et al.</span><span>2011</span></a></cite><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib15"><span>15</span><span>Carro et
                  al.</span><span>2010</span></a></cite></span>. While <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> was also listed as a putative MES
          MR <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib15"><span>15</span><span>Carro et al.</span><span>2010</span></a></cite>,
          its function and mechanism of action have not been further characterized since then. Our
          experimental data show that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> is a key regulator of GBM subtype
          plasticity and MES transition, and define the molecular mechanism through which <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> is regulated. While
          here we have focused on the TFs contributing to MES specifications, previous studies had
          highlighted the role of other TFs, some of which were also identified in our MRA, such as
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">OLIG2, SALL2,</em> and <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">ASCL1</em>, as important
          molecules for non-MES GBM cells <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib76"><span>76</span><span>Suvà et
                al.</span><span>2014</span></a></cite>. Moreover, using a similar MRA, Wu and
          colleagues have recently described also <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SOX10</em> as another TF that contributes to
          the identity of non-MES GBM cells. Strikingly, loss of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SOX10</em> resulted in MES transition
          associated with changes in chromatin accessibility in regions that are specifically
          enriched for FRA-1 binding motifs <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib90"><span>90</span><span>Wu et
                al.</span><span>2020</span></a></cite>. Lastly, using an unbiased CRISPR/Cas9
          genome-wide screening, Richards and colleagues had shown that few of the top TFs
          identified here, such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1, OLIG2,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ASCL1</em>, are genes essential specifically
          either for MES GSCs (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>) or for non-MES GSCs (<em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">OLIG2</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">ASCL1</em>) <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                et al.</span><span>2021</span></a></cite>. This evidence further strengthen the
          relevance of the MRA that we have performed in the identification of important regulators
          of GBM subtype-specific cell biology.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Although consistently defined,
          GBM subtypes do not represent static entities. The plasticity between subtypes happens at
          several levels. Besides the referred MES-to-PN change in cultured GSCs compared to the
          parental tumor <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib10"><span>10</span><span>Bhat et al.</span><span>2013</span></a></cite>, a
          PN-to-MES shift often occurs upon treatment and recurrence. Several independent studies
          comparing matched pairs of primary and recurrent tumors demonstrated a tendency to shift
          towards a MES phenotype, associated with a worse patient survival, likely as a result of
          treatment-induced changes in the tumor and/or the microenvironment <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib64"><span>64</span><span>Phillips
                  et al.</span><span>2006</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib83"><span>83</span><span>Varn et
                  al.</span><span>2021</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib86"><span>86</span><span>Wang et
                  al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                  al.</span><span>2017</span></a></cite></span>. Moreover, distinct
          subtypes/cellular states can coexist within the same tumor <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib60"><span>60</span><span>Neftel
                  et al.</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib63"><span>63</span><span>Patel et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Richards
                  et al.</span><span>2021</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib73"><span>73</span><span>Sottoriva et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib83"><span>83</span><span>Varn et
                  al.</span><span>2021</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib88"><span>88</span><span>Wang et
                  al.</span><span>2019</span></a></cite></span> and targeting these multiple
          cellular components could result in more effective treatments <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib88"><span>88</span><span>Wang et
                al.</span><span>2019</span></a></cite>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">PN-to-MES transition is often
          considered an EMT-like phenomenon, associated with tumor progression <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib29"><span>29</span><span>Fedele et
                al.</span><span>2019</span></a></cite>. The role of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> in EMT has been studied in other
          tumor types. In breast cancer cells, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression correlates with
          mesenchymal features and drives cancer stem cells <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Tam et
                al.</span><span>2013</span></a></cite> and the regulation of EMT seems to happen
          through the direct binding of FRA-1 to promoters of EMT genes such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Tgfb1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Zeb1,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Zeb2</em><span
            data-itemtype="http://schema.org/Number">0</span><cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Bakiri et
                al.</span><span>2015</span></a></cite>. In colorectal cancer cells, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> was also shown to promote cancer
          aggressiveness through EMT by direct transcription regulation of EMT-related genes <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib24"><span>24</span><span>Diesch
                  et al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib50"><span>50</span><span>Liu et
                  al.</span><span>2015</span></a></cite></span>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">It is well established that <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> inactivation is a
          major genetic event associated with the MES subtype <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Verhaak
                  et al.</span><span>2010</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                  al.</span><span>2017</span></a></cite></span>. However, this is probably a late
          event in MES gliomagenesis as all tumors possibly arise from a PN precursor and just later
          in disease progression acquire <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> alterations that are directly
          associated with a transition to a MES subtype <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib62"><span>62</span><span>Ozawa et
                al.</span><span>2014</span></a></cite>. Moreover, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> deficiency has been linked to
          macrophage/microglia infiltration in the MES subtype <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Wang et
                al.</span><span>2017</span></a></cite>. The fact that the enriched
          macrophage/microglia microenvironment is also able to modulate a MES phenotype suggests
          that there might be a two-way interaction between tumor cells and TME. The mechanisms of
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>-regulated chemotaxis
          and whether this relationship between the TME and MGS in GBM is causal remain elusive.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Here, we provide evidence that
          manipulation of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>
          expression levels in patient-derived BTSCs has a direct consequence on the tumor-intrinsic
          MGS activation and that such activation can at least in part be mediated by the modulation
          of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>. Among the
          previously validated MRs, only <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CEBPB</em> appears also to be finely tuned
          by <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em> inactivation.
          This suggests that among the TFs previously characterized (such as <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL2</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">STAT3</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BHLHB2,</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">RUNX1</em>), <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CEBPB</em> might play a dominant role in the
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>-mediated MES
          transition that occurs in a glioma cell-intrinsic manner. However, whether <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> contributes also to
          the cross-talk between the TME and the cell-intrinsic MGS still has to be established.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Furthermore, we show that <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> is a crucial player
          in glioma pathogenesis, particularly in a MAPK-driven MES GBM context (<a href="#fig8"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 8</a>). Most likely, the
          existence of a NF1-MAPK-FOSL1 axis goes beyond GBM pathogenesis since <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> appears to be upregulated in
          concomitance with <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">NF1</em>
          mutations in multiple tumor types (<a href="#fig8s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 8—figure supplement 1</a>). Our findings
          broaden its previously described role in KRAS-driven epithelial tumors, such as lung and
          pancreatic ductal adenocarcinoma <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib82"><span>82</span><span>Vallejo et
                al.</span><span>2017</span></a></cite>. <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NF1</em> inactivation results in Ras
          activation, which stimulates downstream pathways as MAPK and PI3K/Akt/mTOR. RAS/MEK/ERK
          signaling in turn regulates FRA-1 protein stability <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib16"><span>16</span><span>Casalino
                  et al.</span><span>2003</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib84"><span>84</span><span>Verde et
                  al.</span><span>2007</span></a></cite></span>. <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA expression is then most
          likely induced by binding of the SRF/Elk1 complex to the serum-responsive element (SRE) on
          <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> promoter <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib27"><span>27</span><span>Esnault et al.</span><span>2017</span></a></cite>.
          At the same time, FRA-1 can then directly bind to its own promoter to activate its own
          expression <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib24"><span>24</span><span>Diesch et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib48"><span>48</span><span>Lau et
                  al.</span><span>2016</span></a></cite></span> and those of MES genes. This further
          generates a feedback loop that induces MGS, increases proliferation and stemness,
          sustaining tumor growth. FRA-1 requires, for its transcriptional activity,
          heterodimerization with the AP-1 TFs JUN, JUNB, or JUND <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib26"><span>26</span><span>Eferl and
                Wagner</span><span>2003</span></a></cite>. Which of the JUN family members
          participate in the MES gene regulation and whether <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 activates MES gene
          expression and simultaneously represses non-MES genes requires further investigation. Of
          note, pancancer analysis of anatomically distinct solid tumors suggested that c-JUN/JUNB
          and FOSL1/2 are bona fide canonical AP-1 TF configurations in mesenchymal states of lung,
          kidney, and stomach cancers <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib72"><span>72</span><span>Serresi et al.</span><span>2021</span></a></cite>.
          Intriguingly, in support of a direct role in the repression of non-MES genes in GBM cells,
          it has been hypothesized, though not formally demonstrated, that <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FOSL1</em>/FRA-1 could act as a
          transcriptional repressor of a core set of neurodevelopmental TFs, including <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">OLIG2</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">SALL2</em><span
            data-itemtype="http://schema.org/Number">0</span><cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Fiscon et
                al.</span><span>2018</span></a></cite>.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig8" title="Figure 8.">
          <label data-itemprop="label">Figure 8.</label><img src="index.html.media/fig8.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="schematic-model-of-nf1-mapk-fosl1-axis-in-mesenchymal-mes-gliomas">Schematic model
              of NF1-MAPK-FOSL1 axis in mesenchymal (MES) gliomas.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">NF1 alterations or RAS
              mutations lead to the activation of the MAPK signaling that in turn increases FOSL1
              expression both at the mRNA and protein levels. FOSL1 then activates the expression of
              the MES gene signature and possibly inhibits the non-MES gene signature. The scheme
              integrates data presented in this work as well as previously published literature on
              the regulation of <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression by MAPK
              activation.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig8s1"
          title="Figure 8—figure supplement 1."><label data-itemprop="label">Figure 8—figure
            supplement 1.</label><img src="index.html.media/fig8-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="nf1-mutations-are-associated-with-higher-fosl1-expression-in-multiple-cancer-types">
              NF1 mutations are associated with higher <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> expression in multiple cancer
              types.</h3>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FOSL1</em> mRNA expression in the
              indicated tumors of the TCGA, stratified according to <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">NF1</em> mutational status. LGG:
              low-grade glioma; GBM: glioblastoma; ACC: adrenocortical carcinoma; LUAD: lung
              adenocarcinoma; LUSC: lung squamous cell carcinoma; SARC: sarcoma; UCEC: uterine
              corpus endometrial carcinoma; UCS: uterine carcinosarcoma. Wilcoxon p-values are
              indicated.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In conclusion, _FOSL1/_FRA-1 is
          a key regulator of the MES subtype of GBM, significantly contributing to its stem cell
          features, which could open new therapeutic options. Although _FOSL1/_FRA-1 pharmacological
          inhibition is difficult to achieve due to its enzymatic activity, a gene therapy approach
          targeting _FOSL1/_FRA-1 expression through CRISPR/Cas9 or PROTAC, for instance, could
          constitute attractive alternatives to treat mesenchymal GBM patients.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods">
          Materials and methods</h2>
        <table id="keyresource" itemscope="" itemtype="http://schema.org/Table">
          <caption><label data-itemprop="label">Key resources table</label></caption>
          <thead>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Reagent type (species)
                or resource</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Designation</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Source or reference</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Identifiers</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Additional information
              </th>
            </tr>
          </thead>
          <tbody>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Anti-FRA-1 (Rabbit
                polyclonal)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Santa Cruz Biotechnology
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cat#sc-183; RRID:<a
                  href="https://scicrunch.org/resolver/AB_2106928" itemscope=""
                  itemtype="http://schema.stenci.la/Link">AB_2106928</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">WB(1:1000)</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Anti-FRA-1 (Rabbit
                polyclonal)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Santa Cruz Biotechnology
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cat#sc-605, RRID:<a
                  href="https://scicrunch.org/resolver/AB_2106927" itemscope=""
                  itemtype="http://schema.stenci.la/Link">AB_2106927</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">WB(1:1000)</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">anti-CD44 (Rat
                monoclonal)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">BD Biosciences</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cat#550538; RRID:<a
                  href="https://scicrunch.org/resolver/AB_393732" itemscope=""
                  itemtype="http://schema.stenci.la/Link">AB_393732</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">IF(1:100)</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Anti-S100A4 (Rabbit
                polyclonal)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Abcam</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cat#ab27957, RRID:<a
                  href="https://scicrunch.org/resolver/AB_2183775" itemscope=""
                  itemtype="http://schema.stenci.la/Link">AB_2183775</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">IHC(1:300)</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Anti-Ki67 (Rabbit
                monoclonal)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Master Diagnostica</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cat#000310QD</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">IHC(undiluted)</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Anti-FLAG (DYKDDDDK Tag)
                (Rabbit polyclonal)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cell Signaling
                Technology</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cat#2368, RRID:<a
                  href="https: