Phenotypic Mapping of Pathologic Cross-Talk Between Glioblastoma and Innate Immune Cells by Synthetic Genetic Tracing

Published OnlineFirst December 23, 2020; DOI: 10.1158/2159-8290.CD-20-0219

Research Article

Phenotypic Mapping of Pathologic Cross-Talk between Glioblastoma and Innate Immune Cells by Synthetic Genetic Tracing

Matthias Jürgen Schmitt1, Carlos Company1, Yuliia Dramaretska1, Iros Barozzi2, Andreas Göhrig1, Sonia Kertalli1, Melanie Großmann1, Heike Naumann1, Maria Pilar Sanchez-Bailon1, Danielle Hulsman3, Rainer Glass4, Massimo Squatrito5, Michela Serresi1, and Gaetano Gargiulo1

abstract Glioblastoma is a lethal brain tumor that exhibits heterogeneity and resistance to therapy. Our understanding of tumor homeostasis is limited by a lack of genetic tools to selectively identify tumor states and fate transitions. Here, we use glioblastoma subtype signatures to construct synthetic genetic tracing cassettes and investigate tumor heterogeneity at cellular and molecular levels, in vitro and in vivo. Through synthetic locus control regions, we demonstrate that proneural glioblastoma is a hardwired identity, whereas mesenchymal glioblastoma is an adaptive and metastable cell state driven by proinflammatory and differentiation cues and DNA damage, but not hypoxia. Importantly, we discovered that innate immune cells divert glioblastoma cells to a proneural- to-mesenchymal transition that confers therapeutic resistance. Our synthetic genetic tracing methodology is simple, scalable, and widely applicable to study homeostasis in development and diseases. In glioblastoma, the method causally links distinct (micro)environmental, genetic, and pharmacologic perturbations and mesenchymal commitment.

Significance: Glioblastoma is heterogeneous and incurable. Here, we designed synthetic reporters to reflect the transcriptional output of tumor cell states and signaling pathways’ activity. This method is generally applicable to study homeostasis in normal tissues and diseases. In glioblastoma, synthetic genetic tracing causally connects cellular and molecular heterogeneity to therapeutic responses.

Introduction plasticity of genotype and phenotype, in which a dominant mutation and expression profile changes after treatment The cellular and molecular heterogeneity of cancer is (2, 10). This makes it difficult to discern whether GBM sub- thought to contribute to resistance to targeted and immune types represent hardwired entities or transient states imposed therapies. Glioblastoma (GBM) is the most common, het- by differences in signaling or tumor regions. Yet, several erogeneous, and resistant primary adult brain tumor (1). studies established correlations between subtype-specific Compared with most cancers, GBM is highly genomically gene-expression signatures, differential response to therapy, and epigenomically characterized (2–5). Lineage tracing has and overall patient survival; the latter is particularly poor provided important insights into GBM biology in the mouse, in highly mesenchymal tumors that exhibit infiltration of including the cellular origins of individual subtypes (6), and innate immune cells at recurrence (5). A better understanding how aberrant homeostatic regulation can affect responses to of GBM subtype identities and fate changes might be crucial treatments in vivo (7). to develop effective therapies. Transcriptome analyses of human GBM biopsies have Technological advances in single-cell biology confirmed repeatedly yielded a general classification into three subtypes, and extended our understanding of the complexity of brain classic (CL), mesenchymal (MES), and proneural (PN), across tumor homeostasis (11, 12). Yet, as single-cell RNA sequenc- cohorts (2–5). However, a single GBM tumor may exhibit the ing (scRNA-seq) has increasingly expanded the catalogs of coexistence of a predominant subtype along with tumor cells cell populations within tumors, the biological interpretation of other subtypes (8, 9). In addition, recurrent tumors exhibit of novel cell types and disease states has remained difficult, due to a lack of experimental approaches for validation (13). Advanced experimental approaches will be required. 1Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany. Genetic tracing is extremely informative in developmental 2Department of Surgery and Cancer, Imperial College London, London, settings and diseases involving alterations in tissue home- United Kingdom. 3Division of Molecular Genetics, The Netherlands Cancer ostasis including metabolic, immunologic, neurologic, or Institute, Amsterdam, the Netherlands. 4Neurosurgical Research, Depart- psychiatric disorders, as well as inflammation and cancer ment of Neurosurgery, University Hospital, Munich, Germany. 5Seve Ballesteros Foundation Brain Tumor Group, Molecular Oncology Pro- (14–17). However, despite the expansion of sophisticated gramme, Spanish National Cancer Research Center, Madrid, Spain. perturbation tools, such as CRISPR/Cas9 and optogenetics Note: Supplementary data for this article are available at Cancer Discovery (14), genetic tracing has yet to be fully exploited in the dissec- Online (http://cancerdiscovery.aacrjournals.org/). tion of complex disease traits. The pressing need for a novel M.J. Schmitt, C. Company, and Y. Dramaretska are the co–first authors of strategy to characterize brain tumor heterogeneity prompted this article. us to design synthetic reporters based on gene-expression Corresponding Author: Gaetano Gargiulo, Cancer Research, Max-Delbrück signatures. These reporters are designed to integrate multiple Center for Molecular Medicine, Robert-Rössle-Str. 10, Berlin 13125, Germany. pathways into a single genetic cassette, thereby mimicking Phone: 49-30-9406-3861; E-mail: [email protected] endogenous regulatory elements. As an example, the β-globin Cancer Discov 2021;11:1–24 locus control region shows cell type– and developmental doi: 10.1158/2159-8290.CD-20-0219 stage–specific expression and engages transcription factors ©2020 American Association for Cancer Research. independently of its genomic position (15, 16).

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Our method permits us to genetically label individual cell The GBM subtype–specific reporters we generated can populations that share a similar state or undergo similar inform on the transcriptional identity of subsets of patients’ fate transitions within a heterogeneous tissue in vitro and in GBM single cells. Consistently, single-sample gene set enrich- vivo, and to discover mechanisms regulating proneural-to- ment analysis (ssGSEA) showed a high correspondence mesenchymal transition. Whereas a hierarchical and direc- between the potential expression pattern of each individual tional organization of the subtypes is continuously revised reporter, the known cell states of freshly purified single GBM (2, 5, 12, 17), through synthetic genetic tracing in vitro and cells (12), and their corresponding TCGA subtype (Fig. 1C). in vivo, we observed that interconversion between proneural Each sLCR encodes the subtype-specific expression of a fluo- and mesenchymal states is bidirectional. Our results are rescent reporter (mVenus or mCherry) and a second cassette clinically relevant because they expose a causal connection expressing the nuclear H2B–CFP fusion via a ubiquitous between radiotherapy or innate immune cell infiltration and viral promoter enabling reporter-independent selection (Fig. mesenchymal transdifferentiation, which was previously 1D). Reporter expression was validated in live transiently hypothesized on the basis of correlative analyses. Notably, we transfected cells, in stably transduced and cryosectioned link innate immunity and mesenchymal commitment to the tumorspheres, and in fixed tumor cells (Supplementary Fig. acquisition of selective resistance to therapies, which holds S1B–S1D). In the latter, dual RNA FISH and immunofluores- translational implications. cence demonstrated colocalization between nascent MGT#1 RNA and MED1, a master regulator organized in coactivator Results puncta and regulating endogenous cell-identity LCRs (also known as superenhancers; ref. 18). GBM Subtype Genetic Tracing by Synthetic Locus To test the relative expression of synthetic reporters, which Control Regions are representative of two opposite GBM subtypes, we next To trace complex GBM expression subtype identities, we transduced proneural and mesenchymal sLCRs into a near- developed a method that generates synthetic reporters. Our isogenic pair of human glioma-initiating cells (hGIC). These method relies on robust evidence that transcription factors cells were engineered by genetically manipulating sponta- govern cell identity or states through binding to cis-regulatory neously immortalized human neural progenitor cells (19) elements, which in turn control downstream target genes. From and have a proneural-like expression signature, possibly The Cancer Genome Atlas (TCGA) data sets (3), we annotated inherited from the cell of origin (Supplementary Fig. S1E). the genes specific to mesenchymal, classic, and proneural sub- In addition to preexisting and shared aberrations, the near- types. Cell-intrinsic signature genes [i.e., differentially regulated isogenic pair background consists of p53 and NF1 depletion genes (DRG)] were selected as genomic loci containing subtype- or IDH1R132H and p53R273H mutant overexpression. Both lines specific cis-regulatory elements (Fig. 1A and B; Supplementary display a DNA methylation profile concordant with IDH1 Table S1). Next, we used gene expression and standard anno- status in patients with high-grade glioma and are hereafter tation tools to identify their regulators (i.e., genes encoding referred to as IDH-wild-type (WT)-hGICs and IDH-mutant ubiquitous and tissue-specific transcription factors). Finally, (mut)-hGICs, respectively (data not shown). FACS quantifi- we identified DNA elements with the potential of driving GBM cation showed that the proneural reporter PNGT#2 is more subtype–specific expression according to the following criteria: expressed than the mesenchymal one in both near-isogenic a high transcription factor binding sites number (i) and diversity lines, but MGT#1 expression was even lower in the IDH-mut (ii), as well as a known distance from the nearest endogenous genotype (Fig. 1D and E), possibly due to epigenetic suppres- transcriptional start site (iii; Supplementary Table S1). This sion by IDH1R132H-dependent production of the oncome- approach produced a list of potential cis-regulatory elements, tabolite 2-hydroxyglutarate (20). including loci with endogenous binding by the predicted tran- Next, we tested the specificity of our reporters by extending scription factors at endogenous level (Supplementary Fig. S1A). the analysis of the proneural and mesenchymal reporters to Next, we generated distinct synthetic locus control regions patient-derived GBM stem-like cells (GSC), lung and breast (sLCR) to genetically trace mesenchymal, classic, and proneural cancer cell lines of epithelial origin, and cell lines of non- GBM subtypes. This was accomplished by stitching 5 to 6 of the epithelial cancer origin such as leukemia cells. Each line was identified cis-regulatory elements, representing 40% to 60% of purified using FACS to select reporter-expressing cells. We the regulatory potential (Supplementary Table S1 and Methods). detected each reporter using qRT-PCR and normalized their Hereafter, we refer to these GBM subtype genetic tracing tools expression through endogenous GAPDH and the number of as MGT, CLGT, and PNGT. reporter integrations into genomic DNA (see Methods).

Figure 1. GBM subtype sLCRs. A, Schematic representation of GBM sLCR generation from gene-expression data. B, Pairwise correlation heat maps of significant transcription actorf binding site (TFBS) motifs at GBM subtype–specific loci. The number of transcription factors (TF) and signature genes (SG) used in the analysis is indicated above each panel. C, Top, ssGSEA normalized scores for input genes for the indicated sLCRs (see Methods). The cell states identified by Neftel and colleagues (12) are indicated in each quadrant, and the original single-cell position is maintained in the two-dimensional representation (see Methods). Bottom, TCGA subtypes (3) are shown for a head-to-head comparison. D, Top, schematic representation of an sLCR and of the experimental steps to generate reporter cells. Bottom, heat map of MGT#1 and PNGT#2 gene expression normalized by GAPDH and number of integrations relative to hGICs and GSCs. Selected non-brain tumor cell lines are also shown. E, FACS profile of IDH-WT-hGICs and IDH-mut-hGICs transduced with the indicated reporters and FACS sorted for the reporter-independent marker H2B-CFP. F, Top, schematic representation of bulk, MGT#1-, and PNGT#2-expressing hGICs’ transcriptional profiling. Bottom, heat map of GSEA-adjustedP values (see Methods) for the indicated GBM subtypes/state signatures and comparisons in the indicated hGIC lines.

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A D Target phenotype Fluo.marker PGK H2B-CFP (e.g., GBM subtype) GBM sLCR (mesenchymal MGT#1–2, proneural PNGT#1–2, etc.)

Gene expression signature FACS, qPCR, 1.0 0.3 8.8 8.1 1.2 8.6 1.7 4.2 2.0 1.2 5.6 1.4 4.1 0.6 0.9 0.4 MGT#1 RNA-seq Transcription Signature PNGT#2 1.0 0.8 1.4 1.2 3.0 3.2 1.5 2.0 0.9 0.2 1.5 0.6 1.8 0.5 0.3 factor genes genes β K562 A549 BLN5 BLN7 MCF7

GBM2 Fold change/ GDM1 IDH-wt GBM14

IDH-mut IDH-WT-hGICs GBM179 GBM166 MDA-231 NCH421K OCI-AML5

A549+TGF 0.1 2 4 6 8 Transcription factor hGICs GSCs LUAD BRCA LEUK binding sites (TFBS) enrichment genes E CTCF CTCF Near-isogenic glioma-initiating cells

250K 250K 250K Correlate positional information PNGT#2 PNGT#2 MGT#1 MGT#1 200K 200K 200K with TFBS enrichments mCherry high mCherry high mVenus high mVenus high 150K 150K 150K 16% 9% FSC-H 78% 78% FSC-H High 100K 100K 100K

50K 50K 50K TFBS motif 0 0 0 match 101 102 103 104 105 101 102 103 104 105 101 102 103 104 105 101 102 103 104 105 position FSC-H Genomic Low PNGT#2-mCherry Unstained IDH-WT IDH-mut TFBS motif Regulatory potential Supervised assembly inference F In vitro sLCR-high characterisation Natural TSS Fluorescent reporter, IDH-WT-hGICs & RNA-seq Target gene endonuclease, suicide gene, etc. IDH-mut-hGICs sLCR

Laser MGT#1hi Detector B hi MES-GBM TFs = 62 CL-GBM TFs = 34 PN-GBM TFs = 60 PNGT#2 dim MES-GBM SGs = 27 CL-GBM SGs = 32 PN-GBM SGs = 46 sLCR

vs. vs. vs. vs. vs. vs. TFBS motif

TFBS motif −0.4 0.4 Correlation IDH-WT G2–M IDH-mut

G1–S

C Cell-cycle ssGSEA in single glioblastoma cells Neftel_NPC2 MGT#1–2 PNGT#1–2 CLGT#1–2 Neftel_NPC1 OPC-like NPC-like OPC-like NPC-like OPC-like NPC-like 2 Neftel_OPC 1 GBM-PN Verhaak_PN_2010 0 −1 Wang_PN_2017 −2 Neftel_AC AC-like MES-like AC-like MES-like AC-like MES-like Wang_CL_2017 Mesenchymal Proneural Classic

Verhaak et al. 2010 Verhaak et al. 2010 Verhaak et al. 2010 GBM-CL Verhaak_CL_2010 OPC-like NPC-like OPC-like NPC-like OPC-like NPC-like 2 Neftel_MES2 1 Neftel_MES1 0 −1 Wang_MES_2017

−2 GBM-MES Verhaak_MES_2010 AC-like MES-like AC-like MES-like AC-like MES-like −2 −1 0 1 2 −2 −1 0 1 2 −2 −1 0 1 2 −4 4 0 1.3 2 3 4 Z-score −Log10 (Padj)

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Consistent with the specificity of the design, both reporters To test whether sLCRs allow for a genetic tracing of tumor were highly expressed in patient-derived GSCs and exhibited cell–fate changes in vivo, we intracranially transplanted IDH- low expression in leukemia cells (Fig. 1D). In addition to line- WT-hGICs-MGT#1 into immunodeficient mice. Histologi- to-line heterogeneity, an interesting common pattern was the cally, all tumors appeared as grade IV GBM (n = 10), with a high expression of the mesenchymal reporter MGT#1 in well- large proportion of the mouse brain infiltrated by malignant established mesenchymal cells independently of the tissue cells, indicating extensive proliferation and invasion (Fig. 2A). of origin (i.e., GBM166 and MDA-231; Supplementary Fig. IHC staining revealed MGT#1 expression was well confined S1E) and in epithelial cells committed to mesenchymal fate within tumor areas, particularly at the invasive front (Fig. through TGFB signaling (i.e., A549+ TGFβ1; Fig. 1D). 2A and B). We detected tubulin immunostaining in MGT#1- Neurosphere culture conditions permit propagation of negative cells as well as H2B–CFP puncta formation, facili- stem-like and short-lived progenitors and spontaneous dif- tated through mitotic chromatin condensation in dividing ferentiation to occur (21). We exploited this limited degree cells. This ruled out that the cells expressing the reporter were of heterogeneity to test whether cells with high reporter simply located in areas of higher epitope accessibility or were expression are more homogeneous than the bulk. Under escapers to lentiviral silencing (Fig. 2B–D). Thus, MGT#1 these conditions, both near-isogenic hGICs showed high expression reflects functional intratumoral heterogeneity. expression of PNGT#1/#2 and CLGT#1/#2 and low expres- Next, we exploited dual-reporter combinations to gain sion of MGT#1/#2 (Supplementary Table S1). FACS-purified insights in the dynamics of cell states in vivo. For this experi- IDH-mut-hGICs and IDH-WT-hGICs modified by selected ment, we generated IDH-WT or IDH-mut lines that car- reporters could be distinguished by RNA-seq and princi- ried one mVenus-driven mesenchymal reporter (MGT#1 or pal component analysis. Moreover, IDH-WT-hGICs with MGT#2) and one mCherry-driven nonmesenchymal reporter high reporter expression were less variable than bulk hGICs (PNGT#1, PNGT#2, CLGT#1, or CLGT#2). Upon xenograft (Supplementary Fig. S1F). GSEA demonstrated that high formation (n = 18), we applied t-distributed stochastic neigh- expression of MGT#1 in IDH-WT-hGICs enriched for mes- bor embedding (t-SNE) to categorize parallel in vivo/in vitro enchymal GBM gene sets compared with PNGT#2 or bulk flow cytometry data; this consisted of hierarchical compo- unsorted cells (Fig. 1F). Consistently, bulk and PNGT#2 cells nents including cell shape, granularity, viability dyes, mesen- were both enriched for proneural and cell-cycle gene sets. Inter- chymal and nonmesenchymal fate reporters (Supplementary estingly, mesenchymal GBM gene sets were more enriched Fig. S2A and Methods). Strikingly, we observed a proportional in IDH-WT than in IDH-mut cells (Fig. 1F). Together, the increase of IDH-WT mesenchymal reporter–expressing cells data indicate that reporter expression by FACS reflects cell in vivo (P < 0.0001), and alongside a concomitantly milder states at the endogenous level of gene expression. Finally, but significant decrease was observed for classic or proneural the signature genes retrieved in high MGT#1-expressing cells reporter–expressing cells (P < 0.01; Fig. 2E–G) when com- did not change when compared with either high proneural pared with their in vitro culture counterparts. In contrast, or classic reporter-expressing cells. Still, OLIG1 and OLIG2 proportions of MGT#1-high (MGT#1hi) IDH-mut-hGICs did marked proneural reporter-expressing cells, whereas CCNE2 not increase in vivo (Supplementary Fig. S2B and Methods), was enriched in classic reporter-expressing cells (Supplemen- raising the interesting possibility that the IDH1R132H restricts tary Fig. S1G), suggesting that each reporter enriches for cells tumor cell plasticity. in a specific state. To determine the transcriptional identity of the mesen- In summary, this method can systematically leverage bulk chymal state in vivo, we isolated several hundred highly mes- or single-cell gene-expression data representing biologically enchymal and nonmesenchymal reporter–expressing cells or clinically relevant phenotypes into synthetic reporters, (MGT#1, and PNGT#2 or CLGT#1, respectively) from brain which preserve critical features of endogenous cis-regulatory xenografts. We purified and profiled them using FACS and elements and permit a genetic tracing of cell states. RNA-seq. IDH-WT-hGICs with high MGT#1 expression in vivo were significantly enriched for the TCGA–mesenchymal Synthetic Genetic Tracing In Vivo Reveals GBM gene set compared with nonmesenchymal tumor cell frac- Heterogeneity and Hierarchies tions (i.e., PNGT#2 and CLGT#1); the latter were instead The proneural GBM is considered the ancestor of all enriched for TCGA proneural and oligodendrocyte-like gene subtypes (22) and to reflect an oligodendrocytic origin (23). sets (Neftel-OPC; Fig. 2H). Of note, features of highest fit- Recently, however, a mesenchymal-to-proneural hierarchy ness in vivo such as viability and high transcriptome quality emerged by in silico transcriptomic lineage tracing in single (Supplementary Fig. S2C and S2D) were more often associ- cells (17). ated with MGT#1 than with nonmesenchymal reporter cells.

Figure 2. In vivo genetic tracing of mesenchymal transdifferentiation. A, Top, schematic representation of the experiment. Bottom, representative coronal forebrain images of IDH-WT-hGICs-MGT#1 xenografts in NSG mice at humane endpoint (n = 10). Bottom left, hematoxylin and eosin (H&E) staining; bottom right, insets showing magnification of mVenus, Tubulin, and DAPI counterstained tissue with invasive glioma front being homogeneously MGT#1hi. B, Representative lesion with mixed high and low mesenchymal reporter expression. C and D, Representative H2B-CFP expression (arrowhead) in MGT#1- positive and MGT#1-negative lesions, respectively. E, Top, schematic representation of the experiment. Bottom, representative t-SNE map of in vitro and in vivo reporter expression for IDH-WT-hGICs with the indicated dual-reporter combination. Gating strategy is shown in Supplementary Fig. S2. F, Relative quantification of t-SNE data inE . G, Representation of the dual reporters’ expression in vitro and in vivo for the indicated pairs (n = 3/group). Unpaired t test reports significance for eachin vivo reporter group compared with its relative in vitro control (****, P < 0.0001; ns = not significant). H, Bubble plot of hi GSEA Padj values for the indicated GBM subtypes/states and comparisons. I, Volcano plot of the differential expression analysis between in vitro PNGT#2 and in vivo MGT#1hi. Selected genes are highlighted. J, Ingenuity pathway top upstream regulator analysis of differential expression analysis in I.

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A Mesenchymal sLCR E Mesenchymal sLCR Non-mes. sLCR mVenus PGK H2B-CFPH2B-CFP ? mVenus PGK H2B-CFP + mCherry PGK H2B-CFP ? MGT#1hi Both high PNGT#2hi 100 150 100 150 100 150 50 50 50 0 0 0 010150 100 50 0 nvtoIn vivo In vitro 010150 100 50 0

mVenus Tubulin DAPI t-SNE 1 B C D t-SNE 2

11.6% 5.0% F 4.6% 2.3% 4.7% 23% PNGT#2hi Both high MGT#1hi Remaining 81.4% 67.4% % gated cells In vitro In vivo H PN 2010 MES 2010 CL 2010 vs. G OPC 2019 MGT#1 In vivo Mesenchymal sLCR Non-mes. sLCR MES1 2019 mVenus mCherry PGK H2B-CFP AC 2019 PGK H2B-CFP n.s. ** PN 2010 40 **** ** MES 2010 Non-MGT#1 In vitro ** 40 CL 2010 vs. n.s. OPC 2019 MES1 2019 AC 2019 30 30 Padj PN 2010 MES 2010 CLGT1 ns<0.05 CL 2010 20 OPC 2019 vs. CLGT2 20 MES1 2019 Gene ratio AC 2019 PNGT2 0.3 0.5 Gated high (%) PN 2010 10 PNGT1 10 MES 2010 MGT2 CL 2010 0.7 0.9 MGT1 OPC 2019 vs. MES1 2019 0 0 AC 2019 0.0 0.5 1.0 1.5 2.0 In vitro In vivo In vitro In vivo

−Log10(Padj)

I Proneural in vitro Mesenchymal in vivo J LPS hi hi TPA (PNGT#2 ) ns (MGT#1 ) IFNG 300 NFkB EGR1 mVenus F2 FOS Decitabine TNFα JUNB Tretinoin

) IL6 200 EGR2 IFNA adj

P RPTOR ( IKZF1 10 FOSB SREBF1 LY294002

−Log 100 Alpha catenin mCherry FOSL2 SB203580 JUN Bisindolylmaleimide I EGR3 Elaidic acid SREBF2 0 SCAP −5 0 5 10 −3 30 Log2 fold change Z−score

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Compared with homogeneously proneural cells in vitro (i.e., proneural and mesenchymal identities are dependent on PNGT#2hi), MGT#1 expression in vivo significantly enriched intrinsic and external signaling, respectively. for a human mesenchymal GBM gene set (e.g., Neftel-MES1). To investigate whether the mesenchymal state relies on Moreover, the acquisition of an astrocyte-like (AC-like) state upstream signaling cues that are absent in standard in vitro appeared to be a dominant feature of MGT#1hi cells in culture conditions, we modulated external signaling in a phe- vivo (Fig. 2H). This is consistent with the signature of bulk notypic screen. In neurobasal-like serum-free medium (see TCGA–mesenchymal GBM to be a mix of mesenchymal and Methods), which recapitulates GBM heterogeneity in mouse astrocytic phenotypes at the single-cell level (Fig. 1C). xenografts (21, 24), hGICs were stimulated by the addition To identify the hallmarks of proneural-to-mesenchymal of selected cytokines and growth factors. We carried out a transition in vivo, we performed differential expression analy- FACS screen after 48 hours (Fig. 3A) and also tested several sis between the two most abundant homogeneous popula- signaling cues through individual FACS analyses or live-cell tions in vitro and in vivo (proneural PNGT#2hi in vitro vs. imaging (Supplementary Fig. S3A and S3B). Compared with mesenchymal MGT#1hi in vivo). This revealed that in vivo, the naïve cells, IDH-WT-hGICs-MGT#1 swiftly upregulated the MGT#1hi GBM state relies on the activation of early- the reporter in response to TNFα signaling, human/bovine immediate response transcription factors, including EGR1, 2, serum, and leukemia inhibitory factor (LIF), pointing to and 3, JUN, JUNB, FOSL2, FOSB, and FOS (Fig. 2I). A similar these factors as direct mesenchymal triggers, and to a lower outcome was obtained by comparing MGT#1hi in vivo with extent Activin A (Fig. 3B; Supplementary Fig. S3A and S3B). the MGT#1hi in vitro (Supplementary Fig. S2E). This indi- These results were reproducible across two independent cated that those genes represent a signature of tumorigenesis mesenchymal reporters (i.e., MGT#1–2; Fig. 3B). The response in vivo. to the external signaling was generally dampened in IDH- Overall, 954 genes were specifically upregulated in the mut cells. MGT#1 in vivo fraction, and 234 marked PNGT#2 cells in In contrast, in a parallel screen for the same factors using vitro (Padj < 0.05; log2FC ± 1.5; Fig. 2I). mVenus featured one proneural reporters, no factor consistently elicited a response of the most relevant upregulated genes, whereas mCherry was in both reporters, with IFNγ and serum triggering PNGT#1 inversely regulated, although not significantly (log2FC = 5.34; expression only (Fig. 3B). This supports that the proneural Padj = 0 and log2FC = −0.48; Padj = 0.42, respectively; Fig. 2I). identity is largely uncoupled from the microenvironment but Of note, the MGT#1hi state in vivo showed an upregulation rather is either encoded in the cell of origin or embedded in of GFAP and CHI3L1, which mark terminally differentiated the RTK signaling. astrocytic and mesenchymal cells. But their expression in TNFα’s role as a prominent promesenchymal signaling cue the absence of other markers of terminally differentiated is consistent with the transcription factor NFκB binding at astrocytes indicates that most cells in the MGT#1 state are endogenous cis-regulatory elements included in the MGT#1 undifferentiated (Supplementary Fig. S2E). reporter upon TNFα stimulation (Supplementary Fig. S1A). Ingenuity pathway analysis (IPA) established that the genes Importantly, this is in line with our finding that NFκB activa- marking MGT#1 cells in vivo locate downstream of sev- tion is the key connecting pathway for mesenchymal genes eral proinflammatory regulators, including TNFα (z-score = in brain xenografts (Fig. 2J) and previous analyses in patient- 2.655, P = 4.49–24), NFκB (z-score = 1.915, P = 3.05–13), and derived cell lines (10). interferons (Fig. 2J). Among other well-known in vivo–specific Interestingly, side-by-side stimulation in vitro showed that pathways, we found known targets of the synthetic retinoic MGT#1 blandly responded to TNFα in IDH-mut cells (Sup- acid tretinoin (z-score = 2.99, P = 3.31–22), the TGFβ and plementary Fig. S3C), despite both IDH-WT and IDH-mut VEGF pathways (z-score = 2.51, P = 3.28–26 and z-score = bearing comparable levels of MGT#1 expression, and were 1.531, P = 3.2–15, respectively; data not shown). propagated under the same signaling conditions. This is In summary, synthetic genetic tracing in a physiologically reminiscent of the impaired MGT#1 activation in these cells relevant tumor microenvironment revealed that hGICs with in vivo (Supplementary Fig. S2B). Quantitative PCR con- a low mesenchymal identity in vitro generate xenografts with firmed that response to TNFα involved the amplification of phenotypically distinct populations of cells that include mes- promesenchymal genes in both cell types but to a lower extent enchymal GBM cells. in IDH-mut-hGICs (Supplementary Fig. S3D). The specificity of the mesenchymal reporter expression in response to The Mesenchymal GBM Identity Is Adaptive TNFα was further confirmed by using a control reporter and Reversible carrying the ubiquitin C gene promoter (Supplementary Fig. Our hGICs express proneural reporters in a genotype- S3E). Notably, TNFα led to the phosphorylation of NFκB-p65, independent manner, and high levels of mesenchymal activ- STAT3, and p38-MAPK in both cell types (Supplementary ity were seen only in vivo (Fig. 2A–G). We reasoned that the Fig. S3F). Thus, we conclude that our reporters permit the

Figure 3. The mesenchymal GBM identity is adaptive and reversible. A, Schematic description of phenotypic screening using sLCRs. B, Bubble plot visualization of the screening of the indicated factors regulating MGT#1 in IDH-mut- and IDH-WT-hGICs (left) or MGT#1, MGT#2, PNGT#1, or PNGT#2 in IDH-WT-hGICs (right). Bubble size and color indicate the magnitude and the direction of the change. C and D, Bar plot showing the individual response to the indicated factors/sLCRs after 48 hours of induction. E and F, Representation of longitudinal expression of the MGT#1/2-mVenus in response to the indicated factors starting from day 0 (stimulation). The arrows indicate the time point for cytokines withdrawal. G, Bubble plot of GSEA Padj values for the indicated GBM subtypes/states and comparisons between the identified MES-inducing stimuli. H, Upset plot of all intersections for the indicated MGT#1 activation cues sorted by intersection size. Interconnected circles in the matrix indicate common genes.

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A FACS Screen C sLCR PGK sorting GBM subtype– IDH-WT-hGICs-MGT#1 (48 h ON) enhanced cells FACS 20 analysis 18 16 14 B 12 IDH-hGICs WT 10 genotype mut WT 10 8 6 2.3 2.4 TNFα 2.1 2.4 TNFα 4 1.41.4 1.3 2.3 2.1 2 Human serum Human serum 0 1.1

1.9 log FC

2 Fold mVenus FI/untreated FBS IFNγ IL6 1.1 1.7 1.8 pos IGF TnC IL1 β FBS IFN γ Activin A HGF

FBS TGF β neg TNF α NRG1 1.1 TGFβ Control

Activin A Activin A GSK126 Size TGFβ IL6 2.4 ApelinF-13A IL6 Human serum IFNγ 1.1 sTRAIL/Apol2L sTRAIL sTRAIL 0.5 NRG1 Apelin F-13A 0.1 IL1β HGF D Apelin F-13A Tenascin C IDH-WT-hGICs-MGT#2 (48 h ON) HGF NRG1 20 18 IGF IL1β 16 Tenascin C IGF 14 12 MGT PNGT MGT 10 #1|#1 #2|#1 #2|#1 10 8 6 4 2 E F 0

IDH-WT-hGICs-MGT#1 (washout) IDH-WT-hGICs-MGT#2 (washout) Fold mVenus FI/untreated IL6 IGF TnC IL1 β FBS IFN γ Cytokine withdrawal HGF TGF β TNF α 100 100 Cytokine withdrawal TNF NRG1

α Control 80 80 TGFβ Activin A ApelinF-13A

FBS human serum

60 60 sTRAIL/Apol2L HuSer 40 40 Activin A 20 20 H 200 0 0

Fluorescence intensity (%) 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 150 Days Days 100

Set size 50 Intersection size 0 20 40 60 G 0 MES 2010 PN 2010 59 CL 2010 vs. 47 MES 2017 MES1 2019 36 36 MES 2010 PN 2010 24 CL 2010 vs. 23 MES 2017 MES1 2019 18 17

MES 2010

II II

II 14 II

II PN 2010 II CL 2010 vs. 12 MES 2017 11 MES1 2019 7 MES 2010 PN 2010 7 CL 2010 vs. 6 MES 2017 5 MES1 2019 2 MES 2010 2 PN 2010 CL 2010 2 MES 2017 vs. 1 MES1 2019 1 0 1 2 1

−Log10 (Padj) LIF HuS in vivoTNFαActivinControl A

MES In vivo TNFα LIF

Padj ns <0.05

II II II

Non-MES II Gene ratio 0.4 0.6 0.8 In vivo II HuS Activin A

March 2021 CANCER DISCOVERY | OF8

RESEARCH ARTICLE Schmitt et al. identification of differences in the transcriptional responses of the DNA damage marker γH2AX one hour after irra- between IDH-WT-hGICs and IDH-mut-hGICs. diation confirmed that double-strand breaks had occurred Genetic tracing of GBM subtype identities or states offers (Fig. 4A). Forty-eight hours after 20 Gy radiation, in IDH- a means of testing whether the mesenchymal GBM is a stable WT-hGICs-MGT#1, a proneural-to-mesenchymal transition entity (e.g., subtype) or a reversible cell state. To this end, we was supported by RNA-seq followed by GSEA (Fig. 4B and committed IDH-WT-hGICs to a mesenchymal fate using dif- C). Differential expression analysis revealed 135 genes sig- ferent external signaling cues and then performed a washout nificantly upregulated and 31 downregulated (log2FC ± 1.5; experiment. Within the timeframe of five days, the activa- Padj < 0.05), featuring the activation of the ATM signaling that tion of two independent mesenchymal reporters was reset connects the mesenchymal commitment to the DNA damage by the washout of the relevant signaling (Fig. 3C–F). This induced by IR (Supplementary Fig. S4B and S4C). aligns with the observation in dual-reporter cells that MGT#1 To address whether hypoxia also causes mesenchymal tran- expression was inducible but PNGT#2 levels were unchanged sition, we exploited genetic tracing by sLCRs under ambient by the signaling cues tested (Supplementary Fig. S3A–S3C). oxygen tension, physiologic glioma hypoxia, and severe experi- To confirm that the transcriptional states obtainedin vitro are mental hypoxia. Low oxygen levels (3%), which are considered representative of the human mesenchymal GBM in patients’ physiologic in gliomas (27), or hypoxia (1%) activated neither biopsies, we carried out RNA-seq of FACS-sorted MGT#1hi MGT#1 nor MGT#2 in IDH-WT-hGICs or IDH-mut-hGICs- cells after 48 hours of stimulation with TNFα, human serum, MGT#1 (Fig. 4D). Of note, MGT#2 contains a HIF1A motif FBS, LIF, or Activin A. We compared these profiles with naïve (P < 0.001), which makes it potentially responsive to HIF1A control cells as well as with the FACS-sorted in vivo MGT#1hi activation (Supplementary Table S1). However, mesenchymal cells. This revealed that in vitro stimulation with TNFα, reporter expression remained unchanged even in response to human serum, and—to a lower extent—LIF promoted the severe experimental hypoxia (0.5%; Supplementary Fig. S4D). acquisition of a cell state resembling human mesenchymal Consistent with the outcome informed by the reporter, RNA- GBM (Fig. 3G). Hence, the same reporter can detect differ- seq in IDH-WT-hGICs-MGT#1 followed by differential expres- ent signaling cues that lead to a mesenchymal transition. sion analysis indicated that genes that respond to low oxygen In addition, under in vitro conditions, TNFα, human serum, tension experienced significant regulation (Fig. 4E and F), but and LIF appeared to individually activate some of the genes this was not accompanied by mesenchymal commitment. that specifically mark MGT#1-expressing cellsin vivo—even Overall, genetic tracing indicates that both IR and a decrease though, individually, these factors elicited a milder tran- in tissue oxygenation trigger transcriptional responses, but scriptional activation of the MGT#1 reporter (Fig. 3H; Sup- only radiation leads to a swift acquisition of a mesenchymal plementary Fig. S3G). Together with our pathway analysis state in a tumor cell–intrinsic manner. of in vivo MGT#1-expressing IDH-WT-hGICs (Fig. 2J), the data suggest that the mesenchymal transition that occurs Synthetic Genetic Tracing and CRISPR/Cas9 in vivo is driven by specific external signals, most probably in Screens Connect Genetic and Pharmacologic a combinatorial way. Perturbations to Mesenchymal Commitment Overall, our genetic tracing of GBM states revealed that the To exploit synthetic genetic tracing in uncovering the proneural GBM is supported by intrinsic signaling, whereas genetic determinants of mesenchymal GBM, we used a the mesenchymal program is largely adaptive and builds on a genome-wide pooled CRISPR/Cas9 screen to uncover genes reversible, preexisting identity. It is, therefore, non–necessarily modulating MGT#1 expression in IDH-WT-hGICs in their hierarchical. naïve state, or upon induction of the mesenchymal state by external signaling or genotoxic stress (i.e., serum + TNFα Mesenchymal GBM Genetic Tracing Reveals a or temozolomide + IR, respectively; Fig. 5A). Both external Swift Cell-State Change in Response to Ionizing signaling and the exposure to therapy increased the number Radiation, but Not Hypoxia of MGT#1hi cells, as expected. Notably, applying the genome- Mesenchymal transdifferentiation in GBM is dominant wide CRISPR/Cas9 Brunello library alone was also able to at recurrence after standard of care (2), and scRNA-seq has increase the proportion of IDH-WT-hGICs-MGT#1 (Sup- shown a correlation between hypoxia and mesenchymal GBM plementary Fig. S5A), suggesting that this type of screen may (11, 12). However, a causal link had yet to be demonstrated. identify neuroepithelial gatekeepers. Replicates clustering IR is a major component in the standard of care for GBM according to the expected sources of technical and biological (1). To experimentally test whether IR can induce mesen- variability, coupled with the depletion of guide RNAs (gRNA) chymal transdifferentiation in a cell-intrinsic manner, we associated with essential genes but not of the control gRNAs exposed IDH-WT-hGICs and IDH-mut-hGICs to medical overall supported the good quality of the screen (Supplemen- X-rays. A dose-dependent MGT#1 activation in IDH-WT tary Fig. S5B–S5D). cells occurred in response to increasing radiation (Fig. 4A). To identify genes whose activity modulates homeostatic, TNFα amplified mesenchymal transdifferentiation in both therapy-induced, and signaling-induced mesenchymal transi- genotypes (Supplementary Fig. S4A). The IR doses tested tion, we compared the pool of gRNAs statistically depleted in here included a single application of 10 Gy radiation that MGT#1hi fractions against all control fractions. This analysis is sublethal in multiple human GSCs (25, 26). In all of uncovered 341 significant unique genes (256 promoting and the conditions, hGICs remained viable, also in combination 85 opposing to MGT#1 expression, log2FC ± 1; Padj < 0.05; Fig. with other treatments (e.g., TNFα or temozolomide; Sup- 5B; Supplementary Table S2). Among the top hits for which plementary Fig. S4A and data not shown). Phosphorylation a loss of function potentially facilitates the mesenchymal

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE

A B C (48 h) Neftel_MES1-MES2 IDH-WT-hGICs MGT#1 0.6 P < 0.001 30 **** **** **** ** 0.4 adj

Relative expression 0.2 1 20 0.0 0.5 0 5,000 10,000 0 Neftel_OPC 10 IDH-WT IDH-mut −0.5 0.0

gH2AX Enrichment score −0.2 mVenus high (%) Vinculin Condition − + − + 10 Gy −0.4 0 Control Padj < 0.001 20 Gy IR −0.6 0 5,000 10,000

0 Gy 5 Gy Rank 10 Gy 15 Gy 20 Gy

D IDH-WT-hGICs MGT#1 IDH-WT-hGICs MGT#2 IDH-WT-hGICs PNGT#2 IDH-WT-hGICs CLGT#2

Conditions Hypoxia (1%) Low oxygen (3%) Normoxia (20%) Normalized count 101 102 103 104 105 101 102 103 104 105 101 102 103 104 105 101 102 103 104 105 sLCR activation

E 3% O2 20% O2 (24 h) F Gene ontology enrichment

(72 h) Response to hypoxia Padj 1% O 3% O 20% O 2 2 2 1e−08 Response to decreased oxygen levels 2e−08 3e−08

Relative expression Response to oxygen levels 1.5 Count 1 12 0.5 Cellular response to hypoxia 14 0 16 −0.5 −1 Cellular response to Condition decreased oxygen levels Normoxia (20%) Hypoxia (1%) 0.18 0.20 0.22 Low oxygen (3%) Gene ratio

Figure 4. Mesenchymal GBM genetic tracing reveals a swift cell-state change driven by IR but not hypoxia. A, MGT#1 activation in response to increasing doses of IR at the 72-hour time point. The inset shows the immunoblotting of the indicated antibodies and condition at 1 hour after IR hi delivery. B, Heat map of IR-induced significantly differentially regulated genes P( adj < 0.05 and log2FC ± 1.5) in MGT#1 IDH-WT-hGICs fraction (pink; n = 3) against nonirradiated control cells (blue, n = 3). C, GSEA plots for the indicated gene sets. D, Representative FACS quantification of indicated sLCRs under the hypoxic (blue), low oxygen (green), or normoxic (pink) conditions. E, Top, a schematic overview of the RNA-seq experimental design. Bottom, heat map with differentially regulated genes of comparison between the hypoxic (blue, n = 3) and normoxic (pink, n = 3) conditions (Padj < 0.05 and log2FC ± 1.5). Heat map color coding is based on relative rlog-normalized gene-expression values across samples. F, Bubble plot of top gene sets enriched in response to hypoxia. Color codes and size indicate significance and gene ratio, respectively.

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RESEARCH ARTICLE Schmitt et al.

All conditions A IDH-WT-hGICs-MGT#1 B (naïve, TMZ + IR, TNFα + FBS) 30 Naïve Padj < 0.05 )

adj 20 Log2FC < −1 + TMZ/IR ( P Log2FC > 1

10 Not significant SGF29 ATRA/Tretinoin targets FBS/TNFα −log 10 Genome-wide CRISPR/Cas9 (Brunello) MGT#1-low 0 MGT#1-high −10 −5 0 5 10

Log2 fold change

C IDH-WT-hGICs-MGT#1 Naïve TNFα + FBS TMZ + IR 250K MGT#1hi MGT#1hi MGT#1hi 200K

150K

100K FSC-H

50K 5.5% 54.2% 25.2% CTRL 26.6% 82.9% 65% SGF29 KO 101 103 105 101 103 105 101 103 105 FSC-H MGT#1-mVenus

E D IPA upstream regulators RELA-WT RELA-KO (polyclonal) Untreated mVenus high mVenus high −3 TLR2Let−7ValproicGATA4MAPKAPK2 acidMir−155DoxycyclineSCDAR NR1H4MethamphTRAP1CiprofloxacinNR0B2RARA 7% 7%

0 IKK16 5% 4%

Z -score TNFα 70% 61%

IKK16 15% 6% −3 + TNFα 1 2 3 4 5 1 2 3 4 5 IL6 IL4 10 10 10 10 10 10 10 10 10 10 MPA AHR NFkB % of total CTNNB1 TNFSF11 Decitabine P38 MAPKchostatin A Cardiotoxinagen type I 8−br−cAMP Pirinixic acid Tri eldanamycin mVenus G Coll

F Top IPA toxicity G Ratio 0.00 0.25 0.50 0.75 1.00 Renal necrosis/cell death Cardiac fibrosis Liver proliferation I-—I **** Decreases respiration of mitochondria 100 Cardiac hypertrophy I——I *** I———-I * Increases renal damage Untreated (DMSO) Liver necrosis/cell death I—-I ***

Cardiac necrosis/cell death (%) viability 20 Gy IR + 58 µmol/L TMZ Glutathione depletion/ 50 CYP induction and reactive metabolites ATRA (16 h) ≥ IR + TMZ Increases liver hepatitis

FXR/RXR activation Relativ e IKK-16 (16 h) ≥ IR + TMZ RAR activation 0 Aryl hydrocarbon receptor signaling Glutathione depletion − Phase II reactions Ratio Hepatic cholestasis 0 1 2 −Log (P value)

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE

transition, we found a chromatin modulator, the SAGA- cells (i.e., IDH-WT-hGICs with both MGT#1 and PNGT#2) complex acetyltransferase CCDC101/SGF29, which had not to a short pretreatment with IKK-16, ATRA, or DMSO, previously been linked to GBM or epithelial–mesenchymal followed by proneural–mesenchymal induction stimulated transition (EMT). Recently, we had identified another chro- by TNFα. Subsequently, we measured the gene expression matin regulator, the PRC2-complex scaffold EED, as nega- of mVenus (MGT#1 reporter), TNF (an EMT driver gene), tive regulator of EMT (28). Given this, we chose to delete and CHI3L1 (a marker of terminal mesenchymal differentia- CCDC101/SGF29 in IDH-WT-hGICs-MGT#1 cells and subject tion). On the basis of those markers, after eight hours from these to signaling- or therapy-driven EMT. In each case, the induction, we observed that ATRA and IKK-16 had opposing loss of SGF29 increased MGT#1 expression (Fig. 5C), thereby effects on GBM state changes (Supplementary Fig. S5H). This validating the results of the screening. showed that MGT#1 could report on cellular responses to Consistent with our upstream regulator analysis of in vivo targeted compounds. MGT#1-specific gene expression, several single-guide RNAs Overall, these data demonstrate that synthetic genetic trac- (sgRNA) required for MGT#1 activation lie downstream of ing can be an effective method to identify genetic modulators proinflammatory pathways (Fig. 5D). These data connect of cell states and prioritize pharmacologic treatments based the expression of proinflammatory genes to their function on their ability to modulate cell identity. in mesenchymal commitment. Interestingly, the loss of two sgRNAs targeting RELA/p65 occurred in naïve IDH-WT- Non–Cell-Autonomous Phenotypic Consequences hGICs but not under the other conditions (Supplementary of the Cross-Talk between Tumor and Innate Fig. S5E). This suggests that the role of a single one of the Immune Cells NFκB transcription factors in MGT#1 regulation may be lim- Having established that our genetic tracing strategy is well ited to homeostasis. Indeed, deleting RELA alone resulted in versed to address clinically relevant questions, we next dis- the short-lived downregulation of MGT#1 expression at bulk sected the cross-talk between tumor and innate immune cells. protein level, and TNFα-driven MGT#1 activation was only IDH-WT GBM infiltration by GBM-associated microglia/ partially affected by RELA loss (Fig. 5E; Supplementary S5F– monocytes (GAM) correlates with NF1 deficiency and mes- S5G). Consistently, a simultaneous deletion of RELA and enchymal GBM (5), but whether there is a causal relationship NFκB1/p50 drastically impaired TNFα-driven MGT#1 activa- between GAMs and mesenchymal transdifferentiation had tion (Supplementary Fig. S5G). These findings demonstrate yet to be demonstrated. that functional reporters can help to elucidate how a complex Rather than only being recruited by mesenchymal GBM signaling pathway, such as the NFκB pathway, regulates the cells, GAMs might contribute to their specification. To inves- proneural–mesenchymal transition. Importantly, pharmaco- tigate this, we cocultured NF1-depleted IDH-WT cells with logic interference with the pathway by the IκB kinase (IKK) an early-passage immortalized human microglia cell line inhibitor-16 impaired MGT#1 activation in both WT and (hMG; cl.C20; ref. 29). The in vitro coculture between hGIC RELA KO cells, at concentrations that did not affect viability tumorspheres and hMG cells was set up using transwell (Fig. 5E and data not shown). Thus, hits from the phenotypic inserts, which enable cell–cell communication but maintain screen can predict pharmacologic switches such as the notion a physical separation between the two populations. Under that inhibiting IKK-mediated NFκB activation could prevent these conditions, hMG drove the upregulation of MGT#1 mesenchymal transition. expression to an extent comparable to TNFα (Fig. 6A–D). Having identified a clinically relevant compound that Microglia also activated MGT#1 in IDH-mut-hGICs, but at antagonizes the proneural–mesenchymal transition, we next lower levels (Fig. 6B). focused on mesenchymal state amplifiers. All-trans-retinoic Next, to test whether non-brain innate immune cells can acid (ATRA; aka tretinoin) is a RAR/RXR agonist whose also instruct mesenchymal differentiation in human GICs, targets are expressed in MGT#1 cells in vivo (Fig. 2J), and we differentiated primary human CD34+ cells into immature can potentially induce its targets identified by sgRNAs in cells broadly referred to as myeloid-derived suppressor-like the screen (Fig. 5B and F). In response to a short pre- (MDSC-like) and the human monocytic cell line THP-1 into treatment with ATRA, IDH-WT-hGICs appeared protected M1 or M2 macrophage-like cells. MGT#1 was strongly upreg- from the detrimental effects of radiotherapy and temozo- ulated when cocultured with macrophage-like cells, with lomide, as compared with their responses to IKK-16 (Fig. M1-polarized cells driving the strongest phenotypic commit- 5G). To correlate this phenotype with the modulation of ment, whereas MDSC-like cells triggered only a mild MGT#1 the GBM state, we first subjected dual-reporter-expressing activation (Fig. 6A–C). This aligns with our observation that

Figure 5. Genetic and pharmacologic modulation of the mesenchymal state. A, Experimental design for the functional dissection of MGT#1 activation. B, Volcano plot of sgRNA targets regulating MGT#1 expression in the screen (A). Fold changes were calculated for all MGT#1hi fractions [naïve, temozolomide (TMZ) + IR, TNFα + FBS, n = 3 each] relative to all MGT#1lo fractions and unsorted controls (n = 6 each). SGF29/CCDC101 highlighted in red as one of the top significantly upregulated hits within the comparison. sgRNA targets associated with RAR/RXR agonist tretinoin are labeled in yellow. C, Representative FACS profiles of MGT#1 activation by the indicated conditions inSGF29 KO or control IDH-WT-hGICs-MGT#1 cells. D, Ingenuity pathway top upstream regulator analysis of differential expression analysis in B. Categories associated with acute inflammation pathways are in bold. E, Representative FACS quantification of MGT#1 activation by the indicated treatments at 48 hours. F, Ingenuity pathway top upstream regulator analysis of differential expression analysis in B. Categories associated with retinoic receptors signaling are in bold. G, Bar plot representation of IDH-WT- hGICs-MGT#1 cells’ relative viability upon the indicated treatments. Error bars, ± SEM, two-way ANOVA followed by Welch correction (n = 5; *, P < 0.05; ***, P < 0.001; ****, P < 0.0001).

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RESEARCH ARTICLE Schmitt et al.

A Transwell D insert Control TNFα hMG coculture 100 100 200 hGICs Microporous 80 80 membrane 150 60 mVenus mVenus 60 mVenus 100 high 40 high 40 high Normalized To Mode Normalized To Mode 50 9% 55% 48% or or hMDSC, hM1Φ, hM2Φ, hMG 20 20 coculture 0 0 0 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Count mVenus Adaptation in RHBA NFkB-associated hi MGT#1hi+TNFα MGT#1 +MG/ MGT#1hi+TNFα/ hGICs MDSC-like Macrophages-like hGICs 0 257 differentiation differentiation/polarization 89 0 43 (M1-M2) 128 0 0 Brain-derived immortalized 24 hCD11b+ microglia 23 0 0 0 Private to hMG Donor-derived Human monocytic (hMG-cl.C20) Private to TNFα 0 hCD34+ cell line (THP-1) Shared hMG/TNFα NFkB-associated MGT#1hi+hMG E B Differentially regulated genes IDH-mut-hGICs (Padj < 0.05)

+MDSCs#1 MGT#1 WWTR1 NFKB1 +MDSCs#2 TNFAIP3 SOD2 +hMG#cl.20 NFKBIA Control L1CAM F FBOX32 FABP3 FDFT1 GPX3 ELOVL6 HMGCR IDH-WT-hGICs MSMO1 DHCR7 HMGCS1

MGT#1 HMGCS1 ADGRG1 +MDSCs#1 HMGCR INSIG1 INSIG1 ACSS2 +MDSCs#2 FDFT1 IRS2 SCD ACSL4 +hMG#cl.20 SC5D SREBF1 LDLR

% of total LDLR ABCD2 Control ACSS2 SREBF2 LPIN1 TMEM97 101 102 103 104 105 LSS STARD4 MEF2C MGT#1-mVenus SELENOP SCD MSMO1 SC5D MVD C RETREG1 PLA2G6 NUPR1 IDH-WT-hGICs MGT#1 PNPLA3 IDH-WT-hGICs 48 h 16% 72 h 16% Control MGT#1hi+hMG MGT#1hi+TNFα −2 2

Z-scores (log2)

42% 69% THP-1(M1) G UMAP DEG MGT#1 activation

34% 60% THP-1(M2) 1 % of total HuS IR 101 102 103 104 105 Control MGT#1-mVenus LIF 0 Activin A H C20 hMG

68 UMAP2

60 −1 OxLDL TNFα

40 NOC-18 33 29 −2 −1 0 1 2 21 UMAP1 Intersection size 20 20 20 16 13 11 11 9 9 8 7 7 6 6 6 5 5 0 OxLDL Activin A IR NOC-18 C20 hMG TNFα LIF HuS 200 050100150 Set size

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE cytokines associated with innate immunity such as TNFα NOC-18, TNFα, and—to a lesser extent—serum and LIF (Fig. trigger a stronger direct MGT#1 activation to a larger extent 6G and H; Supplementary Fig. S6D). than signaling molecules derived from adaptive immunity or Overall, our data establish a causal relationship between stroma (IFNγ/IL2 and IL6, respectively; Fig. 3A and B; Sup- innate immune cell infiltration and mesenchymal transdif- plementary Fig. S3A and S3B). ferentiation in GBM. To determine how microglia induce a mesenchymal GBM state in a nonautonomous manner, we used FACS to purify Fate Mapping by sLCRs Reveals Phenotypic and then transcriptionally profile MGT#1hi-expressing IDH- and Molecular Features Conserved in WT-hGICs upon coculture. TNFα treatment served as control, Patients with GBM because innate immune cells are capable of secreting TNFα in To test the relevance of the predictions based on genetic mouse and human glioma (30, 31). The coculture transcrip- tracing experiments that our models generated, we looked tionally remodeled both cell types (Supplementary Fig. S6A). for the molecular signatures we had found in patients’ gene- Pathway analysis revealed that both microglia and TNFα lead expression profiles. Patients with GBM were clustered using to NFκB-related gene activation in hGICs, yet largely through ssGSEA and several gene sets experimentally determined in a private set of genes (Fig. 6D and E). Our system provided our cells (Fig. 7A). Importantly, the microglia-driven pheno- no evidence to suggest that TNFα triggered the hMG-driven type and its related oxLDL and NOC-18 signatures clearly phenotype (data not shown). In fact, a pathway analysis connected mesenchymal patients. on MGT#1hi-expressing IDH-WT-hGICs activated by micro- The patient groups identified by these signatures had glia uncovered a remodeling of the metabolic transcriptome, statistical features, such as a trend toward poor survival, including genes in the cholesterol biosynthesis and SREBP1/2 which were comparable to human mesenchymal GBM sig- pathway (Fig. 6E and F). The presence of extracellular lipid natures that had been directly obtained from patients’ biop- droplets in GBM has been well established (32), and GBM sies (Supplementary Fig. S7). These analyses were replicated cells rely on extracellular cholesterol uptake for growth (33), in patient-derived single GBM cells (12) and in a large set so we next treated our cells with oxidized low-density lipo- of patient-derived GSCs, including a well-characterized one protein (oxLDL), which is known to activate the SREBP1/2 (ref. 35; Fig. 7B; Supplementary Fig. S7C). The similarities pathway through PPARγ. OxLDL drove MGT#1 activation between gene-expression data generated in our models and in both IDH-WT and IDH-mut models (Supplementary Fig. freshly derived patients’ GBM cells or GSC-like cells argue S6B). Moreover, in a hypothesis-driven approach, we tested that hypotheses generated with our genetic tracing strategy whether the nitric oxide (NO) synthesis pathway is capable are possibly falsifiable in human GBM. of driving mesenchymal transdifferentiation. Upregulation of the activity of the NO pathway is a common feature Therapeutic Implications of Phenotypic Changes of innate immune cell activation, and endothelial cell NO Induced by Innate Immune Cells in hGICs activity has previously been linked to glioma growth and inva- EMT has been linked to resistance to chemotherapy but siveness (34). Strikingly, NO donor NOC-18 also triggered also offers therapeutic opportunities (36, 37). Thus, we MGT#1 expression to levels comparable to hMG or TNFα exploited sLCRs’ ability to identify a mesenchymal state to stimulation (Supplementary Fig. S6C). However, neither anti- determine whether the microglia-driven state we had discov- LDLR treatment nor the NOS inhibitor L-NG-nitroarginine ered might have therapeutic implications. methyl ester (L-NAME) could rescue MGT#1 induction by In gene-expression signature seen in patients, both TNFα- microglia. This suggests that several concurrent mechanisms and the microglia-driven signatures scored high in a similar regulate the phenotypic changes driven by innate immune cohort of patients and single GBM cells by ssGSEA (Fig. cells in glioma. Next, we performed gene-expression profiling 7A and B; Supplementary Fig. S7). Yet, they had unique of the oxLDL- and NOC-18–driven MGT#1-expressing IDH- molecular features. Specifically, microglia appeared to impair WT-hGICs and analyzed these along with all the MGT#1hi expression of DNA damage and cell-cycle genes in GBM cells expression profiles. This allowed us to search for common (Fig. 7C and D). traits and to look into how different upstream signaling cues GBM cells must activate DNA-damage responses and are integrated into the transcriptional response detected by undergo proliferation in order for the standard-of-care (1) to our reporter. We found that each type of upstream signaling work. This means that the microglia-driven program identi- led to a specific transcriptional output, yet the microglia- fied here may have significant therapeutic implications, such driven mesenchymal transition shares features with oxLDL, as that GBM cells exposed to microglia respond differently

Figure 6. Innate immune cells drive non–cell-autonomous mesenchymal commitment in tumor cells. A, Schematic representation of contact-free hGIC coculture with immune cells (see Methods). B, Representative FACS profiles of IDH-WT- or IDH-mut-hGICs-MGT#1 alone or cocultured with human microglia (hMG#cl.20) or human CD34+ in vitro–derived MDSCs. C, Representative FACS profiles of IDH-WT-hGICs-MGT#1 alone or cocultured for the indicated time with human THP1-derived M1 or M2 macrophage-like cells. D, Representative FACS profiles and gating strategy of IDH-WT-hGICs-MGT#1 alone or stimulated with TNFα or hMG coculture. Bottom, Venn diagram of NFκB-related genes by IPA of DRGs for the indicated conditions. DRGs are computed relative to control hGICs (log2FC > 1, Padj < 0.05). E, Heat map of DRGs for the indicated conditions. RNA-seq reads were normalized as tran- script per million, log2-transformed and z-scored. Statistical significance was assessed by using the limma R package (control,n = 3, hMG, n = 3; TNFα, n = 2; hi Padj < 0.05). F, Ingenuity upstream regulator analysis of upregulated genes by hMG coculture compared with TNFα in IDH-WT-hGICs-MGT#1 . G, UMAP dimensional reduction of MGT#1 activation cues expression profiles combining all upregulated genes (see Methods). H, Upset plot of all intersections for the indicated MGT#1 activation cue comparison sorted by intersection size. Interconnected circles in the matrix indicate common genes.

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A Subtype IDH_status NF1_status OxLDL MES1_2019 MES_2017

4 Normalized ssGSEA score Subtype MES_2010 Classic MES2_2019 Mesenchymal NOC-18 2 Proneural C20hMG IDH_status IR 0 HuS Mutated TNFα Wild-type −2 PN_2017 NF1_status PN_2010 Altered CL_2017 −4 Not altered CL_2010 LIF Activin A

B C D S-phase gene set hGICs + hMG hGICs + TNFα 0.0 OPC-like NPC-like OPC-like NPC-like −0.2 Padj < 0.001 −0.4 −0.6

+hMG +TNF α hMG vs. TNF −0.8 α hi hi

AC-like MES-like AC-like MES-like DNA-damage response gene set MGT#1 IDH-WT-hGICs MGT#1 0.0 Neftel_MES2/MES1 Verhaak_PN_2010 S_phase Padj < 0.001 OPC-like NPC-like OPC-like NPC-like −0.2 G2–M −0.4 DDR hMG vs. TNFα SREBP1/2 −0.6 TNFα SREBP1/2 target gene set MES 0.8 AC-like MES-like AC-like MES-like 0.6 hMG vs. ctrl z-score Enrichment score 0.00 1.00 0.4 Normalized ssGSEA score −1 0 1 0.2 Padj < 0.001 E IDH-WT or IDH-mut-hGICs-MGT#1 IDH-WT-hGIC IDH-mut-hGICs 0.0 −3 +hMG-cl.C20 10 TMZ TMZ −4 Olaparib MES GBM (all) gene set 10 Olaparib VE-821 0.6 5 VE-821 10− LXR623 0.4 hMG vs. ctrl [M] LXR623 WP1066 50 −6 WP1066 Naïve hGICs 10 Topotecan 0.2 P < 0.001 Topotecan MitomycinC adj 10−7 MitomycinC 0.0 Log IC 10−8 Bay11-7085 Bay11-7085 0 10,000 20,000 −9 10 Rank MGT#1low MGT#1high Naïve

F IDH-WT-hGICs-MGT#1 G Ionizing radiation

Serum Chemotherapy

II II

100 100 II

II II 80 80 Activin-A II ? LIF

60 60

TNFα i

i i i Hypoxia i i i

40 lo 40 lo i

MGT1 MGT1 Nitric oxide i i hi hi i Viability [%] 20 MGT1 20 MGT1 oxLDL i Naïve Naïve 0 0 10−12 10−9 10−6 10−3 10−12 10−9 10−6 10−3 Glioma-associated Log[M] Topotecan Log[M] Mitomycin C microglia/macrophages

100 100

80 80 MES state 60 60 Non-MES state 1 Non-MES state 2 40 MGT1lo 40 MGT1lo Pro-MES MGT1hi MGT1hi Mild inducer 20 20 ? Nonautonomous

Naïve Naïve i 0 0 i Cycling cells 10−12 10−9 10−6 10−3 10−12 10−9 10−6 10−3 Log[M] LXR623 Log[M] Bay11-7085

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE to treatments. To validate our prediction, we FACS-sorted (10, 38–40), providing the basis for our genetic tracing using MGT#1hi- and MGT#1lo-expressing hGICs after microglia- sLCRs. The proneural and the mesenchymal reporters showed driven conversion and exposed these cells to a selected battery a higher relative expression level in patient-derived GBM cells of standard and targeted chemotherapeutics. Strikingly, in than in unrelated counterparts. Further efforts and models contrast to their sLCR-low and sLCR-naïve counterpart, both will be required to determine whether the reporters are well MGT#1hi-expressing IDH-WT-hGICs and IDH-mut-hGICs suited to perform “absolute subtyping.” proved to be more resistant to therapies based on DNA-dam- The in vitro derivation and propagation of GBM stem-like age responses (olaparib, ATR inhibitor VE-821, topotecan, cell lines results in generally high mesenchymal signaling mitomycin C; Fig. 7E and F; Supplementary Fig. S7D and in vitro (41). Yet, evidence presented here and in very recent S7E). Moreover, compared with MGT#1lo-expressing hGICs, work by others (42) suggests that these in vitro conditions MGT#1hi cells were found more resistant to LXR623, an LXR favor proliferation, but lack critical features of microen- agonist that regulates cholesterol efflux (Fig. 7E and F)—a vironmental complexity. Consistently, our data show that mechanism proposed as a synthetic vulnerability for GBM synthetic genetic tracing is best suited to trace changes in cells (33). Our data suggest that the therapeutic benefit of cell states. Using our transformed neural stem cell model and lowering cell-intrinsic cholesterol levels in GBM cells may synthetic genetic tracing, the proneural GBM emerged as a be unattained when innate immune cells induce GBM cells default entity. In the absence of a tumor microenvironment, to activate cholesterol biosynthesis. Moreover, hGICs puri- the proneural state appeared hardwired even in cells with a fied using either MGT#1hi or MGT#2hi, which both identify genotype often associated with mesenchymal GBM (e.g., NF1 phenotypically distinct cells through different regulatory ele- depletion). However, the mesenchymal identity was swiftly ments, exhibited similar dose–response patterns (Supplemen- amplified by acute inflammatory and proastroglial differenti- tary Fig. S7D), thereby indicating that the program identified ation stimuli, experimentally supporting previous correlative by individual reporters in these cells is causal to the resist- evidence (3, 10). Interestingly, the presence of the IDH1R132H ance. Importantly, IDH-WT/mut-hGICs had a dose response mutation correlated with a restriction in cells’ ability to enter to targeted agents such as BAY11-7085 (IκB inhibitor) and a mesenchymal state, in vitro and in vivo, in line with IDH-mut WP1066 (STAT3 inhibitor) independent of stratification by dampening transcriptional responses and validating its use as MGT#1 expression (Fig. 7E and F; Supplementary Fig. S7E). a genetic biomarker for disease classification (43). By captur- This indicates that the microglia activate a selective response ing the activation status of signaling pathways and develop- to chemotherapeutics in hGICs. mental stages (e.g., inflammation and differentiation), and In summary, synthetic genetic tracing established a causal any preexisting context-dependent difference (e.g., IDH-WT link between the innate immune cells and acquisition of two vs. mutant background), genetic tracing supports a mixed functionally relevant and therapeutically distinct GBM states. model in which the proneural GBM is an entity (2–4) and the mesenchymal GBM is a state (12). Yet the proneural GBM can be further subgrouped into specific “homeostates,” in which Discussion oncogenic, cell-of-origin signatures and the microenviron- GBM remains a lethal disease despite deep (epi)genomic ment make distinct contributions. characterization. Here, we approached the problem by com- Our data on the reversibility of the mesenchymal state sug- bining epigenomic information and classic genetic tracing. gest an alternative view of proneural and mesenchymal GBM We used synthetic genetic tracing of human GBM subtype as sharp cell identities with a fixed hierarchical relationship, expression programs to trace the fates of cells with homo- and support rather the potential for a fluid interconversion geneous phenotypes. This way, we discovered causal rela- between states. This view is consistent with both genetic tionships between perturbations, cell-fate commitment, and evidence from patients and mouse models (2–4, 12, 39, 40) response to therapies (Fig. 7G). Our data provide direct and the apparently discordant finding of a mesenchymal-to- evidence that links the tumor heterogeneity to patients’ proneural transition predicted by in silico lineage tracing (17). responses to treatments. Of note, the mesenchymal conversion observed in our in vivo Proneural and mesenchymal GBM subtypes have con- genetic tracing is consistent with either a proneural-to-mes- sistently been identified across expression platforms, read- enchymal transition contingent on the microenvironment, outs, and patient cohorts. Yet, the origins of these subtypes, with mesenchymal hGICs being more fit during tumor initia- their location or spatiotemporal evolution, and—most tion and progression, thereby leading to their positive selec- importantly—their therapeutic significance have remained tion, or a combination of both. Along this line, even though obscure. GBM subtypes rely on transcriptional programs the highly mesenchymal cells constituted only a fraction of

Figure 7. Therapeutic implications of phenotypic changes in GICs driven by innate immune cells. A, Heat map of the relative ssGSEA-normalized score for the indicated gene sets in patients with GBM from the TCGA data set, including gene sets representing specific GBM subtype/state and upregulated MGT#1 activation cues (Fig. 6G and H). The status of IDH1 and NF1 mutations and the corresponding GBM subtypes are also indicated. B, ssGSEA-normalized scores for upregulated MGT#1hi genes indicated in Fig. 6D (see Methods). Cell states identified by Neftel et al. (12) are indicated in each quadrant, and the original dots position is maintained in the two-dimensional representation of GBM cell states (or meta-modules; Methods). C and D, Differential GSEA for the indicated comparisons. Significance is independently calculated byt test and Kolmogorov–Smirnov. E, Left, schematic depiction of chemosensitivity profiling of hi sLCR-high and -low states. Right, dot denotes log[IC50] value in response to increasing concentrations of the indicated drugs for FACS-sorted MGT#1 and MGT#1lo fractions of the indicated genotypes. Dotted line indicates threshold of 10 μmol/L concentration, unattainable in the brain tissue. F, Dose–response curves of FACS-sorted MGT#1hi, MGT#1lo, or naïve IDH-WT-hGICs subjected to increasing concentrations of selected compounds as summarized in E. G, A model for modulation of mesenchymal fate in GBM.

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RESEARCH ARTICLE Schmitt et al. the bulk tumor, they more often appeared viable and pro- resistance. The indications on LXR agonists, aimed at duced high-quality cDNA libraries. It is tempting to speculate exploiting low cholesterol biosynthesis in GBM (33), are par- that mesenchymal cells are better adjusted to survive in vivo ticularly timely given the identification of innate immune cells and experimental stressors. A scenario in which mesenchy- as the culprit in patients’ lack of response to checkpoint inhib- mal fate activation enhances cell fitness would be consistent itor immunotherapy (48) and the current clinical trial com- with its identification as a dominant entity at recurrence bining immunotherapy and an LXR agonist (NCT02922764). (2); it would also explain the presence of the mesenchy- The data support the combination of multicellular systems mal surface receptor CD44 on a sizable fraction of primary and sLCRs as preclinical tools to test therapeutic strategies GBM–propagating cells (44). To systematically discriminate that are more likely to succeed in clinical trials. between all the plausible models, future genetic and tran- In summary, the synthetic genetic tracing developed here scriptional lineage tracing using genetic barcodes is war- uncovered causal and mechanistic evidence for a pathologic ranted, including secondary transplantations. role of innate immune cells in GBM through mesenchymal Substantial correlative evidence supports a link between transition—a relationship that has been hinted at for a long inflammatory signaling, EMT, the infiltration of innate time. The method is effective, scalable, and can be extended immune cells, and radioresistance (25, 26). Here, fate map- to a wide range of ex vivo or in vivo systems. The further devel- ping by sLCRs established a causal link for the clinical opment of the algorithm will help to broadly dissect cell- observation that DNA-damage therapy is associated with intrinsic and non–cell-autonomous mechanisms that control mesenchymal transdifferentiation (2). Conversely, it offered normal and tumor homeostasis in diverse contexts of clinical no support for a causal hypothesis of hypoxia-driven mesen- relevance. chymal commitment. Hypoxia regulates tumor homeostasis, GBM cells surrounding areas of pseudopalisading necro- Methods sis overexpress hypoxia-inducible factor-1, and scRNA-seq revealed a correlation between the hypoxia gene-expression GBM sLCR Generation signature and the mesenchymal GBM (11, 12, 45). Although To focus on cell-intrinsic gene signatures, in a pilot approach, we individual genes of the mesenchymal GBM signature may be filtered out genes with low expression in GSCs from our previous directly regulated by changes in levels of tissue oxygenation, experiments and confirmed their potential intrinsic expression in a our data support a model in which hypoxic gene expression validated cohort of GSCs from others. A database of 1,818 motifs [position weight matrices (PWM)] representing known transcrip- is additive to rather than causal for the mesenchymal GBM tion factor binding preferences was generated from the literature program. This conclusion does not, however, exclude that (49–53). PWMs were preselected on the basis of subtype-specific the hypoxic microenvironment might trigger a recruitment TFs. Regions corresponding to DRGs were retrieved from the hg19 of innate immune cells in vivo, in turn leading to a non–cell- (Refseq table downloaded from UCSC genome browser on October autonomous mesenchymal commitment in vivo (Fig. 7G). 5, 2012) and decomposed in windows of 150 bp and 50 bp steps In line with a model of tumor EMT as the hijacking of a (hereafter referred to as CRE). The scanned area surrounding each conserved developmental process, the mesenchymal reporters signature gene was manually delimited by two distal CCCTC-binding presented here are likely to uncover cell-fate mechanisms in factor (CTCF) sites, positioned ~10 kb away from the transcription a broader set of epithelioid cancers and may serve as tools to start site or transcription end site. High-affinity TF-binding sites in discover optimal treatment strategies, such as sequential dos- defined genomic regions were identified using Find Individual Motif Occurrences (PMID: 21330290) with –output-pthresh 1e-4 –no-qvalue. ing of targeted drugs to send cancer cells into a state followed For each window, whenever multiple matches for the same PWM were by hitting such state with targeted treatments (46). identified, theP adj < 0.01 of the best match (multiple backgrounds) was Finally, our strategy provides evidence of a causal link considered as a proxy for the affinity of that TF over that region. TFBS to support the clinical association between the mesen- pairwise correlation heat maps in Fig. 1A used the top 500 regions in chymal GBM subtype and a specific immune landscape terms of the score defined as −log10 (P value). Genomic coordinates (5). Although TNFα is believed to drive therapeutic resist- versus TFBS correlation heat maps, including the representative one in ance (10) and GAMs to be the source for TNFα in mouse Fig. 1B, were generated with the top 100 scoring regions. models (30) and human tumors (31), we uncover TNFα- independent routes to the GAM-driven mesenchymal GBM Vector Generation state. The signatures experimentally identified in this study The sLCRs were synthetized initially at IDT, later at GenScript, resemble those observed in single mesenchymal GBM cells and currently at VectorBuilder. MGT#1-mVenus was cloned in the from patients, suggesting a relevance for our findingsin vivo. PacI-BsrGI fragment of the Mammalian Expression, Lentiviral FUGW In addition to identifying a pathophysiologically relevant (gift from David Baltimore; Addgene #14883). Additional modifica- non–cell-autonomous interaction, mesenchymal genetic tions, such as insertion of H2B-CFP (gift of Elaine Fuchs, Addgene #25998), swapping of mVenus to mCherry or MGT#1 with all other tracing pointed toward the mechanistic activation of the sLCRs used either restriction enzyme digestion or Gibson cloning. NO and SREBP1/2 pathways as relevant for microglia- The sequence of the Igk-mVenus-TM was from ref. 54. The mCherry driven phenotypes. was modified with a nuclear localization signal or sequence. The The transcriptome remodeling induced in GAMs by cross- sLCR vectors are third-generation lentiviral system and have been talk between glioma cells and innate immunity is consistent used together with pCMV-G (Addgene #8454), pRSV-REV (Addgene with regulation of inflammatory and metabolism genes (47). #12253), and pMDLG/pRRE (Addgene #12251). A unique finding of this study is to show that the phenotypic changes induced by innate immune cells in tumor cells drive Oligonucleotides and Primers mesenchymal commitment and lead to selective therapeutic All oligos used in this study are available upon request.

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE

Cell Lines Human Monocyte Cell Line Differentiation All lines used in this study were thawed from frozen batches and Human monocytic THP-1 cells (ATCC TIB-202) were acquired propagated for a limited number of passages (5–10×), and all lines from S. Minucci (IEO Milan, Milan, Italy) and maintained in RPMI regularly tested with the Mycoplasma Detection Kit (Jena Biosci- 1640 (Thermo Fisher Scientific) culture medium supplemented with ence; 11828383, PP-401L) to exclude contamination. For all glioma- 10% FBS (Gibco, 10270106), 1 mmol/L pyruvate (Life Technologies), initiating cells and GSCs, cell line authentication occurred through 2 mmol/L GlutaMAX (Thermo Fisher Scientific, 35050-038). THP-1 global expression profiling. monocytes are differentiated into macrophages by 48 hours of incubation with 150 nmol/L phorbol 12-myristate 13-acetate (PMA; Human Glioma Cell Lines Cayman Chemicals; Cay10008014) followed by 24-hour incubation The IDH-WT-hGICs and IDH-mut-hGICs were generated by our lab in RPMI medium. Macrophages were polarized into M1 macrophages and will be described elsewhere. Briefly, IDH-mut-hGICs were generated by incubation with 20 ng/mL of IFNγ (R&D Systems, 285-IF) and by transforming human neural progenitor cells (NPC; kindly provided 10 pg/mL of lipopolysaccharide (Sigma, L2630). Macrophage M2 by R. Glass, LMU Munich, Munich, Germany), by means of: pLenti6.2/ polarization was obtained by incubation with 20 ng/mL of IL4 V5-IDH1-R132H (kindly provided by Hai Yan, Duke University, (Sigma, A3134) and 20 ng/mL of IL13 (PeproTech, 200-13). Durham, NC), p53R173H and p53R273H (point mutations introduced into TP53 ccsbBroad304_07088 from the CCSB–Broad Lentiviral Expression Transfection and Transduction Library), and pRS-Puro-sh-PTEN (#1; kind gift from D. Peeper). IDH- Transfection and transduction were previously described in detail WT-hGICs were generated by transforming human NPCs with the con- (59). Briefly, 12 μg of DNA mix (lentivector, pCMV-G, pRSV-REV, structs pRSPURO-sh-PTEN (#1), pLKO.1-sh-TP53 (TRCN0000003754), pMDLG/pRRE) was incubated with the FuGENE (Promega, E2311)– and pRS-shNF1. For these lines, thorough genetic, transcriptional, and DMEM/F12 (Life Technologies, 31331) mix for 15 minutes at room epigenetic characterization has been performed, as well as in vivo tumor temperature, added to the antibiotic-free medium covering the 293T formation and phenotypic mimicking ability. cells, and the first tap of viral supernatant was collected at 40 hours Patient-derived GSC lines GBM2 (ref. 55; TCP#2), GBM14, and after transfection. Titer was assessed using the Lenti-X p24 Rapid NCH421K (56) were kindly provided by Rainer Glass, LMU Munich Titer Kit (Takara, 631280) according to the manufacturer’s instruc- (Munich, Germany). Lines GBM166 and GBM179 were kindly pro- tions. We applied viral particles to target cells in the appropriate com- vided by Peter Dirks (University of Toronto, Toronto, Canada; ref. plete medium supplemented with 2.5 μg/mL protamine sulfate. After 21), and lines BLN-5 and BLN-7 (57) were kindly provided by Phillip 12 to 14 hours of incubation with the viral supernatant, the medium Euskirchen, Charité Berlin, Germany. was refreshed with the appropriate complete medium. In vitro, all glioma lines were propagated as described (58) with one modification. In addition to EGF (20 ng/mL; R&D Systems, 236- FACS EG), bFGF (20 ng/mL; R&D Systems, 233-FB), heparin (1 μg/mL; Transduced cell lines were harvested into single-cell suspensions Sigma, H3149), and 1% penicillin and streptomycin, PDGF-AA and resuspended into cold medium and filtered into FACS tubes. (20 ng/mL; R&D Systems, 221-AA) is also supplemented to RHB-A Sorting was conducted using BD FACSAria III or Fusion. The appro- (Takara, Y40001). This medium composition will be referred to as priate laser-filter combinations were chosen depending on the fluo- RHB-A complete. hGICs were cultured at 37°C in a 5% CO –95% air 2 rophores being sorted for. Typically, to remove dead cells, events were incubator, 3% O , and humidified incubator. 2 first gated on the basis of shape and granularity (FSC-A vs. SSC-A) Cancer Cell Lines and doublets were excluded (FSC-A vs. FSC-H). Positive gates were established on PGK-driven and constitutively expressed H2B-CFP as The MCF7 and MDA-231 cell lines (kindly provided by the Rene sorting reporter, to sort for populations with low to medium inten- Bernards lab, NKI Amsterdam, Amsterdam, the Netherlands) were sity of sLCR-dependent fluorophore expression. cultured in RPMI medium (Life Technologies, 21875091). Both cell lines were supplemented with 10% FBS, and 1% penicillin and strep- FACS Analysis tomycin at 37°C in a 5% CO2–95% air incubator. A549 and H1944 cell lines (kindly provided by the Rene Bernards lab) were cultured in All analyses were performed using FlowJo_v10. For analysis of RPMI medium. Both cell lines were supplemented with 10% FBS, and in vivo data in Fig. 2E–G, freshly dissociated mouse brain tumor sam-

1% penicillin and streptomycin at 37°C in a 5% CO2–95% air incubator. ples were pregated for viable cells before dimensionality reduction of all acquired parameters (FSC-H/W/A, SSC-H/W/A, positive and Human Microglia Cell Line negative viability dies and mVenus/mCherry sLCR expression) was Immortalized primary human microglia C20 cells (kindly provided performed using the inbuilt auto t-distributed SNE (opt-SNE) algo- by David Alvarez-Carbonell, Case Western Reserve University, Cleve- rithm (Perplexity 30, Iterations 1000). From resulting t-SNE maps, land, Ohio; ref. 29) were cultured in RHB-A medium supplemented the glioma cell clusters separating apart from mouse cells were iden- with 1% FBS, 2.5 mmol/L glutamine (Thermo Fisher; 35050038), tified and gated by overlaying sLCR expression heat maps. Further 1 μmol/L dexamethasone (Sigma; D1756), and 1% penicillin and t-SNE dimensionality reduction of gated glioma cells was performed to assess clustering of sLCR reporter distribution for individual streptomycin at 37°C in a 5% CO2–95% air incubator. in vivo–derived tumor cells and compared with simultaneously ana- Human Hematopoietic Progenitor CD34 Differentiation lyzed in vitro–cultured cells used for transplantation. Quantification of sLCR-high cells was established by defining a four-quadrant gating Donor-derived CD34+ cells (kind gift from K. Rajewsky, MDC Berlin, in mVenus versus mCherry plots on in vitro cells to mark sLCR-high Berlin, Germany) were propagated in SFEM II (STEMCELL Technolo- populations and applying this gating to t-SNE–gated in vivo glioma gies, 09605), SCF, FLT3-L, TPO, IL6 (all 100 ng/mL; easyexperiments. cells (see Supplementary Fig. S2A). com), UM171 (Selleck, 35 nmol/L), SR1 (Selleck, 0.75 μmol/L), and 19-deoxy-9-methylene-16,16-dimethyl PGE2 (Cayman Chemicals, 10 μmol/L). CD34+ differentiation toward immature MDSC-like cells RNA FISH and Dual FISH-IF was induced by switching culture medium to RHB-A-medium supple- Cells were permeabilized in 70% ethanol (RNA FISH only) or with mented with human SCF (50 ng/mL) and human GM-CSF (100 ng/ 0.5% triton X-100 (for dual IF-RNA FISH), washed in RNase-free PBS mL) for 7 to 12 days (cl.#1 and cl.#2), prior to coculture. (Life Technologies, AM9932), fixed with 10% deionized formamide

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RESEARCH ARTICLE Schmitt et al.

(EMD Millipore, S4117) in 20% Stellaris RNA FISH Wash Buffer A IDH-WT-hGICs-MGT#1 using the CA-138 pulse program of the (Biosearch Technologies, Inc., SMF-WA1-60) and RNase-free PBS, for 4D-Nucleofector Core Unit (Lonza). Approximately seven days after 5 minutes at room temperature. IgK-MGT#1-mVenus and H2B-CFP electroporation, the gDNA was extracted with AMPure XP beads were probed using SMF-1084-5 CAL Fluor Red 635 and SMF-1063-5 (Beckman Coulter, A63881), eluted in 50 μL of elution buffer with Quasar 570 custom Stellaris FISH Probes (oligo sequence available downstream PCR amplification of the target site of interest using 800 upon request) in 10% deionized formamide 90% Stellaris RNA FISH to 1,200 bp products centered around the gRNA target loci (primers Hybridization Buffer (Biosearch Technologies, SMF-HB1-10) at available upon request). The efficiency of the knockouts was assessed 31.5 μmol/L in 100 μL transferred to the coverglass, hybridized at using TIDE (NKI, https://tide.nki.nl/) or T7EI assays. The alteration 37°C in the dark. After overnight incubation, slides were washed of the MGT#1 fluorescence in bulkSGF29/CCDC101 -KO cells was three times with RNase-free PBS for 5 minutes. If primary/secondary directly assayed by FACS using BD LSRFortessa and FlowJo. staining occurred, it was performed as described in the Immunofluo- rescence (IF) section. Genome-Wide CRISPR Knockout In Vitro Screen Phenotypic Screening For the genome-wide pooled CRISPR knockout screen, we utilized the Brunello library consisting of 77,441 sgRNAs targeting 19,114 Tumor cells were propagated as described above until the screen- genes (average of 4 sgRNAs per gene) and 1,000 nontargeting con- ing. We seeded 15,000 cells/50 μL/well in 384-well plates (Corning) trols. To achieve a library representation over 100×, we transduced in Gibco FluoroBrite DMEM supplemented with the appropriate a total of 16 × 106 IDH-WT-hGICs-MGT#1 cells at a multiplicity growth factors. Cells were dispensed as 50 μL suspension into each of infection of ∼0.5 and amplified the cells for 10 days prior to well using the SPARK 20M Injector system (50 μL injection volume; introducing the treatment. At day 10, the cells were either treated 100 μL/second injection speed). For nonadherent cells (e.g., hGICs), with TNFα (10 ng/mL) and FBS (0.5%); temozolomide (50 μmol/L) cells were further centrifuged at 1,500 rpm for 1 hour 30 minutes and irradiation (20 Gy), or left untreated. Before the gDNA extrac- at 37°C. Bottom reading fluorescence was scanned using a SPARK tion, we performed a FACS sorting of each condition, collecting 20M TECAN plate reader at 37°C in a 5% CO (additionally 3% O 2 2 the IDH-WT-hGICs-MGT#1hi, IDH-WT-hGICs-MGT#1lo, and the for hGICs) in a humidified cassette, with the following settings for unsorted populations. Although the time window of the experiment mVenus: Monochromator, Ex 505 nm ± 20 nm, Em 535 nm ± 7.5 nm. is compatible with gene essentiality, we have specifically ruled out In independent replica, cell viability was measured with 0.02% Alamar- that our hits are essential in validation experiments. The genomic Blue solution in FluoroBrite medium with the following settings: DNA was extracted by lysing the cell pellets for 10 minutes at 56°C Fluorescence Top reading, Monochromator, Ex 565 nm ± 10 nm, in AL buffer (Qiagen, 19075), supplemented with Proteinase K Em 592 nm ± 10 nm. (Invitrogen, AM2548) and RNAse A (Thermo Scientific, 10753721), DMSO-soluble compounds such as GSK126 were robotically subsequently purified with AMPure XP beads (Beckman Coulter, aliquoted using a D300e compound printer (TECAN), whereas A63881), and eluted in EB buffer (Qiagen, 19086). Next-generation cytokines were robotically aliquoted to each well using an Andrew sequencing (NGS) libraries were constructed in a two-step PCR setup, pipetting robot (AndrewAlliance). Data were imported in PRISM7 where the PCR1 is used to amplify the sgRNA scaffold and insert a (GraphPad). Fluorescence intensity from control dead cells was sub- stagger sequence to increase library complexity across the flow cell, tracted as background from all values. Individual values were normal- while the PCR2 introduced Illumina compatible adaptors with unique ized to the mean of controls and represented as fold change. P7 barcodes, allowing sample multiplexity. For the PCR1, 5 μg of each gDNA sample was divided over five parallel reactions, which Irradiation of hGICs were subsequently pooled together and purified using AMPure XP Irradiation was delivered using the XenX irradiator platform beads. The optimal cycle numbers for PCR2 were determined for (XStrahl Life Sciences), equipped with a 225 kV X-ray tube for tar- 1 μL of each PCR1 individually by conducting a qPCR amplification geted irradiation. hGICs cultured in either 6-well plates or 96-well using KAPA HiFi HotStart Ready Mix (Roche, 7958927001) and 1× plates were placed in the focal plane of the beamline and exposed EvaGreen (Biotium, 31000). Ten microliters of the purified PCR1 to irradiation for a specific time, depending on the target dosage, as of each sample was used as input for the final PCR2. Both PCR1 calculated with an internal calculation software. and PCR2 were performed using KAPA HiFi HotStart Ready Mix. Primers are available upon request. Quality control of the final Induction of Hypoxia libraries was performed using the Qubit dsDNA HS kit (Invitro- gen, Q32854) for quantification and TapeStation High Sensitivity To investigate effects of hypoxia on IDH-WT-hGICs, cells were D1000 ScreenTapes (Agilent, 5067-5584) for determination of PCR moved from the standard 3% O culture to ambient O levels for 2 2 fragment size. The barcoded libraries were pooled together in equal 24 hours. Cells were then seeded at 250,000 cells/well into 6-well molarities and sequenced on an Illumina NextSeq500 using the 75 plates and moved to 1% O , 3% O , and ambient O , respectively. 2 2 2 cycles V2 chemistry (1 × 75 nt single-read mode). Reads were aligned In case of severe hypoxia (Supplementary Fig. S4D), plates were to the Brunello library using bwa and a custom script to generate cultured in pressurized incubators (Avatar, Xcellbio) at 0.5% O and 2 the gRNA read counts. The resulting sgRNA read counts were pre- 5 Psi (∼344 mbar) over the usual atmospheric pressure in Berlin of filtered >( 10 counts) and aggregated to the gene level with caRpools 14.7Psi (1,010–1,030 mbar). After three days of culture, cells were R package, and DESeq2 was applied for the identification of poten- harvested into single-cell suspensions using Accutase for assessing tial MGT#1 expression regulating hits. A hit was considered to be sLCR expression using FACS, and RNA was extracted as described significant atP < 0.05 and log FC ± 1. The identified significant for RNA-seq. adj 2 differentially regulated target genes were used as input for the IPA (Qiagen Bioinformatics), predicting the upstream regulators and Gene Knockout Using CRISPR/Cas9 toxicity of MGT#1 activation (Fig. 5D and F). For the density plot

The SGF29/CCDC101 deletion was performed using the Gene in Supplementary Fig. S5D, the count data were log2-normalized Knockout Kit v2 (Synthego). The sgRNAs were dissolved in nucle- and averaged between the replicates. Log2-normalized counts of the ase-free 1xTE buffer to a stock concentration of 30 μmol/L. RNP Brunello library were subtracted from the unsorted IDH-WT-hGIC complexes were formed by mixing the Cas9 nuclease-gRNAs in conditions to calculate the log2FC. sgRNAs with <10 read counts in a ratio of 6:1. Each RNP complex was nucleofected into 250,000 the plasmid library were removed prior to log2FC calculation.

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE qRT-PCR were carried out as two independent technical replicate experiments, cDNA was generated using the SuperScript VILO MasterMix (Inv- and data from both runs were combined for final normalization. All primers are available upon request. itrogen, 11755050) starting with 0.5–2.5 μg RNA as input in 20 μL reaction, incubated at 25°C for 10 minutes, at 42°C for 60 minutes, Final normalization as presented in Fig. 1D was done by first cor- and at 85°C for 5 minutes. qRT-PCR was performed with 10 ng recting GAPDH-normalized sLCR expression divided over N2-nor- cDNA/well, in a 384w ViiA 7 System using 1× Power SYBR Green malized copy-number abundance and then setting IDH-WT-hGICs as reference, by calculating the fold-change increase of copy-number PCR Master Mix (Applied Biosystems, 4368702), in 10 μL/well. Prim- ers are available upon request. normalized sLCR expression.

Automated Longitudinal Live-Cell Imaging Intracranial Orthotopic Glioma Xenograft All mouse studies were conducted in accordance with a protocol IncuCyte automated longitudinal imaging was performed in approved by the Institutional Animal Care and Use Committee 96-well black-walled plates (Greiner). 3 × 105 cells per plate were and in agreement with regulations by the European Union. Ortho- seeded to reach optimal confluence at the end of the experiment. topic glioma xenograft studies were conducted in NOD.Cg-Prkdcscid GSK126 was aliquoted using a D300e, whereas TGFβ1+2 were manu- Il2rgtm1Wjl/SzJ (NSG) mice purchased from The Jackson Labora- ally aliquoted to each well. Both were refreshed every second day. tory and maintained in specific pathogen-free conditions. We used The last time point was independently verified using a plate reader male and female mice between 7 and 12 weeks of age. Orthotopic (BMC Clariostar). glioma xenograft studies were conducted as described previously with modifications (58, 59). Immunoblot NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) male and female mice Cell pellets were lysed in RIPA buffer (20 mmol/L Tris-HCl pH7.5, between 7 and 12 weeks of age were used for injections. Briefly, the 150 mmol/L NaCl, 1 mmol/L EDTA, 1 mmol/L EGTA, 1% NP-40) head of the mouse was immobilized within the stereotactic frame supplemented with a 1× Protease inhibitor cocktail (Roche), 10 and the skull was exposed through a small skin incision. A small mmol/L NaPPi, 10 mmol/L NaF, and 1 mmol/L sodium orthovana- burr hole was made using a drill with the following stereotactic coor- date. The lysates were sonicated if necessary, and electrophoresis was dinates: 1.0 mm anterior and 2.0 mm lateral of the bregma. During performed using NuPAGE Bis-Tris precast gels (Life Technologies) in the entire procedure, mice were kept under 1.5% to 2% isoflurane NuPAGE MOPS SDS running buffer (50 mmol/L MOPS, 50 mmol/L mixed with ambient air and oxygen anesthesia on a warming pad. Tris Base, 0.1% SDS, 1 mmol/L EDTA). Protein was transferred onto hGIC tumorspheres were treated with accutase and resuspended nitrocellulose membranes in transfer buffer (25 mmol/L Tris-HCl as single cells at a concentration of 50,000 cells/μL. Generally, 4 to pH 7.5, 192 mmol/L glycine, 20% methanol) at 120 mA for 1 hour. 5 μL of hGICs was stereotactically injected into the corpus callosum Protein transfer was assessed through staining with Ponceau Red for at a 3 mm depth to the cortical surface. Injection flow was set at 5 minutes, following two washes with TBS-T. Blocking of membranes 0.4 μL/minute, and the needle was withdrawn after one-minute rest, was done for 1 hour at room temperature with 5% BSA in PBS. Dilu- to avoid intracranial pressure and reflux, respectively. The burr hole tions of primary antibodies were prepared in PBS + 5% BSA, and was sealed with bone wax and the scalp with surgical clips. Mice were membranes were incubated overnight at 4°C. Following three washes returned to their cages, monitored until full recovery, and checked for 5 minutes with TBS-T, dilutions of appropriate HRP-coupled sec- daily for signs of neurologic symptoms. Mice were also monitored ondary antibodies were prepared in PBS + 5% BSA, and membranes by imaging using in vivo imaging system (IVIS) as needed until neu- were incubated for 45 minutes at room temperature. After washing rologic symptoms occurred and mice were to be sacrificed. Brains three times for 5 minutes with TBS-T, enhanced chemiluminescence were collected directly after euthanasia, examined under fluorescence (ECL) detection reagent (Sigma, RPN2209) was applied and mem- microscope to identify the tumor border, and xenografted tumors branes were exposed to ECL Hyperfilms (Sigma, GE28-9068-37) to were processed the same day for FACS analysis and sorting or cells detect chemoluminescent signals. were frozen in medium plus 10% DMSO until needed. The remaining brain tissues were then subjected to fixation as described below. For Copy Number Normalized sLCR Expression the experiment in Fig. 2E–L, performed at EPO GmbH, 8-week-old female NOG mice were used under similar experimental protocols, and hGICs, patient-derived GSCs, lung adenocarcinoma, breast ade- welfare standards were approved by the Berlin authorities (LaGeSo). nocarcinoma, and leukemia cell lines were transduced with the sLCRs MGT#1 and PNGT#2 as described. To perform lentiviral copy-number normalization, gDNA was extracted using AMPure XP Tissue Dissociation and Brain Tumor Cell Sorting beads according to the manufacturer’s protocol. Relative amounts of Brain tumor dissection was previously described in detail (58, 59). sLCR integration sites into the genome of target cells were assessed Briefly, the tissue was dissected with a scalpel and digested in Accutase/ by qPCR, using mVenus (MGT#1)- and mCherry (PNGT#2)-specific DNaseI (947 μL Accutase, 50 μL DNase I Buffer, 3 μL DNase I) primers and N2 primers targeting a genomic region in chromo- at 37°C using C-tubes in an OctoMACS dissociator (Miltenyi Biotec) some 13 for input normalization between samples. One nanogram using program 37C_BTDK_1. Suspensions were filtered first through of gDNA was amplified using respective primers and Power SYBR a 100-μm cell strainer, following a 70-μm cell strainer before RBC Green PCR Master Mix in a total reaction volume of 10 μL in quad- lysis (NH4Cl, 155 mmol/L; KHCO3, 10 mmol/L; EDTA, pH 7.4, ruplicates. Relative DNA amounts of MGT#1 or PNGT#2 were nor- 0.1 mmol/L). After washing in cold PBS, viability and cell count malized over N2 levels to calculate copy-number abundance for each were assessed automatically with 0.4% Trypan Blue staining using a sLCR in each sample. TECAN SPARK20M. Expression levels of sLCRs in corresponding samples were assessed When surface markers were assessed, typically, 200,000 cells/ in quadruplicates by qPCR using One-Step TB Green PrimeScript antibody were used in 15 mL Falcons. Staining volume was 50 μL RT-PCR Kit II (Takara, RR086A) with an input of 2 ng total RNA in RHB-A medium with primary antibody (e.g., CD133-APC; Milte- using mVenus (MGT#1) and mCherry (PNGT#2)-specific primers nyi Biotec), on ice, in the dark, for 30 minutes. Unbound antibody and GAPDH primers for normalization. Relative sLCR expression was removed with two washes of PBS. Depending on whether cells of MGT#1 or PNGT#2 was calculated by normalizing over GAPDH were analyzed or sorted, data acquisition was performed on the expression for each sLCR in each sample. Both qPCR normalizations BD LSRFortessa or cells were sorted using the BD Aria II/III or an

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Astrios Moflo. The appropriate laser-filter combinations were chosen once with PBS before 1 mL of RHB-A complete medium was added. depending on the fluorophores being analyzed. Typically, to remove Transwell inserts were placed into plates, and 5 × 105 single hGICs in dead cells, events were first gated on the basis of shape and granular- a total volume of 1 mL of RHB-A complete medium were plated on ity (FSC-SSC), and we used as viability dyes either Calcein UltraBlue insert surface. hGICs and C20 human microglia were harvested after or DRAQ5 and ZombieRed (depending on the fluorophores being 48 hours of coculture for further analysis. analyzed). Analysis was performed with FlowJo_V10. Drug Dose–Response Screening Preparation of Cryosections These experiments were performed as previously described (46). Tumorspheres were allowed to settle by gravity, fixed in fresh Transduced hGICs from transwell coculture experiments were har- prepared formaldehyde in PBS (1.0%), which was blocked with 140 vested into single-cell suspension and sorted into mVenus-high and mmol/L glycine, rinsed with 30% sucrose, followed by addition of mVenus-low populations using a BD FACSAria III. Cells were counted freezing medium (O.C.T./cryomold). Frozen blocks were obtained by and 7,000 cells/50 μL/well were seeded onto 384-well black-walled dry ice freezing and stored at -80°C until used. The blocks were cut plates in RHB-A complete medium using the SPARK20M Injector with Leica CM 1950. system (50 μL injection volume; 100 μL/second injection speed). Drugs were typically dissolved as a 10 mmol/L stock in DMSO and Immunohistochemistry dispensed using the D300e compound printer (TECAN) for targeted Tissues or tumorspheres were fixed in 4% paraformaldehyde (PFA) dose response with plate randomization and DMSO normalization. for 20 minutes. Following fixation, dehydration was performed with After 72 hours of incubation, cell viability was measured after 2 to increasing EtOH from 70% to 100%, Xylene, and overnight paraffin 6 hours of incubation with 10 μL of Cell-Titer-Blue (Promega, 2813) incubation. Paraffin-embedded samples were cut using a HM 355S assay reagent with the following settings: Fluorescence top reading, microtome (Thermo Scientific). Hematoxylin/eosin (H&E) stain- Monochromator, Ex 565 nm ± 10 nm, Em 592 nm ± 10 nm. Data ing was performed with standard protocols, and slide images were were imported in PRISM7 (GraphPad). Fluorescence intensities from acquired with an automated microscope (Keyence). empty wells were subtracted as background from all values. Concen- trations were log10-transformed into log[M] scale, and individual Immunofluorescence values were normalized to the mean of untreated positive and SDS- treated negative control conditions. Nonlinear regression modeling At room temperature, cells were grown on coverslips or spheroids [log (inhibitor) vs. normalized response − variable slope) was used to spun down on glass followed by 4% PFA (Sigma-Aldrich, 16005) derive dose–response curve and IC50 values. in PBS for 10-minute fixation, washed in PBS 5 minutes (3×), permeabilized with 0.5% triton X-100 in PBS for 5 minutes, blocked Cell Viability Assay 15 minutes with 4% BSA (Roth, 3854.4), stained with primary and secondary antibodies, and 20 μg/mL Hoechst 33258 (Cayman Chem- For Fig. 5G, IDH-WT-hGICs-MGT#1 were plated in 5 replicates icals, 16756–50), and finally mounted onto glass slides using nail pol- and pretreated for 16 hours with 5 μmol/L ATRA or 0.2 μmol/L IKK- ish and Vectashield (Linaris, H1000). On paraffin-embedded tissues, 16, with subsequent exposure to ionizing irradiation (20 Gy) and we performed deparaffinization and citrate buffer antigen retrieval temozolomide (58 μmol/L). 72 hours after treatment, the viability with standard protocols. Permeabilization was performed with was measured by the addition of Calcein UltraBlue at 1.5 μmol/L and Triton X-100 0.25% in PBS and—when appropriate—endogenous 4 images were taken per well with the Operetta CLS (PerkinElmer) high-content imaging system. Integrated Harmony software was used peroxidases were blocked with 3% H2O2 in water. Typically, we performed blocking with 5% normal goat serum. Primary antibodies to analyze the images, obtaining the mean intensity of the Calcein were anti-GFP (Abcam, ab6556, 1:1,000), anti-MED1 (Abcam, ab64965 UltraBlue from the spheres per well. Data were analyzed with Prism8. 1:500), and anti-Tubulin (BD T5168, 1:2,000), and secondary antibodies (1:200) were A31573, A11055, A31571 Alexa Fluor 647, A21206 RNA-seq Generation Alexa Fluor 488, and A31570 Alexa Fluor 555. Standardly, the RNA was extracted using the TRIzol Reagent (Inv- itrogen, 15596026), with subsequent isopropanol precipitation and Imaging AMPure XP beads purification. The concentration of the RNA was assessed by the Qubit RNA HS Assay Kit (Invitrogen) and/or qPCR Microscopes used in this study were Zeiss LSM800, Leica SP5-7-8, quantification with the One-Step TB Green PrimeScript RT-PCR Kit and Nikon Spinning Disk. Confocal images in Supplementary Fig. II (Takara Bio). The integrity of the RNA was determined by means of S1B were acquired with a Leica SP5, where mVenus fluorescence was the High-Sensitivity RNA ScreenTape System (Agilent, 5067-5581). acquired using Ex = 488 nm, Em = 535 nm. Images in Supplementary To generate the GSC expression profiles for GBM sLCR con- Fig. S1C were acquired with a Zeiss LSM800, using Ex = 558 nm, Em = struction, the TruSeq Stranded Total RNA Library Prep (Illumina, 575 nm for mVenus-QUASAR570 and Ex = 653 nm, Em = 668 nm for 20020596) and the Ribo-Zero Gold rRNA Removal Kits (Illumina, BRD4- or MED1-AF647, respectively. For Supplementary Fig. S1D, MRZG12324) were utilized following the manufacturer’s protocol. the H2B-CFP-QUASAR670 used Ex = 631 nm, Em = 670 nm. Images The final libraries were profiled for quantity using the Qubit dsDNA were processed using ImageJ or Photoshop. Confocal live-cell imag- HS kit (Invitrogen) and/or the KAPA Library Quantification Kit ing in Supplementary Fig. S3A was done using a Zeiss LSM700 with (Roche, 7960204001). The appropriate library size distribution was appropriate excitation laser/emission filter combinations to detect assessed by the TapeStation High-Sensitivity D1000 ScreenTapes kit endogenous mVenus, mCherry, and CFP expression. (Agilent). Pooled libraries were sequenced on the Illumina HiSeq2500 or HiSeq4000 platforms in either single-read 51 bp or paired-end Transwell Coculture 100 bp mode. Illumina adaptors were trimmed using raw reads Cocultures of hGICs and immortalized primary human microglia with Cutadapt (https://cutadapt.readthedocs.io/en/stable/), and C20 or human monocytes were set up using hydrophilic PTFE 6-well raw reads were aligned to the human genome (hg19 or hg38) with cell culture inserts with a pore size of 0.4 μm (Merck). Human micro- TopHat (Tophat2 v2.1.0; parameters: –library-type fr-firststrand -g 1 glia, CD34+ monocytes, or THP-1–derived M1/M2 macrophages were -p 8 -G ENSEMBL_Annotation_v82.gtf). HTSeq-count v0.6.1p1 was seeded at 1.5 × 105 cells/well for 24 hours on 6-well plates in respec- used to assess the number of uniquely assigned reads for each gene tive medium. The medium was aspirated, and cells were washed (parameters: -m intersection-nonempty -a 10 -i gene_id -s reverse -f

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bam). Reads were normalized and log2-transformed to obtain log2 counts (>50). The heat maps depicting the significantly differentially counts per millions. expressed genes (Padj < 0.05, log2FC ± 1.5) in Fig. 4B and E were The TruSeq Stranded Total RNA Library Prep Gold Kit (Illumina, generated using the pheatmap package. Color coding represents 20020598) was utilized for the generation of the in vitro RNA-seq data relative rlog-normalized gene-expression values across samples. The (Figs. 3, 4, 6, and 7) according to the manufacturer’s protocol with enrichGO and the dotplot function from the clusterProfiler R pack- 0.1–1 μg of total RNA input. The SMARTer Stranded Total RNA-Seq age v.3.16.1 were used to conduct and visualize the Gene Ontology Kit v2–Pico Input Mammalian (Takara Bio, 634413) was used for the enrichment analysis are shown in Fig. 4F. in vivo RNA profiling (Figs. 1 and 2). The library construction was In Fig. 6D–F, transcriptome profiles were analyzed using Seq- performed following the manufacturer’s protocol with 0.2–10 ng Monk, and reads were normalized by the standard pipeline, applying of total RNA input. Quantity and quality controls were performed DNA contamination correction. Raw counts were used to perform as described above. Pooled libraries were sequenced on the Illumina DESeq2 differential gene-expression analysis. The same pipeline NextSeq500 or NovaSeq 6000 platforms in a 2 × 75 bp or 2 × 100 with log-transformation was used for visualization. Significance was bp mode. The demultiplexing was performed using the bcl2fastq determined using standard SeqMonk settings, P < 0.05 after Benja- conversion software (v2.20.0). The reads were aligned to a custom mini and Hochberg correction, followed by independent intensity genome (GRCh38 containing sLCRs and reporter sequences) using filtering. Quantitation was done as above. NFκB-related genes in STARv2.6.0c. HTSeq was utilized to assess the number of uniquely hMG versus hGIC and TNFα versus hGIC comparisons were deter- assigned reads for each gene (–m intersection-nonempty -a 10 -i mined using IPA (Qiagen Bioinformatics). MES-GBM signatures gene_id -s reverse -f bam). were obtained by the respective publications and plots were generated using Venny. GSEA significance was determined for MES-GBM RNA-seq Analysis log2FC > 0.5-fold with Padj = 0, for PN log2FC < −0.4, Padj = 0, and for RNA-seq analysis of in vivo and in vitro high/low data set was con- SREBP log2FC > 1-fold with Padj = 0. ducted using R v3.6 (Figs. 1 and 2). After the data processing step The interaction map in Fig. 6F was generated using the func- (above), the quality of each sample was individually assessed using tion Ingenuity upstream regulator from IPA for the comparison of dupRadar v1.18 R package (default parameters) and subsequently MGT#1hi TNFα with MGT#1hi C20MG coculture. In comparing reevaluated by the correlation between the number of genes and TNFα and hMG samples, independent filter ofP adj < 0.05, log2FC > average counts for each file (Supplementary Fig. S2C and S2D). 1, and log2Avg > 5 was applied to the DESeq2 results, selecting only Then, differential expression analyses between specific sLCR activa- upregulated genes to conform the TNFα and hMG signature gene set. tion, high/low, and in vivo/in vitro were conducted using DESeq2 v1.24 on raw prefiltered counts >( 100 and >50). Of note, principal GSEA component analysis was used to identify potential outliers in the GSEA was used to identify the enrichment of any of the GBM in vivo samples data set (Supplementary Fig. S2D), and only MGT#1hi subtypes using GBM subtype/state public gene sets (3, 5, 12) in the homogeneous samples were used in different comparisons (Fig. 2H; different comparisons of FACS sorted upon MGT#1 activation RNA- Supplementary Fig. 2E). Differential regulated genes were consid- seq profiles (above). The enrichment was generated using the piano ered if log2FC > ±1, Padj < 0.05, and base mean > 5 (Fig. 2I; Supple- v2.0.2 R package function runGSA (parameters: geneSetStat=“page,” mentary Fig. S2E). The in vivo MGT#1hi gene set contains only the signifMethod=“geneSampling,” and nPerm=1000). Graphical repre- upregulated genes of the comparison between in vivo MGT#1hi and sentation was generated by computing the positive gene set enriched lo MGT#1 samples. Graphical representations in this analysis were as −1*log10 (Padj (dist.dir.up) for the corresponding comparison (Figs. generated using ggplot2 v3.3.2. 1F, 2H, and 3G). GSEA for GBM signatures (12) in Fig. 4C and Sup- Differential expression analyses of MGT#1 activation cues RNA- plementary Fig. S4C were conducted and visualized using the fast- seq profiles were conducted using DESeq2 v1.24. Each condition preranked GSEA (fgsea) R package v.1.14.0, using all genes included [TNFα, Human Serum (huSer), IR, Activin A, NOC-18, oxidized in the comparison. The genes were preranked based on the log-fold LDL (OxLDL), and C20-human microglia coculture] was individu- change derived from the differential expression analysis for the indi- ally compared against control samples after filtering for low-count cated comparisons, with the number of permutations = 100,000 for genes (filterByExpr function from the edgeR v3.26 R package). To assessing the enrichment significance. account for technical differences between sequencing runs, batch Graphical representations of this analysis were generated using correction using the sva v3.32 R package was applied when neces- ggplot2 v3.3.2, with the exception of the heat map in Fig. 1F, which sary. Upregulated genes (log2FC > 1, Padj < 0.05, and base mean > was generated using pheatmap v1.0.12 R package (hierarchical clus- 5) were considered as gene sets for the different representations tering is based on Manhattan distance and the ward.D2 clustering (Figs. 3H, 6G and H, and 7A; Supplementary Figs. S3G, S6D, S7A, method). and S7B). Common genes between MGT#1 activation cues were ssGSEA using gsva (ssgsea.norm=TRUE) from GSVA v1.32.0 R identified using the UpSetR v1.4 R package (Figs. 3H and 6H). package was applied to obtain a signature score-matrix of GBM Of note, the control gene set was obtained by the comparison bulk (TCGA) and single-cell patients’ expression profiles (12) per between control samples and the rest of MGT#1hi samples. UMAP each gene set (Figs. 1C, 7A–C; Supplementary Figs. S1E, S7A–S7C). dimensional reduction (Fig. 6G) was generated using the umap Box-plot comparison (t test) in Supplementary Fig. S7A–S7D was function from the uwot R package (n_neighbors = 10, metric = carried out between each GBM transcriptional subtype. Heat map in “manhattan,” search_k = 100). The input to run the algorithm Fig. 7A was directly generated using this matrix (pheatmap v1.0.12 R was the batch corrected matrix (removeBatchEffect limma v3.46 R package; hierarchical clustering is based on Manhattan distance and package function) filtered by the combination of all upregulated the ward.D2 method). The division between patients with high- and genes in each comparison. Graphical representations of this analy- low-expression GBM in the survival plots (Supplementary Fig. S7B) sis were generated using ggplot2 v3.3.2, with the exception of the was established using the 0.9 percentile as a threshold given for each upset plots (above) and the heat map in Supplementary Fig. S3G, signature. Statistical tests log rank and Mantel–Cox were applied to which was generated using pheatmap v1.0.12 R package (hierarchi- contrast differences between survival distributions. cal clustering is based on Manhattan distance and the ward.D2 For GSC microarray analyses (Supplementary Fig. S7C), the brain clustering method). tumor stem cell line data set was assembled with previously gener- The RNA-seq analysis in Fig. 4 was conducted for the indi- ated transcriptomic data: 44 RNA-seq (samples Illumina HiSeq cated comparisons applying the DESeq2 pipeline on raw prefiltered 2500) from GSE119834, GSE67089, and GSE8049. At the exception

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RESEARCH ARTICLE Schmitt et al. of the GSE119834, for which preprocessed data were used, raw data were downloaded from the Gene Expression Omnibus repository References (https://www.ncbi.nlm.nih.gov/geo/) and, subsequently, the affy R 1. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJB, package v1.64.0 was used for robust multiarray average normaliza- Janzer RC, et al. Effects of radiotherapy with concomitant and tion followed by quantile normalization. For genes with several adjuvant temozolomide versus radiotherapy alone on survival in probe sets, the median of all probes had been chosen and only com- glioblastoma in a randomised phase III study: 5-year analysis of the mon genes. Heat map representation was generated using pheatmap EORTC-NCIC trial. Lancet Oncol 2009;10:459–66. v1.0.12 R package (hierarchical clustering is based on Euclidean 2. Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, distance and the complete clustering method). et al. 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Dirks, transgenic mice. Cell 1987;51:975–85. and P. Euskirchen, respectively. The Human Brunello CRISPR knock- 16. Ragoczy T, Bender MA, Telling A, Byron R, Groudine MT. The locus out pooled library was a gift from David Root and John Doench control region is required for association of the murine beta-globin (Addgene #73178); hMG-cl.20, CD34+ cells, and THP-1 cells were locus with engaged transcription factories during erythroid matura- kind gifts from D. Alvarez-Carbonell, K. Rajewsky, and S. Minucci, tion. Genes Dev 2006;20:1447–57. respectively. Data analyses include data generated by the TCGA 17. Wang L, Babikir H, Müller S, Yagnik G, Shamardani K, Catalan F, Research Network: https://www.cancer.gov/tcga. C. Company, M.J. et al. The phenotypes of proliferating glioblastoma cells reside on a Schmitt, and Y. Dramaretska are graduate students with Humboldt single axis of variation. Cancer Discov 2019;9:1708–19. University and S. Kertalli with BSIO-Charitè Medical University. 18. Sabari BR, Dall’Agnese A, Boija A, Klein IA, Coffey EL, Shrinivas K, et al. Coactivator condensation at super-enhancers links phase sepa- M. Squatrito is supported by a grant from the Seve Ballesteros ration and gene control. Science 2018;361:eaar3958. Foundation. The G. Gargiulo lab acknowledges funding from MDC, 19. Stock K, Kumar J, Synowitz M, Petrosino S, Imperatore R, Smith ESJ, Helmholtz (VH-NG-1153), ERC (714922), and KWF (NKI-2013 and et al. Neural precursor cells induce cell death of high-grade astrocyto- NKI-2014-7208). mas through stimulation of TRPV1. Nat Med 2012;18:1232–8. 20. Xu W, Yang H, Liu Y, Yang Y, Wang P, Kim S-H, et al. Oncometabolite Received February 24, 2020; revised September 21, 2020; accepted 2-hydroxyglutarate is a competitive inhibitor of α-ketoglutarate- December 11, 2020; published first December 23, 2020. dependent dioxygenases. Cancer Cell 2011;19:17–30.

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Glioblastoma Homeostasis Revealed by Synthetic Genetic Tracing RESEARCH ARTICLE

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March 2021 CANCER DISCOVERY | OF24

Phenotypic Mapping of Pathologic Cross-Talk between Glioblastoma and Innate Immune Cells by Synthetic Genetic Tracing

Matthias Jürgen Schmitt, Carlos Company, Yuliia Dramaretska, et al.

Cancer Discov Published OnlineFirst December 23, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/2159-8290.CD-20-0219

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