Phenotypic mapping of pathological crosstalk between glioblastoma and innate immune cells by synthetic genetic tracing

Abstract

Glioblastoma is a lethal brain tumor which 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 level, in vitro and in vivo. Through synthetic locus control regions, we demonstrated that proneural glioblastoma is a hardwired identity, whereas the mesenchymal glioblastoma is an adaptive and metastable cell state driven by pro-inflammatory 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 which 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 pharmacological perturbations and mesenchymal commitment.

Introduction

The cellular and molecular heterogeneity of cancer is thought to contribute to resistance to targeted and immune therapies. Glioblastoma(GBM) is the most common, heterogeneous and resistant primary adult brain tumor(1). Compared to most cancers, glioblastoma is highly genomically and epigenomically characterized(2–5). Lineage tracing has provided important insights into glioblastoma biology in the mouse, including the cellular origins of individual subtypes(6), and how aberrant homeostatic regulation can affect responses to treatments in vivo(7).

Transcriptome analyses of human glioblastoma biopsies have repeatedly yielded a general classification into three subtypes, classical(CL), mesenchymal(MES) and proneural(PN) across cohorts(2–5). However, a single glioblastoma tumor may exhibit the coexistence of a predominant subtype along with tumor cells of other subtypes(8,9). Additionally, recurrent tumors exhibit plasticity of genotype and phenotype, in which a dominant mutation and expression profile changes after treatment(2,10). This makes it difficult to discern whether glioblastoma subtypes represent hard-wired entities or transient states imposed by differences in signaling or tumor regions. Yet, several studies established correlations between subtype-specific gene expression signatures, differential response to therapy and overall patients’ survival; the latter is particularly poor in highly mesenchymal tumors which exhibit infiltration of innate immune cells at recurrence(5). A better understanding of glioblastoma subtype identities and fate changes might be crucial to develop effective therapies.

Technological advances in single-cell biology confirmed and extended our understanding of the complexity of brain tumor homeostasis(11,12). Yet, as scRNA-seq has increasingly expanded the catalogues of cell populations within tumors, the biological interpretation 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. Genetic tracing is extremely informative in developmental settings and diseases involving alterations in tissue homeostasis including metabolic, immunological, neurological or psychiatric disorders as well as inflammation and cancer(14–17). However, despite the expansion of sophisticated perturbation tools, such as CRISPR/Cas9 and optogenetics(14), genetic tracing has yet to be fully exploited in the dissection of complex disease traits. The pressing need for a novel strategy to characterize brain tumor heterogeneity prompted us to design synthetic reporters based on gene expression signatures. These reporters are designed to integrate multiple pathways into a single genetic cassette, thereby mimicking endogenous regulatory elements. As an example, the β-globin locus control region shows cell-type- and developmental-stage-specific expression and engages transcription factors independently of its genomic position(15,16).

Our method permits to genetically label individual cell populations that share a similar state or undergo similar fate transitions within a heterogeneous tissue in vitro and in vivo, and to discover mechanisms regulating proneural-to-mesenchymal transition. Whereas a hierarchical and directional organization of the subtypes is continuously revised(2,5,12,17), through synthetic genetic tracing in vitro and in vivo, we observed that interconversion between proneural and mesenchymal states is bidirectional. Our results are clinically relevant because they expose a causal connection between radiotherapy or innate immune cell infiltration and mesenchymal trans-differentiation, which was previously hypothesized based on correlative analyses. Notably, we link innate immunity and mesenchymal commitment to the acquisition of selective resistance to therapies, which holds translational implications.

Results

Glioblastoma subtype genetic tracing by synthetic locus control regions (sLCRs)

To trace complex glioblastoma expression subtype identities, we developed a method that generates synthetic reporters. Our method relies on robust evidence that transcription factors govern cell identity or states through binding to cis-regulatory elements, which in turn control downstream target genes. From the TCGA datasets(3), we annotated the genes specific to mesenchymal, classical and proneural subtypes. Cell-intrinsic signature genes(i.e. differentially-regulated genes) were selected as genomic loci containing subtype-specific cis-regulatory elements(Fig. 1A–B; Supplementary Table S1). Next, we used gene expression and standard annotation tools to identify their regulators (i.e. genes encoding ubiquitous and tissue-specific transcription factors). Finally, we identified DNA elements with the potential of driving glioblastoma subtype-specific expression according to the following criteria: a high transcription factor binding sites number (i) and diversity (ii), as well as a known distance from the nearest endogenous transcriptional start site(iii; Supplementary Table S1). This approach produced a list of potential cis-regulatory elements, including loci with endogenous binding by the predicted transcription factors at endogenous level(Supplementary Fig. S1A). Next, we generated distinct synthetic locus control regions(sLCRs) to genetically trace mesenchymal, classical and proneural glioblastoma subtypes. This was accomplished by stitching 5-6 of the identified cis-regulatory elements, representing 40-60% of the regulatory potential(Supplementary Table S1 and methods). Hereafter, we refer to these glioblastoma subtype genetic tracing tools as MGT, CLGT, and PNGT.

Figure 1. Glioblastoma-subtype synthetic Locus Control Regions (sLCR).

(A) Schematic representation of glioblastoma sLCRs generation from gene expression data. (B) Pairwise correlation heatmaps of significant TFBS motifs at glioblastoma subtype-specific loci. The number of transcription factors and signature genes used in the analysis are indicated above each panel. MES=Mesenchymal; PN=Proneural; CL=Classical. (C) Above, ssGSEA normalized scores for input genes for the indicated sLCRs (methods). The cell states identified by (12) are indicated in each quadrant, and the original single-cell position is maintained in the two-dimensional representation (methods). Below, TCGA subtypes (3) are shown for a head-to-head comparison. (D) Above, schematic representation of a sLCR and of the experimental steps to generate reporter cells. Below, heatmap of MGT#1 and PNGT#2 gene expression normalized by GAPDH and number of integrations relative to hGICs=human glioma-initiating-cells; GSCs=glioma-stem-cells. 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) Above, schematic representation of bulk, MGT#1- and PNGT#2-expressing hGICs’ transcriptional profiling. Below, heatmap of GSEA adjusted p-values (see methods) for the indicated glioblastoma subtypes/state-signatures and comparisons in the indicated hGIC lines.

The glioblastoma subtype-specific reporters we generated can inform on the transcriptional identity of subsets of patients’ glioblastoma single cells. Consistently, single-sample gene set enrichment analysis(ssGSEA) showed a high correspondence between the potential expression pattern of each individual reporter, the known cell states of freshly purified single glioblastoma cells(12), and their corresponding TCGA subtype(Fig. 1C). Each sLCR encodes the subtype-specific expression of a fluorescent reporter(mVenus or mCherry) and a second cassette expressing the nuclear H2B-CFP fusion via a ubiquitous viral promoter enabling reporter-independent selection(Fig. 1D). Reporter expression was validated in live transiently transfected cells, in stably transduced and cryosectioned tumorspheres, and in fixed tumor cells(Supplementary Fig. S1B–D). In the latter, dual RNA-FISH and immuno-fluorescence demonstrated co-localization between nascent MGT#1 RNA and MED1, a master regulator organized in coactivator puncta and regulating endogenous cell-identity LCRs(also known as super-enhancers(18)).

To test the relative expression of synthetic reporters, which are representative of two opposite glioblastoma subtypes, we next transduced proneural and mesenchymal sLCRs into a near-isogenic pair of human glioma-initiating cells(hGICs). These cells were engineered by genetically manipulating spontaneously immortalized human neural progenitor cells(19), and have a proneural-like expression signature, possibly inherited from the cell of origin(Supplementary Fig. S1E). In addition to pre-existing and shared aberrations, the near-isogenic pair background consists of TP53 and NF1 depletion or IDH1^R132H and TP53^R273H mutants’ over-expression. Both lines display a DNA methylation profile concordant with the IDH1 status in high-grade glioma patients and are hereafter referred to as IDH-wt- and IDH-mut-hGICs, respectively (data not shown). FACS quantification showed that the proneural reporter PNGT#2 is more expressed than the mesenchymal one in both near-isogenic lines, but MGT#1 expression was even lower in the IDH-mutant genotype(Fig. 1D–E), possibly due to epigenetic suppression by IDH1^R132H-dependent production of the oncometabolite 2-Hydroxyglutarate(20).

Next, we tested the specificity of our reporters by extending the analysis of the proneural and mesenchymal reporters to patient-derived glioma stem cells(GSCs), lung and breast cancer cell lines of epithelial origin, and cell lines of non-epithelial cancers origin such as leukaemia cells. Each line was purified using FACS to select reporter expressing cells. We detected each reporter using RT-qPCR and normalized their expression through endogenous GAPDH and the number of reporter integrations into genomic DNA(methods).

Consistent with the specificity of the design, both reporters were highly expressed in patient-derived GSCs and exhibited low expression in leukaemia cells(Fig. 1D). In addition to line-to-line heterogeneity, a common interesting pattern was the high expression of the mesenchymal reporter MGT#1 in well-established mesenchymal cells independently of the tissue of origin(i.e. GBM166 and MDA-231, Supplementary Fig. S1E) and in epithelial cells committed to mesenchymal fate through TGFB-signaling(i.e. A549+ TGF-β1; Fig. 1D).

Neurosphere culture conditions permit to propagate stem-like and short-lived progenitors and spontaneous differentiation to occur(21). We exploited this limited degree of heterogeneity to test whether cells with high reporter expression are more homogeneous than the bulk. Under these conditions, both near-isogenic hGICs showed high expression of PNGT#1/#2, CLGT#1/#2 and low expression of MGT#1/#2(Supplementary Table S1). FACS-purified IDH-mut-hGICs and IDH-wthGICs modified by selected reporters could be distinguished by RNA-seq and principal component analysis. Moreover, IDH-wt-hGICs with high reporter expression were less variable than bulk hGICs(Supplementary Fig. S1F). GSEA demonstrated that high expression of MGT#1 in IDH-wt-hGICs enriched for mesenchymal glioblastoma gene sets compared to PNGT#2 or bulk unsorted cells(Fig. 1F). Consistently, bulk and PNGT#2 cells were both enriched for proneural and cell cycle gene sets. Interestingly, mesenchymal glioblastoma gene sets were more enriched in IDH-wild-type than IDH-mutant cells(Fig. 1F). Together, the data indicate that reporter expression by FACS reflects cell states at endogenous level of gene expression. Finally, the signature genes retrieved in high MGT#1 expressing cells did not change if compared to either high proneural or classical reporter expressing cells. Still, OLIG1 and OLIG2 marked proneural reporter expressing cells, while CCNE2 was enriched in classical reporter expressing cells(Supplementary Fig. S1G), suggesting that each reporter enriches for cells in a specific state.

In summary, this method can systematically leverage bulk or single-cell gene expression data representing biologically- or clinically-relevant phenotypes into synthetic reporters, which preserve critical features of endogenous cis-regulatory elements and permit a genetic tracing of cell states.

Synthetic genetic tracing in vivo reveals glioblastoma heterogeneity and hierarchies

The proneural glioblastoma is considered the ancestor of all subtypes(22) and to reflect an oligodendrocytic origin(23). Recently, however, a mesenchymal-toproneural hierarchy emerged by in silico transcriptomic lineage tracing in single cells(17).

To test whether sLCRs allow for a genetic tracing of tumor cells fate changes in vivo, we intracranially transplanted IDH-wt-hGICs-MGT#1 into immunodeficient mice. Histologically, all tumors appeared as grade IV GBM (n=10), with a large proportion of the mouse brain infiltrated by malignant cells, indicating extensive proliferation and invasion(Fig. 2A). Immunohistochemical staining revealed MGT#1 expression was well confined within tumor areas, particularly at the invasive front(Fig. 2A–B). We detected tubulin immunostaining in MGT#1-negative cells as well as H2B-CFP puncta formation, facilitated through mitotic chromatin condensation in dividing cells. This ruled out that the cells expressing the reporter were simply located in areas of higher epitope accessibility or were escapers to lentiviral silencing(Fig. 2B–D). Thus, MGT#1 expression reflects functional intratumoral heterogeneity.

Figure 2. In vivo genetic tracing of mesenchymal trans-differentiation.

(A) Above, schematic representation of the experiment. Below, representative coronal forebrain images of IDH-wt-hGICs-MGT#1 xenografts in NSG mice at humane end point (n=10). Lower left, HE staining; lower right, insets showing magnification of mVenus, Tubulin and DAPI counterstained tissue with invasive glioma front being homogeneously MGT#1-high. (B) Representative lesion with mixed high and low mesenchymal reporter expression. (C-D) Representative H2B-CFP expression (arrowhead) in MGT#1 positive and negative lesions, respectively. (E) Above, schematic representation of the experiment. Below, 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 Figure S2). (F) Relative quantification of t-SNE data in (E). (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 each in vivo reporter group compared to its relative in vitro control (****P<0.0001, ns=not significant). (H) Bubble plot of GSEA adjusted p-values for the indicated glioblastoma subtypes/states and comparisons. (I) Volcano plot of the differential expression analysis between in vitro PNGT#2-high and in vivo MGT#1-high. Selected genes are highlighted. (J) Ingenuity pathway top upstream regulator analysis of differential expression analysis in (I).

Next, we exploited dual-reporter combinations to gain insights in the dynamics of cell states in vivo. For this experiment, we generated IDH-wild-type or IDH-mutant lines that carried one mVenus-driven mesenchymal reporter (MGT#1 or MGT#2) and one mCherry-driven non-mesenchymal reporter (PNGT#1, PNGT#2, CLGT#1 or CLGT#2). Upon xenograft formation (n=18), we applied t-Distributed Stochastic Neighbour Embedding(t-SNE) to categorize parallel in vivo/in vitro flow cytometry data; this consisted of hierarchical components including cell shape, granularity, viability dyes, mesenchymal and non-mesenchymal fate reporters(Supplementary Fig. S2A and methods). Strikingly, we observed a proportional increase of IDH-wild-type mesenchymal reporter-expressing cells in vivo (p<0.0001), and alongside, a concomitantly milder but significant decrease was observed for classical or proneural reporter-expressing cells (p<0.01; Fig. 2E–G) when compared to their in vitro culture counterparts. In contrast, proportions of MGT#1-high IDH-mut-hGICs did not increase in vivo(Supplementary Fig. S2B and methods), raising the interesting possibility that the IDH1^R132H restricts tumor cell plasticity.

To determine the transcriptional identity of the mesenchymal state in vivo, we isolated several hundreds of highly mesenchymal and non-mesenchymal reporter-expressing cells (MGT#1, and PNGT#2 or CLGT#1, respectively) from brain xenografts. We purified and profiled them using FACS and RNA-seq. IDH-wt-hGICs with high MGT#1 expression in vivo were significantly enriched for the TCGAmesenchymal gene set compared to non-mesenchymal tumor cell fractions (i.e. PNGT#2, CLGT#1); the latter were instead enriched for TCGA-proneural and oligodendrocyte-like gene sets (Neftel-OPC; Fig. 2H). Of note, features of highest fitness in vivo such as viability and high transcriptome quality(Supplementary Fig. S2C–D), were more often associated with MGT#1 than non-mesenchymal reporter cells. Compared to homogeneously proneural cells in vitro (i.e. PNGT#2-high), MGT#1 expression in vivo significantly enriched for a human mesenchymal glioblastoma gene set (e.g. Neftel-MES1). Moreover, the acquisition of an astrocyte-like state (AC-like) appeared to be a dominant feature of MGT#1-high cells in vivo(Fig. 2H). This is consistent with the signature of bulk TCGA-mesenchymal glioblastoma to be a mix of mesenchymal and astrocytic phenotypes at the single-cell level(Fig. 1C).

To identify the hallmarks of proneural-to-mesenchymal transition in vivo, we performed differential expression analysis between the two most abundant homogeneous populations in vitro and in vivo (proneural PNGT#2-high in vitro vs. mesenchymal MGT#1-high in vivo). This revealed that in vivo, the MGT#1-high glioblastoma state relies on the activation of early-immediate response transcription factors, including EGR-1, -2 and -3, JUN, JUNB, FOSL2, FOSB and FOS(Fig. 2I). A similar outcome was obtained by comparing MGT#1-high in vivo to the MGT#1-high in vitro(Supplementary Fig. S2E). This indicated that those genes represent a signature of tumorigenesis in vivo.

Overall, 954 genes were specifically up-regulated in the MGT#1 in vivo fraction, and 234 marked PNGT#2 cells in vitro (padj<0.05; log2FC±1.5; Fig. 2I). mVenus featured one of the most relevant upregulated genes, whereas mCherry was inversely regulated, although not significantly (log2FC=5.34; padj=0 and log2FC=-0.48; padj=0.42, respectively; Fig. 2I). Of note, the MGT#1-high state in vivo showed an upregulation of GFAP and CHI3L1, which mark terminally differentiated astrocytic and mesenchymal cells. But their expression in absence of other markers of terminally differentiated astrocytes indicates that most cells in the MGT#1-state are undifferentiated(Supplementary Fig. S2E).

Ingenuity pathway analysis established that the genes marking MGT#1-cells in vivo locate downstream of several pro-inflammatory regulators, including TNFα (z-score=2.655, p=4.49⁻²⁴), NFκB (z-score=1.915, p=3.05⁻¹³), and interferons(Fig. 2J). Among other well-known in vivo-specific pathways, we found known targets of the synthetic retinoic acid tretinoin (z-score=2.99, p=3.31⁻²²), the TGFB and VEGF pathways (z-score=2.51, p=3.28⁻²⁶ and z-score=1.531, p=3.2⁻¹⁵, respectively; data not shown).

In summary, synthetic genetic tracing in a physiologically relevant tumor microenvironment revealed that glioma-initiating cells with a low mesenchymal identity in vitro generate xenografts with phenotypically distinct populations of cells that include mesenchymal glioblastoma cells.

The mesenchymal glioblastoma identity is adaptive and reversible

Our hGICs express proneural reporters in a genotype-independent manner and high levels of mesenchymal activity were only seen in vivo(Fig. 2A–G). We reasoned that the proneural and mesenchymal identities are dependent on intrinsic and external signaling, respectively.

To investigate whether the mesenchymal state relies on upstream signaling cues that are absent in standard in vitro culture conditions, we modulated external signaling in a phenotypic screen. In neurobasal-like serum-free medium(methods), which recapitulates glioblastoma heterogeneity in mouse xenografts(21,24), hGICs were stimulated by the further addition of selected cytokines and growth factors. We carried out a FACS screen after 48h(Fig. 3A) and also tested several signaling cues through individual FACS analyses or live-cell imaging(Supplementary Fig. S3A–B). Compared to the naïve cells, IDH-wt-hGICs-MGT#1 swiftly upregulated the reporter in response to TNFα signaling, human/bovine serum and LIF, pointing to these factors as direct mesenchymal triggers, and to a lower extent Activin A(Fig. 3B and Supplementary Fig. S3A–B). These results were reproducible across two independent mesenchymal reporters(i.e. MGT#1-2; Fig. 3B). The response to the external signaling was generally dampened in IDH-mutant cells.

Figure 3. The mesenchymal glioblastoma 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-D) Bar plot showing the individual response to the indicated factors/sLCRs after 48h of induction. (E-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 adjusted p-values for the indicated glioblastoma 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.

In contrast, in a parallel screen for the same factors using proneural reporters, no factor consistently elicited a response in both reporters, with interferon gamma and serum triggering PNGT#1 expression only(Fig. 3B). This supports that the proneural identity is largely uncoupled from the microenvironment but rather is either encoded in the cell of origin or is embedded in the RTK signaling.

TNFα"s role as a prominent pro-mesenchymal signaling cue is consistent with the transcription factor NFκB binding at endogenous cis-regulatory elements included in the MGT#1 reporter upon TNFα’stimulation(Supplementary Fig. S1A). Importantly, this is in line with our finding that NFκB activation is the key connecting pathway for mesenchymal genes in brain xenografts(Fig. 2J) and previous analyses in patient-derived cell lines(10).

Interestingly, side-by-side stimulation in vitro showed that MGT#1 blandly responded to TNFα in IDH-mutant cells(Supplementary Fig. S3C), despite both IDH-wt and IDH-mut bearing comparable levels of MGT#1 expression and were propagated under the same signaling conditions. This is reminiscent of the impaired MGT#1 activation in these cells in vivo(Supplementary Fig. S2B). Quantitative PCR confirmed that response to TNFα involved the amplification of pro-mesenchymal genes in both cell types but to a lower extent in IDH-mut-hGICs (Supplementary Fig. S3D). The specificity of the mesenchymal reporter expression in response to TNFα was further confirmed by using a control reporter carrying the ubiquitous UBC promoter(Supplementary Fig. S3E). Notably, TNFα led to the phosphorylation of NFκB-p65, STAT3 and p38-MAPK in both cell types(Supplementary Fig. S3F). Thus, we conclude that our reporters permit the identification of differences in the transcriptional responses between IDH-wt- and IDH-mut-hGICs.

Genetic tracing of glioblastoma subtype identities or states offers a means of testing whether the mesenchymal glioblastoma is a stable entity (e.g. subtype) or a reversible cell state. To this end, we committed IDH-wt-hGICs to a mesenchymal fate using different external signaling cues and then performed a washout experiment. Within the timeframe of five days, the activation of two independent mesenchymal reporters was reset by the washout of the relevant signaling(Fig. 3C–F). This aligns with the observation in dual-reporter cells that MGT#1 expression was inducible but PNGT#2 levels were unchanged by the signaling cues tested(Supplementary Fig. S3A–S3C). To confirm that the transcriptional states obtained in vitro are representative of the human mesenchymal glioblastoma in patients’ biopsies, we carried out RNA-seq of FACS sorted MGT#1-high cells after forty-eight hours of stimulation with TNFα, human serum, FBS, LIF or Activin A. We compared these profiles to naïve control cells as well as to the FACS sorted in vivo MGT#1-high cells. This revealed that in vitro stimulation with TNFα, human serum and -to a lower extent LIF -promoted the acquisition of a cell state resembling human mesenchymal glioblastoma(Fig. 3G). Hence, the same reporter can detect different signaling cues that lead to a mesenchymal transition. In addition, under in vitro conditions, TNFα, human serum and LIF appeared to individually activate some of the genes that specifically mark MGT#1-expressing cells in vivo – even though individually, these factors elicited a milder transcriptional activation of the MGT#1 reporter(Fig. 3H and Supplementary Fig. S3G). Together with our pathway analysis of in vivo MGT#1-expressing IDH-wt-hGICs(Fig. 2J), the data suggest that the mesenchymal transition which occurs in vivo is driven by specific external signals, most probably in a combinatorial way.

Overall, our genetic tracing of glioblastoma states revealed that the proneural glioblastoma is supported by intrinsic signaling whereas the mesenchymal program is largely adaptive and builds on a reversible, pre-existing identity. It is, therefore, non-necessarily hierarchical.

Mesenchymal glioblastoma genetic tracing reveals a swift cell state change in response to ionizing radiation but not hypoxia

Mesenchymal trans-differentiation in glioblastoma is dominant at recurrence after standard of care(2) and single-cell RNA-seq has shown a correlation between hypoxia and mesenchymal glioblastoma(11,12). However, a causal link had yet to be demonstrated.

Ionizing radiation is a major component in the standard of care for glioblastoma(1). To experimentally test whether IR can induce mesenchymal trans-differentiation in a cell-intrinsic manner, we exposed IDH-wt- and IDH-mut-hGICs to medical X-rays. A dose-dependent MGT#1 activation in IDH-wt-cells occurred in response to increasing radiation(Fig. 4A). TNFα amplified mesenchymal trans-differentiation in both genotypes(Supplementary Fig. S4A). The IR doses tested here included a single application of 10Gy radiation that is sub-lethal in multiple human GSCs(25,26). In all of the conditions, hGICs remained viable, also in combination with other treatments (e.g. TNFα or Temozolomide; Supplementary Fig. S4A and data not shown). Phosphorylation of the DNA damage marker γH2AX one-hour post irradiation confirmed that double-strand breaks had occurred(Fig. 4A). Forty-eight hours post 20Gy radiation, in IDH-wt-hGICs-MGT#1, a proneural-to-mesenchymal transition was supported by RNA-seq followed by GSEA(Fig. 4B–C). Differential expression analysis revealed 135 genes significantly up-regulated, and 31 down-regulated(log2FC±1.5; padj<0.05), featuring the activation of the ATM signaling that connects the mesenchymal commitment to the DNA damage induced by ionizing radiation(Supplementary Fig. S4B–C).

Figure 4. Mesenchymal glioblastoma genetic tracing reveals a swift cell state change driven by ionizing radiation but not hypoxia.

(A) MGT#1 activation in response to increasing doses of ionizing radiation (IR), at the 72h time-point. The insert shows the immunoblotting of the indicated antibodies and condition at 1h post ionizing radiation delivery. (B) Heatmap of ionizing radiation-induced significantly differentially regulated genes (padj<0.05 and log2FC±1.5) in MGT#1-high IDH-wt-hGICs fraction (pink, n=3) against non-irradiated 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) Above, a schematic overview of the RNA-seq experimental design. Below, heatmap 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). Heatmap 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.

To address whether hypoxia also causes mesenchymal transition, we exploited genetic tracing by sLCRs under ambient oxygen tension, physiological glioma hypoxia, and severe experimental hypoxia. Low oxygen levels (3%), which are considered physiological in gliomas(27), or hypoxia (1%), activated neither MGT#1 nor MGT#2 in IDH-wt-hGICs or IDH-mut-hGICs-MGT#1 (Fig. 4D). Of note, MGT#2 contains an HIF1A motif(p<0.001), which makes it potentially responsive to HIF1A activation(Supplementary Table S1). However, mesenchymal reporter expression remained unchanged even in response to severe experimental hypoxia(0,5%; Supplementary Fig. S4D). Consistent with the outcome informed by the reporter, RNA-seq in IDH-wt-hGICs-MGT#1 followed by differential expression analysis indicated that genes that respond to low oxygen tension experienced significant regulation(Fig. 4E–F), but this was not accompanied by mesenchymal commitment.

Overall, genetic tracing indicates that both ionizing radiation and a decrease in tissue oxygenation both trigger transcriptional responses, but only radiation leads to a swift acquisition of a mesenchymal state in a tumor cell-intrinsic manner.

Synthetic genetic tracing and CRISPR/Cas9 screens connect genetic and pharmacological perturbations to mesenchymal commitment

To exploit synthetic genetic tracing in uncovering the genetic determinants of mesenchymal glioblastoma, we used a genome-wide pooled CRISPR/Cas9 screen to uncover genes modulating MGT#1 expression in IDH-wt-hGICs cells in their naïve state, or upon induction of the mesenchymal state by external signaling or genotoxic stress(i.e. Serum+TNFα’or TMZ+IR, respectively; Fig. 5A). Both, external signaling and the exposure to therapy increased the number of MGT#1-high cells, as expected. Notably, applying the genome-wide CRISPR/Cas9 Brunello library alone was also able to increase the proportion of IDH-wt-hGICs-MGT#1(Supplementary Fig. S5A), suggesting that this type of screen may identify neuroepithelial gatekeepers. Replicates clustering according to the expected sources of technical and biological variability, coupled with the depletion of gRNAs associated with essential genes but not of the control gRNAs overall supported the good quality of the screen(Supplementary Fig. S5B–D).

Figure 5. Genetic and pharmacological 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#1-high fractions (naïve, TMZ+IR, TNFα+FBS, n=3 each) relative to all MGT#1-low fractions and unsorted controls (n=6 each). SGF29/CCDC101 highlighted in red as one of the top significantly upregulated hits within the comparison. sgRNAs associated with RAR/RXR agonist Tretinoin are labeled in yellow. (C) Representative FACS profiles of MGT#1 activation by the indicated conditions in SGF29 KO or control IDH-wt-hGICs-MGT#1 cells. (D) Ingenuity pathway analysis top upstream regulators 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 48h. (F) Top Ingenuity Pathway Analysis toxicity categories 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, 2-way ANOVA followed by Welch’s correction (n=5, *p<0.05, ***p<0.001 and ****p<0.0001).

To identify genes whose activity modulates homeostatic, therapy- and signaling-induced mesenchymal transition, we compared the pool of gRNAs statistically depleted in MGT#1-high fractions against all control fractions. This analysis uncovered 341 significant unique genes(256 promoting and 85 opposing to MGT#1 expression, log2FC±1; padj<0.05; Fig. 5B and supplementary table S2). Among the top hits for which a loss of function potentially facilitates the mesenchymal transition, we found a chromatin modulator, the SAGA-complex acetyltransferase CCDC101/SGF29, which had not previously been linked to glioblastoma or epithelialmesenchymal transition(EMT). Recently we had identified another chromatin regulator, the PRC2-complex scaffold EED as negative regulator of EMT(28). Given this, we chose to delete CCDC101/SGF29 in IDH-wt-hGICs-MGT#1 cells and subject these to signaling- or therapy-driven EMT. In each case, the loss of SGF29 increased MGT#1 expression(Fig. 5C), thereby validating the results of the screening.

Consistent with our upstream regulator analysis of in vivo MGT#1-specific gene expression, several sgRNAs required for MGT#1 activation lie downstream of pro-inflammatory pathways(Fig. 5D). These data connect the expression of pro-inflammatory genes to their function in mesenchymal commitment. Interestingly, the loss of two sgRNAs targeting RELA/p65 occurred in naïve IDH-wt-hGICs but not under the other conditions(Supplementary Fig. S5E). This suggests that the role of a single one of the NFκB transcription factors in MGT#1 regulation may be limited to homeostasis. Indeed, deleting RELA alone resulted in the short-lived downregulation of MGT#1 expression at bulk protein level, and TNFα-driven MGT#1 activation was only partially affected by RELA loss(Fig. 5E and Supplementary S5F–G). Consistently, a simultaneous deletion of RELA and NFκB1/p50 drastically impaired TNFα-driven MGT#1 activation(Supplementary Fig. 5G). These findings demonstrate that functional reporters can help to elucidate how a complex signaling pathway such as the NFκB pathway, regulates the proneural-mesenchymal transition. Importantly, pharmacological interference with the pathway by the IκB kinase(IKK) inhibitor-16 impaired MGT#1 activation in both WT and RELA KO cells, at concentrations that did not affect viability(Fig. 5E and data not shown). Thus, hits from the phenotypic screen can predict pharmacological switches such as the notion that inhibiting IKK-mediated NFκB activation could prevent mesenchymal transition.

Having identified a clinically-relevant compound that antagonizes the proneural-mesenchymal transition, we next focused on mesenchymal state amplifiers. All-trans-retinoic acid(ATRA; aka Tretinoin) is a RAR/RXR agonist whose targets are expressed in MGT#1-cells in vivo(Fig. 2J), and can potentially induce its targets identified by sgRNAs in the screen(Fig. 5B and 5F). In response to a short pre-treatment with ATRA, IDH-wt-hGICs appeared protected from the detrimental effects of radiotherapy and temozolomide, as compared to their responses to IKK-16(Fig. 5G). To correlate this phenotype with the modulation of the glioblastoma state, we first subjected dual-reporter expressing cells (i.e. IDH-wt-hGICs with both, MGT#1 and PNGT#2) to a short pre-treatment with IKK-16, ATRA or DMSO, followed by proneural-mesenchymal induction stimulated by TNFα. Subsequently, we measured the gene expression of mVenus (MGT#1 reporter), TNF (an EMT driver gene) and CHI3L1 (a marker of terminal mesenchymal differentiation). Based on those markers, after eight hours from induction, we observed that ATRA and IKK-16 had opposing effects on glioblastoma state changes(Supplementary Fig. S5H). This showed that MGT#1 could report on cellular responses to targeted compounds.

Overall, these data demonstrate that synthetic genetic tracing can be an effective method to identify genetic modulators of cell states and prioritize pharmacological treatments based on their ability to modulate cell identity.

Non-cell autonomous phenotypic consequences of the crosstalk between tumor and innate immune cells

Having established that our genetic tracing strategy is well versed to address clinically-relevant questions, we next dissected the crosstalk between tumor and innate immune cells. IDH-wild-type glioblastoma infiltration by glioblastoma-associated microglia/monocytes(GAMs) correlates with NF1 deficiency and mesenchymal glioblastoma(5), but whether there is a causal relationship between GAMs and mesenchymal trans-differentiation had yet to be demonstrated.

Rather than only being recruited by mesenchymal glioblastoma cells, GAMs might contribute to their specification. To investigate this, we co-cultured NF1-depleted IDH-wild-type cells with an early-passage immortalized human microglia cell line(hMG; cl.C20 (29)). The in vitro co-culture between hGICs tumorspheres and hMG cells was set up using trans-well inserts, which enable cell-cell communication but maintain a physical separation between the two populations. Under these conditions, hMG drove the upregulation of MGT#1 expression to an extent comparable to TNFα(Fig. 6A–D). Microglia also activated MGT#1 in IDH-mut-hGICs, but at lower levels(Fig. 6B).

Figure 6. Innate immune cells drive non-cell autonomous mesenchymal commitment in tumor cells.

(A) Schematic representation of contact-free hGICs co-culture with immune cells (see methods). (B) Representative FACS profiles of IDH-wt- or IDH-mut-hGICsMGT#1 alone or co-cultured with human microglia (hMG#cl.20) or human CD34+ in vitro-derived myeloid-derived suppressor-like cells (MDSCs). (C) Representative FACS profiles of IDH-wt-hGICs-MGT#1 alone or co-cultured 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 co-culture. Below, Venn diagram of NFκB-related genes by Ingenuity Pathway Analysis of DRGs for the indicated conditions. DRGs are computed relative to control hGICs (log2FC>1, padj<0.05). (E) Heatmap of DRGs for the indicated conditions. RNA-seq reads were normalized as transcript 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; padj<0.05). (F) Ingenuity Upstream Regulator Analysis of up-regulated genes by hMG co-culture compared to TNFα in IDH-wt-hGICs-MGT#1-high. (G) UMAP dimensional reduction of MGT#1 activation cues expression profiles combining all up-regulated 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.

Next, to test whether also non-brain innate immune cells can also instruct mesenchymal differentiation in human GICs, we differentiated primary human CD34+ cells into immature cells broadly referred to as myeloid-derived suppressor-like(MDSC-like) and the human monocytic cell line THP-1 into M1 or M2 macrophage-like cells. MGT#1 was strongly upregulated when co-cultured with macrophage-like cells, with M1 polarized cells driving the strongest phenotypic commitment, whereas MDSC-like cells only triggered a mild MGT#1 activation(Fig. 6A–C). This aligns with our observation that cytokines associated with innate immunity such as TNFα trigger a stronger direct MGT#1 activation to a larger extent than signaling molecules derived from adaptive immunity or stroma(IFNγ/IL-2 and IL-6, respectively; Fig. 3A–B; Supplementary Fig. S3A–B).

To determine how microglia induce a mesenchymal glioblastoma state in a non-autonomous manner, we used FACS to purify and then transcriptionally profile MGT#1-high expressing IDH-wt-hGICs upon co-culture. TNFα treatment served as control, since innate immune cells are capable of secreting TNFα in mouse and human glioma(30,31). The co-culture transcriptionally remodeled both cell types (Supplementary Figure S6A). Pathway analysis revealed that both, microglia and TNFα lead to NFκB-related gene activation in hGICs, yet largely through a private set of genes(Fig. 6D–E). Our system provided no evidence to suggest that TNFα triggered the hMG-driven phenotype(data not shown). In fact, a pathway analysis on MGT#1-high expressing IDH-wt-hGICs activated by microglia uncovered a remodeling of the metabolic transcriptome including genes in the cholesterol biosynthesis and SREBP1/2 pathway(Fig. 6E–F). The presence of extracellular lipid droplets in glioblastoma has been well established(32) and glioblastoma cells rely on extracellular cholesterol uptake for growth(33), so we next treated our cells with oxidized Low-Density Lipoprotein(oxLDL), which are known to activate the SREBP1/2 pathway through PPARγ. OxLDL drove MGT#1 activation in both IDH-wild-type and -mutant models(Supplementary Fig. S6B). Moreover, in a hypothesis-driven approach, we tested whether the nitric oxide(NO) synthesis pathway is capable of driving mesenchymal trans-differentiation. Upregulation of the activity of the NO pathway is a common feature of innate immune cell activation and endothelial cell NO activity has previously been linked to glioma growth and invasiveness(34). Strikingly, NO-donor NOC-18 also triggered MGT#1 expression to levels comparable to hMG or TNFα stimulation(Supplementary Fig. S6C). However, neither anti-LDLR treatment nor the NOS inhibitor L-NG-nitroarginine methyl ester(L-NAME) could rescue MGT#1 induction by microglia. This suggests that several concurrent mechanisms regulate the phenotypic changes driven by innate immune cells in glioma. Next, we performed gene expression profiling of the oxLDL- and NOC-18-driven MGT#1-expressing IDH-wt-hGICs and analysed these along with all the MGT#1-high expression profiles. This allowed us to search for common traits and to look into how different upstream signaling cues are integrated into the transcriptional response detected by our reporter. We found that each type of upstream signaling led to a specific transcriptional output, yet the microglia-driven mesenchymal transition shares features with oxLDL, NOC-18, TNFα and -to a lower extent -with serum and LIF(Fig. 6G–H and Supplementary Fig. S6D).

Overall, our data establish a causal relationship between innate immune cell infiltration and mesenchymal trans-differentiation in glioblastoma.

Fate mapping by sLCRs reveals phenotypic and molecular features conserved in glioblastoma patients

To test the relevance of the predictions based on genetic tracing experiments that our models generated, we looked for the molecular signatures we had found in patients’ gene expression profiles. Glioblastoma patients were clustered using ssGSEA and several gene sets experimentally determined in our cells(Fig. 7A). Importantly, the microglia-driven phenotype and its related oxLDL- and NOC-18-signatures clearly connected mesenchymal patients.

Figure 7. Therapeutic implications of phenotypic changes in glioma initiating cells driven by innate immune cells.

(A) Heatmap of the relative ssGSEA normalized score for the indicated gene sets in glioblastoma patients from the TCGA dataset. Including gene sets representing specific GBM subtype/state and up-regulated MGT#1 activation cues (Fig. 6 G–H). The status of IDH1 and NF1 mutations and the corresponding GBM subtypes are also indicated. (B) ssGSEA normalized scores for up-regulated MGT#1-high genes indicated in Figure 6D (see methods). Cell states identified by (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-D) Differential GSEA for the indicated comparisons. Significance is independently calculated by t-test and Kolmogorov-Smirnov. (E) Left, schematic depiction of chemosensitivity profiling of 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-high and MGT#1-low fractions of the indicated genotypes. Dotted line indicates threshold of 10μM concentration, unattainable in the brain tissue. (F) Dose-response curves of FACS sorted MGT 1-high, -low or na ve IDH-wt-hGICs subjected to increasing concentrations of selected compounds as summarized in (E). (G) A model for modulation of mesenchymal fate in glioblastoma.

The patient groups identified by these signatures had statistical features, such as a trend towards poor survival, which were comparable to human mesenchymal glioblastoma signatures that had been directly obtained from patients’ biopsies(Supplementary Fig. S7). These analyses were replicated in patient-derived single glioblastoma cells(12) and in a large set of patient-derived GSCs, including a well-characterized one(35)(Fig. 7B and Supplementary Fig. S7C). The similarities between gene expression data generated in our models and freshly-derived patients’ glioblastoma cells or GSC-like cells argue that hypotheses generated with our genetic tracing strategy are possibly falsifiable in human glioblastoma.

Therapeutic implications of phenotypic changes induced by innate immune cells in glioma initiating cells

EMT has been linked to resistance to chemotherapy but also offers therapeutic opportunities(36,37). Thus, we exploited sLCRs’ ability to identify a mesenchymal state to determine whether the microglia-driven state we had discovered might have therapeutic implications.

In gene expression signature seen in patients, both TNFα-’and the microglia-driven-signatures scored high in a similar cohort of patients and single glioblastoma cells by ssGSEA(Fig. 7A–B; Supplementary Fig. S7). Yet, they had unique molecular features. Specifically, microglia appeared to impair expression of DNA damage and cell cycle genes in glioblastoma cells(Fig. 7C–D).

Glioblastoma cells must activate DNA damage responses and undergo proliferation in order for the standard-of-care functions(1) to work. This means that the microglia-driven program identified here may have significant therapeutic implications, such as that glioblastoma cells exposed to microglia respond differently to treatments. To validate our prediction, we FACS sorted MGT#1-high and MGT#1-low expressing hGICs cells after microglia-driven conversion and exposed these cells to a selected battery of standard and targeted chemotherapeutics. Strikingly, in contrast to their sLCR-low and naïve counterpart, both MGT#1-high expressing IDHwt- and IDH-mut-hGICs proved to be more resistant to therapies based on DNA damage responses (Olaparib, ATR inhibitor VE-821, Topotecan, Mitomycin C; Fig. 7E–F, S7D–E). Moreover, compared to MGT#1-low expressing hGICs, MGT#1-high cells were found more resistant to LXR623, an LXR agonist which regulates cholesterol efflux(Fig. 7E–F) – a mechanism proposed as a synthetic vulnerability for glioblastoma cells(33). Our data suggest that the therapeutic benefit of lowering cell-intrinsic cholesterol levels in glioblastoma cells may be unattained when innate immune cells induce glioblastoma cells to activate cholesterol biosynthesis. Moreover, hGICs purified using either MGT#1-high or MGT#2-high, which both identify phenotypically distinct cells through different regulatory elements, exhibited similar dose-response patterns(Supplementary Fig. S7D), thereby indicating that the program identified by individual reporters in these cells is causal to the resistance. Importantly, IDH-wt/mut-hGICs had a dose-response to targeted agents such as BAY11-7085(IκB inhibitor) and WP1066(STAT3 inhibitor) independent of stratification by MGT#1 expression(Fig. 7E–F; Supplementary Fig. S7E). This indicates that the microglia activate a selective response to chemotherapeutics in hGICs.

In summary, synthetic genetic tracing established a causal link between the innate immune cells and acquisition of two functionally relevant and therapeutically distinct glioblastoma states.

Discussion

Glioblastoma remains a lethal disease despite deep (epi)genomic characterization. Here, we approached the problem by combining epigenomic information and classic genetic tracing. We used synthetic genetic tracing of human glioblastoma subtype expression programs to trace the fates of cells with homogeneous phenotypes. This way, we discovered causal relationships between perturbations, cell fate commitment and response to therapies(Fig. 7G). Our data provide direct evidence that links the tumor heterogeneity to patients’ responses to treatments.

Proneural and mesenchymal glioblastoma subtypes have consistently been identified across expression platforms, readouts, and patients’ cohorts. Yet, the origins of these subtypes, their location or spatiotemporal evolution and – most importantly – their therapeutic significance has remained obscure. Glioblastoma subtypes rely on transcriptional programs(10,38–40), providing the basis for our genetic tracing using sLCRs. The proneural and the mesenchymal reporters showed a higher relative expression level in patient-derived glioblastoma cells than in unrelated counterparts. Further efforts and models will be required to determine whether the reporters are well suited to perform “absolute subtyping.”

The in vitro derivation and propagation of glioblastoma stem-like cell lines results in generally high mesenchymal signaling in vitro(41). Yet, evidence presented here and in very recent work by others(42) suggests that these in vitro conditions favor proliferation, but lack critical features of microenvironmental complexity. Consistently, our data show that synthetic genetic tracing is best suited to trace changes in cell states. Using our transformed neural stem cells model and synthetic genetic tracing, the proneural glioblastoma emerged as a default entity. In the absence of a tumor microenvironment, the proneural state appeared hardwired even in cells with a genotype often associated with mesenchymal glioblastoma(e.g. NF1 depletion). However, the mesenchymal identity was swiftly amplified by acute inflammatory and pro-astroglial differentiation stimuli, experimentally supporting previous correlative evidence(3,10). Interestingly, the presence of the IDH1^R132H mutation correlated with a restriction in cells’ ability to enter a mesenchymal state, in vitro and in vivo, in line with IDH-mutant dampening transcriptional responses and validating its use as a genetic biomarker for disease classification(43). By capturing the activation status of signaling pathways and developmental stages (e.g. inflammation and differentiation), and any pre-existing context-dependent difference (e.g. IDH-wild-type vs mutant background), genetic tracing supports a mixed model in which the proneural glioblastoma is an entity(2–4) and the mesenchymal glioblastoma is a state(12). Yet the proneural glioblastoma can be further sub-grouped into specific "homeostates’, in which oncogenic, cell-of-origin-signatures and the microenvironment make distinct contribution.

Our data on the reversibility of the mesenchymal state suggest an alternative view of proneural and mesenchymal glioblastoma as sharp cell identities with a fixed hierarchical relationship, and support rather the potential for a fluid interconversion between states. This view is consistent with both, genetic evidence from patients and mouse models(2–4,12,39,40) and the apparently discordant finding of a mesenchymal-to-proneural transition predicted by in silico lineage tracing(17). Of note, the mesenchymal conversion observed in our in vivo genetic tracing is consistent with either a proneural-to-mesenchymal transition contingent on the microenvironment, with mesenchymal glioma-initiating cells being more fit during tumor initiation and progression, thereby leading to their positive selection, or a combination of both. Along this line, even though the highly mesenchymal cells constituted only a fraction of the bulk tumor, they more often appeared viable and produced high-quality cDNA libraries. It is tempting to speculate that mesenchymal cells are better adjusted to survive in vivo and experimental stressors. A scenario in which mesenchymal fate activation enhances cell fitness would be consistent with its identification as a dominant entity at recurrence(2); it would also explain the presence of the mesenchymal surface receptor CD44 on a sizeable fraction of primary glioblastoma-propagating cells(44). To systematically discriminate between all the plausible models, future genetic and transcriptional lineage tracing using genetic barcodes is warranted, including secondary transplantations.

Substantial correlative evidence supports a link between inflammatory signaling, EMT, the infiltration of innate immune cells and radio-resistance(25,26). Here, fate mapping by sLCRs established a causal link for the clinical observation that DNA damage therapy is associated with mesenchymal trans-differentiation(2). Conversely, it offered no support for a causal hypothesis of hypoxia-driven mesenchymal commitment. Hypoxia regulates tumor homeostasis, glioblastoma cells surrounding areas of pseudopalisading necrosis overexpress hypoxia-inducible factor-1 and scRNA-seq revealed a correlation between the hypoxia gene expression signature and the mesenchymal glioblastoma(11,12,45). While individual genes of the mesenchymal glioblastoma signature may be directly regulated by changes in levels of tissue oxygenation, our data support a model in which hypoxic gene expression is additive to rather than causal for the mesenchymal glioblastoma program. This conclusion does not, however, exclude that the hypoxic microenvironment might trigger a recruitment of innate immune cells in vivo, in turn leading to a non-cell-autonomous mesenchymal commitment in vivo(Fig. 7G).

In line with a model of tumor EMT as the hijacking of a conserved developmental process, the mesenchymal reporters presented here are likely to uncover cell fate mechanisms in a broader set of epithelioid cancers and may serve as tools to discover optimal treatment strategies, such as sequential dosing of targeted drugs to send cancer cells into a state followed by hitting such state with targeted treatments(46).

Finally, our strategy provides evidence of a causal link to support the clinical association between the mesenchymal glioblastoma subtype and a specific immune landscape(5). While TNFα is believed to drive therapeutic resistance(10) and GAMs to be the source for TNFα in mouse models(30) and human tumors(31), we uncover TNFα-independent routes to the GAM-driven mesenchymal glioblastoma state. The signatures experimentally identified in this study resemble those observed in single mesenchymal glioblastoma cells from patients, suggesting a relevance for our findings in vivo. In addition to identifying a pathophysiologically relevant non-cell autonomous interaction, mesenchymal genetic tracing pointed towards the mechanistical activation of the NO and SREBP1/2 pathways as relevant for microglia-driven phenotypes.

The transcriptome remodeling induced in GAMs by crosstalk between glioma cells and innate immunity is consistent with regulation of inflammatory and metabolism genes(47). A unique finding of this study is to show that the phenotypic changes induced by innate immune cells in tumor cells drive mesenchymal commitment and lead to selective therapeutic resistance. The indications on LXR agonists, aimed at exploiting low cholesterol biosynthesis in glioblastoma(33), are particularly timely given the identification of innate immune cells as the culprit in patients’ lack of response to checkpoint inhibitor immunotherapy(48) and the current clinical trial combining immunotherapy and an LXR agonist (NCT02922764). The data support the combination of multicellular systems and sLCRs as pre-clinical tools to test therapeutic strategies that are more likely to succeed in clinical trials.

In summary, the synthetic genetic tracing developed here uncovered causal and mechanistic evidence for a pathological role of innate immune cells in glioblastoma through mesenchymal transition – a relationship that has been hinted at for a long time. The method is effective, scalable and can be extended to a wide range of ex vivo or in vivo systems. The further development of the algorithm will help to broadly dissect cell-intrinsic and non-cell-autonomous mechanisms that control normal and tumor homeostasis in diverse contexts of clinical relevance.

methods

GBM-sLCR generation

To focus on cell intrinsic gene signatures, in a pilot approach, we filtered out genes with low expression in GBM stem-like cells(GSCs) from our previous experiments and confirmed their potential intrinsic expression in a validated cohort of GSCs from others. A database of 1,818 motifs(position weight matrices, PWM) representing known transcription factor binding preferences was generated from the literature(49–53). PWMs were pre-selected based on subtype-specific TFs. Regions corresponding to DRGs were retrieved from the hg19(Refseq table downloaded from UCSC genome browser on October 5, 2012) and decomposed in windows of 150bp and 50bp steps(hereafter referred to as CRE). The scanned area surrounding each signature gene was manually delimited by two distal CTCF sites, positioned >10 kb away from the TSS or TES. High-affinity, TF-binding sites in defined genomic regions were identified using FIMO(PMID: 21330290) with --output-pthresh 1e-4 --no-qvalue. For each window, whenever multiple matches for the same PWM were identified, the adj. p-value < 0.01 of the best match(multiple backgrounds) was considered as a proxy for the affinity of that TF over that region. TFBS pairwise correlation heatmaps in (Fig. 1A) used the top 500 regions in terms of the score defined as -log10(p-value). Genomics coordinates vs TFBS correlation heatmaps, including the representative one in Fig. 1B, were generated with the top 100 scoring regions.

Vectors generation

The sLCRs were synthetized initially at IDT, later at GenScript and currently at VectorBuilder. MGT#1-mVenus was cloned in the PacI-BsrGI fragment of the Mammalian Expression, Lentiviral FUGW(gift from David Baltimore; Addgene#14883). Additional modifications, such as insertion of H2B-CFP (gift of Elaine Fuchs, Addgene#25998), swapping of mVenus to mCherry, or MGT#1 with all other sLCR used either restriction enzyme digestion or Gibson cloning.

The sequence of the Igk-mVenus-TM was from(54). The mCherry was modified with a NLS. The sLCRs vectors are 3rd gen lentiviral system and have been used together with pCMV-G(Addgene#8454), pRSV-REV(Addgene#12253) and pMDLG/pRRE(Addgene#12251).

Oligonucleotides and Primers

All oligos used in this study are available upon request.

Cell lines

All lines used in this study were thawed from frozen batches and propagated for a limited number of passages(5-10x), and all lines regularly tested with the Mycoplasma Detection kit(Jena Bioscience11828383, PP-401L) to exclude contamination. For all glioma initiating cells and GSCs cell line authentication occurred through global expression profiling.

Human glioma cell lines

The IDH-wt-hGICs and IDH-mut-hGICs were generated by our lab and will be described elsewhere. Briefly, IDH-mut-hGICs were generated by transforming human NPC(kindly provided by R. Glass, LMU Munich, Germany), by means of: pLenti6.2/V5-IDH1-R132H(kindly provided by Hai Yan, Duke University, USA), TP53^R173H and TP53^R273H(point mutations introduced into TP53 ccsbBroad304_07088 from the CCSB-Broad Lentiviral Expression Library, and pRSPuro-sh-PTEN(#1; kind gift from D. Peeper). IDH-wt-hGICs were generated by transforming human NPC with the constructs pRSPURO-sh-PTEN(#1), pLKO.1-shTP53(TRCN0000003754) and pRS-shNF1. For these lines, thorough genetic, transcriptional, and epigenetic characterization has been performed, as well as in vivo tumor formation and phenotypic mimicking ability.

Patient-derived glioma stem cell lines GBM2(55; TCP#2), GBM14 and NCH421K(56) were kindly provided by Rainer Glass, LMU Munich, Germany. Lines GBM166 and GBM179 were kindly provided by Peter Dirks (University of Toronto, Canada (21)) and lines BLN-5 and BLN-7(57) were kindly provided by Phillip Euskirchen, Charité Berlin, Germany.

In vitro, all glioma lines were propagated as described(58) with one modification. In addition to with EGF(20ng/ml; R&D, 236-EG), bFGF(20ng/ml; R&D, 233-FB), heparin(1μg/ml; Sigma, H3149) and 1% penicillin and streptomycin, PDGFAA(20ng/ml; R&D, 221-AA) is also supplemented to RHB-A(Takara, Y40001). This medium composition will be referred to as RHB-A complete. hGICs were cultured at 37°C in a 5% CO₂–95% air incubator, 3% O₂ and humidified incubator.

Cancer cell lines

The MCF7 and MDA-231 cell lines(kindly provided by the Rene Bernards lab, NKI Amsterdam, Netherlands) were cultured in RPMI medium(Life Technologies, 21875091). Both cell lines were supplemented with 10% FBS, and 1% penicillin and streptomycin at 37°C in a 5% CO₂–95% air incubator. A549 and H1944 cell lines(kindly provided by the Rene Bernards lab, NKI Amsterdam, Netherlands) were cultured in RPMI medium. Both cell lines were supplemented with 10% FBS, and 1% penicillin and streptomycin at 37°C in a 5% CO₂–95% air incubator.

Human Microglia cell line

Immortalized primary human Microglia C20(kindly provided by David Alvarez-Carbonell, Case Western Reserve University, Cleveland, USA (29)) were cultured in RHB-A medium supplemented with 1% FBS, 2.5mM Glutamine(Thermofisher; 35050038), 1μM Dexamethasone(Sigma; D1756) and 1% penicillin and streptomycin at 37°C in a 5% CO₂–95% air incubator.

Human hematopoietic progenitor CD34 differentiation

Donor-derived CD34+ cells(kind gift from K. Rajewsky, MDC Berlin, Germany) were propagated in SFEM II(StemCell Technologies, 09605), SCF, FLT3-L, TPO, IL6(all 100ng/ml; easyexperiments.com), UM171(Selleck, 35nM), SR1(Selleck, 0.75μM), 19-deoxy-9-methylene-16,16-dimethyl PGE2(Cayman, 10μM). CD34+ differentiation towards immature MDSC-like cells was induced by switching culture medium to RHB-A-medium supplemented with human SCF(50ng/ml) and human GM-CSF(100ng/ml) for 7-12 days(cl.#1, cl.#2), prior to co-culture.

Human Monocyte cell line differentiation

Human monocytic THP-1 cells(ATCC TIB-202) were acquired from S. Minucci (IEO Milan, Italy) and maintained in Roswell Park Memorial Institute medium(RPMI 1640, Thermofisher) culture medium supplemented with 10% fetal bovine serum(Gibco, 10270106), 1mM pyruvate(Life Technologies), 2mM GlutaMAX(Thermofisher, 35050-038). THP-1 monocytes are differentiated into macrophages by 48h incubation with 150nM phorbol 12-myristate 13-acetate(PMA, Cayman Chemicals; Cay10008014) followed by 24h incubation in RPMI medium. Macrophages were polarized into M1 macrophages by incubation with 20ng/ml of IFNγ(R&D system, 285-IF) and 10pg/ml of LPS(Sigma, L2630). Macrophage M2 polarization was obtained by incubation with 20ng/ml of interleukin 4(Sigma, A3134) and 20ng/ml of interleukin 13(PeproTech, 200-13).

Transfection/Transduction

Transfection and transduction were previously described in detail(59). Briefly, 12μg of DNA mix(lentivector, pCMV-G, pRSV-REV, pMDLG/pRRE) was incubated with the FuGENE(Promega, E2311) -DMEM/F12(Life Technologies, 31331) mix for 15min at RT, added to the antibiotic-free medium covering the 293T cells, and the a first-tap of viral supernatant was collected at 40h after transfection. Titer was assessed using Lenti-X p24 Rapid Titer Kit(Takara, 631280) according to the manufacturer’s instructions. We applied viral particles to target cells in the appropriate complete medium supplemented with 2.5μg/ml protamine sulfate. After 12-14h of incubation with the viral supernatant, the medium was refreshed with the appropriate complete medium.

Fluorescence-activated cell sorting (FACS)

Transduced cell lines were harvested into single cell suspensions and resuspended into cold medium and filtered into FACS tubes. Sorting was conducted using BD FACSAria III or Fusion. The appropriate laser-filter combinations were chosen depending on the fluorophores being sorted for. Typically, to remove dead cells, events were first gated on the basis of shape and granularity(FSC-A vs. SSCA) and doublets were excluded(FSC-A vs. FSC-H). Positive gates were established on PGK-driven and constitutively expressed H2B-CFP as sorting reporter, to sort for populations with low to medium intensity of sLCR-dependent fluorophore expression.

FACS analysis

All analyses were performed using FlowJo_v10. For analysis of in vivo data in (Fig. 2E–G), freshly dissociated mouse brain tumor samples were pre-gated for viable cells before dimensionality reduction of all acquired parameters(FSC-H/W/A, SSC-H/W/A, positive and negative viability dies and mVenus/mCherry sLCR expression) was performed using the inbuilt auto t-distributed stochastic neighbor embedding(opt-SNE) algorithm(Perplexity 30, Iterations 1000). From resulting t-SNE maps, the glioma cell clusters separating apart from mouse cells were identified and gated by overlaying sLCR expression heatmaps. Further t-SNE dimensionality reduction of gated glioma cells was performed to assess clustering of sLCR reporter distribution for individual in vivo-derived tumor cells and compared to simultaneously analysed in vitro-cultured cells used for transplantation. Quantification of sLCR-high cells was established by defining a four-quadrant gating in mVenus vs. mCherry plots on in vitro cells to mark sLCR high populations and applying this gating to t-SNE gated in vivo glioma cells(see Supplementary Fig. S2A).

RNA FISH and dual FISH-IF

Cells were permeabilized in 70% ethanol(RNA FISH only) or with 0.5% triton X-100(for dual IF-RNA FISH), washed in RNase-free PBS(Life Technologies, AM9932), fixed with 10% deionized Formamide(EMD Millipore, S4117) in 20% Stellaris RNA FISH Wash Buffer A(Biosearch Technologies, Inc., SMF-WA1-60) and RNase-free PBS, for 5min at room temperature. IgK-MGT#1-mVenus and H2B-CFP were probed using SMF-1084-5 CAL Fluor® Red 635 and SMF-1063-5 Quasar® 570 custom Stellaris® FISH Probes(oligo sequence available upon request) in 10% deionized Formamide 90% Stellaris RNA FISH Hybridization Buffer(Biosearch Technologies, SMF-HB1-10) at 31.5μM in 100μL transferred to the coverglass, hybridized at 37°C in the dark. After O/N incubation, slides were washed 3x with RNase-free PBS 5min. If primary/secondary staining occurred, it was performed as described in the immunofluorescence section.

Phenotypic screening

Tumor cells were propagated as described above until the screening. We seeded 15,000 cells/50μl/well in 384 well plates(Corning), in Gibco FluoroBrite DMEM medium supplemented with the appropriate growth factors. Cells were dispensed as 50μl suspension into each well using the SPARK 20M Injector system(50μl injection volume; 100μl/s injection speed). For non-adherent cells(e.g. hGICs), cells were further centrifuged at 1500rpm for 1h 30min at 37°C. Bottom reading fluorescence was scanned using a SPARK 20M TECAN plate reader at 37°C in a 5% CO₂ (additionally 3% O₂ for hGICs) in a humidified cassette, with the following settings for mVenus: Monochromator, Ex 505nm±20nm, Em 535nm±7.5nm. In independent replica, cell viability was measured with 0.02% AlamarBlue solution in FluoroBrite medium with the following settings: Fluorescence Top reading, Monochromator, Ex 565nm±10nm, Em 592nm±10nm.

DMSO-soluble compounds such as GSK126, were robotically aliquoted using a D300e compound printer(TECAN), whereas cytokines were robotically aliquoted to each well using an Andrew pipetting robot(AndrewAlliance). Data were imported in PRISM7(GraphPad). Fluorescence intensity from control dead cells was subtracted as background from all values. Individual values were normalized to the mean of controls and represented as fold change.

Irradiation of hGICs

Irradiation was delivered using the XenX irradiator platform(XStrahl Life Sciences), equipped with a 225 kV X-ray tube for targeted irradiation. hGICs cultured in either 6-well plates or 96-well plates were placed in the focal plane of the beamline and exposed to irradiation for a specific time, depending on the target dosage, as calculated with an internal calculation software.

Induction of hypoxia

To investigate effects of hypoxia on IDH-wt-hGICs, cells were moved from the standard 3% O₂ culture to ambient O₂-levels for 24h. Cells were then seeded at 250,000 cells/well into 6-well plates and moved to 1% O₂, 3% O₂ and ambient O₂, respectively. In case of severe hypoxia(Supplementary Fig. S4D), plates were cultured in pressurized incubators(Avatar, Xcellbio) at 0.5% O2 and 5 Psi(~344 mbar) over the usual atmospheric pressure in Berlin of 14.7Psi(1010-1030 mbar). After 3d culture, cells were harvested into single cell suspensions using Accutase for assessing sLCR expression using FACS and RNA was extracted as described for RNA-sequencing.

Gene Knock-out using CRISPR/Cas9

The SGF29/CCDC101 deletion was performed using the Gene Knockout Kit v2 (Synthego). The sgRNAs were dissolved in nuclease free 1xTE buffer to a stock concentration of 30μM. RNP complexes were formed by mixing the Cas9 nuclease-gRNAs in a ratio of 6:1. Each RNP complex was nucleofected into 250,000 IDH-wthGICs-MGT#1 using the CA-138 pulse program of the 4D-Nucleofector Core Unit(Lonza). Approximately 7d after electroporation, the gDNA was extracted with AMPure XP beads (Beckman Coulter, A63881), eluted in 50μl of Elution Buffer with downstream PCR-amplification of the target site of interest using 800 to 1,200 bp products centered around the gRNA target loci(primers available upon request). The efficiency of the knock-outs was assessed using TIDE(NKI, https://tide.nki.nl/) or T7EI assays. The alteration of the MGT#1 fluorescence in bulk SGF29/CCDC101-KO cells was directly assayed by FACS using a BD LSRFortessa and FlowJo.

Genome-wide CRISPR Knock-out in vitro screen

For the genome-wide pooled CRISPR Knock-out screen, we utilized the Brunello library consisting of 77,441 sgRNAs targeting 19,114 genes(average of 4 sgRNAs per gene) and 1,000 non-targeting controls. To achieve a library representation over 100x, we transduced a total of 16x10⁶ IDH-wt-hGICs-MGT#1 cells at a MOI of ~0.5 and amplified the cells for 10d prior introducing the treatment. At day 10, the cells were either treated with TNFα(10ng/ml) and FBS(0.5%); Temozolomide(50μM) and Irradiation(20Gy) or left untreated. Before the gDNA extraction, we performed a FACS sorting of each condition, collecting the IDH-wt-hGICs-MGT#1-high, IDH-wt-hGICs-MGT#1-low and the unsorted populations. While the time-window of the experiment is compatible with gene essentiality, we have specifically ruled out that our hits are essential in validation experiments. The genomic DNA was extracted by lysing the cell pellets for 10min at 56°C in AL buffer(Qiagen, 19075), supplemented with Proteinase K(Invitrogen, AM2548) and RNAse A(Thermo Scientific, 10753721), subsequently purified with AMPure XP beads(Beckman Coulter, A63881) and eluted in EB buffer(Qiagen, 19086). NGS libraries were constructed in a two-step PCR setup, where the PCR1 is used to amplify the sgRNA scaffold and insert a stagger sequence to increase library complexity across the flow cell, while the PCR2 introduced Illumina compatible adaptors with unique P7 barcodes, allowing sample multiplexity. For the PCR1, 5μg of each gDNA sample were divided over 5 parallel reactions, that were subsequently pooled together and purified using AMPure XP beads. The optimal cycle numbers for PCR2 were determined for 1μl of each PCR1 individually by conducting a qPCR amplification using KAPA HiFi HotStart Ready Mix(Roche, 7958927001) and 1x EvaGreen(Biotium, 31000). 10μl of the purified PCR1 of each sample were used as input for the final PCR2. Both PCR1 and PCR2 were performed using KAPA HiFi HotStart Ready Mix. Primers are available upon request. Quality control of the final libraries was performed using the Qubit dsDNA HS kit(Invitrogen) for quantification and TapeStation High Sensitivity D1000 ScreenTapes(Agilent, 5067-5584) for determination of PCR fragment size. The barcoded libraries were pooled together in equal molarities and sequenced on an Illumina NextSeq500 using the 75 cycles V2 chemistry(1x75nt single read mode). Reads were aligned to the Brunello library using bwa and a custom script to generate the gRNA read-counts. The resulting sgRNA read-counts were pre-filtered (> 10 counts) and aggregated to the gene level with caRpools R package and DESeq2 was applied for identification of potential MGT#1 expression regulating hits. A hit was considered to be significant at padj < 0.05 and log2FC ±1. The identified significant differentially regulated target genes were used as input for the IPA analysis(Qiagen Bioinformatics), predicting the upstream regulators and toxicity of MGT#1 activation(Fig. 5D–F). For the density plot in Fig. S5D, the count data was log2-normalized and averaged between the replicates. log2-normalized counts of the Brunello library were subtracted from the unsorted IDH-wthGICs conditions to calculate the log2FC. sgRNAs with <10 read counts in the plasmid library were removed prior log2FC calculation.

RT-qPCR

cDNA was generated using the SuperScript™ VILO™ MasterMix(Invitrogen, 11755050) starting with 0.5-2.5μg RNA as input in 20μL reaction, incubated at 25°C for 10min, at 42°C for 60min and at 85°C for 5min. RT-qPCR was performed with 10ng cDNA/well, in a 384w ViiA™ 7 System using 1x Power SYBR Green PCR Master Mix(Applied Biosystems, 4368702), in 10μl/well. Primers are available upon request.

Automated longitudinal live-cell imaging

IncuCyte automated longitudinal imaging was performed in 96 wells black walls plates(Greiner). 300,000 cells per plate were seeded to reach optimal confluence at the end of the experiment. GSK126 was aliquoted using a D300e, whereas TGF-β1+2 were manually aliquoted to each well. Both were refreshed every second day. The last time-point was independently verified using a plate reader(BMC Clariostar).

Immunoblot

Cell pellets were lysed in RIPA buffer(20mM Tris-HCl pH7.5,150mM NaCl, 1mM EDTA, 1mM EGTA, 1% NP-40) supplemented with a 1x Protease inhibitor cocktail(Roche), 10mM NaPPi, 10mM NaF, and 1mM Sodium orthovanadate. The lysates were sonicated if necessary, and electrophoresis was performed using NuPAGE Bis-Tris precast gels(Life Technologies) in NuPAGE MOPS SDS Running Buffer(50mM MOPS, 50mM Tris Base, 0.1% SDS, 1mM EDTA). Protein was transferred onto Nitrocellulose membranes in transfer buffer(25mM Tris-HCl pH 7.5, 192mM Glycine, 20% Methanol) at 120mA for 1h. Protein transfer was assessed through staining with Ponceau Red for 5min, following two washes with TBS-T. Blocking of membranes was done for 1h at room temperature with 5% BSA in PBS. Dilutions of primary antibodies were prepared in PBS+5% BSA and membranes were incubated over night at 4°C. Following three washes for 5min with TBS-T, dilutions of appropriate HRP-coupled secondary antibodies were prepared in PBS+5% BSA and membranes were incubated for 45min at room temperature. After washing three times for 5min with TBS-T, ECL detection reagent(Sigma, RPN2209) was applied and membranes were exposed to ECL Hyperfilms(Sigma, GE28-9068-37) to detect chemoluminescent signals.

Copy number normalised sLCR expression

Human glioma initiating cells(hGICs), patient-derived glioma stem cells(GSCs), lung adenocarcinoma, breast adenocarcinoma and leukemia cell lines were transduced with the sLCRs MGT#1 and PNGT#2 as described. To perform lentiviral copy number normalisation, gDNA was extracted using AMPure XP beads according to the manufacturers protocol. Relative amounts of sLCR integration sites into the genome of target cells was assessed by qPCR, using mVenus(MGT#1) and mCherry(PNGT#2) specific primers and N2 primers targeting a genomic region in Chromosome 13 for input normalisation between samples. 1ng of gDNA was amplified using respective primers and Power SYBR Green PCR Master Mix in a total reaction volume of 10μl in quadruplicates. Relative DNA amounts of MGT#1 or PNGT#2 were normalised over N2 levels to calculate copy number abundance for each sLCR in each sample.

Expression levels of sLCRs in corresponding samples were assessed in quadruplicates by qPCR using One Step TB Green PrimeScript RT-PCR Kit II(Takara, RR086A) with an input of 2ng total RNA using mVenus(MGT#1) and mCherry(PNGT#2) specific primers and GAPDH primers for normalisation. Relative sLCR expression of MGT#1 or PNGT#2 were calculated by normalising over GAPDH expression for each sLCR in each sample. Both qPCR normalisations were carried out as two independent technical replicate experiments and data from both runs was combined for final normalisation. All primers are available upon request.

Final normalisation as presented in (Fig. 1D) was done by first correcting GAPDH-normalised sLCR expression divided over N2-normalised copy number abundance and then setting IDH-wt-hGICs as reference, by calculating the fold-change increase of copy number normalised sLCR expression.

Intracranial orthotopic glioma xenograft

All mouse studies were conducted in accordance with a protocol approved by the Institutional Animal Care and Use Committee and in agreement with regulations by the European Union. Orthotopic glioma xenograft studies were conducted in NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ(NSG) mice purchased from The Jackson Laboratory and maintained in specific-pathogen-free(SPF) conditions. We used male and female mice between 7-12 weeks of age. Orthotopic glioma xenograft studies were conducted as previously described with modifications(58,59).

NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ(NSG) male and female mice between 7-12 weeks of age were used for injections. Briefly, the head of the mouse was immobilized within the stereotactic frame and the skull was exposed through a small skin incision. A small burr hole was made using a drill with the following stereotactic coordinates: 1.0 mm anterior and 2.0 mm lateral of the bregma. During the entire procedure, mice were kept under 1.5-2% isoflurane mixed with ambient air and oxygen anaesthesia on a warming pad. hGICs tumorspheres were treated with accutase and resuspended as single cells at a concentration of 50,000 cells/μl. Generally, 4-5μl of hGICs were stereotactically injected into the corpus callosum at a 3mm depth to the cortical surface. Injection flow was set at 0.4μL/min and the needle was withdrawn after one minute rest, to avoid intracranial pressure and reflux, respectively. The burr hole was sealed with bone wax and the scalp with surgical clips. Mice were returned to their cages, monitored until full recovery and checked daily for signs of neurological symptoms. Mice were also monitored by imaging using IVIS as needed until neurological symptoms occurred and mice were to be sacrificed. Brains were collected directly after euthanasia, examined under fluorescence microscope to identify the tumor border and xenografted tumors were processed the same day for FACS analysis and sorting or cells were frozen in medium plus 10% DMSO until needed. The remaining brain tissues were then subjected to fixation as described below. For the experiment in (Fig. 2E–L), performed at EPO GmbH (BerlinBuch, Germany), 8 weeks old female NOG mice were used under similar experimental protocols and welfare standards approved by the Berlin authorities(LaGeSo).

Tissue dissociation and brain tumor cell sorting

Brain tumor dissection was previously described in detail(58,59). Briefly, the tissue was dissected with a scalpel and digested in Accutase/DNaseI(947μl Accutase, 50μl DNase I Buffer, 3μl DNase I) at 37°C using C-tubes in a OctoMACS dissociator(Miltenyi Biotec) using program 37C_BTDK_1. Suspensions were filtered first through a 100μm cell strainer, following a 70μm cell strainer before RBC lysis(NH4Cl, 155 mM; KHCO3, 10 mM; EDTA, pH 7.4, 0.1 mM). After washing in cold PBS, viability and cell count were assessed automatically with 0.4% Trypan Blue staining using a TECAN SPARK20M.

When surface markers were assessed, typically, 200,000 cells/antibody were used in 15ml Falcons. Staining volume was 50μl in RHB-A medium with primary antibody(e.g. CD133-APC; Miltenyi), on ice, in the dark, for 30min. Unbound antibody was removed with two washes of PBS. Depending on whether cells were analyzed or sorted, data acquisition was performed on the BD LSRFortessa or cells were sorted using the BD Aria II/III or an Astrios Moflo. The appropriate laser-filter combinations were chosen depending on the fluorophores being analyzed. Typically, to remove dead cells, events were first gated on the basis of shape and granularity(FSC-SSC), and we used as viability dyes either Calcein UltraBlue or DRAQ5 and ZombieRed(depending on the fluorophores being analyzed). Analysis was performed with FlowJo_V10.

Preparation of cryosections

Tumorspheres were allowed to settle by gravity, fixed in fresh prepared formaldehyde in PBS(1.0%), which was blocked with 140mM glycine 2M, rinsed with 30% sucrose, followed by addition of freezing medium(O.C.T/cryomold). Frozen blocks were obtained by dry ice freezing and stored at -80°C until used. The blocks were cut with Leica CM 1950.

Immunohistochemistry

Tissues or tumorspheres were fixed in 4% PFA for 20 min. Following fixation, dehydration was performed with increasing EtOH from 70% to 100%, Xylene and overnight Paraffin incubation. Paraffin-embedded samples(PES) were cut using a HM 355S microtome(Thermo Scientific). Hematoxylin/Eosin(HE) staining was performed with standard protocols and slide images were acquired with an automated microscope(Keyence).

Immunofluorescence

At room temperature, cells were grown on coverslips or spheroids spun down on glass followed by 4% paraformaldehyde(PFA, Sigma Aldrich, 16005) in PBS for 10min fixation, washed in PBS 5min (3x), permeabilized with 0.5% triton X-100 in PBS for 5 min, blocked 15min with 4% BSA(Roth, 3854.4), stained with primary and secondary antibodies and 20μg/ml Hoechst 33258(Cayman, 16756-50), finally mounted onto glass slides using nail polish and Vectashield(Linaris, H1000). On paraffin-embedded tissues, we performed deparaffinization and citrate buffer antigen retrieval with standard protocols. Permeabilization was performed with Triton X-100 0.25% in PBS and – when appropriate - endogenous peroxidases were blocked with 3% H₂O₂ in water. Typically, we performed blocking with 5% normal goat serum(NGS). Primary antibodies were: anti-GFP(Abcam, ab6556, 1:1,000), anti-MED1(Abcam, ab64965 1:500), anti-Tubulin(BD T5168, 1:2,000), and secondary antibodies (1:200) were: A31573, A11055 and A31571 Alexa Fluor 647, A21206 Alexa Fluor 488, A31570 Alexa Fluor 555.

Imaging

Microscopes used in this study were Zeiss LSM800, Leica SP5-7-8, Nikon Spinning Disk. Confocal images in (Supplementary Fig. S1B) were acquired with a Leica SP5, where mVenus fluorescence was acquired using Ex=488nm, Em=535nm. Images in (Supplementary Fig. S1C) were acquired with a Zeiss LSM800, using Ex=558nm, Em=575nm for mVenus-QUASAR570 and Ex=653nm, Em=668nm for BRD4- or MED1-AF647, respectively. For (Supplementary Fig. S1D), the H2BCFP-QUASAR670 used Ex=631nm, Em=670nm. Images were processed using ImageJ or Photoshop. Confocal live-cell imaging in (Supplementary Fig. S3A) was done using a Zeis LSM700 with appropriate excitation laser/emission filter combinations to detect endogenous mVenus, mCherry and CFP expression.

Transwell co-culture

Co-cultures of hGICs and immortalized primary human Microglia C20 or human monocytes were set up using hydrophilic PTFE 6-well cell culture inserts with a pore size of 0.4μm(Merck). Human Microglia, CD34+ monocytes or THP-1 derived M1/M2 macrophages were seeded at 1.5x10⁵ cells/well for 24h on 6-well plates in respective medium. Medium was aspirated and cells were washed once with PBS before 1ml of RHB-A complete medium was added. Transwell inserts were placed into plates and 5x10⁵ single hGICs in a total volume of 1ml of RHB-A complete medium were plated on insert surface. hGICs and C20 human Microglia were harvested after 48h of co-culture for further analysis.

Drug dose-response screening

These experiments were performed as previously described(46). Transduced hGICs from transwell co-culture experiments were harvested into single cell suspension and sorted into mVenus high and low populations using a BD FACSAria III. Cells were counted and 7,000 cells/50μl/well were seeded onto 384-well black walled plates in RHB-A complete medium using the SPARK20M Injector system(50μl injection volume; 100μl/s injection speed). Drugs were typically dissolved as a 10mM stock in DMSO and dispensed using the D300e compound printer(TECAN) for targeted dose-response with plate randomization and DMSO normalization. After 72h of incubation, cell viability was measured after 2-6h incubation with 10μl of Cell-Titer-Blue(Promega, 2813) assay reagent with the following settings: Fluorescence top reading, Monochromator, Ex 565nm±10nm, Em 592nm±10nm. Data were imported in PRISM7(GraphPad). Fluorescence intensities from empty wells was subtracted as background from all values. Concentrations were log10-transformed into log[M] scale and individual values were normalized to the mean of untreated positive and SDS treated negative control conditions. Non-linear regression modelling(log(inhibitor) vs. normalized response --Variable slope) was used to derive dose-response curve and IC50 values.

Cell viability assay

For (Fig. 5G), IDH-wt-hGICs-MGT#1 were plated in 5 replica and pre-treated for 16h with 5μM ATRA or 0.2μM IKK-16, with subsequent exposure to ionizing irradiation(20Gy) and TMZ(58μM). 72h post treatment the viability was measured by addition of Calcein UltraBlue at 1.5μM and 4 images were taken per well with the Operetta CLS (Perkin Elmer) high-content imaging system. Integrated Harmony software was used to analyze the images, obtaining the mean intensity of the Calcein UltraBlue from the spheres per well. Data were analyzed with Prism8.

RNA-seq Generation

Standardly, the RNA was extracted using the TRIzol™ Reagent(Invitrogen, 15596026), with subsequent isopropanol precipitation and AMPure XP beads purification. The concentration of the RNA was assessed by the Qubit™ RNA HS Assay Kit(Invitrogen) and/or qPCR quantification with the One-Step TB Green® PrimeScript™ RT-PCR Kit II(Takara Bio). The integrity of the RNA was determined by means of the High Sensitivity RNA ScreenTape System(Agilent, 5067-5581).

To generate the GSC expression profiles for GBM sLCR construction, the TruSeq Stranded Total RNA Library Prep(Illumina, 20020596) and the Ribo-Zero Gold rRNA Removal Kit(Illumina, MRZG12324) kits were utilized following the manufacturers’ protocol. The final libraries were profiled for quantity using the Qubit dsDNA HS kit(Invitrogen) and/or the KAPA Library Quantification Kit(Roche, 7960204001). The appropriate library size distribution was assessed by the TapeStation High Sensitivity D1000 ScreenTapes kit(Agilent). Pooled libraries were sequenced on the Illumina HiSeq2500 or HiSeq4000 platforms in either single-read 51bp or paired-end 100bp mode. Illumina adaptors were trimmed using from the raw reads with Cutadapt(https://cutadapt.readthedocs.io/en/stable/) and raw reads were aligned to the human genome(hg19 or hg38) with TopHat(Tophat2 v2.1.0; parameters: --library-type fr-firststrand -g 1 -p 8 -G ENSEMBL_Annotation_v82.gtf). HTSeq-count v0.6.1p1 was used to assess the number of uniquely assigned reads for each gene(parameters: -m intersection-nonempty -a 10 -i gene_id -s reverse -f bam). Reads were normalized and log2 transformed to obtain log2 counts per millions(CPM).

The TruSeq Stranded Total RNA Library Prep Gold Kit(Illumina, 20020598) was utilized for the generation of the in vitro RNA-seq data(Fig. 3–4, 6–7) according to the manufacturers protocol with 0.1-1μg of total RNA input. The SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian(Takara Bio, 634413) was used for the in vivo RNA profiling(Fig. 1–2). The library construction was performed following the manufacturers protocol with 0.2-10ng of total RNA input. Quantity and quality controls were performed as described above. Pooled libraries were sequenced on the Illumina NextSeq500 or NovaSeq 6000 platforms in a 2x75bp or 2x100bp mode. The demultiplexing was performed using the bcl2fastq conversion software(v2.20.0). The reads were aligned to a custom genome(GRCh38 containing sLCRs and reporter sequences) using STARv2.6.0c. HTSeq was utilized to assess the number of uniquely assigned reads for each gene(–m intersection-nonempty -a 10 -i gene_id -s reverse -f bam).

RNA-seq Analysis

RNA-seq analysis of in vivo and in vitro high/low data-set was conducted using R v3.6 (Fig. 1 and Fig. 2). After the data processing step (above), the quality of each sample was individually assessed using dupRadar v1.18 R package (default parameters) and subsequently re-evaluated by the correlation between the number of genes and average counts for each file (Supplementary Fig. S2C–D). Then, differential expression analyses between specific sLCR activation, high/low, and in vivo/in vitro, were conducted using DESeq2 v1.24 on raw pre-filtered counts (>100 and >50). Of note, principal component analysis was used to identify potential outliers in the in vivo samples data-set (Supplementary Fig. S2D), and only MGT#1-high homogenous samples were used in different comparisons (Fig. 2H and Supplementary Fig. 2E). Differential regulated genes were considered if log2FC>±1, padj<0.05, and baseMean>5 (Fig. 2I and Supplementary Fig. S2E). The in vivo MGT#1-high gene-set contains only the up-regulated genes of the comparison between in vivo MGT#1-high and -low samples. Graphical representations in this analysis were generated using ggplot2 v3.3.2.

Differential expression analyses of MGT#1 activation cues RNA-seq profiles were conducted using DESeq2 v1.24. Each condition (TNFα, Human Serum (huSer), ionizing radiation (IR), Activin A, NOC-18, oxidized LDL (OxLDL), and C20-human microglia co-culture) was individually compared against control samples after filtering for low-counts genes (filterByExpr function from the edgeR v3.26 R package). To account for technical differences between sequencing runs, batch correction using the sva v3.32 R package was applied when necessary. Up-regulated genes (log2FC>1, padj<0.05, and baseMean>5) were considered as gene sets for the different representations (Fig. 3H, 6G–H, 7A, and Supplementary Fig. S3G, S6D, S7A–B). Common genes between MGT#1 activation cues were identified by using the UpSetR v1.4 R package (Fig. 3H and 6H). Of note, the control gene-set was obtained by the comparison between control samples and the rest of MGT#1-high samples. UMAP dimensional reduction (Fig. 6G) was generated using the umap function from the uwot R package (n_neighbors=10, metric=“manhattan”, search_k=100). The input to run the algorithm was the batch corrected matrix (removeBatchEffect limma v3.46 R package function) filtered by the combination of all up-regulated genes in each comparison. Graphical representations of this analysis were generated using ggplot2 v3.3.2, with the exception of the upset plots (above) and the Heatmap in (Supplementary Fig. S3G), which was generated using pheatmap v1.0.12 R package (Hierarchical clustering is based on Manhattan distance and the ward.D2 clustering method).

The RNA-seq analysis in (Fig. 4) was conducted for the indicated comparisons applying the DESeq2 pipeline on raw pre-filtered counts(>50). The heatmaps depicting the significantly differentially expressed genes(padj<0.05, log2FC±1.5) in (Fig. 4B and E) were generated using the pheatmap package. Color coding represents relative rlog-normalized gene expression values across samples. The enrichGO and the dotplot function from the clusterProfiler R package v.3.16.1 were used to conduct and visualize the Gene Ontology enrichment analysis and in (Fig. 4F).

In (Fig. 6D–F), transcriptome profiles were analyzed using SeqMonk and reads were normalized by the standard pipeline, applying DNA contamination correction. Raw counts were used to perform DESeq2 differential gene expression analysis. The same pipeline with log-transformation was used for visualization. Significance was determined using standard SeqMonk settings, p<0.05 after Benjamini and Hochberg correction, followed by independent intensity filtering. Quantitation was done as above. NFκB-related genes in MG vs hGICs and TNFα vs hGICs comparisons were determined using IPA(Qiagen Bioinformatics). MES GBM signatures were obtained by the respective publications and plots were generated using Venny. GSEA significance was determined for MES-GBM log2FC>0.5 fold with padj=0, for PN log2FC<-0.4, padj=0 and for SREBP log2FC>1 fold with padj=0.

Interaction map in (Fig. 6F) was generated using the function Ingenuity upstream regulator from IPA for the comparison of MGT#1-high TNFα vs MGT#1-high C20MG co-culture. In comparing TNFα vs hMG samples, independent filter of padj<0.05, log2FC>1, and log2Avg>5 was applied to the DESeq2 results, selecting only up-regulated genes to conform the TNFα and MG signature gene-set.

Gene Set Enrichment Analysis

Gene-set enrichment analysis(GSEA) was used to identify the enrichment of any of the GBM subtypes enrichment using GBM subtype/state public gene-sets (3, 5, 12) in the different comparisons of FACS sorted upon MGT#1 activation RNA-seq profiles (above). The enrichment was generated using the piano v2.0.2 R package function runGSA (parameters: geneSetStat=“page”, signifMethod=“geneSampling”, and nPerm=1000). Graphical representation was generated by computing the positive gene-set enriched as -1*log10(p adj (dist.dir.up) for the corresponding comparison (Fig. 1F, 2H, and 3G). GSEA for GBM signatures (12) in (Fig. 4C and Supplementary Fig. S4C) was conducted and visualized using the fast-pre-ranked gene set enrichment analysis(fgsea) R package v.1.14.0, using all genes included in the comparison. The genes were pre-ranked based on the log-fold change derived from the differential expression analysis for the indicated comparisons, with the number of permutations=100,000 for assessing the enrichment significance. Graphical representations of this analysis were generated using ggplot2 v3.3.2, with the exception of the Heatmap in (Fig. 1F), which was generated using pheatmap v1.0.12 R package (Hierarchical clustering is based on Manhattan distance and the ward.D2 clustering method).

Single-sample Gene set enrichment analysis (ssGSEA) using gsva (ssgsea.norm=TRUE) from GSVA v1.32.0 R package was applied to obtain a signature score-matrix of GBM bulk (TCGA) and single-cell patients’ expression profiles (12) per each gene-set (Fig. 1C, 7A–C and Supplementary Fig. S1E, S7AC). Boxplots comparison (t-test) in (Fig. S7A–D) between each GBM transcriptional subtype. Heatmap in (Fig. 7A) was directly generated using this matrix (pheatmap v1.0.12 R package; hierarchical clustering is based on Manhattan distance and the ward.D2 method). The division between high- and low-expression GBM patients in the survival plots (Supplementary Fig. 7B), were established using the 0.9 percentile as a threshold given for each signature. Statistical tests log.rank and mantle.cox were applied to contrast differences between survival distributions.

For GSCs microarray analyses (Supplementary Fig. 7C), the brain tumor stem cell lines dataset was assembled with previously generated transcriptomic data: 44 RNA-seq (samples Illumina HiSeq 2500) from GSE119834, GSE67089, and GSE8049. At the exception of the GSE119834, for which pre-processed data were used, raw data were downloaded from the GEO repository(https://www.ncbi.nlm.nih.gov/geo/) and subsequently, the affy R package v1.64.0 was used for robust multi-array average normalization followed by quantile normalization. For genes with several probe sets, the median of all probes had been chosen, and only common genes. Heatmap representation was generated using pheatmap v1.0.12 R package (Hierarchical clustering is based on Euclidean distance and the complete clustering method).

Deposited data and code

All data and codes used in this study have been deposited to the relevant repositories (Supplementary Table S3). Additional protocols, data and codes, are available upon reasonable request.

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.