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Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity

Abstract

Cultured cell lines are the workhorse of cancer research, but the extent to which they recapitulate the heterogeneity observed among malignant cells in tumors is unclear. Here we used multiplexed single-cell RNA-seq to profile 198 cancer cell lines from 22 cancer types. We identified 12 expression programs that are recurrently heterogeneous within multiple cancer cell lines. These programs are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial–mesenchymal transition and protein metabolism. Most of these programs recapitulate those recently identified as heterogeneous within human tumors. We prioritized specific cell lines as models of cellular heterogeneity and used them to study subpopulations of senescence-related cells, demonstrating their dynamics, regulation and unique drug sensitivities, which were predictive of clinical response. Our work describes the landscape of heterogeneity within diverse cancer cell lines and identifies recurrent patterns of heterogeneity that are shared between tumors and specific cell lines.

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Fig. 1: Characterizing expression heterogeneity within cell lines by multiplexed scRNA-seq.
Fig. 2: Discrete and continuous patterns of heterogeneity within cell lines.
Fig. 3: Functional annotation of RHPs.
Fig. 4: In vitro RHPs recapitulate in vivo programs of heterogeneity.
Fig. 5: Interrogating the EpiSen RHP in primary cells and model cell lines.
Fig. 6: Genetic and microenvironmental factors partially explain expression heterogeneity.
Fig. 7: Coexisting cellular states differ in drug sensitivity.

Data availability

Raw and processed scRNA-seq data are available through the Broad Institute’s single-cell portal (SCP542) and at the Gene Expression Omnibus (GEO) (accession number GSE157220). Publicly available databases used in our analysis included the DepMap portal (18q3 data release; https://depmap.org/), the CCLE portal (https://portals.broadinstitute.org/ccle), the CTD2 portal (https://ocg.cancer.gov/programs/), GTRD database version 20.06 (http://gtrd.biouml.org) and MSigDB version 7.0 (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp).

Code availability

R code for reproducing the analyses shown in the main figures is available at https://github.com/gabrielakinker/CCLE_heterogeneity. Additional code related to extended data and supplementary figures is available upon request from the corresponding author. Code used for the assignment of cells to reference expression profiles is available at https://github.com/broadinstitute/single_cell_classification.

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Acknowledgements

This work was supported by funding from the Israel Science Foundation (I.T.), the Zuckerman STEM leadership program (I.T.), a Mexican Friends New Generation grant (I.T.), the Rising Tide Foundation (I.T.), the AMN Fund for the Promotion of Science, Culture and Arts in Israel (I.T.), the Estate of Dr. David Levinson, the Dr. Celia Zwillenberg-Fridman and Dr. Lutz Zwillenberg Career Development Chair (I.T.), the Sao Paulo Research Foundation (FAPESP) (fellowships 2014/27287-0 and 2017/24287-8 (G.S.K.)), the Clore Foundation Postdoctoral Fellowship (A.C.G.), the Klarman Cell Observatory (A.R.), the Howard Hughes Medical Institute (A.R.), the National Cancer Institute (K08CA237732; S.V.P.), a V Foundation V Scholars Award (S.V.P.), a Cancer Research Foundation Young Investigator Award (S.V.P.) and the Dorris Duke Fund to Retain Clinical Scientists (S.V.P.).

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Authors

Contributions

G.S.K., A.C.G. and I.T. conceived and designed the study. J.A.R., S.A.B., C.C.M., B.K., J.W.R. and S.V.P. provided cell lines and pools. A.C.G., R.T., M.S.C. and C.R. performed scRNA-seq experiments. J.M.M., A.W., A.T. and I.T. assigned cells to cell lines. G.S.K., A.C.G. and I.T. analyzed the scRNA-seq data and interpreted the results. A.C.G., R.T. and Z.O. designed and performed follow-up experiments with selected cell lines. A.C.G., R.T., A.P. and H.B. performed drug screening. A.C.G. and H.K.-S. performed bulk RNA-seq experiments. A.C.G. and I.T. analyzed the drug screening and bulk RNA-seq data. V.K. was consulted on senescence. O.R.-R., A.R. and P.A.C.M.F. provided resources and supervision. I.T. supervised the work. G.S.K., A.C.G. and I.T. wrote the manuscript with input from all other authors.

Corresponding author

Correspondence to Itay Tirosh.

Ethics declarations

Competing interests

A.R. is a cofounder and equity holder of Celsius Therapeutics, an equity holder of Immunitas and was an SAB member of Neogene Therapeutics, Thermo Fisher Scientific, Asimov and Syros Pharmaceuticals until 31 July 2020. Since 1 August 2020, A.R. is an employee of Genentech, a member of the Roche group. O.R.-R. is a co-inventor on patent applications filed by the Broad Institute for inventions relating to single-cell genomics, such as PCT/US2018/060860 and US provisional application no. 62/745,259. No other authors declare competing interests.

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Extended data

Extended Data Fig. 1 Identifying discrete clusters and continuous expression programs.

a, Performance of DBSCAN using different sizes of epsilon neighborhood (eps) and minimum numbers of points required to form a dense region (MinPts). We randomly selected cells from two different cell lines and tested the ability of DBSCAN to distinguish between them (two-sided Fisher’s exact test, P < 0.001) using different parameter combinations. The procedure was repeated 1,000 times and the combination yielding the highest rate of correct classification was applied in the subsequent analyses. b, t-SNE plots for additional two examples of cell lines from each of the four classes defined by presence and number of discrete subpopulations identified by DBSCAN (as in Fig. 2b). c, d, Identification of discrete programs of heterogeneity, as in Fig. 2b, using less stringent eps (1.2 and 1.5) highlights common trends. e, Number of heterogeneity programs identified per cell line using NMF. NMF was applied to each cell line using k (number of factors) of 6–9, and gene programs identified as variable with 2 or more values of k were retained (left panel, n = 1,445). To identify common expression programs varying within multiple cell lines, we excluded programs with limited similarity to all other programs as well as those associated with technical confounders (right panel, n = 800). f, Pairwise similarities between programs identified by NMF across all the cell lines analyzed, with cell lines ordered by hierarchical clustering. Programs with limited similarity to all other programs were excluded. Top panel indicates correlations between program scores and cell complexity (that is number of genes detected per cell). The cluster of programs that correlates with complexity (indicated by dashed lines) was excluded from subsequent analyses.

Extended Data Fig. 2 In vivo programs of variability and comparison to in vitro cell cycle programs.

a, Heatmap depicts pairwise similarities between programs identified in tumor samples using NMF. Programs with limited similarity to all other programs were excluded. Top panel shows tumor type and correlations between program scores and cell complexity (that is number of genes detected per cell). Hierarchical clustering emphasizes multiple clusters (shown by squares), one of which is correlated with cell complexity and thus excluded as a potential technical artifact. b, NMF scores of G1/S genes (top panel) and G2/M genes (bottom panel) across all NMF programs associated with the corresponding cell cycle phase; each program is from a different cell line. Genes are ranked in each panel by average scores, and their assignment to in vitro and in vivo cell cycle programs is indicated in the right bar, demonstrating that G1/S programs differ both across cell lines and between cell lines and tumors, while G2/M programs are more consistent. Venn diagrams (right) illustrate the overlap of genes between in vivo and in vitro RHPs. c, Single-cell profiles (n = 264 cells from NCIH2126) showing G1/S and G2/M program score thresholds used to assign cells to different cell cycle phases. d, Examples of genes with distinct cell cycle upregulation in vitro and in vivo. Expression of HIST genes (preferentially induced in vitro) and MCM genes (preferentially induced in vivo) is shown along the cell cycle (relative to cells in G0) in cell lines (C, green lines) and tumors (D, yellow lines). e, Comparison of cell cycle phase distribution in vitro and in vivo. Scatterplot shows the percentage of cells in G0 (x-axis) and the ratio between the percentage of cells in G1/S and G2/M (y-axis) for each cell line (green, n = 198) and tumor (yellow, n = 25) analyzed. Cell lines display a significantly lower percentage of cells in G0 cells (P=2e−10, two-sided t-test).

Extended Data Fig. 3 Association of RHPs with cell cycle, pool identity and cancer types.

a, Average distribution of cell cycle phases in cells with high RHP scores (top 5%). b, Median RHP scores of cells in each phase of the cell cycle. Cell cycle state was estimated for each individual cell based on the relative expression of the G1/S and G2/M metaprograms (see Extended Data Fig. 2c). For each RHP in (a) and (b) we only considered the respective model cell lines (see Supplementary Table 3). c, Distribution of RHPs (n = 680) across the 9 pools (H corresponds to the custom HNSCC pool). Each RHP was observed in multiple pools, underscoring the lack of pool-specific effects and the robustness of RHPs. d, The fraction of cell lines from each cancer type (rows) observed in each RHP (columns) are indicated by circle size. Red circles depict significant enrichments (P<0.001 by hypergeometric test). Left panel shows the fraction of each cancer type in our dataset and the number of cell lines profiled. e, Hierarchical clustering of cell lines based on their set of RHPs (bottom panel). Clusters composed of more than 5 cell lines are annotated (1–9). Relevant cell line features are shown on top and their associations with each cell line cluster was tested using hypergeometric test. Significant associations (P < 0.001) are indicated by black squares and arrows. These include associations of cluster 2 with melanoma, cluster 4 with HNSCC and squamous cells, and cluster 8 with wild-type p53.

Extended Data Fig. 4 Cell lines recapitulate programs of heterogeneity observed in tumor samples.

ad, Combined PCA of in vitro and in vivo cells in melanoma (A-B) and HNSCC (C-D). (a, c) Top 10 genes by positive (left) and negative (right) loadings of PC1–5; Genes of respective RHPs (SkinPig. and EMT-I in melanoma, EpiSen and EMT-II in HNSCC) are emphasized (bold). (b, d) Coordinates of the three PCs associated with the respective RHPs across in vitro (left, melanoma = 3,033 cells, HNSCC = 2,780 cells) and in vivo (right, melanoma = 1,169 cells, HNSCC = 1,078 cells) cells. Cells are colored by their cell line or tumor. Similar results were obtained when PCs were not combined (not shown). (e) Heatmap shows relative expression of genes shared by paired in vivo and in vitro programs in selected melanoma (top panels) and HNSCC (bottom panels) cell lines and tumors, highlighting similar patterns of variability in vivo and in vitro. Cells are sorted according to the relative average expression of genes in each program, showing the negative correlation between the AXL and MITF programs in melanomas and the pEMT and EpiSen programs in HNSCC. Programs are annotated (right) and selected genes are indicated (left).

Extended Data Fig. 5 Determinants and consequences of cellular heterogeneity.

a, Association between RHP variability scores and somatic non-silent mutations. We compared the variability of each program in mutated and non-mutated cell lines using two-sided t-test. Model cell lines (high variability) of EMT-II, IFN response and p53-dependent senescence RHPs are depleted of NOTCH4, MRE11 and TP53 mutations, respectively. Horizontal lines indicate the median. b, Association between drug response (CTRP database) and program variability calculated using linear regression including tumor type and program variability as independent variables. Increased sensitivity to NOTCH inhibition (gamma secretase inhibitors) and MDM2 inhibition (Nutlin-3) were observed in model cell lines (high variability) of the EMT-II and the p53-dependent senescence respectively.

Extended Data Fig 6 Similarities and differences between senescence-related expression programs.

a, Significance (-log10(P), hypergeometric test) of the overlaps between sets of genes upregulated upon senescence, as defined by multiple studies (see Table S8). b, Most common genes across the different senescence programs, including all genes appearing in at least three programs, ranked from top to bottom by the number of programs. c, Density of RHP signature genes within sliding windows of 300 genes, among the top 6,000 expressed genes, arranged by their expression response to etoposide, from the most upregulated (top) to the most downregulated (bottom), as also shown in Fig. 6b. RHPs are sorted from left to right by their enrichment with upregulated and downregulated genes, respectively. d, e, Significance of gene-set overlaps (d), and the most common genes (e), as in (a, b), when restricting the analysis to secreted genes, in order to define SASP.

Extended Data Fig. 7 Isolating EpiSen-high and EpiSen-low cells in model cell lines.

a, Gating scheme for isolation of EpiSen-high and EpiSen-low subpopulations in the JHU006 cell line including doublet and dead cell exclusion. For sorting, the top 10% high and bottom 10% low cells were sorted. Validation of AXL+CLDN4- (EpiSen-high)-sorted cells enriching for the full EpiSen RHP was performed by bulk RNA-Seq of sorted cells (Fig. 5c, Methods). b, FACS analysis of cell cycle by the DNA binding dye propidium iodide (PI) on sorted EpiSen-high and EpiSen-low cells in SCC47, as shown for JHU006 in Fig. 5d. The table summarizes the results. c, Cell cycle scores of the G1/S (X-axis) and G2/M (Y-axis) programs, shown for in vivo HNSCC cells from six tumors (panels). Top 10% of cells scoring highly for the EpiSen-related program (EpiDif1) are shown in black, demonstrating their enrichment among non-cycling cells in each of the tumors (P<0.001, hypergeometric test). d, Three subpopulations were isolated by FACS (EpiSen-high: AXL-CLDN4+, EpiSen-low population: AXL+CLDN4-, control: unsorted) from SCC47 (left) and JHU006 (right), and analyzed immediately after sorting (day 0, top) and at two additional time points in culture (day 7 and 14, middle and bottom, respectively). Density plots correspond to the pie charts in Fig. 5e.

Extended Data Fig 8 Activation and isolation of the EMT-II program.

a, Average expression log-ratio for the gene-sets representing each RHP (each row represents an RHP), upon treatment of SCC47 or JHU006 with multiple perturbations (each column represents one perturbation in one cell line, as indicated at the bottom averaged over duplicates). b, Comparison of the EMT program induced upon TGFβ treatment of unsorted cells (X-axis) vs. the EMT-II RHP gene scores (Y-axis). In both axes, data was averaged over the results for JHU006 and SCC47. c, Isolation by FACS of the EMT-II-high population (AXL+ITGA6+) and the EMT-II-low population (AXL-ITGA6-) in JHU006. d, Both TGFβ3-treated cells and EMT-II-high sorted cells are associated with increased migration. Shown is a gap closure (migration) assay, performed on unsorted, unsorted but TGFβ3-treated, EMT-II-high, and EMT-II-low cells, at 0 h and 12 h following gap generation. Scale bar length is 1000 μM.

Extended Data Fig. 9 Drug sensitivity of EpiSen-high cells and EpiSen-low cells.

a, Pie charts depict the proportions of primary screen hits by type. b, Shared hits between SCC47 and JHU006 for compounds that preferentially killed the EpiSen-high (green) and EpiSen-low (orange) states. Selected hits are labeled. c, Viability of the control population (X-axis) and differential viability of the EpiSen-high vs. EpiSen-low populations (Y-axis) upon treatment with 2198 compounds in JHU006 (left) and SCC47 (right). Dotted lines represent thresholds for differential sensitivity, and hits are colored as defined in the lower legend. d, Dose response curves of selected compounds in three SCC47 subpopulations at seven concentrations measured in duplicate (continued from Fig. 6c). Change in viability was calculated relative to vehicle (DMSO-treated) controls. Error bars represent standard deviation, data points represent the mean of replicates.

Extended Data Fig. 10 Association between the abundance of EpiSen cells and clinical response of HNSCC patients to Cetuximab.

a, Volcano plots depict differential expression analysis comparing bulk pretreatment samples of HSNCC patients with short (n = 14) and long (n = 26) PFS following Cetuximab treatment plus platinum-based chemotherapy. Comparisons were performed using two-sided t-test, and P values were adjusted using the FDR procedure. Genes in the HNSCC EpiSen program from cell lines (in vitro, top panel) and tumors (in vivo, middle panel) are highlighted, as well as those shared between the two programs (bottom panel). b, Heatmap showing the expression of shared EpiSen genes in samples stratified into short and long PFS. Genes are order by differential expression (log2(fold change)), as shown in (a), and tumors are ordered within each group according to the EpiSen score. Top panels depicts sample type, tumor stage (T), tumor grade, and site of primary tumors.

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Kinker, G.S., Greenwald, A.C., Tal, R. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat Genet 52, 1208–1218 (2020). https://doi.org/10.1038/s41588-020-00726-6

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