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1 Supplementary Materials for Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq Andrew S. Venteicher, Itay Tirosh,* Christine Hebert, Keren Yizhak, Cyril Neftel, Mariella G. Filbin, Volker Hovestadt, Leah E. Escalante, McKenzie L. Shaw, Christopher Rodman, Shawn M. Gillespie, Danielle Dionne, Christina C. Luo, Hiranmayi Ravichandran, Ravindra Mylvaganam, Christopher Mount, Maristela L. Onozato, Brian V. Nahed, Hiroaki Wakimoto, William T. Curry, A. John Iafrate, Miguel N. Rivera, Matthew P. Frosch, Todd R. Golub, Priscilla K. Brastianos, Gad Getz, Anoop P. Patel, Michelle Monje, Daniel P. Cahill, Orit Rozenblatt-Rosen, David N. Louis, Bradley E. Bernstein, Aviv Regev,* Mario L. Suvà* *Corresponding author. (M.L.S.); (A.R.); (I.T.) Published 31 March 2017, Science 355, eaai8478 (2017) DOI: /science.aai8478 This PDF file includes: Figs. S1 to S13 Other supplementary material for this manuscript includes the following: Tables S1 to S3 (Excel format)

2 Supplementary Figures Figure S1. Representative histology of our IDH-A cohort. Hematoxyline & Eosin stains of our tumors show both intra- and inter-tumoral heterogeneity with various degrees of cellularity and cytonuclear pleomorphism. In MGH45, microvascular proliferations (arrow) and mitoses are observed, consistent with a grade IV tumor. Immunohistochemistry (IHC) for P53 in MGH42 shows strong nuclear staining consistent with the mutant protein. IHC for ATRX in MGH61 shows loss of expression in cancer cells (arrow) and retained nuclear expression in endothelial and immune cells (arrowheads).

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4 Figure S2. Cell classification by expression profiles and inferred CNVs. (A) Classification by expression profiles. Shown are Pearson correlation coefficients for the relative expression profiles of all analyzed genes, among all IDH-A single cells. Cells were ordered first based on assignment to three clusters as identified by hierarchical clustering; within each cluster cells were further ordered based on their tumor of origin, as indicated at the bottom panel, and within each sub-cluster that reflects a given tumor the cells were ordered based on hierarchical clustering. The three clusters were annotated as oligodendrocytes, microglia/macrophages, and malignant based on the top differentially expressed genes (Methods). (B-C) classification by CNVs. We estimated CNVs based on the relative expression of genes in a sliding window of 100 genomically contiguous genes (Methods). (B) Shown are the estimated CNV values of all cells (rows) across all genomic positions (columns). Cells were sorted as in (A), demonstrating that the two clusters we inferred as non-malignant have consistent CNV patterns despite harboring cells from different tumors, while the cells inferred as malignant have tumor-specific CNV patterns as expected for malignant cells. (C) Comparison of CNVs inferred from gene expression and averaged over cells from each tumor (RNA), to those defined from bulk whole-exome sequencing (WES) for three tumors. The consistency between CNV estimates was high in all three cases (Pearson R>0.6, P<10-16 in all cases); the remaining inconsistencies could reflect spatial differences between the tumor region used for single cell analysis and the one used for WES, as well as quantitative differences due to the limited sensitivities and caveats of both approaches. (D) Fluorescent in situ hybridization (FISH) analysis, demonstrating amplification of chromosome arm 7q and deletion of chromosome arm 10q by comparison of centromeric probes (CEP) to locus-specific probes (two probes for each chromosome arm).

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6 Figure S3. Consistency between expression and genetic analysis, and integrated cell classification. (A) Each cell was scored for the overall signal of CNVs (X-axis) and for the correlation between the CNV pattern of that cell and the average CNV pattern of all malignant cells from the same tumor (when malignant cells were determined based on gene expression clustering) (Y-axis). Cells are colored based on their assignment to the non-malignant expression clusters (blue and purple for microglia and oligodendrocytes, as well as 11 cells which we identified as expressing a T-cell signature and are shown in green), while all other cells (malignant and unresolved cells) are colored in red. (B) Integrated expression and CNV classification. Shown are the expression and CNV scores for all cells which were retained for final analysis and classified as non-malignant (top) and malignant (bottom). Note that only three cells were excluded due to discordant classification by expression and CNV analysis, and 44 more cells were excluded due to unresolved assignment by both methods. (C-F) tsne analysis of all cells profiled in this work, as well as fetal NPCs profiled recently (12), in which cells are grouped based on global similarity in gene expression (33). The same tsne analysis is shown in each panel, but cells are colored by different criteria, demonstrating expression or genetic features. (C) Colors represent the tumor-of-origin of each cell, demonstrating that tumors primarily form distinct clusters, except for two main clusters that include cells from many tumors and correspond to immune cells (top left cluster) and oligodendrocytes (bottom left cluster) as demonstrated in (D). (D) Colors correspond to high expression levels (32-fold above average) of signatures specific to four cell types; the remaining cells that do not pass the threshold for any of the signatures are shown in red, and primarily correspond to cancer cells. (E) Colors represent cell classification as malignant based on CNV analysis: cells passing both thresholds in (A) are shown in red and all others are shown in black. (F) Cells in which an IDH1 mutation (p.r132c/h mutation) is identified in scrna-seq reads are colored in red and all others are colored in black; right panel: Shown are the percentages of cells classified as malignant (left) and non-malignant (right) in which we observed at least one read covering the site of the mutation and identified either a mutant allele (shown in red), a wild-type allele (blue), or both (purple). The ~100-fold enrichment of mutant IDH reads in cells classified as malignant (P<10-16, hypergeometric test) further supports our classification, but these results also highlight the limited sensitivity of scrna-seq mutation calling. This limitation is also observed for the wild-type allele, which is detected in a comparable fraction of cells as the mutant allele, indicating that scrna-seq reads cover the exact site of the IDH1 mutation in <40% of the cells. Moreover, while the IDH mutation is heterozygote we detect both the wild-type and the mutant alleles only in ~5% of the malignant cells (and in none of the non- malignant cells), again reflecting the limited sensitivity and indicating that in most cells either none or one of the alleles is detected due to insufficient coverage of the mutation site (12). (G) Restricting the analysis to cancer cells with detected IDHmutation does not affect our results. The analysis presented in Fig. 2C is repeated when restricting IDH-A (left) and IDH-O (right) cancer cells to those with an identified IDH mutation.

7 Figure S4. The tumor microenvironment contributes to bulk differences between IDH-A and IDH-O. (A) Analysis of the fraction of expression differences between IDH-A and IDH-O, identified in bulk analysis, which are recapitulated in single cell analysis, and those that may be accounted by the tumor-microenvironment (TME). The fraction of bulk differences recapitulated in single cell analysis depends on the threshold for defining expression differences in single cell analysis, while the fraction of remaining bulk differences that may be accounted by TME (i.e. preferential IDH-A expression of immune-specific genes and preferential IDH-O expression of neuron-specific genes) depends on the thresholds to assign genes as immune-specific and neuron-specific. We thus examined these fractions with multiple fold-change thresholds for defining differential expression in single cell analysis, and for two stringencies of defining genes as cell type specific (strict and lenient, see Methods). For each fold-change threshold, the red line indicates the fraction of bulk differences recapitulated in single cell analysis above that threshold, while the dashed red line indicates the expected fraction based on the overall frequency of genes with IDH-A vs. IDH-O differences above that threshold in single cell analysis. The fraction of remaining differences which may be accounted by TME is shown for each fold-change threshold, based on the strict TME-specific genes definition (black line) and the lenient TME-specific gene definition (grey line); dashed black and grey lines indicate the expected fractions based on the overall number of immune-specific and neuron-specific genes. While the results are threshold dependent, in all cases a considerable fraction of bulk differences are not recapitulated by single cell analysis (38-64%) and, of those, most differences may be accounted by TME (53-75%). (B) Estimation of the relative abundance of microglia/macrophages (X-axis) and neurons (Y-axis) in bulk TCGA samples, based on the average expression of all genes specific to each cell type. Shown are IDH-A (purple) and IDH-O (blue) bulk samples separated by grade (grade II, top panel; grade III; bottom panel). The differences between IDH-A and IDH- O are significant within each of the grades, for both microglia/macrophages and neurons (P<0.01 in all comparisons, t-test).

8 Figure S5. Glial differentiation programs are largely independent of technical (A) or batch (B) effects and are reproduced in an alternative single cell RNA-seq platform (C). (A) Technical complexity of RNA-seq libraries is reflected in the number of genes detected by at least one mapping read. The number of genes detected (Y-axis) is shown for all IDH-A (top) and IDH-O (bottom) malignant cells, which were ranked by glial differentiation scores, as shown in Fig. 2C; red lines indicate a moving average of 200 genes. (B) Each row corresponds to a distinct 96- well plate which corresponds to sorting and sequencing batches. Shown is the frequency of cells, log 2 (number of cells + 1), in bins defined by glial differentiation scores. This analysis demonstrates that different batches for the same tumor are highly consistent, while some differences are observed between tumors which are shown as separate panels from top to bottom and tumor names are indicated at the right. (C) Glial differentiation programs are reproduced in single cell RNA-seq analysis using the 10X genomics platform ( that contains unique-molecular identifiers (UMIs). We profiled two of the IDH-A tumors in our cohort (MGH103 and MGH107) with the 10X platform, processed the data with the 10X analysis pipeline (CellRanger), and identified malignant cells as described in fig. S2. Shown is the expression of oligo-specific and astro-specific genes across all malignant cells, ranked by the relative expression of oligo-specific and astro-specific genes, as done in Fig. 2C for the main dataset with the Smart-Seq2 platform.

9 Figure S6. Glial differentiation patterns. (A) Each tumor has a wide distribution of glial differentiation states, although many tumors are enriched with certain cellular states. IDH-A (top) and IDH-O (bottom) malignant cells were sorted from oligodendrocytic-like to astrocytic-like as shown in Fig. 2C. Shown is the frequency of cells in a sliding window of 250 cells that are derived from each tumor, where tumor names are indicated at the right. (B) The distribution of astrocytic scores (top) and oligodendrocytic scores (bottom) are shown for all malignant cells from IDH-O (blue), IDH-A (purple) and GBM (4) (black). (C) Average relative expression of IDH-A specific genes whose expression difference between IDH-A cells (purple) and IDH-O (blue) cells could be accounted by IDH-O specific genetic (i.e. co-deletion of chromosome arms 1p and 19q and CIC mutations); lines indicate a LOWESS regression with a window spanning 20% of the cells. (D-E) Astrocytic and oligodendrocytic scores are negatively correlated preferentially in IDH-O. (D) Density of cells in combinations of astrocytic scores (X-axis) and oligodendrocytic scores (Y-axis) are shown for all malignant cells from IDH-O (left panel) and IDH-A (right panel). The range of values for each score (oligo. and astro.) was divided to 70 equal bins, and the number of cells (N) in each combination of bins is color coded by log 2 (N+1). (E) Averaged astrocytic scores over cells primarily differentiated into the oligodendrocytic lineage in sliding windows of 200 IDH-A (purple) and IDH-O (blue) cells ranked by their oligodendrocytic scores. Undifferentiated cells (differentiation score below 0.3) and cells with higher astrocytic than oligodendrocytic scores were excluded from this analysis in order to focus on the oligodendrocytic compartment of the tumors. The Spearman correlations (R) between astrocytic and oligodendrocytic scores (before smoothing) and the P-values (P) from t-tests comparing astrocytic scores between third of the cells with the highest oligo scores and third of the cells with the lowest oligo scores, are also indicated at the left for both IDH-O (blue) and IDH-A (purple).

10 Figure S7. Comparison of genome-wide DNA methylation profiles of IDH-O, IDH-A, IDH-wildtype glioma (2), IDH1/2-mutant AML (25), and IDH mutant chondrosarcoma (26). Heatmap representation of the 10,000 most variably methylated CpG probes (excluding probes mapping to chrx/y) indicates high similarity of IDH-O and IDH-A relative to other tumor classes. CpG probes (rows) are ordered by hierarchical clustering. AML and Chondrosarcoma derive from distant developmental lineages, whereas differences to IDH-wildtype glioma (mostly gain of CpG methylation) could to a large degree be attributed to the G-CIMP phenotype.

11 Figure S8. Cell cycle analysis. G1/S score (X-axis) and G2/M score (Y-axis) defined as the average relative expression of the corresponding gene-sets are shown for all IDH-A and IDH-O malignant cells. Cells defined as cycling were color coded in blue for IDH-O and purple for IDH-A cells.

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13 Figure S9. Combined IDH-A and IDH-O stemness program. (A-B) The putative stemness program, defined by enrichment and co-expression in undifferentiated IDH-A and IDH-O cells, is consistent with expression programs of neural progenitor cells (NPCs) and neural stem cells (NSCs). (A) Shown are the distribution of correlation values of genes (X axis) with the NPC (left panel) and NSC (right panel) expression programs, across all genes (gray), or across genes enriched but not co-expressed (black), or across genes enriched and co-expressed among the undifferentiated IDH-A and IDH-O cells. The NSC activation program was defined by single cell analysis of mice NSCs, as previously quantified by pseudotime (34). The NPC expression program was defined by the first principal component in analysis of single cell RNA-seq data of human fetal NPCs, as described previously (12). Correlations were defined across the respective single cell datasets (NPCs or NSCs). The genes enriched and coexpressed (red) have significantly higher correlations compared either to all other genes (grey) or to enriched but not co-expressed genes (black), with both the NPC and the NSC program (P<0.01 in all cases, t-test). (B) Correlations of genes enriched in undifferentiated IDH-A and IDH-O cells with the NSC (X-axis) and NPC (Y-axis) programs. Genes which are also co-expressed in IDH-A and IDH-O are marked in red and labeled. (C) Each panel shows the differentiation and stemness scores of malignant cells from a particular tumor. Tumor names are indicated at the right top corner with a one-letter code for tumor type (A and O for IDH-A and IDH-O, respectively).

14 Figure S10. Apparent differences between IDH-O and IDH-A (in the frequency of cycling and undifferentiated cells and in the negative association between the two lineage scores) are correlated with grade in IDH-A, and may reflect the higher grades of IDH-A tumors in our cohort. (A-B) Shown are the percentages of undifferentiated cells (X-axis), cycling cells (A, Y-axis) or Astro-Oligo lineage correlation (B, Y- axis) for each of the IDH-O (circle) and IDH-A (square) tumors, which are also colored by grade (grey, grade II; black, grade III; red, grade IV). These analyses demonstrate that MGH107, a grade II IDH-A in our cohort, resembles IDH-O grade II tumors, and that the two IDH-A grade IV tumors are especially distinct from IDH-O grade II tumors, suggesting that the observed variability between IDH-A and IDH-O tumors may be derived from differences in grades. (C) Grade-related differences in cell cycle frequencies are recapitulated in analysis of bulk TCGA samples. Each bulk sample was scored for the expression of G1/S-specific (X-axis) and G2/M-specific (Yaxis) genes, and the average scores shown for sets of tumors with the same tumor type (IDH-A in squares and IDH- O in circles) and the same grade (grade II, III and IV, in grey, black and red, respectively). Cell cycle scores (for both G1/S and G2/M) were significantly different (P<0.05, t-test) in all comparisons between distinct grades for the same tumor types (as illustrated by dashed lines), and were not significant (P>0.05) in comparisons of the same grade across tumor types (for grade II and for grade III).

15 Figure S11. Genetic intra-tumor heterogeneity identified by CNVs and associated differences in cell cycle and glial differentiation. CNV analysis (left panels) of three tumors - MGH44 (A), MGH103 (B), and MGH57 (C) - revealed large-scale CNVs which vary between cells of the same tumor. We ranked cells (A,B) or clustered cells (C) based on their estimated copy numbers at these chromosomal regions and defined putative subclones, while excluding cells with intermediate values that cannot be assigned confidently (A). For each of the three tumors, we then compared the two clones with respect to the distribution of glial differentiation scores (middle panels, showing astrocytic and oligodendrocytic scores), stemness vs. differentiation scores (top right panels, as defined in Fig. S9) and the fraction of cycling cells (right panels, showing the fraction in all cells, and in clone 1 and clone 2). In MGH57 we focused on the two largest clones, since analysis of other clones was limited by cell numbers. In all three tumors we found significant differences in differentiation patterns (Kolmogorov-Smirnov test; * and ** correspond to P<0.05 and P<0.001, respectively) and in one case (C) we also found significant difference in fraction of cycling cells (hypergeometric test, P=0.004). In MGH44 (A) and MGH57 (C), there are small subsets of cells that may reflect stem cells, with low differentiation score and high stemness score (top left cells in the top right panel), while a similar subset is not found in MGH103 (B); In MGH57 this subset contains cells of both clones, and in MGH44 this subset may be biased to clone 1, although this cannot be determined confidently due to limited cell number and dependence on exact threshold for defining the subset.

16 Figure S12. PCA of macrophage/microglia cells from three IDH-O (A) and nine IDH-A (B) tumors. (A) PCA was performed over all macrophage/microglia cells from the twelve tumors (shown in black) and each panel highlights the cells from one tumor (shown in red). PC2 reflects an inflammatory program, while PC1 reflects macrophage (PC1-low vs. microglia (PC1-high) expression programs (table S3). Four tumors that we profiled (MGH60, MGH93, MGH97 and MGH103) are not included in this analysis since we only sequenced CD45- plates after FACS sorting of those tumors and identified at most 2 macrophage/microglia cells per tumor.

17 Figure S13. Expression program associated with immune infiltration. (A) Top: correlation of the expression of 24 selected genes (column) with microglia or macrophage (row) scores across IDH-A (top two rows) and across IDH-O (bottom two rows) bulk tumors. Bottom: differential expression of the same genes between IDH-A and IDH-O bulk tumors. Three genes from the complement system are marked. (B) Immune scores (X-axis: macrophage, left; microglia, right) correlate with the average expression of the 24 non-immune genes from (F) (Yaxis) across bulk IDH-A (purple) and IDH-O (blue) TCGA tumors.