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Published: 07 December 2020

Single-cell analyses reveal YAP/TAZ as regulators of stemness and cell plasticity in glioblastoma

Nature Cancer volume 2, pages 174–188 (2021)Cite this article

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Abstract

Glioblastoma (GBM) is a devastating human malignancy. GBM stem-like cells (GSCs) drive tumor initiation and progression. Yet the molecular determinants defining GSCs in their native state in patients remain poorly understood. Here we used single-cell datasets and identified GSCs at the apex of the differentiation hierarchy of GBM. By reconstructing the GSCs’ regulatory network, we identified the YAP/TAZ coactivators as master regulators of this cell state, irrespectively of GBM subtypes. YAP/TAZ are required to install GSC properties in primary cells downstream of multiple oncogenic lesions and are required for tumor initiation and maintenance in vivo in different mouse and human GBM models. YAP/TAZ act as main roadblock of GSC differentiation, and their inhibition irreversibly locks differentiated GBM cells into a nontumorigenic state, preventing plasticity and regeneration of GSC-like cells. Thus, GSC identity is linked to a key molecular hub integrating genetics and microenvironmental inputs within the multifaceted biology of GBM.

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Main

GBM is the most frequent and lethal form of brain cancer. GBM is characterized by a high degree of intratumoral cellular heterogeneity and plasticity, contributing to therapeutic resistance and recurrence¹. At the cellular level, many of the malignant traits of GBM have been interpreted through the biology of a stem-like cell population (GSCs), endowed with the ability to self-renew, to initiate tumors in vivo and to give rise to a hierarchy of more-differentiated progeny^2,3,4. These attributes establish operational definitions and corresponding bioassays for GSCs, retrospectively identifying GSCs in primary GBM⁵. However, although these operational definitions are effective at revealing bona fide GSCs and allowing their experimental manipulation, they also necessarily introduce experimental biases that are hardly compatible with the need of molecularly characterizing GSCs as they exist in vivo. For example, GSCs are typically sorted from other GBM cell populations through cell surface markers of disputed and incomplete specificity⁵; moreover, GSCs need to be expanded ex vivo under the confounding influence of artificial culturing conditions, clearly different from those existing in vivo (for example in term of growth factors availability, oxygen or mechanical gradients and more). Then, GSCs are typically cultured ex vivo as neurospheres, where they coexist with their more-differentiated progeny⁶. Thus, the molecular portrait of GSCs that one can obtain through these procedures may differ substantially from one of tumor-resident, native GSCs. In turn, these confounding caveats have so far ultimately limited our understanding of the molecular underpinning of the native GSC state, its key determinants and associated vulnerabilities. Addressing this gap is essential for the design of more effective therapies.

Single-cell analyses have the potential to overcome the above limitations, allowing for a less-biased identification of GSC-like cells in their native environments. Identifying the molecular features of native GSCs in single-cell data is the subject of intense investigation, with some studies focusing on plasticity, some on hierarchies and some on subtype-specific stem cells^7,8,9. Here we started from single-cell data to identify gene regulatory networks of native GSCs, leading to the discovery of YAP/TAZ as key molecular engines at the heart of GSC biology.

Results

A gene expression program identifying native GSCs

To identify native GSC-like cells we aimed at visualizing the natural trajectory of differentiation of GBM cells. For this, we used single-cell (sc)RNA-seq profiles from 32 patients with IDH wild-type GBM^7,8 and then validated our conclusions in large patient cohorts of TCGA and REMBRANDT datasets.

We started our investigation using Monocle¹⁰, an algorithm allowing the ordering of cells in a trajectory on the basis of ‘pseudotime’, a quantitative measure of the progress along a biological process defined by changes in cellular transcriptional programs. By applying this method to a scRNA-seq study of primary GBM samples (Darmanis dataset)⁷, we found that neoplastic cells organized along a tripartite trajectory (Fig. 1a and Extended Data Fig. 1a). To gain more insights on the nature of the cells that are at the opposite ends of this pseudotime trajectory (Fig. 1b), we compared their transcriptomes. Taking advantage of signatures derived from scRNA-seq studies of human neural development (Supplementary Table 1), we first found that neoplastic cells endowed with the highest pseudotime value were enriched for markers of differentiated neural cells (astrocytes and neurons) or of committed precursors, both oligodendrocyte progenitor cells (OPCs) and intermediate neuronal precursors (INPs), as defined by gene set enrichment analysis (GSEA) (Extended Data Fig. 1b). Thus, these neoplastic cells likely represent a population of differentiated glioblastoma cells (DGCs). Conversely, cells with the lowest pseudotime value were enriched for markers of neural stem cells (NSCs) and of early neural progenitors, such as the outer radial glia (oRG), a primate-specific neural progenitor cell type endowed with migratory properties¹¹ (Extended Data Fig. 1b,c). These cells expressed higher levels of markers previously associated with GSCs and NSCs, such as nestin¹², vimentin¹¹, integrin-α6 (ref. ¹³) and SOCS3 (ref. ¹⁴) and lower levels of INP markers in comparison to DGCs (Extended Data Fig. 1c). All together, these data support the identification of the cell population at the start of the pseudotime trajectory as prospective native GSCs.

**Fig. 1: A gene expression program identifying native GSCs.**

Although Monocle is one of the commonly used tools for delineating cell differentiation trajectories, it should be noted that such analyses do not provide a sense of direction. For this, we complemented the above analyses with ‘RNA velocity’¹⁵, a tool allowing the reconstruction of the differentiation trajectory of sole neoplastic cells. Based on the dynamic of mRNA processing and degradation, this algorithm returns, for each cell, a vector (visualized as an arrow) that ultimately indicates the direction of the differentiation. As shown in Extended Data Fig. 1d, these vectors identify a differentiation trajectory that starts from GSCs (red dots) and gradually transits into DGCs (blue dots), as such, solidifying the results previously obtained with Monocle.

At this point, we characterized the gene expression program typifying this GSC-like cell population. We derived a gene signature, hereafter G-STEM, consisting of the genes more significantly upregulated in this cell population when compared to DGCs (Fig. 1c and Supplementary Table 2). By Monocle analyses, the G-STEM signature identifies cells at the start of the pseudotime trajectory in each individual patient (Extended Data Fig. 2a,b). Moreover, to avoid the risk of any polarization in cell trajectory caused by the presence of scRNA of normal cells, we repeated the Monocle analyses using only neoplastic cells (Darmanis dataset), finding that, also in these conditions, G-STEM identifies cells at the start of the differentiation trajectory (Extended Data Fig. 2c).

As revealed by gene ontology analysis (Extended Data Fig. 3a,b and Supplementary Table 3), G-STEM contains genes coding for proteins involved in ECM organization, cell–ECM adhesion, promotion of cell migration, control of cell proliferation and survival, recruitment of innate immune cells, protection against immune responses and transduction of various extracellular signals. Overall, this molecular profile is consistent with the view that native GSCs entertain mutual relationships with their microenvironement^16,17,18. In contrast, the gene expression program of DGCs (DGC signature; Supplementary Table 2) mostly contains genes coding for factors involved in the early steps of neuronal differentiation (Extended Data Fig. 1c), indicating that DGCs encompass neuronal precursor-like cell states.

GBMs can be classified in at least three transcriptional subtypes, defined as proneural, classical and mesenchymal; proneural and mesenchymal GBMs represent two extremes in terms of molecular marker expression and patient survival¹. Subsequent studies have shown that the GSCs of proneural and mesenchymal GBMs are characterized by expression of subtype-specific cell surface markers¹⁹, raising the possibility that different GBM subtypes may originate from biologically distinct GSCs. To tackle this hypothesis, we used an independent scRNA-seq dataset from Neftel et al.⁸, containing 28 tumors including all GBM subtypes. Applying Monocle to the sole neoplastic cells of the Neftel dataset retrieved a complex pseudotime trajectory with many branches (Fig. 1d), consistently with the previously reported heterogeneity of GBM cells and associated plasticity. Nonetheless, at the start of the pseudotime trajectory we could identify tumor cells characterized by the high level of the G-STEM signature (Fig. 1e). Notably, by GSEA, this cell population resembled human NSCs and early neural progenitors (Extended Data Fig. 4a). Collectively, these analyses identified ostensible GSC-like cells in the largest GBM scRNA-seq collection to date. When we focused on the distinct proneural, classical or mesenchymal GBM subtypes, the G-STEM was invariably enriched in the cells at the start of the pseudotime trajectory (Extended Data Fig. 4b). Together, these data indicate that the GSC-like cells across all GBM subtypes share the G-STEM signature.

Data presented so far provided a molecular characterization of GSCs; next, we tested whether this correlated with the biological properties of prospective GSCs within GBM. Consistently with the role of GSCs in tumor initiation, aggressiveness and relapse, a number of studies highlighted that the GSC representation in tumor samples represents a considerable prognostic factor for poor clinical outcome in patients with GBM^20,21,22, raising the possibility that the G-STEM signature may correlate with outcome in patients. To test this prediction, we applied the G-STEM for Kaplan–Meier survival analysis of patients with GBM from two large datasets, the TCGA and the REMBRANDT projects^23,24. Patients were stratified in two groups, according to high or low expression of the G-STEM signature. As shown in Extended Data Fig. 4c, high levels of G-STEM expression are indeed predictive of worse outcome in both datasets. We conclude from this collective set of results that the G-STEM signature represents a new transcriptional program that identifies native GSCs at the top of the differentiation hierarchy in patients with GBM.

Identification of candidate master transcriptional regulators of the GSC state

Next, we aimed to attain a deeper understanding of the molecular nature of native GSCs, whose gene expression and functional attributes ultimately depend on transcriptional regulators (TRs) regulating each other and their downstream target genes (‘regulons’), as such defining gene regulatory networks (GRNs)²⁵. Here we aimed at the identification of master TRs of the GSC state. This quest was challenged by the fact that computational tools able to unbiasedly infer master TRs from scRNA-seq data are currently under-developed; indeed, the intrinsic characteristics of scRNA data (high variability per gene detected between cells and high rates of zero values (dropouts) inherent to mRNA undersampling^26,27), present great technical challenges for TR inference. To overcome these limitations, we combined the established algorithms of ARACNE and VIPER in a multistep computational pipeline, named ‘Rhabdomant’ (Extended Data Fig. 5a and Methods), which first reconstructs a coarse GRN from single-cell gene expression profiles and transcription factors active in GBM (step 1), then ‘prunes’ this GRN by anchoring inferred regulatory interactions to putative direct target genes of TRs (step 2) and finally prioritizes master TRs from the differential enrichment of their regulons in different cell states (step 3; Extended Data Fig. 5a).

Of the list of TRs we obtained from the Rhabdomant pipeline (Supplementary Table 4), we decided to focus our attention on the 27 TRs with the largest regulons (Extended Data Fig. 5b), controlling more than 95% of the GRN (1,409 out of 1,465 genes). Of these TRs, 15 were candidate ‘master’ TRs of the GSC state, indeed cumulatively controlling a large part (96%) of the G-STEM signature. Several of these factors (SALL2, NOTCH2, ETV5 and FOXO1/3) are known regulators of stemness properties in both GSCs and NSCs alike^28,29,30,31. As for the DGC state, we retrieved seven TRs, most of which (TCF12, NFIA, NFIB, SOX9 and SOX4) are in fact known to be involved in NSC differentiation toward neuronal or glial fates^32,33,34. Together these results nicely validate the ability of our computational approach to identify biologically meaningful TRs of cell states out of single-cell transcriptomic data.

Scoring the list of the most significant (false discovery rate (FDR) < 0.0001) candidate master TRs of the GSC state, we focused our attention on the transcriptional coactivators YAP1 and WWTR1 (also known as TAZ). In epithelial tumors, YAP/TAZ are essential for tumor initiation and progression by inducing stemness, proliferation and chemoresistance³⁵. Notably, YAP/TAZ activation has recently emerged as hub for tumor–stromal interaction³⁵, integrating multiple inputs, including mechanical signaling and hypoxia, which are indeed profoundly dysregulated in the GBM microenvironment and are associated with GBM recurrence^1,16,18. YAP/TAZ have been reported to be regulated by CD109 (ref. ¹⁹), a marker of mesenchymal GBM, although the functional significance of this regulation remains undefined. Moreover, elevated YAP/TAZ expression levels have been noted in GBM and correlated with shorter survival of patients with glioma^36,37, but their functional involvement in GSCs remains unexplored. Our interest on YAP/TAZ was further motivated by the fact that AP-1 family members also scored at the top of the candidate master TRs of the GSC state in our analyses. Indeed, a series of recent findings have revealed that AP-1 is a pervasive transcriptional partner of YAP/TAZ on a vast number of cis-regulatory elements and that AP-1 is functionally required for YAP/TAZ responses^38,39,40. Collectively, these considerations prompted us to focus on YAP/TAZ activity as a candidate overarching factor in defining the GSC state.

YAP/TAZ activity is associated with the GSC state

We addressed more directly whether YAP/TAZ are indeed specifically active in native GSCs. We thus zoomed into the GRN architecture and asked to what extent the GSC state, as defined by G-STEM, could be explained by placing YAP/TAZ at the center of a gene regulatory cascade controlling progressive layers of downstream TRs and their targets (Fig. 2a). The YAP/TAZ-controlled GRN structure contains FOXO1 as immediate YAP/TAZ regulated downstream TR; FOXO1 regulates FOS and SALL1 that in turn regulate FOXO3, BCL6 and ERF. Collectively the target genes of these TRs account for a remarkable 73% of the whole G-STEM gene list, with YAP/TAZ alone directly controlling one-third of this network (Supplementary Table 5).

**Fig. 2: YAP/TAZ are master TRs of the GSC state.**

Next, we addressed experimentally to what extent YAP/TAZ are in fact required for G-STEM expression and, more broadly, for GSC biology in vivo. For this, we triggered YAP/TAZ knockout in pre-established, full blown GBM-like neoplasms derived from transformed cells bearing different oncogenic insults (KRasG12V/shp53, HER2CA or shNF1/shp53); as detailed below, these transformed cells display GSC-like properties, including the ability to generate orthotopic and subcutaneous tumors driven by YAP/TAZ and displaying several features of human GBM. To assess the effects of YAP/TAZ depletion in pre-established tumors, we opted for subcutaneous injection¹², as this setup allows careful monitoring of tumor growth and unambiguous retrieval of neoplastic cells. While YAP/TAZ wild-type tumors kept expanding, YAP/TAZ knockout halted tumor growth (Extended Data Fig. 6a–e), a result consistent with loss of GSCs in tumorigenic cell populations^12,41. From YAP/TAZ wild-type tumors (four out of four mice), we could retrieve gliomasphere-forming cells that could be passaged ex vivo and that are able to re-initiate tumorigenesis in vivo (Extended Data Fig. 6f). In contrast, no gliomasphere-forming cells could be retrieved from YAP/TAZ-knockout tumors (n = 5). Notably, at the molecular level, YAP/TAZ inactivation in tumors caused loss of G-STEM expression (Fig. 2b) and also led to the collapse of the TR architecture identified in our GRN as operating downstream of YAP/TAZ (with loss of FOXO1, SALL1, FOS, FOXO3, BCL6 and ERF expression; Fig. 2c). Collectively the findings indicate that YAP/TAZ are required to preserve the GSC state, which is identified by a YAP/TAZ-dependent G-STEM signature in vivo.

To further validate the connection between YAP/TAZ activity and GSCs, we quantified by immunohistochemistry (IHC) active TAZ (nuclear and stabilized) in human GBM sections, revealing massive activation in the perinecrotic areas and a progressively more salt-and-pepper, heterogeneous staining toward the tumor periphery (Fig. 2d); these findings are nicely consistent with previous reports suggesting that the GBM perinecrotic areas are indeed enriched in GSC representation^1,18. Moreover, we also detected the highest levels of G-STEM in perinecrotic areas of the Ivy Atlas⁴², a molecular pathology atlas providing gene expression data from human GBM after laser microdissection and RNA-seq of different histologically defined tumor areas (Fig. 2e). Thus, G-STEM expression peaks in the same tumor areas where active TAZ peaks.

YAP/TAZ are required for oncogenes to confer GSC properties to normal neural cells

Given the activation of YAP/TAZ transcriptional programs in prospective GSCs, we next asked whether YAP/TAZ activity may represent an early addiction in gliomagenesis. Compelling evidence indicate that the cells of origin of GBM include various cell types, including astrocytes^43,44, OPCs^45,46,47 and, as recently described in human GBMs, cells of the subventricular zone (SVZ)⁴⁸ that display mixed astrocyte-like and NSC-like features, such as expression of GFAP, NESTIN and SOX2 (ref. ⁴⁸ and Extended Data Fig. 7a). Typical oncogenic lesions in GBM entail overactivation of the RTK/RAS pathway through genetic amplification or activating mutations of RTK-coding genes (PDGFRα, EGFR and HER2) or mutation of the RAS inhibitor NF1 (ref. ⁴⁹).

To recapitulate, at least in part, the early step of gliomagenesis, we introduced activated oncogenes in astroglial cells from newborn mice that are highly similar to the SVZ cell population⁵⁰, expressing mixed astrocyte-like (GFAP) and NSC markers (NESTIN, SOX2) (Extended Data Fig. 7b,c). Newborn astroglial cells were transduced with lentiviral vectors coding for the activated forms of PDGFR-α, EGFR or HER2, with vectors coding for oncogenic KRasG12V plus short hairpin (sh)RNA against p53 or shRNAs against NF1 and p53; this list includes established drivers of human GBM⁴⁹. After approximately 3 weeks, gliomaspheres emerged from all oncogene-expressing primary astroglial monolayers and never from controls (Fig. 3a). Gliomaspheres could be dissociated as single cells and expanded over several passages in suspension (Extended Data Fig. 7d); when transplanted orthotopically (intracranially) in immunocompromised mice, dissociated gliomasphere cells gave rise to tumors well-recapitulating key histological features of human GBM, also resembling giant cell glioblastoma⁵¹ and displaying elevated levels of active TAZ in the perinecrotic regions (Extended Data Fig. 7e). Similar tumors emerged after subcutaneous cell transplantation, where they also included areas of necrosis surrounded by pseudopalisading cells, also common features of human GBM (Extended Data Fig. 7f).

**Fig. 3: YAP/TAZ are required for oncogene-dependent transformation of primary normal neural cells.**

Upon limiting-dilution transplantation, we found that 1,000 dissociated gliomasphere cells were sufficient to seed tumor formation (Extended Data Fig. 7g). When explanted and re-cultured ex vivo, tumor cells remained able to form gliomaspheres that could be expanded again over several passages in culture, retaining the capacity of tumor initiation upon serial transplantation in vivo (Extended Data Fig. 7h). As control, we could not detect any formation of spheroids from control newborn astroglial cells ex vivo (Fig. 3a); then, no outgrowth whatsoever could be detected when these parental cells were injected in recipient mice. In other words, overexpression of RAS/RTK oncogenes in otherwise normal primary cells specifically converts them into cells endowed with typical properties of GSCs, such as tumorigenic potential and the ability to undergo self-renewal, in vitro and in vivo. In accordance, by RNA-seq, tumors displayed elevated expression of the G-STEM signature and low expression of the DGC signature when compared with the original astroglial cell cultures (Extended Data Fig. 7i).

Are YAP/TAZ activated by oncogenic mutations during acquisition of a GSC-like state? To address this question, we first verified that YAP/TAZ were activated during the early phases of reprogramming of newborn astroglial cells into GSC-like cells by oncogenes. For this, we transduced early postnatal astroglial cells with 8xGTIIC-RFPDD, a YAP/TAZ-responsive lentiviral reporter⁵², and then monitored the expression of red fluorescent protein (RFP) during oncogene-mediated reprogramming. As shown in Fig. 3b, oncogenes specifically induced activation of the reporter within 24 h of reprogramming; RFP-positive cells acquired elongated shapes reminiscent of that of the radial glia, spreading and migrating, typically converging toward one cell cluster (Extended Data Fig. 8a). Overall, these data indicate that YAP/TAZ activation is an early event during reprogramming of normal astroglial cells into tumorigenic GSC-like cells.

We next asked whether YAP/TAZ are required for oncogene-mediated reprogramming of normal cells into GSC-like cells. To this end, we derived astroglial cells from newborn R26^CAGCreERT2;Yap^fl/fl;Taz^fl/fl mice, in which genetic ablation of YAP/TAZ was induced after CRE activation by 4OH-tamoxifen (4OH-TAM) treatment. As shown in Fig. 3c, gliomasphere formation by various RTK oncogenes, either transduced alone or in combination with shRNA-targeting tumor suppressors, was completely abolished by YAP/TAZ deletion.

Next, we investigated at the molecular level how YAP/TAZ contribute to oncogenic reprogramming of normal neural cells into GSCs. We performed transcriptomic analyses by RNA-seq of astrocytes from Yap^fl/fl;Taz^fl/fl mice transduced with lentiviral vectors encoding CreERT2 and a doxycycline-inducible HER2CA, in absence or presence of 4OH-TAM at the incipit of cell transformation. By hierarchical clustering of samples using genes whose expression is significantly altered by HER2CA, we found that the transcriptome of YAP/TAZ-knockout cells remained remarkably similar to that of control/nontransformed cells in spite of HER2 overexpression (Fig. 3d). Specifically, HER2CA expression in newborn astroglial cells induces YAP/TAZ-dependent downregulation of astrocyte markers and upregulation of NSCs markers⁵³ (compare lanes 1 and 2 of Fig. 3e with lanes 1 and 2 of Fig. 3f). Notably, 45 out of 46 NSCs marker genes upregulated by HER2CA in control cells are dependent on YAP/TAZ (Fig. 3g); however, at the incipit of oncogenic transformation, YAP/TAZ seem dispensable for the expression of genes involved in proliferation (Fig. 3f; lane 4). Overall, these results indicate that the main function of YAP/TAZ during GBM initiation is to repress differentiation and promote the acquisition of NSC-like properties.

YAP/TAZ are required for the intrinsic differentiation plasticity of GBM cells

One of the most lethal properties of GBM cells is their intrinsic plasticity⁵⁴, allowing for interconversion between GSC and non-GSC states depending on a number of factors, as such promoting tumor relapse⁵⁵. We thus tested the role of YAP/TAZ in GBM plasticity. For this, we took advantage of a previously reported differentiation/de-differentiation protocol for human GBM cells^54,56 (Fig. 4a). Specifically, we used two independent primary GBM cell lines, HuTu10 and HuTu13 (ref. ⁵⁷), corresponding, by transcriptomics, to proneural and mesenchymal subtypes, respectively. When cultured in serum-free, growth-factor-rich conditions, both cell types display no expression of the astrocyte marker GFAP; however, after exposure to serum and BMP2-containing medium, cells en masse differentiate into GFAP-positive cells with a stellate morphology, reminiscent of normal astrocytes (Fig. 4b,d,e; compare lanes 1 and 2). This event is accompanied by a dramatic decrease in the ability to form gliomaspheres (Fig. 4f,g; compare lanes 1 and 2), indicating that acquisition of astrocyte-like properties is associated with loss of stemness properties. Of note, this process is also accompanied by relocalization of TAZ in the cytoplasm, indicating that YAP/TAZ are inactivated during differentiation (Fig. 4b,c; compare lanes 1 and 2). The return to a GSC-like, less-differentiated phenotype can be induced by placing cells back into serum-free, growth-factor-rich culture conditions (de-differentiation), promoting YAP/TAZ re-entry in the nucleus, progressive disappearance of GFAP and re-acquisition of gliomasphere-forming abilities (Fig. 4b–g; compare lanes 2 and 3). Remarkably, under these conditions, YAP/TAZ depletion prevented de-differentiation, as cells remained as differentiated astrocyte-like cells in spite of being exposed to GSC-inducing medium, as revealed by their retaining high levels of GFAP (Fig. 4b,d,e; compare lanes 3 and 4), and permanent loss of gliomasphere-forming ability (Fig. 4f,g; compare lane 3 with lanes 4 and 5).

**Fig. 4: YAP/TAZ control GBM cell plasticity.**

YAP/TAZ preserve the GSC state by preventing differentiation

Having shown above the role of YAP/TAZ at inducing GSC-like properties in otherwise normal cells at the incipit of oncogenic transformation or in differentiated tumor cells, we next asked whether YAP/TAZ are required to preserve GSC-like cells after their establishment. For this, we first monitored the effects of YAP/TAZ ablation within pre-established gliomaspheres. YAP/TAZ depletion induced progressive disaggregation and demise of gliomaspheres, irrespectively of the type of oncogenic lesion that drove gliomasphere emergence (Fig. 5a–e). After single-cell replating, YAP/TAZ-knockout cells were unable to sustain any outgrowth/passaging (Extended Data Fig. 9b), indicating that YAP/TAZ are essential for self-renewal.

**Fig. 5: YAP/TAZ are required to prevent GSC differentiation.**

To investigate at the molecular level the consequences of YAP/TAZ ablation, we first compared the transcriptomes of gliomasphere cultures arising from KRasG12V/shp53-transformed R26^CAGCreERT2;Yap^fl/fl;Taz^fl/fl astroglial cells, either treated with vehicle control or with 4OH-TAM. Notably, loss of YAP/TAZ in gliomaspheres was accompanied by upregulation of proliferation markers (Fig. 5f; lane 4), indicating that processes other than loss of proliferation are relevant for gliomasphere demise after YAP/TAZ inactivation. As shown in Fig. 5f, upon YAP/TAZ ablation, NSCs markers were strongly downregulated, whereas markers of neuroblasts were induced (including Dlx2, Stmn1, TUJ1 and many others; Supplementary Table 6), suggesting that YAP/TAZ are primarily required in GSCs to prevent their differentiation along the neuronal lineage. Consistently, YAP/TAZ ablation in gliomaspheres also caused upregulation of markers of INPs (Fig. 5f; lane 3), that is, proliferating neuroblast precursors.

YAP/TAZ control GBM initiation and differentiation in vivo

In keeping with the relevance of YAP/TAZ at preserving GSCs-like cell population in vitro, we next verified whether YAP/TAZ are required for one of the cardinal features of GSCs in vivo, tumor initiation. We tested this idea by injecting GBM cells orthotopically, in the brain of immunocompromised mice. We first used transformed Yap^fl/fl;Taz^fl/fl cells carrying either shNF1/shp53 or KRasG12V/shp53 and dual luciferase-GFP expression vectors, allowing for a noninvasive readout of tumor growth. Mice injected intracranially with parental GBM cells invariably formed large tumor masses with invasive behavior, as revealed by bioluminescence and by histological analyses (Fig. 6a–d and Extended Data Fig. 10a–c). In contrast, YAP/TAZ-knockout cells failed to form any outgrowths (Fig. 6a–c and Extended Data Fig. 10a–c). Similar results were obtained in immunocompromised mice by orthotopically injected control versus YAP/TAZ-depleted human GBM cells (HuTu13 cells, Extended Data Fig. 10d–f).

**Fig. 6: YAP/TAZ are required for GBM initiation by preventing GSC differentiation.**

Next, we validated these findings using some of the most established models of mouse GBM, the mouse glioma cell lines GL261 and CT2A. After orthotopic transplantation in syngeneic mice, these cells form tumors displaying several characteristics of human GBMs, including intratumoral heterogeneity, pseudopalisading necrosis, radio resistance and chemoresistance⁵⁸. To study the role of YAP/TAZ in these models, we injected GL261 or CT2A expressing anti-YAP/TAZ doxycycline-inducible shRNAs in the brain of immunocompetent syngeneic mice. Control cells formed large tumor masses, whereas upon doxycycline treatment, YAP/TAZ-depleted cells did not (Fig. 6e–g and Extended Data Fig. 10g–i). However, in some brain sections we could still detect residual YAP/TAZ-depleted cells tumor cells, thus allowing in vivo investigation of their differentiation state by immunofluorescence. As shown in Fig. 6h for GL261 cells, YAP/TAZ depletion causes a strikingly wholesale differentiation toward the neuronal lineage, being essentially all cells positive for TUJ1. In contrast, tumors generated by parental GL261 cells are almost invariably negative for TUJ1, with the exception of a minority of cells (about 3% of tumor cells typically found as small clusters).

We then confirmed that tumor cell differentiation is also at the root of halted tumor growth after YAP/TAZ inactivation in pre-established lesions in vivo (as in Extended Data Fig. 6). As mentioned above, loss of YAP/TAZ causes depletion of GSCs and downregulation of the G-STEM signature. Remarkably, this is accompanied by massive upregulation in the expression of early markers of neuronal differentiation (for example, Ascl1, Tau, Tuj1, NCAM, Stathmin and SOX11) (Fig. 6i). By IF, YAP/TAZ-knockout tumor cells acquired the expression of TUJ1, confirming that YAP/TAZ prevents GSC differentiation, with their ablation skewing the fate of GBM cells toward a neuronal-like fate (Fig. 6j and Extended Data Fig. 10j). We conclude from this collective set of results that YAP/TAZ are required for tumorigenesis and to prevent differentiation in vivo in multiple cellular and experimental contexts, in primary astroglial/SVZ-like cells transformed with different activated oncogenes and in classic GBM models growing in a syngeneic context.

Discussion

In this work, we advance on the molecular foundations of the GSC state, identifying YAP/TAZ as the transcriptional determinants that define GSC populations in their native state. YAP/TAZ activation occurs downstream of classic oncogenic drivers of GBM to induce GSC-containing tumorigenic cell populations; within such populations, YAP/TAZ remain key for self-renewal of gliomaspheres in vitro and for both tumor initiation and maintenance in vivo, as shown in different mouse and human GBM cellular contexts. Consistently, YAP/TAZ activity, as monitored by G-STEM signature, can identify prospective GSCs in heterogeneous GBM cell populations, at least in IDH wild-type GBMs.

A recent seminal study by Neftel et al.⁸ presented a model in which cellular heterogeneity of IDH wild-type GBM reflects the coexistence, within each individual tumor, of four cellular states, able to interconvert into each other. Still unclear is whether these cell subtypes are connected to a shared GSC state or whether different stem cell populations exist in different GBM subtypes. The present identification of the G-STEM only in part advances on these open issues. Our analyses of the scRNA-seq data of Neftel et al. reveal GSC populations earmarked by the G-STEM transcriptional program in different GBM subtypes. However, projecting a shared molecular signature into cell populations should be interpreted with caution, as this may imply either of two scenarios: one in which the G-STEM indeed identifies a specific GSC population common to all GBM subtypes or alternatively, that distinct stem cells in different GBM may share part of their anti-differentiation mechanisms and that one of this overlapping program may be highlighted by elevated YAP/TAZ activity as denoted by G-STEM.

Notably, we found that these native GSCs display a hybrid phenotype between the astrocyte-like and mesenchymal-like cell states of Neftel et al. For example, we found that native GSCs are enriched in molecular markers typical of both astrocyte-like state (such as HOPX, GFAP and MLC1) and of the mesenchymal-like state (such as VIM, CD44 and LGALS3) (Supplementary Table 2). This conclusion is apparently in contrast with our observation that GSCs acquire astrocyte-like features after in vitro differentiation induced by serum. However, normal astrocytes do not express mesenchymal markers¹¹ and seem transcriptionally distinct from GSCs in our analyses. Rather, it is tempting to speculate that native GSCs may resemble fetal-like cells, such as early neural progenitors, which also coexpress markers of astrocyte (such as HOPX and GFAP) and mesenchymal-like states (such as VIM and LGALS3)¹¹.

Restoring differentiation capacity of GBM might represent a therapeutic option⁵⁶, although this is complicated by the ability of differentiated cells to revert back to a GSC state⁵⁴. Here we advance in these directions by showing that YAP/TAZ activity peaks in GSCs and that targeting YAP/TAZ is instrumental to cause their irreversible conversion into committed neural progenitors and more-differentiated neural cell types. Of note, this occurs independently of proliferation control, providing a departure from current models envisioning a central role of YAP/TAZ as regulators of cell-cycle progression in cancer³⁸. We propose that an anti-YAP/TAZ therapy has the potential to be more effective than current chemotherapeutic regimens targeting cell proliferation, whose efficacy in patients with GBM is in fact very limited. Strategies aimed at blunting YAP/TAZ activity in vivo have been recently proposed, including inhibitors of Brd4, a YAP/TAZ co-activator⁵⁹ that represents an addiction of GBM although only in vivo⁶⁰, consistently with the identification of YAP/TAZ activity at the core of the native GSC state.

In conclusion, we have here advanced on a key molecular underpinning of the GSC native state, thus unveiling a core vulnerability and addiction of GBM, all in all hinting at new perspectives to ameliorate treatment of a devastating malignancy.

Methods

Analysis of scRNA-seq data from primary glioblastomas

Darmanis dataset

We analyzed scRNA-seq data of primary glioblastoma samples from Darmanis et al.⁷, retaining the cell annotation provided by the authors. Raw reads were downloaded from the Gene Expression Omnibus (GEO) under accession code GSE84465 and mapped to the human reference genome GRCh38 using STAR⁶¹. Raw gene counts were obtained using the featureCounts function of the Rsubread R package⁶² and the GENCODE release 25 (GRCh38.p7) basic gene annotation. Quality controls and normalization were carried out using Seurat⁶³ (v.2.3.1) with default parameters. We retained for subsequent analyses 3,188 cells (out of 3,588 cells) with (1) number of unique detected genes between 500 and 8,000; (2) total number of detected molecules between 1 × 10⁵ and 1.5 × 10⁶; and (3) fraction of reads mapping to the mitochondrial genome ≤0.2. Pseudotime trajectories on neoplastic and normal neural cells were constructed using Monocle2 (ref. ¹⁰; v.2.8.0); we used the unsupervised ‘dpFeature’ procedure to order cells based on genes that differ between clusters and the ‘DDRTree’ algorithm for the dimensionality reduction step, as recommended by the authors. Based on the distribution of pseudotime values, we selected two populations of neoplastic cells, with pseudotime values below the first quartile (low), and above the third quartile (high), respectively. We compared the transcriptomes of these two populations using the FindMarkers function of Seurat, setting the following parameters: only.pos = F, min.pct = 0.01, logfc.threshold = 0.01, min.cells.gene = 1 and min.cells.group = 1. Results from the analysis of differential expression were functionally annotated using GSEA and gene sets derived from previously published gene signatures^{11,64,65,66,67} (Supplementary Table 1). The GSEA software (http://software.broadinstitute.org/gsea/index.jsp) was applied in preranked mode to the gene list ranked on log₂ fold change. Gene sets were considered significantly enriched at FDR ≤ 0.05 when using classic enrichment statistics and 1,000 permutations of gene sets.

The same pseudotime trajectory analysis was applied to the neoplastic and normal cells of each single patient (with the exclusion of patient BT-S6 that was characterized by a very limited number of sequenced cells) and to the sole neoplastic cells of all patients. In both cases, we defined two populations of neoplastic cells, one with pseudotime values below the first quartile (Q1 cells, low) and the other with pseudotime values above the third quartile (Q3 cells, high) of the distribution of pseudotime values, and compared the transcriptomes of these two populations (low versus high) as described above. In the case of the pseudotime trajectory analyses of single patients, we set the root on the end that is opposite to those containing normal cells.

To reconstruct the differentiation trajectory of the neoplastic cells, we estimated the RNA velocities of neoplastic cells using velocyto.R, a package for the analysis of expression dynamics in scRNA data¹⁵. In velocyto.R, we estimated the RNA velocity with the gene-relative model, which combines cell kNN pooling with the γ fit based on an extreme quantiles and set the parameter kCells = 25 and the parameter fit.quantile = 0.02.

G-STEM signature

From the differentially expressed genes between low and high cells, we selected 895 genes with fold change ≥1.25 and adjusted P value ≤0.05 to define the G-STEM signature (Supplementary Table 2). We annotated the G-STEM signature on the Biological Process gene ontologies using the Enrichr website (https://maayanlab.cloud/Enrichr/).

Neftel dataset

We analyzed scRNA-seq data of primary glioblastoma samples from Neftel et al.⁸, retaining the cell annotation provided by the authors. Expression matrix and metadata were downloaded from the Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma#study-summary). Pseudotime trajectories on the sole neoplastic cells were constructed using Monocle2 (ref. ¹⁰; v.2.8.0); we used the unsupervised ‘dpFeature’ procedure to order cells based on genes that differ between clusters and the ‘DDRTree’ algorithm for the dimensionality reduction step. Based on the distribution of pseudotime values, we select two populations of neoplastic cells, the first with pseudotime values below the first quartile (<Q1), and the second with pseudotime values above the first quartile (>Q1). We compared the transcriptomes of the two populations of neoplastic cells calculating the log₂ fold change of the expression level for each gene. We applied the GSEA software in preranked mode to the gene list ranked on log₂ fold change to evaluate the functional enrichment for the same gene sets tested in the Darmanis dataset (Supplementary Table 1). Gene sets were considered significantly enriched at FDR ≤ 0.05 when using classic enrichment statistics and 1,000 permutations of gene sets.

Signature scores

Signature scores have been calculated as the average expression of the genes comprised in each signature.

All analyses were performed in R 3.5.0.

Collection and processing of GBM transcriptomes from the TCGA and REMBRANDT studies

Gene expression data of TCGA and REMBRANDT were obtained from the GEO (Supplementary Tables 8 and 9). For the TCGA dataset, related clinical and molecular subclass data were obtained from Supplementary Table 7 of ref. ²³ and NCI Genomic Data Commons (https://gdc.cancer.gov/about-data/publications/lgggbm_2016), whereas clinical data for the REMBRANDT dataset were downloaded from GEO (GSE108474).

For the TCGA dataset, expression values were generated from intensity signals using a custom definition file (CDF) for Affymetrix HT HG-U133A arrays based on Entrez genes (hthgu133ahsentrezgcdf v.21.0.0; http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/21.0.0/entrezg.asp). Intensity values for 552 samples (n = 542 brain glioblastomas and n = 10 brain tissues; Supplementary Table 8) were background-adjusted, normalized using quantile normalization and gene expression levels were calculated using median polish summarization of Bioconductor affy package (multi-array average procedure, RMA⁶⁸). For the REMBRANDT dataset, after removing low grade gliomas, probe-level signals for a total of 248 samples (n = 220 glioblastomas and n = 28 nontumor brain tissues; Supplementary Table 9) were converted to expression values using RMA and a custom CDF for Affymetrix HG-U133Plus2 arrays based on Entrez genes (hgu133plus2hsentrezgcdf v.21.0.0; http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/21.0.0/entrezg.asp). All analyses were performed in R 3.5.0.

Kaplan–Meier survival analysis of human GBM datasets

To identify two groups of tumors with either high or low G-STEM signature we applied the following classification rule. Briefly, each tumor was classified as G-STEM signature high if the average standardized expression level of G-STEM signature genes (fold change ≥2; Supplementary Table 2) was larger than the mean of average standardized expression signals of all samples and as G-STEM signature low and vice versa. This classification was applied to expression values of TCGA and REMBRANDT glioblastomas. To evaluate the prognostic value of the G-STEM signature, we applied the Kaplan–Meier method on the patients’ survival data to estimate the probabilities that patients classified as ‘G-STEM high’ and ‘G-STEM low’ would survive. To confirm these findings, Kaplan–Meier curves were compared using the log-rank (Mantel–Cox) test. P values were calculated according to the standard normal asymptotic distribution. Survival analysis was performed in GraphPad Prism.

Identification of candidate master transcriptional regulators

To identify candidate master transcriptional regulators we assembled a three-step computational workflow named ‘Rhabdomant’.

In the first step, to reconstruct the GRN, we initially defined a list of transcription factors active in GBM, defined as TRs potentially associated with chromatin in GBM tumors. For this, we took advantage of the epigenetic analyses provided by a recent large-scale ATAC-seq profiling of several human tumor types, including GBM⁶⁹. We carried out a DNA-binding motif enrichment analysis using the HOMER algorithm on the open chromatin regions of GBMs and then selected the list of transcription factors whose DNA-binding motifs were highly enriched (FDR < 0.0001) in these genomic regions. We manually implemented this list with partner transcriptional cofactors (Supplementary Table 10) for a total list of 151 TRs.

Next, we applied the reverse-engineering algorithm ARACNe-AP⁷⁰ to gene expression signals of neoplastic cells of the Darmanis scRNA-seq dataset, allowing to map the interactions between GBM-specific transcription factors and their co-regulated genes. To limit the effects of scRNA-seq data sparsity, we removed genes expressed only in a limited number of cells (normalized counts >0 in fewer than 100 cells).

In the second step, in order overcome spurious TR-target gene association intrinsic to the noise generated by scRNA-seq data, we pruned the GRN by retaining only candidate direct targets genes of each TR. For this, we took advantage of the association map of between each ATAC-seq peak and its target genes, as provided by Corces et al.⁶⁹ (Supplementary Table 11). We then intersected this map with the gene interactomes obtained in step 1, in so doing retaining in the GRN only the lists of target genes (regulons) associated to a binding motif for a candidate TR in their cis-regulatory elements.

In the third step, we interrogated the GRN with the VIPER algorithm⁷¹ to identify candidate master TRs of GSCs and DGCs, namely, transcriptional determinants whose regulons were enriched of genes activated in one of the two opposite cell populations of the pseudotime trajectory of neoplastic cells, that is the G-STEM and DGC signatures. Based on VIPER analysis, we defined as candidate master TRs those regulomes (TR and its regulon) with an FDR ≤ 0.05 and a number of target genes >70. This resulted in 27 candidate master regulators, of which 15 were candidate master TRs of the GSC state and 7 were candidate master TRs of the DGC state.

An extended version of these procedures is provided as Protocol Exchange, at https://doi.org/10.21203/rs.3.pex-1230/v1.

Comparison of G-STEM signature and master TR regulomes

To compare the G-STEM signature with the GBM regulomes, we calculated the enrichment of G-STEM genes in the regulomes of YAP/TAZ and of their downstream TRs. Specifically, we considered the first three layers of the YAP/TAZ gene regulatory network and identified six TRs (FOXO1, FOS, SALL1, FOXO3, BCL6 and ERF); then we calculated the intersection between the 313 G-STEM signature genes contained in the GBM regulons resulting from step 2 and the targets positively interacting (mode of action >0) with TAP/TAZ or one of their downstream TRs (Supplementary Table 5). The statistical significance of the overlaps was calculated in R using the fisher.test function of the stat package.

Analysis of samples from the Ivy Glioblastoma Atlas

RNA-seq data from six GBM were downloaded from the Anatomic Structures RNA-Seq repository of the Ivy Glioblastoma Atlas Project⁴² (Supplementary Table 12). Gene expression was quantified in R 3.3.1 using the featureCounts function of the Rsubread R package⁶² and the UCSC gene annotation (GRCh37/hg19). Data normalization was performed using the edgeR package⁷² (v.3.20.0); briefly, raw counts were normalized to counts per million (CPM) mapped reads and to fragments per kilobase per million mapped reads (FPKM). Gene expression data were then standardized between the samples composing each tumor. Signature scores were calculated as the average expression of the genes comprised in each signature. For each area of each tumor, the signature scores were calculated as the average of the signature scores of the different samples of the same area from the same tumor.

TAZ immunohistochemistry

Archival frozen GBM specimens were collected at the Azienda Ospedaliera of Padua. For IHC, 4-μm thick sections were obtained from tumor samples. IHC was performed with rabbit polyclonal anti-TAZ (Sigma, HPA007415; 1:50 dilution) as previously described⁷³. For mouse tissues, IHC was performed using a fully automated system (Bond-maX; Leica).

Slide images were captured using the D-Sight-F system for digital pathology (Menarini Diagnostics) and the percentage of TAZ-positive nuclei was determined using the Nuclei Analysis module of the D-Sight Viewer software.

Reagents and plasmids

Doxycycline hyclate, tamoxifen, 4-hydroxytamoxifen, hygromycin and puromycin were obtained from Sigma. Fibronectin was obtained from Santa Cruz Biotechnologies. Recombinant human/mouse/rat BMP2, hEGF, hbFGF, mEGF and mbFGF were obtained from Peprotech. BIT9500 Serum Substitute was from StemCell Technologies. Growth-factor-reduced Matrigel (Phenol Red-free) was obtained from Corning. XenoLight D-Luciferin K+ Salt Bioluminescent Substrate was obtained from PerkinElmer. Cre- and GFP-expressing adenoviruses were obtained from the University of Iowa, Gene Transfer Vector Core.

For inducible expression of HER2CA, HER2CA cDNA (from pcDNA3-HER2CA; Addgene, 16259) was subcloned in FUW-tetO-MCS (Addgene, 84008). Empty vector (FUW-tetO-MCS) was used as negative control. Inducible lentiviral vectors were used in combination with FUdeltaGW-rtTA (Addgene, 19780).

For constitutive expression of RTKs, cDNAs of HER2CA (from FUW-tetO-HER2CA), PDGFRαCA (from pcDNA5FRT-EF-Pdgfrα-CA-EGFPN, Addgene, 66789) and EGFRCA (from pBabe EGFR-L858R/T790M, Addgene, 32073) were subcloned in CSII-CMV-MCS-IRES-puro empty vector backbone (obtained by substituting blasticidin resistance of CSII-CMV-MCS-IRES-bsd (a gift from H. Miyoshi) with puromycin resistance).

For constitutive depletion of Nf1 and p53 in mouse cells, we used pTomo-shNF1-shp53 (a gift from I. Verma⁴³). For constitutive lentiviral expression of mutant KRas, pTomo-KRasG12V was generated by subcloning the KRasG12V from the pBabe KRasG12V (Addgene, 46746) to the pTomo-MCS empty vector, obtained by substituting the loxP-RFP-loxP cassette from the pTomo vector (Addgene, 26291) with a synthetic multiple cloning site. For constitutive expression of mutant KRas and shp53, pTomo-KRasG12V-shp53 was created by inserting the shp53-containing cassette (from the pTomo-HRas-shp53, a gift from I. Verma⁴³) into pTomo KRas G12V with SalI/SfiI restriction sites. The 8xGTIIC-RFP-DD lentiviral vector was a gift from J. Massagué⁵².

For doxycycline-inducible downregulation of mouse YAP and TAZ in GL261 and CT2A cell lines, we used Tet-pLKO-puro (a kind gift from G. Del Sal) lentiviral vectors expressing doxycycline-inducible sh-mouseTAZ in combination with Tet-pLKO-hygro lentiviral vectors (obtained by substituting the puromycin resistance cassette in Tet-pLKO-puro with the hygromycin resistance cassette from pBABEhygro) expressing doxycycline-inducible sh-mouseYAP. Tet-pLKO-puro shControl lentiviral vectors were used as control.

LV-CreERT2 was obtained by substituting the Cre coding sequence of LV-Cre-SD (Addgene, 12106) with CreERT2 of pCAG-CreERT2 (Addgene, 14797).

All constructs were confirmed by sequencing.

Cell cultures

HuTu10 and HuTu13 patient-derived GBM cell lines were donated by G. Basso⁵⁷ and were cultured on fibronectin-coated dishes in DMEM/F12 (Gibco), 10% BIT9500, 25 ng ml⁻¹ hEGF, 25 ng ml⁻¹ hbFGF, glutamine and antibiotics.

HEK293T cells were from the American Type Culture Collection and were cultured in DMEM (Gibco) supplemented with 10% FBS, glutamine and antibiotics. HEK293T cells were authenticated by DSMZ/Eurofins Genomics. CT2A and GL261 mouse glioma cell lines were purchased from Millipore (cat. no. SCC194) and from DSMZ (cat. no. ACC 802), respectively and cultured as described in manufacturer’s instructions. All cells were routinely tested negative for Mycoplasma.

For experiments with inducible transgenes, cells were treated with 2 μg ml⁻¹ doxycycline for the whole duration of the experiments.

siRNA transfections were performed with lipofectamine RNAi-MAX (Thermo Fisher Scientific) in antibiotic-free medium according to manufacturer instructions. Sequences of siRNAs are provided in Supplementary Table 13. Lentiviral particle preparation and cell culture infections were as described elsewhere⁷⁴.

Mice

Animal experiments were performed adhering to our institutional and national guidelines as approved by OPBA (Padova) and the Ministry of Health of Italy. The housing conditions comprised a diet with 28% protein. A maximum of five adult mice weighing up to 20 g were homed in a single cage, maintaining the ambient temperature at 19–23 °C, the humidity at 55 ± 10% and a 12-h light/12-h dark cycle.

Female 6–8-week-old NOD-SCID mice (Charles River) were used for subcutaneous injections and female 6–8-week-old NSG or C57BL/6 mice (Charles River) were used for intracranial injections.

Transgenic lines used in the experiments were provided by D. Pan (Yap^fl/fl mice⁷⁵); D. Saur and J. Siveke (R26^{CAG-FSF-CreER} mice⁷⁶) and P. Bonaldo (CMV-Flp mice). Double Yap^fl/fl;Taz^fl/fl conditional KO mice were as described elsewhere⁷⁷. To obtain R26^CAG-CreER;Yap^fl/fl;Taz^fl/fl mice, first we obtained the R26^CAG-CreER line by crossing R26^{CAG-FSF-CreER} mice with CMV-Flp mice, R26^CAG-CreER mice were then intercrossed with Yap^fl/fl;Taz^fl/fl mice.

Gliomasphere preparation

Primary newborn astroglial cells were isolated and maintained as previously described⁷⁸. For gliomasphere preparation, astroglial cells were plated at 20–30% confluence in six-well plates in 2 ml astrocyte medium (DMEM medium supplemented with 10% FBS, glutamine and antibiotics). The next day, cells were transduced with lentiviral vectors coding for oncogenes or with empty vector as negative controls. After 24 h (day 3), transduced astrocytes were switched to NSC medium (DMEM/F12 supplemented with 100× N2, 20 ng ml⁻¹ mEGF, 20 ng ml⁻¹ mbFGF, glutamine and antibiotics). Spheres arising from the cell monolayer were evident after approximately 3 weeks. Sphere passaging was performed as described previously⁷⁴.

To evaluate self-renewal properties (Extended Data Figs. 7d and 9b), gliomaspheres were dissociated to single cells and replated in Ultra-Low-Attachment 24-well plates (Corning), at the concentration of 2,000 cells per well; fully formed gliomaspheres were counted by visual inspection.

Monitoring YAP/TAZ activity during gliomagenesis

Newborn mouse astroglial cells were plated at 20–30% confluence in six-well plates in 2 ml of astrocyte medium. At 24 h after seeding, cells were infected with the 8xGTIIC-RFP-DD lentiviral vector⁵². Transduced cells were infected with oncogene-expressing or empty lentiviral vectors and, the day after, switched to NSC medium supplemented with trimethoprim (10 μM). Bright-field and fluorescent images were acquired daily with a Leica DMIL LED microscope equipped with a Leica DFC 3000G camera using LAS AF version X software.

For the experiments depicted in Fig. 3d–g, astroglial cells from Yap^fl/fl;Taz^fl/fl mice were transduced with LV-CreERT2 and then with FUdeltaGW-rtTA and doxy-inducible HER2CA lentiviruses. After 24 h, infected newborn astroglial cells were switched to NSC medium containing 2 μg ml⁻¹ doxycycline to induce HER2CA ± 1 μM 4OH-TAM to induce YAP/TAZ knockout. Medium was replaced every 3–4 d, till day 19, when cells were collected for RNA extraction. Negative controls were provided by astroglial cells transduced with empty vector.

Testing YAP/TAZ requirement for gliomasphere maintenance

Single glioblastoma cells obtained from R26^CAG-CreERT2;Yap^fl/fl;Taz^fl/fl P2 gliomaspheres were seeded to form P3 gliomaspheres. Fully formed P3 gliomaspheres were treated with either vehicle (ethanol) or 1 μM 4OH-TAM. Fresh 4OH-TAM was added every 3 d and sphere morphology and size was evaluated 3 d and 1 week after the first treatment. Bright-field images were acquired with a Leica DMIL LED microscope equipped with a Leica DFC 3000G camera using LAS version X software.

Subcutaneous tumor experiments

Oncogene-induced P2/P3 gliomaspheres were dissociated into single cells with Tryple, resuspended in ice-cold Matrigel and injected into the flank of NOD/SCID mice (750,000 cells 200 μl⁻¹ Matrigel). Tumor growth was followed over time and masses were collected for histological analyses when they reached 1–2 cm in diameter, before any apparent ulceration of the skin.

To test the requirement of YAP/TAZ on the maintenance of subcutaneous tumors, we injected in the flank of NOD/SCID mice single cells dissociated from oncogene-induced R26^CAG-CreERT2;Yap^fl/fl;Taz^fl/fl P2–P3 gliomaspheres (750,000 cells 200 μl⁻¹ Matrigel). At the appearance of palpable masses, a group of mice received a TAM400/CreER diet (Envigo) to promote YAP/TAZ KO in tumor cells (YAP/TAZ KO), whereas a second group, serving as control, continued to be fed with normal diet (YAP/TAZ wt). Tumor growth was followed over time and masses were collected for histological analyses when YAP/TAZ wt tumors reached 1–2 cm in diameter.

For serial passaging experiments, cell cultures were obtained from primary tumor masses and expanded in vitro as gliomaspheres in NSC medium for three passages. Gliomaspheres were then dissociated into single cells and subcutaneously transplanted (10,000 cells 200 μl⁻¹ Matrigel) for secondary tumor formation. This procedure was repeated again for tertiary tumor formation of Extended Data Fig. 7h.

For the experiments in Extended Data Fig. 7g, P3 KRasG12V/shp53 gliomaspheres were dissociated with Tryple, resuspended at different cell dilutions in 200 μl ice-cold Matrigel (100,000; 10,000; 1,000; 100 cells) and injected into the flank of NOD/SCID mice.

Tumor growth was followed over time and masses were collected for histological analyses when they reached 1–2 cm in diameter, before any apparent ulceration of the skin.

Brain tumor experiments

For experiments depicted in Fig. 6a–d and Extended Data Fig. 8a–c, single cells obtained from oncogene-induced Yap^fl/fl;Taz^fl/fl gliomaspheres were transduced with adenoviruses encoding for CRE recombinase (Ad-Cre) to induce YAP/TAZ KO or for GFP (Ad-GFP) as negative controls. Cells were then orthotopically injected into NSG mice (300,000 cells 2 μl⁻¹ PBS).

For the experiments in Extended Data Fig. 10d–f, HuTu13 cells were transfected with siControl or siYAP/TAZ for 48 h and then orthotopically injected into 6–8-week-old NSG mice (300,000 cells 2 μl⁻¹ PBS).

For the experiments in Fig. 6e–h and in Extended Data Fig. 10g–i, CT2A or GL261 shControl and shYAP/TAZ lines were generated by infecting glioma cells either with the pLKO-hygro-puro-Tet-On-shControl or with pLKO-hygro-Tet-On-shYap and pLKO-puro-Tet-On-shTaz lentiviral particles, respectively; cells were expanded in hygromycin/puromycin-containing medium to select for transduced cells. GL261 and CT2A cells were then treated for 48 h with 2 μg ml⁻¹ doxycycline and then orthotopically injected into the brain of 6–8-week-old C57BL/6J mice (300,000 cells 2 μl⁻¹ PBS). To sustain YAP/TAZ depletion after injection, doxycycline was added to the drinking water of all mice.

Before injection, all cells were transduced with a lentiviral construct coding for eGFP and firefly luciferase (GFP/Luc)⁷⁹. The procedure and the coordinates used for the injection were as described previously⁷⁹. Brain tumor growth was monitored by in vivo luciferase assay, by intraperitoneal injection of 150 μg g⁻¹ of XenoLight D-luciferin in PBS (PerkinElmer) and detecting brain luminescence with a Xenogen IVIS Lumina II System (Xenogen Corporation). Data analysis was performed using Living Image software v.4.7.2 (PerkinElmer) and intensity of signal was quantified in the regions of interest. Mouse brains were collected as described previously⁷⁹, fixed overnight in 4% PFA and then processed for H&E staining and immunofluorescence. NDPscan3.1 was used to acquire H&E images.

HuTu cell plasticity assay

HuTu cells were classified on the basis of their transcriptional profiles (centroids are reported in Supplementary Table 14). Details of the procedure are provided as Protocol Exchange at https://doi.org/10.21203/rs.3.pex-1218/v1.

HuTu cells were plates on fibronectin-coated six-well plates and cultured in stem medium (DMEM/F12, 10% BIT9500, 25 ng ml⁻¹ hEGF, 25 ng ml⁻¹ hbFGF, glutamine and antibiotics). Differentiation was established by switching to differentiation medium (DMEM/F12, 10% FBS, BMP2 50 ng ml⁻¹, glutamine and antibiotics) for 15 d. De-differentiation was induced through medium switch to stem medium. When indicated, siRNA transfections were performed the last day of differentiation, before starting de-differentiation. De-differentiation was maintained for 5 d before collecting for western blot. For immunofluorescence, at the end of the de-differentiation process cells were plated on fibronectin-coated glass slides for 24 h and then fixed with 4% PFA.

For experiments in Fig. 4f,g, cells were transfected with control siRNA or with two independent YAP/TAZ siRNA mixes after 15 d of differentiation. Cells were then plated in low-attachment 24-well plates (2,000 cells per well) in HuTu stem medium; growing spheres were counted after 4 d.

Gene expression analyses by RNA-seq

RNA extraction from cells was performed with NucleoSpin 8 RNA Core kit (Macherey-Nagel) according to the manufacturer’s instructions, using an automated system (Freedom EVO, Tecan). Preparation and sequencing of RNA-seq libraries are described elsewhere⁵⁹. Raw reads were mapped to the mouse reference genome (GRCm38) using STAR⁶¹. Raw gene counts were obtained using the featureCounts function of the Rsubread R package⁶² and the UCSC gene annotation (GRCm38/mm10). Raw counts were normalized to CPM mapped reads and FPKM using the edgeR package⁷²; only genes with CPM > 1 in at least one sample (or two samples when replicates were available) were further retained for differential analysis. Differential gene expression analysis was performed using the exactTest function of the edgeR package⁷².

Hierarchical clustering of Fig. 3d was performed using the Hierarchical Clustering of the MultiExperiment Viewer (MeV 4.8) package with Pearson correlation as distance metric and average linkage clustering, using the row-wise standardized FPKM of genes significantly (FDR ≤ 0.05) upregulated (fold change ≥1.33; FPKM ≥ 1 in YAP/TAZ wt HER2CA-expressing astrocytes) or downregulated (fold change ≤0.75; FPKM ≥ 1 in control newborn astroglial cells) in YAP/TAZ wt astrocytes expressing HER2CA compared to control newborn astroglial cells. Gene expression heat maps have been generated in GraphPad Prism 8.0.2 software using row-wise standardization of the expression values.

Average log₂ gene expression changes were calculated as the standardized average log₂ fold change of signature genes in all samples and plotted as mean ± s.e.m. For Fig. 3e,f, displaying log₂ gene expression changes between HER2CA-expressing and control astrocytes, we considered only genes expressed either in HER2CA-expressing astrocytes for upregulated genes or in control astrocytes for downregulated genes, that is, genes displaying FPKM ≥ 1 in HER2CA astrocytes for genes with log₂ fold change >0 and genes displaying FPKM ≥ 1 in control astrocytes for genes with log₂ fold change <0. For Fig. 5f, displaying log₂ gene expression changes between YAP/TAZ KO and YAP/TAZ wt gliomaspheres, we considered only genes expressed either in YAP/TAZ KO gliomaspheres for upregulated genes or in YAP/TAZ wt gliomaspheres for downregulated genes, that is, genes displaying FPKM ≥ 1 in YAP/TAZ KO gliomaspheres for genes with log₂ fold change >0 and genes displaying FPKM ≥ 1 in YAP/TAZ wt gliomaspheres for genes with log₂ fold change <0.

Quantitative real-time PCR

Real-time PCR was performed as described previously⁵⁹, using System thermal cycler and analyzed with QuantStudio software (Thermo Fisher Scientific; v.1.4.3). Expression levels were normalized to GAPDH. PCR oligonucleotide sequences are listed in Supplementary Table 15.

Immunofluorescence

Immunofluorescence on PFA-fixed cells and tissue samples was performed as previously described⁸⁰. Primary and secondary antibodies and their working dilutions are described in Supplementary Table 16. Slides were mounted with Fluoroshield Mounting Medium with DAPI (F6057, Sigma). Images were acquired with Leica TCS SP5II confocal microscope equipped with a CCD camera using LAS AF 2.7.3.9723 software and analyzed using Volocity software 6.0 (PerkinElmer).

Western blot

Immunoblots were performed as previously described⁷³. Chemiluminescence was digitally acquired by ImageQuant LAS 4000 1.2 (GE healthcare). Primary and secondary antibodies and their working dilutions are described in Supplementary Table 16.

Statistics and reproducibility

Data are mean ± s.d. or s.e.m. as indicated in figure legends. Statistical tests (Student’s t-test, ANOVA and Kaplan–Meier survival analyses) are indicated in the figure legends and were performed with GraphPad Prism 8.0.2 software. Sample sizes for each experiment are stated in the corresponding figure legends. No statistical method was used to predetermine sample size.

All tested animals were included in the analysis. All experiments were reproducible. Every figure states how many times each experiment was performed with similar results. Mice were randomly allocated to experimental or treatment groups. Investigators were not blinded to mouse grouping. Pathological examination of histological sections was carried out by M. Fassan (a professional pathologist), who was blind to animal treatments.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All RNA-seq and microarray raw data generated for the present study, along with counts matrices and metadata for each sample, are publicly available in GEO under accession code GSE133471. The scRNA-seq data of primary glioblastoma samples from Darmanis et al. were downloaded as raw reads from GEO (GSE84465). The expression matrix and metadata of the Neftel dataset were downloaded from the Single Cell Portal of the Broad Institute (https://singlecell.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma#study-summary). Raw gene expression data (.CEL files) of the GBM TCGA cohort were downloaded from GEO (GSE83130). Raw gene expression (.CEL files) and clinical data of the REMBRANDT study were downloaded from GEO (GSE108474). BAM files of Ivy Atlas GBM samples were downloaded from the Anatomic Structures RNA-Seq repository of the Ivy Glioblastoma Atlas Project (http://glioblastoma.alleninstitute.org/rnaseq/bam.csv). Source data for Figs. 1e, 3b,e–f, 4c,f,g, 5a–f, 6b,f and Extended Data Figs. 1c, 2b,c, 3b, 4b, 6a–d, 7d, 8b, 9a,b and 10b,e,f have been provided with the paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.

Code availability

All code used R 3.5.0 and publicly available packages cited in the paper. No custom functions were written for the analysis. STAR is available in GitHub (https://github.com/alexdobin/STAR). ARACNe-AP is available in GitHub (https://github.com/califano-lab/ARACNe-AP). HOMER is available at http://homer.ucsd.edu/homer/. MultiExperiment Viewer is available at http://mev.tm4.org.

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Acknowledgements

We thank I. Verma, J. Massagué and L. Naldini for plasmids and colleagues sharing their plasmids through Addgene (M.-C. Hung, L. Pedersen, C. Counter, C. Cepko, K. Hochedlinger and M. Meyerson). We thank D.J. Pan, D. Saur, J. Siveke and P. Bonaldo for gifts of mice, G. Basso for HuTu cells, G. Zuccolotto for the GFP/Luc-expressing lentiviral construct, V. Barbieri for in vivo experiments, V. Guzzardo for histology, S. Bresolin for microarrays, G. Leo for TAZ IHC analysis and M. Forcato for comments. M. Castellan was supported by a FIRC-AIRC fellowship for Italy. O.R. is supported by Fondazione Umberto Veronesi (Post-Doctoral Fellowship 2020). The research leading to these results has received funding from AIRC 5×1000 2018 ‘Metastasis as mechanodisease’ (ID, 22759) grant to S.P.; from AIRC IG Grant 2019 (ID, 23307) to S.P.; from the Italian Ministry of Education, University and Research under a MIUR-FARE grant to S.P. and a MIUR-PRIN Bando 2017 grant to S.P. (cod. 2017HWTP2K); from the European Research Council under the European Union’s Horizon 2020 research and innovation program (DENOVOSTEM grant agreement No 670126) to S.P.

Author information

These authors jointly supervised this work: M. Cordenonsi, S. Piccolo.

Authors and Affiliations

Department of Molecular Medicine, University of Padua, Padua, Italy
Martina Castellan, Alberto Guarnieri, Atsushi Fujimura, Francesca Zanconato, Giusy Battilana, Tito Panciera, Hanna Lucie Sladitschek, Paolo Contessotto, Anna Citron, Michelangelo Cordenonsi & Stefano Piccolo
Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
Andrea Grilli, Oriana Romano & Silvio Bicciato
Department of Medicine - Surgical Pathology and Cytopathology Unit, University of Padua, Padua, Italy
Matteo Fassan
Department of Woman and Children Health, University of Padua, Padua, Italy
Elena Porcù
Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
Antonio Rosato
Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
Antonio Rosato
IFOM, the FIRC Institute of Molecular Oncology, Milan, Italy
Stefano Piccolo

Authors

Martina Castellan
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Alberto Guarnieri
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Atsushi Fujimura
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Francesca Zanconato
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Giusy Battilana
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Tito Panciera
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Hanna Lucie Sladitschek
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Paolo Contessotto
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Anna Citron
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Andrea Grilli
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Oriana Romano
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Silvio Bicciato
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Elena Porcù
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Antonio Rosato
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Michelangelo Cordenonsi
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Stefano Piccolo
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Contributions

M. Castellan performed most of the in vitro and in vivo experiments and contributed to writing. A.F. carried out the initial experiments of this study. A. Guarnieri and G.B. carried out experiments with HuTu cells. T.P. optimized mouse astrocyte isolation and infection. F.Z. contributed to manuscript preparation. F.Z., H.L.S., P.C. and A.C. optimized technical procedures critical for experiments in vivo and in vitro; M.F. performed histology and histopathological evaluations. E.P. and A.R. performed brain tumor experiments. O.R., A. Grilli and S.B. performed bioinformatic analyses. S.P. and M. Cordenonsi conceived the initial hypothesis and experimental design, organized the work and wrote the manuscript.

Corresponding authors

Correspondence to Michelangelo Cordenonsi or Stefano Piccolo.

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Extended data

Extended Data Fig. 1 Identification of the gene expression program of GSCs.

a, Single-cell differentiation trajectory of GBM cells reconstructed by Monocle2 using single-cell RNA-seq data of the indicated cell populations from primary GBM samples of the Darmanis dataset. b, Gene set enrichment analysis (GSEA) for association between the cell populations at the start and at the end of the pseudotime trajectory of the neoplastic cells of the Darmanis datasets (as depicted in Fig. 1c), and gene sets denoting the identity of specific cell types. Gene lists denoting early neural progenitor cells (RG: Radial Glia; oRG: outer Radial Glia; vRG: ventricular Radial Glia) or neural stem cells (NSC) are indicated in red; those identifying neurons, astrocytes or committed neuronal progenitors (OPC: Oligodendrocyte Progenitor Cells; INP: Intermediate Neuronal Progenitors) are, respectively, in blue, purple and blue-green colors; gene lists enriched in the putative GSC and DGC populations are highlighted in orange and in light blue, respectively. Signatures are available in Supplementary Table 1. GSEA calculated FDR adjusting for multiple comparisons; details of p-value and FDR calculation are described in the GSEA website (http://software.broadinstitute.org/gsea/index.jsp). Related to Fig. 1c. c, Log2 expression levels of the indicated oRG (top graphs), NSC and GSC (middle graphs) and INP markers (bottom graphs) in the subpopulations of neoplastic cells of the Darmanis dataset that are at the start (GSC, n = 221 cells) and at the end (DGC, n = 221 cells) of the pseudotime trajectory depicted in Fig. 1c. Data are presented as mean + s.d. p-values were determined by unpaired two-tailed t test. d, RNA velocities (arrows) of neoplastic cells of the Darmanis dataset projected in the space of the first two principal components. Red and blue dots are the cells that are at the start (GSC) and at the end (DGC) of the pseudotime trajectory depicted in Fig. 1c.

Source data

Extended Data Fig. 2 Validation of the G-STEM signature.

a-b, Violin plots showing the expression of the G-STEM signature (right panels in (b)) on the cells at the start (Low; red dots in the left panels in (b)) of the pseudotime trajectories (a) of patient-specific cohorts of the Darmanis dataset, vs. the neoplastic cells that are on the opposite ends of the same trajectories (High; blue dots in the left panels in (b)). The p-values were determined by two-tailed Mann-Whitney test. c, Violin plots showing the expression of the G-STEM signature (right panel) on the cells at the start (Low; red dots in the middle panel) of the pseudotime trajectory (left panel) of the sole neoplastic cells of the Darmanis dataset, vs. the cells that are on the opposite ends of the same trajectory (High; blue dots in the middle panel). The p-values were determined by two-tailed Mann-Whitney test.

Source data

Extended Data Fig. 3 Characterization of the G-STEM signature.

a, Graphs depicting the most significant GO terms emerging from the Gene Ontology analyses of the genes composing the G-STEM and the DGC signatures. The full lists of significant GO terms of both signatures are in Supplementary Table 3. b, Log2 expression levels of the indicated components of the G-STEM signature in the subpopulations of neoplastic cells of the Darmanis dataset that are at the start (GSC, n = 221 cells) and at the end (DGC, n = 221 cells) of the pseudotime trajectory depicted in Fig. 1c. Data are presented as mean + s.d. p-values were determined by unpaired two-tailed t test.

Source data

Extended Data Fig. 4 Validation of the G-STEM signature in large datasets of GBM patients.

a, Gene set enrichment analysis (GSEA) for association between the cell population at the start of the pseudotime trajectory of the neoplastic cells of the Neftel datasets (as depicted in Fig. 1e) vs. all the other neoplastic cells and gene sets denoting the identity of specific cell types. Abbreviations and color codes are as in Extended Data Fig. 1b. Signatures are available in Supplementary Table 1. GSEA calculated FDR adjusting for multiple comparisons; details of p-value and FDR calculation are described in the GSEA website (http://software.broadinstitute.org/gsea/index.jsp). Related to Fig. 1e. b, Violin plots showing the expression of the G-STEM signature (bottom panels) on the cells at the start of the pseudotime trajectory (GSC; red dots in the top panels) of small tumor cohorts of the Neftel dataset, pre-sorted according to the Proneural, Classical or Mesenchymal classification of GBMs, vs. all the other neoplastic cells of the same cohorts (NON GSC; light blue dots in the top panels) of the same dataset. The p-values were determined by two-tailed Mann-Whitney test. (c) Kaplan–Meier analysis representing the probability of survival in n = 541 GBM patients from the TCGA dataset (left panel), n = 210 GBM patients from the REMBRANDT dataset (middle panel), and n = 390 GBM patients carrying wild-type IDH1 from the TCGA dataset (right panel), stratified according to high or low GSC-signature. The p-value of the Log-rank (Mantel-Cox) test reflects the significance of the association between GSC-signature ‘low’ and longer survival. G-STEM expression is prognostic for the vast majority of GBM, that is IDH1-wild type tumors (93%, of those annotated in the TGCA dataset; n = 390 out of 419 IDH1-annotated samples).

Source data

Extended Data Fig. 5 A computational procedure to identify candidate TRs controlling the gene expression program of GSCs.

a, Overview of the experimental flow for inference of the master Transcriptional Regulators (TRs) of the GSC state using the Rhabdomant pipeline on the Darmanis sc-RNA-seq dataset of primary GBM samples. See Methods for details. b, List of candidate master Transcriptional Regulators (TRs) emerging from the analysis of the Darmanis dataset of scRNA-seq dataset with the Rhabdomant pipeline, ordered on the base of their normalized enrichment signal (NES). The Rhabdomant pipeline calculated FDR adjusting for multiple comparisons; see Methods for details about p-value and FDR calculation. The lists of candidate master TRs of the GSC and of the DGC state are highlighted in orange and in light blue, respectively. The most significant candidate master TRs of the GSC state are indicated in red.

Extended Data Fig. 6 YAP/TAZ are required for GSC maintenance in vivo.

a-c, Effects of YAP/TAZ knockout on the growth of established subcuteaneous GBM-like lesions. Transformed cells were obtained by dissociation of gliomaspheres obtained from HER2CA- (a), shNF1/shp53- (b) or KRasG12V/shp53- (c) transformed R26^CAGCreERT2; Yap^fl/fl; Taz^fl/fl newborn mouse astroglial cells (as in Fig. 3), and then injected in NOD-SCID mice. When subcutaneous tumors reached approximately 0.5 cm of diameter, mice were either fed with Tamoxifen food to induce YAP/TAZ knockout (YAP/TAZ KO), or maintained under normal diet (YAP/TAZ wt). Graphs are growth curves of YAP/TAZ wt (KRasG12V/shp53-, n = 4 mice; HER2CA, n = 6 mice; shNF1/shp53, n = 5 mice) and YAP/TAZ KO (KRasG12V/shp53-, n = 4 mice; HER2CA, n = 4; shNF1/shp53, n = 8 mice) tumors (average volume ± s.e.m.). d,e, Effects of YAP/TAZ knockout in tumors derived from KRasG12V/shp53 gliomaspheres, following the experimental setup described in a-c. d, Dot plot for tumor weight at sacrifice (YAP/TAZ wt, n = 8; YAP/TAZ KO, n = 6). Mean ± s.e.m. of the distribution are also shown. p-value was calculated by unpaired two-tail t-test. e, Representative H&E stainings. Scale bar, 2.5 mm. N, necrotic area; *, Matrigel residue. f, Tabular results showing the number of NOD/SCID mice displaying subcutaneous tumor formation after injection of cells dissociated either from gliomaspheres derived from HER2CA-transformed primary newborn astroglial cells (Primary tumors), or from HER2CA-gliomaspheres derived from one of the Primary tumors (Secondary tumors).

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Extended Data Fig. 7 Ex-vivo reprogramming of normal neural cells into GSC-like cells.

a, GFAP and SOX2 stainings (scale bars, 50 μm) of the mouse SVZ, representative of n = 3 mice. Nuclei were counterstained with DAPI. b,c, GFAP, NESTIN and SOX2 stainings (scale bars, 50 μm) in mouse newborn astroglial cells, representative of two independent experiments. d, Gliomaspheres emerging from newborn astroglial cell cultures transformed by the indicated oncogenes (P0 spheres) were dissociated to single cells and replated at clonal density for gliomasphere formation (P1 to P10 spheres). Results are representative of three experiments with n = 3 replicates each. Data are presented as scatter dot plots and bar graphs showing mean with s.d. e, Left panel: H&E staining of a lesion obtained after intracranial transplantation of shNf1/shp53-transformed astroglial cells. N, necrotic area. Scale bar, 2.5 mm. Middle panel: High magnification of the same tumor, showing large polynucleated cells (arrowheads). Right panel: TAZ IHC on the same tumor. Scale bars, 100 μm. Experiments were independently repeated on n = 10 mice, with similar results. f, H&E staining of subcutaneous tumors obtained by injecting cells dissociated from gliomaspheres carrying the indicated oncogenic lesions, representative of: KRasG12V/shp53, n = 4 tumors; HER2CA, n = 6 tumors; shNf1/shp53, n = 5 tumors. N, necrotic areas. Scale bars, 250 μm. g, Number of mice displaying tumor formation after injection of cells dissociated from KRasG12V/shp53-gliomaspheres at the indicated cell dilutions. h, Top, Schematic representation of the serial transplantation assay performed with HER2CA-transformed cells (see Methods for details). Bottom, H&E staining (scale bars, 2.5 mm) of tumors obtained after each round of transplantation, representative of n = 4 primary tumors, n = 8 secondary tumors and n = 4 tertiary tumors, respectively. Numbers of mice developing tumors per numbers of transplanted mice are indicated in each picture. i, GSEA curves of the G-STEM and the DGC signatures in KRasG12V/shp53-tumors compared to the astroglial cells from which they derive. Signatures are available in Supplementary Table 7.

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Extended Data Fig. 8 Oncogenic insults activate YAP/TAZ in transformed primary astroglial cells.

a, Bright-field and fluorescent pictures (representative of n = 5 independent samples each) of newborn astroglial cells transduced with lentiviral vectors encoding for the YAP/TAZ reporter 8xGTIIC-RFP-DD⁵², and with lentiviral vectors encoding for the indicated oncogenes or, as negative control, with empty vector, as in Fig. 3b. Images were taken 4 days after inducing oncogenic reprogramming by incubating cells in NSC medium. Scale bars, 50 μm. b, Compendium of Fig. 3c. Efficiency of Yap and Taz downregulation in R26CAG-CreERT2; Yap^fl/fl; Taz^fl/fl mouse newborn astroglial cells treated with either vehicle (Control) or 4OH-TAM (YAP/TAZ KO), as measured by qRT-PCR (mean + s.d. of all independent samples of three experiments). p-values are calculated by two-way ANOVA with Sidak’s multiple comparisons.

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Extended Data Fig. 9 YAP/TAZ are required for GSC maintenance in vitro.

a, Control experiment of Fig. 5a–e. Gliomaspheres derived from HER2CA-transformed Yap^fl/fl; Taz^fl/fl newborn astroglial cells, not expressing CRE^ERT2, were treated with either ethanol (Vehicle) or 4OH-TAM (TAM). Panels are representative images (left; scale bar, 100 µm) and quantifications (right; mean ± s.d. of two independent experiments, each performed with two replicates) of the number of gliomaspheres/cm² in vehicle versus 4OH-TAM-treated samples. p-values were determined by two-way ANOVA with Sidak’s multiple comparisons test. In the absence of CRE^ERT2 expression, treatment with 4OH-TAM is inconsequential for gliomasphere formation, indicating that gliomasphere disaggregation shown in Fig. 4a–e is specifically caused by YAP/TAZ deletion. b, P2 gliomaspheres derived from R26^CAG-CreERT2; Yap^fl/fl; Taz^fl/fl newborn astroglial cells transformed with the indicated oncogenes were dissociated to single cells and replated at clonal density for P3 gliomasphere formation in presence of ethanol (YAP/TAZ wt), or of 4OH-TAM to induce YAP/TAZ knockout (YAP/TAZ KO). Data are presented as scatter dot plots (n = 3 replicates each) and bar graphs showing mean with s.d. The p-values were calculated by unpaired two-tailed t-test.

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Extended Data Fig. 10 YAP/TAZ are required for GBM initiation in vivo.

a-c, Immunocompromised mice were injected intracranially with KRasG12V/shp53-transformed Yap^fl/fl;Taz^fl/fl cells, also transduced with dual luciferase-GFP expression vectors. Control animals (n = 6) were injected with cells transduced with Ad-GFP, whereas YAP/TAZ KO animals (n = 5) were injected with cells transduced with Ad-Cre. a, Representative images of brain bioluminescence. b, Bioluminescence quantification shown as scatter dot plots and bar graphs showing mean with s.d; p-value was calculated by unpaired two-tailed t-test. c, Representative H&E stainings. Scale bars, 2.5 mm in left panels and 250 μm in the magnification shown on the right. Arrowheads highlight the presence of large, polynucleated cells. d-f, Immunocompromised mice were injected intracranially with HuTu13 cells transduced with dual luciferase-GFP expression vectors, and transfected with siCo (Control; n = 5) or siYAP/TAZ (YAP/TAZ depleted; n = 5). d, Representative images of brain bioluminescence. e, Bioluminescence quantification shown as scatter dot plots and bar graphs showing mean with s.d.; unpaired two-tailed t-test p-values are shown. f, Representative H&E stainings. Scale bars, 2.5 mm in left panels and 250 μm in the magnification shown on the right. ‘N’ indicates necrosis. g-i, CT2A cells were transduced with dual luciferase-GFP expression vectors and injected intracranially in syngeneic mice. Control animals (n = 5) were injected with cells expressing anti-GFP shRNA, whereas YAP/TAZ-depleted animals (n = 5) were injected with cells expressing doxycycline-inducible YAP and TAZ shRNAs. g, Representative brain bioluminescences at one day and 14 days after injection. h, Bioluminescence quantification at three different time points shown as scatter dot plots and bar graphs showing mean with s.d.; unpaired two-tailed t-test p-values are shown. i, Representative H&E stainings. Scale bars, 2.5 mm in left panels and 250 μm in the magnification shown on the right. N, necrotic areas. j, GFP and TUJ1 stainings in sections from YAP/TAZ-wt and YAP/TAZ-KO subcutaneous shNF1/shp53-induced tumors (representative of n = 3 independent samples each). Scale bars, 50 µm.

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Supplementary Tables 1–16

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Castellan, M., Guarnieri, A., Fujimura, A. et al. Single-cell analyses reveal YAP/TAZ as regulators of stemness and cell plasticity in glioblastoma. Nat Cancer 2, 174–188 (2021). https://doi.org/10.1038/s43018-020-00150-z

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Received: 06 August 2019
Accepted: 28 October 2020
Published: 07 December 2020
Issue Date: February 2021
DOI: https://doi.org/10.1038/s43018-020-00150-z

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Abstract

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Main

Results

A gene expression program identifying native GSCs

Identification of candidate master transcriptional regulators of the GSC state

YAP/TAZ activity is associated with the GSC state

YAP/TAZ are required for oncogenes to confer GSC properties to normal neural cells

YAP/TAZ are required for the intrinsic differentiation plasticity of GBM cells

YAP/TAZ preserve the GSC state by preventing differentiation

YAP/TAZ control GBM initiation and differentiation in vivo

Discussion

Methods

Analysis of scRNA-seq data from primary glioblastomas

Darmanis dataset

G-STEM signature

Neftel dataset

Signature scores

Collection and processing of GBM transcriptomes from the TCGA and REMBRANDT studies

Kaplan–Meier survival analysis of human GBM datasets

Identification of candidate master transcriptional regulators

Comparison of G-STEM signature and master TR regulomes

Analysis of samples from the Ivy Glioblastoma Atlas

TAZ immunohistochemistry

Reagents and plasmids

Cell cultures

Mice

Gliomasphere preparation

Monitoring YAP/TAZ activity during gliomagenesis

Testing YAP/TAZ requirement for gliomasphere maintenance

Subcutaneous tumor experiments

Brain tumor experiments

HuTu cell plasticity assay

Gene expression analyses by RNA-seq

Quantitative real-time PCR

Immunofluorescence

Western blot

Statistics and reproducibility

Reporting Summary

Data availability

Code availability

References

Acknowledgements

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Competing interests

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Extended data

Supplementary information

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