Abstract
Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1,2,3,4,5,6,7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7,8,9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.
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Data availability
All raw and processed imaging data generated in this study are on BioStudies (accession S-BIAD696). All raw and processed FateMap single-cell barcoding data generated in this study are at Figshare (https://doi.org/10.6084/m9.figshare.22798952 and https://doi.org/10.6084/m9.figshare.22802888). All raw and processed gDNA barcoding data generated in this study are at Figshare (https://doi.org/10.6084/m9.figshare.22806494). All raw and processed WGS data for individual clones used in this manuscript are at BioProject accession PRJNA972638 and Figshare (https://doi.org/10.6084/m9.figshare.23255273), respectively. All raw and processed bulk RNA-seq data used in this manuscript are at the Gene Expression Omnibus accession GSE233622. All raw and processed scRNA-seq data used in this manuscript are at Gene Expression Omnibus accession GSE233766. All raw and processed data from the GeoMx spatial transcriptomics used in this manuscript can be found at BioProject Accession PRJNA976929 and Figshare (https://doi.org/10.6084/m9.figshare.23248199), respectively.
Code availability
All code used in this study is available at Zenodo (https://doi.org/10.5281/zenodo.8000328).
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Acknowledgements
The authors thank S. Ramdas, C. Jiang, L. Beck, P. Burnham, L. Richman, M. Melzer, S. Reffsin, E. Torre, C. Cote, A. Cote, V. Rebecca and G. Allard for insightful discussions related to this work; the Genomics Facility at the Wistar Institute, especially S. Majumdar and S. Widura, for assistance with sequencing and single-cell partitioning and addition of 10X cell identifiers; the Flow Cytometry Core Laboratory at the Children’s Hospital of Philadelphia Research Institute for assistance with flow cytometry and fluorescence-activated cell sorting; the Histology Core at the Penn Center for Musculoskeletal Disorders for their assistance with tissue sectioning (P30 AR069619); the Center for Genetic Medicine, NUSeq Core, Feinberg Information Technology, Northwestern University Information Technology, and Quest High Performance Computing Cluster at Northwestern University Feinberg School of Medicine for their assistance; B. Singh for histotechnology expertise; I. Raman, G. Chen and C. Zhu from the Microarray Core at UT Southwestern Medical Center in Dallas, TX for their expert help with ROI selection on the GeoMx DSP and data generation; and J. Villanueva, R. Inga and L. Li from the Herlyn laboratory at the Wistar Institute for their assistance with identifying and obtaining the NRAS-mutant melanoma cell lines and the melanocyte cell line. Y.G. thanks R. J. Valadka and M. Temkin for their prompt support in setting up the wet and dry lab space at Northwestern University. M.E.F. and A.T.W. thank the Core Facilities of the Johns Hopkins Kimmel Cancer Center, P30CA00697356. Y.G. acknowledges support from Northwestern University’s startup funds, Research Catalyst Program from the McCormick School of Engineering at Northwestern University, Cancer Research Foundation Young Investigator Award, the Burroughs Wellcome Fund Career Awards at the Scientific Interface, the Jane Coffin Childs Memorial Fund, and the Schmidt Science Fellowship. G.T.B. acknowledges support from NSF GRFP DGE-1845298. M.P. was supported by grants to Y.G. including the Burroughs Wellcome Fund Career Awards at the Scientific Interface and Northwestern University’s startup funds. R.H.B. acknowledges support from NIH T32 HG000046 and T32 GM007170. E.I.G. acknowledges support from the NSF NRT 2021900-Synthesizing Biology Across Scales. B.E. acknowledges support from NIH F30 CA236129, NIH T32 GM007170 and NIH T32 HG000046. A.K. acknowledges support from NIH K00-CA-212437-02. N.J. acknowledges support from NIH F30 HD103378. N.B. acknowledges support from NIH T32 GM144295. I.A.M. acknowledges support from NIH F30 NS100595. K.K. acknowledges support from NIH T32 GM008216. P.T.R. acknowledges support by NIH Medical Scientist Training Program T32 GM07170. D.F. and M.H. acknowledge support from NIH grants RO1 CA238237, U54 CA224070, PO1 CA114046, P50 CA174523 and the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation. S.A. was supported by grants to Y.G. including the Burroughs Wellcome Fund Career Awards at the Scientific Interface, Northwestern University’s startup funds, and Research Catalyst Grant from McCormick School of Engineering. C.C. was supported by grants to Y.G. including the Burroughs Wellcome Fund Career Awards at the Scientific Interface and Northwestern University’s startup funds. M.E.F. and A.T.W. acknowledge support from R01CA174746 and R01CA207935. A.T.W. is also supported by a Team Science Award from the Melanoma Research Alliance and P01 CA114046. A.J.L. and J.A.W. are supported by the NCI Melanoma SPORE (P50CA221703), MD Anderson Melanoma Moonshot, TRANSCEND Cancer Initiative and Platform for Innovative Microbiome and Translational Research (PRIME-TR). A.R. acknowledges support from NIH Director’s Transformative Research Award R01 GM137425, NIH R01 CA238237, NIH R01 CA232256, NIH P30 CA016520, NIH SPORE P50 CA174523 and NIH U01 CA227550.
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Authors and Affiliations
Contributions
Y.G. and A.R. conceived and designed the project. Y.G. designed, performed and analysed all experiments, supervised by A.R. M.P., G.T.B. and E.I.G. assisted Y.G. with FateMap experiments and analysis. R.H.B., P.T.R., J.L. and M.P. assisted Y.G. with bulk RNA-seq experiments and analysis. M.P. performed specific analysis for revisions with inputs from Y.G. and A.R. I.P.D., G.T.B., S.S.A., E.I.G., M.C.D. and C.C. assisted Y.G. with tissue sectioning and automated RNA FISH and DAPI scans and analysis. Y.G., B.E. and K.K. designed and optimized the PCR ‘side reaction’ primers for recovering the barcodes from scRNA-seq libraries. R.H.B., G.T.B. and J.L. extracted gDNA for WGS experiments and N.B. performed the WGS analysis with inputs from Y.G. and A.R. A.K. assisted Y.G. in the design and implementation of spheroid experiments. G.T.B., N.J., J.L., J.B., M.P. and I.A.M. assisted Y.G. with barcode library preparation and the computational pipeline. Y.G. designed the mouse barcoding experiments, and D.F., H.L., Y.C., G.M.A. and M.E.F. performed the mouse experiments with input from Y.G., M.H., A.R. and A.T.W. Y.G. and G.T.B. prepared barcode libraries for mouse experiments. M.C., R.H.B., R.G.W., R.L., D.R.I., S.B.J., K.W., M.P., A.J.L. and J.A.W. performed human patient experiments and analysis with inputs from Y.G. and A.R. Y.G., G.T.B. and E.I.G. prepared all illustrations used in this study. Y.G. and A.R. wrote the manuscript with input from all authors.
Corresponding authors
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Competing interests
A.R. receives royalties related to Stellaris RNA FISH probes. Y.G. received consultancy fees from the Schmidt Science Fellows and the Rhodes Trust. A.J.L. reports financial relationships with AbbVie, Adaptimmune, AstraZeneca, Bain Capital, Bayer, Bio-AI Health, BMS, Caris, Deciphera, Foghorn Therapeutics, Gothams, GSK, Illumina, Invitae/Archer DX, Iterion Therapeutics, Merck, Novartis, Nucleai, OncoKB (MSKCC), Pfizer, Regeneron, Roche/Genentech, SpringWorks, Tempus and Thermo Fisher. J.A.W. is an inventor on US patent application no. PCT/US17/53.717 submitted by the University of Texas MD Anderson Cancer Center, which covers methods to enhance immune checkpoint blockade responses by modulating the microbiome. J.A.W. reports compensation for the speaker’s bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, MedImmune, Exelixis and Bristol Myers Squibb; and has served as a consultant and/or advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol Myers Squibb, Micronoma, OSE therapeutics, Merck and Everimmune. J.A.W. receives stock options from Micronoma and OSE therapeutics. All other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 FateMap reveals between-clone fate type diversity in treatment-naive cells, albeit to a lesser degree compared to resistant cells.
a. (left) UMAP of all barcoded treatment-naive cells. Total 16,432 cells (8,420 split A and 8,012 cells in split B) are colored by clusters determined using Seurat’s FindClusters command at a resolution of 0.6 (i.e. “Seurat clusters, resolution = 0.6”). (right) On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed in drug resistant cells via the Seurat pipeline (Cell counts available in Supplementary Table 11). b. Five representative examples demonstrate that a clone (cells sharing the same barcode) is constrained largely in a specific transcriptional cluster such that cells within a clone are more transcriptionally similar to each other than cells in other clones. c. Average pairwise correlation between cells within a clone was estimated based on the expression levels of the top 500 most variable genes. Each point represents the average value for Spearman’s correlation coefficient for all possible pairs of cells within a clone. For each clone, a paired control was created by randomly sampling an equivalent number of cells from the entire population. Higher average correlation coefficient in clones indicates higher transcriptional similarity among cells within a clone, as compared to cells that are not clones. Wilcoxon signed rank exact test (paired, two-sided) was used to compare the difference in average correlation coefficient. d. Fraction of variance explained by the experimental data and randomized data for the top 50 principal components (PCs). The number of PCs needed to explain the actual variance in data (indicated by the dotted line) is a measure of the degrees of freedom of variability of a given dataset. There was an increase (see Extended Data Fig. 1e for statistical testing) in the number of PCs needed to explain the variance in data from resistant cells (43 PCs) as compared to naive (30 PCs) and primed cells (23 PCs), suggesting that there is an increase in overall variability in samples when cells transition to becoming drug resistant. Primed cells were identified as cells where at least 40% of pre-resistant markers identified in (Emert et al. 2021) are higher than their average expression level. e. Average number of PCs needed to explain the variance in resistant, naive and pre-resistant cells. Error bars represent standard deviation over 100 simulations of randomized data. Mann-Whitney U-Test was used to estimate a p-value for pairwise difference in means. f. Comparison of Euclidean distances between clusters across resistant and naive populations of melanoma cells for varying numbers of clusters. We used the first 50 principal components to calculate the Euclidean distance between cells across clusters. We used Wilcoxon signed rank exact test (paired, two-sided) for statistical comparisons. g. Comparison between resistant and naive populations for total number of clusters, given fixed number of cells and shared nearest neighbor (snn) resolution. We used Wilcoxon signed rank exact test (paired, two-sided) for statistical comparisons of average number of clusters across resolutions. h. UMAPs of representative twin clones (sharing the same barcode) across the two splits A and B. The twins largely end up with the same transcriptional fate. This observation suggests that cells have similar transcriptional states prior to drug treatment.
Extended Data Fig. 2 Whole genome sequencing of treatment-naive and drug resistant fate type clones.
a. We performed a pairwise hypergeometric test for variants in all clones to determine statistical significance of variant overlap between clones. This was calculated with the following parameters (M = all CADD > 15 variants, n = # Variants in Sample 1, N = # Variants in Sample 2, X = # Variants in intersection of Samples 1 & 2). P-values are plotted on the heatmap where the p-value represents the probability of observing at least as large an overlap as observed if the two clones in fact had independently randomly selected variants from the full list of CADD > 15 variants. P-values below 0.05 represent two clones that are not genetically independent. b. Heatmap of genes with deleterious variants (CADD > 15) that were present with allele frequencies between 25% and 75% in both naive and resistant clones, colored by their CADD deleteriousness score. For genes that include multiple, unique variants, the variants were collapsed into one row, where the variant with the highest CADD score was plotted for that sample. The curated gene set represents the lack of variation in (Shaffer et al. 2017; Garman et al. 2017). Differentially expressed genes from the FateMap dataset show eight genes with variation. c. The expression patterns of the eight genes from the DEG list from FateMap with heterogeneously present genetic variants, visualized on UMAP (Cell counts available in Supplementary Table 11). d. To evaluate for acquired genetic resistance to therapy in the resistant clones, we next plotted variants on a heatmap (colored by their CADD deleteriousness score) if there was a significant difference in the allele frequencies of variants between naive and resistant clones by Fisher’s exact test (P < 0.05). Variants were only included if they were not present in any naive clones. The curated gene set represents the lack of acquired variants in genes from [2017 paper genes], [FateMap DEG], [Clone Genes], [Top 500 most Variable Genes], [Known Epigenetic Modifiers]. The [“all variants with CAAD > 15”] includes all variants with CADD c-scores over 15. e. We analyzed 143 genes classified as epigenetic modifiers for deleterious variants (CADD > 15) within naive clones. The chart shows the number of genes with variants in a subset of naive clones (2 genes) and in all naive clones (10 genes). f. Heatmap of deleterious variants in epigenetic modifier genes, colored by their CADD deleteriousness score.
Extended Data Fig. 3 Isolation, longitudinal profiling and functional mapping of drug resistant clones.
a. Schematic for longitudinal tracking and profiling of drug resistant colonies. Colonies were isolated, expanded and maintained over 4 to 6 weeks. Paired initial and late samples were sequenced at a bulk-level. b. Paired initial and late samples display minimal phenotypic drift in principal component (PC) space for top 500 most variable genes. Insets show brightfield images of representative samples. c. Euclidean distance (in PC1 and PC2) measured between paired initial and late samples and equivalent number of random initial-late pairs of samples. Lower Euclidean distance in true pairs as compared to random pairs implies that paired initial and late samples are transcriptionally more similar (closer in PC space) than any pair of initial and late samples. d. Scree plot depicting cumulative variance explained by PCs. Dotted line represents that most of the variance can be explained by the first 25 PCs alone. e. Euclidean distance (in first 25 PCs) measured between paired initial and late samples and equivalent number of random initial-late pairs of samples. Lower Euclidean distance in true pairs as compared to random pairs implies that paired initial and late samples are transcriptionally more similar (closer in PC space) than any pair of initial and late samples. f. Euclidean distance measured between paired early and late samples and equivalent number of random initial-late pairs of samples. Euclidean distance was measured in PC1 and PC2 space for top 200, 500 and 1000 variable genes. g. Euclidean distance measured between paired initial and late samples and equivalent number of random initial-late pairs of samples. Euclidean distance was measured in the PC space created by the first 25 PCs for top 200, 500 and 1000 variable genes. h. Mapping of invasiveness onto the single-cell RNA sequencing dataset from FateMap by comparing genes differentially expressed between the two slowest and the two fastest invading resistant colonies (UMAP colored for similarity score). The slowest invading colonies have a high similarity score for cluster 15 (and to some extent 4 and 6), while the fastest invading colonies have a high similarity score for cluster 8 (and to some extent 1).
Extended Data Fig. 4 FateMap on BRAF and NRAS mutant melanoma cell lines reveals between-clone fate type diversity.
a. (left) For another single-cell derived melanoma cell line WM983B E9-C6, we traced representative resistant cells in Adobe Illustrator and created cartoon schematics based on visual inspection of orientation and density. (right) Brightfield images of resistant colonies exhibiting different types of morphologies. b. We applied the Uniform Manifold Approximation and Projection (UMAP) algorithm within Seurat to the first 50 principal components to visualize differences in gene expression. Cells are colored by clusters determined using Seurat’s FindClusters command at a resolution of 0.5 (i.e. “Seurat clusters, resolution = 0.5”) (13,869 and 11,249 total cells respectively for split A and B). c. On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed via the Seurat pipeline and marked different clusters. MLANA, which marks melanocytes, is found largely in clusters 1,3, and 5; IFIT2, which marks type-1 interferon signaling, is found largely in cluster 4; NGFR, which marks neural crest cells, is found largely in cluster 2 and 4; AXL, which is a canonical resistance marker, is found largely in cluster 5 and 7. d. Six examples to demonstrate that a clone (cells sharing the same barcode) is constrained largely in a specific transcriptional cluster such that cells within a clone are more transcriptionally similar to each other than cells in other clones. Some clones are larger in size than others, and some exist as singletons, meaning they survive vemurafenib treatment but do not necessarily divide while exposed to the drug. e. We quantified the preference for a specific cluster across all barcode clones (clone size>4). Specifically, we calculated the fraction of dominant clusters for each clone and found it to be significantly higher (Wilcoxon test, two-sided, unpaired, p-value = 1.49e-15) than that for randomly selected cells. The analysis plotted here is for a cluster resolution of 0.5. f. Painting of singletons and colonies onto the UMAP demonstrated that singletons and colonies belonged to distinct regions and clusters. g. UMAPs of representative twin clones (sharing the same barcode) across the two splits A and B. The twins largely end up with the same transcriptional fate type, invariant of the clone size. This observation suggests that cells are predestined for distinct resistant fate types upon exposure to vemurafenib. h. NRAS mutant cell line WM3623 treated with three different doses of trametinib (10 nM, 20 nM, and 40 nM). Representative brightfield images after 2.5 and 5 weeks of drug treatment are shown for each dose. i. (left) UMAP of all barcoded 3623 cell line cells treated with Trametinib. 6,397 cells are colored by clusters determined using Seurat’s FindClusters command at a resolution of 0.6 (i.e. “Seurat clusters, resolution = 0.6”). (right) On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed in drug resistant cells via the Seurat pipeline. j. On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed in drug resistant cells via the Seurat pipeline. k. Five representative examples demonstrate that a clone (cells sharing the same barcode) is constrained largely in a specific transcriptional cluster such that cells within a clone are more transcriptionally similar to each other than cells in other clones. l. UMAPs of representative twin clones (sharing the same barcode) across the two splits A (6,397 cells) and B (7,538 cells). The twins largely end up with the same transcriptional fate type. This observation suggests that drug resistant cells are derived from the same clones having similar transcriptional states and are constrained in the gene expression space. One of the clones appears to be a dominant clone and gives rise to a large fraction of sequenced cells.
Extended Data Fig. 5 FateMap on an NRAS mutant melanoma cell line reveals between-clone fate type diversity.
a. NRAS mutant cell line WM3451 treated with three different doses of trametinib (20 nM, 40 nM, and 50 nM). Representative brightfield images after 2.5 and 5 weeks of drug treatment are shown for each dose. b. (left) UMAP of all barcoded 3451 cells treated with Trametinib. 5,789 cells are colored by clusters determined using Seurat’s FindClusters command at a resolution of 0.6 (i.e. “Seurat clusters, resolution = 0.6”). (right) On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed in drug resistant cells via the Seurat pipeline. c. Five representative examples demonstrate that a clone (cells sharing the same barcode) is constrained largely in a specific transcriptional cluster such that cells within a clone are more transcriptionally similar to each other than cells in other clones. d. Average pairwise correlation between cells within a clone was estimated based on the expression levels of the top 500 most variable genes. Each point represents the average value for Spearman’s correlation coefficient for all possible pairs of cells within a clone. For each clone, a paired control was created by randomly sampling an equivalent number of cells from the whole population. Higher average correlation coefficient in clones indicates higher transcriptional similarity among cells within a clone, as compared to cells that are not clones. Wilcoxon signed rank test (paired, two-sided) was used to compare the difference in average correlation coefficient. e. UMAP of all barcoded WM3451 P2G7 cells treated with Trametinib. Cells are colored by whether they are a singleton (i.e. clone size = 1). f. UMAPs of representative twin clones (sharing the same barcode) across the two splits A (5,789 cells) and B (7,473 cells). The twins largely end up with the same transcriptional fate type. This observation suggests that drug resistant cells are derived from the same clones having similar transcriptional states and are constrained in the gene expression space.
Extended Data Fig. 6 FateMap on treatment-naive primary human melanocytes reveals between-clone diversity.
a. (left) UMAP of all barcoded naive primary melanocyte cells. Cells are colored by clusters determined using Seurat’s FindClusters command (“Seurat clusters, resolution = 0.6”). (right) On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed in drug resistant cells via the Seurat pipeline. b. Six representative examples demonstrate that a clone is constrained largely in a specific transcriptional cluster such that cells within a clone are more transcriptionally similar to each other than cells in other clones. c. Average pairwise correlation between cells within a clone was estimated based on the expression levels of the top 500 most variable genes. Each point represents the average value for Spearman’s correlation coefficient for all possible pairs of cells within a clone. For each clone, a paired control was created by randomly sampling an equivalent number of cells from the whole population. Higher average correlation coefficient in clones indicates higher transcriptional similarity among cells within a clone, as compared to cells that are not clones. Wilcoxon signed rank test (paired, two-sided) was used to compare the difference in average correlation coefficient. d. UMAP of all barcoded naive primary melanocyte cells. 2,868 cells are colored by whether they are a singleton (i.e. clone size = 1). Cluster 10 is enriched for singletons and displays high expression of S100B, a marker identified to be associated with single cell colonies by FateMap. e. UMAPs of representative twin clones (sharing the same barcode) across the two splits A (2,868 cells) and B (3,333 cells). The twins largely end up with the same transcriptional fate type. This observation suggests that primary melanocyte cells derived from the same clone have similar transcriptional states and are constrained in the gene expression space.
Extended Data Fig. 7 FateMap on a triple negative breast cancer cell line reveals between-clone fate type diversity.
a. Nuclei scans (DAPI-stained) of resistant colonies emerging from treatment of the single-cell derived triple negative breast cancer cell line MDA-MB-231-D4 with 1nM paclitaxel. b. For the MDA-MB-231-D4 cell line, we traced representative resistant cells in Adobe Illustrator and created cartoon schematics based on visual inspection of orientation and density. c. Brightfield images of resistant colonies exhibiting different types of morphologies. d. We applied the Uniform Manifold Approximation and Projection (UMAP) algorithm within Seurat to the first 50 principal components to visualize differences in gene expression. 6,535 cells are colored by clusters determined using Seurat’s FindClusters command (“Seurat clusters, resolution = 0.5”). e. We observed silencing of the transcribed barcodes in a subset of colonies, as revealed by epifluorescence imaging of the GFP signal. The colony on the left is strongly expressing a GFP signal while the colony on the right has a very dim GFP signal. f. Cells with assigned barcodes were evenly distributed throughout the UMAP with no clear bias for any specific resistant fate types. g. Four examples from split A (6,535 cells) demonstrate that a clone is constrained largely in specific UMAP regions such that cells within a clone are more transcriptionally similar to each other than cells in other clones. h. Four examples from split B (8,745 cells) demonstrate that a clone is constrained largely in specific UMAP regions such that cells within a clone are more transcriptionally similar to each other than cells in other clones. i. We quantified the preference for a specific cluster across all barcode clones (clone size>4). Specifically, we calculated the fraction of dominant clusters for each clone and found it to be significantly higher (Wilcoxon, unpaired, two-sided) than that for randomly selected cells. The analysis plotted here is for a cluster resolution of 0.5. j. We found that our UMAP had superclusters defined by cell cycle (S, G1, G2M). Of the 3,720 clonal DEGs, 63 are cell cycle genes. We therefore regressed out cell cycle genes and the cell-cycle-genes-regressed data with UMAP. k. We quantified the preference for a specific cluster across all barcode clones after cell cycle regression (clone size>4). Specifically, we calculated the fraction of dominant clusters for each clone and found it to be significantly higher (Wilcoxon, unpaired, two-sided) than that for randomly selected cells. The analysis plotted here is for a cluster resolution of 0.5. l. UMAPs of representative twin clones across the two splits A and B. The twins largely end up with the same transcriptional fate type, invariant of the clone size. This observation suggests that cells are predestined for distinct resistant fate types upon exposure to chemotherapy drug paclitaxel.
Extended Data Fig. 8 FateMap reveals differences in clonal fate type outcomes between continuous and discontinuous therapy.
a. Schematic of the experimental design where we exposed single-cell-derived WM989 A6-G3 melanoma cells to continuous and discontinuous doses of targeted therapy drug vemurafenib. b. UMAP of all barcoded cells. 17,634 cells are colored by clusters determined using Seurat’s FindClusters command (“Seurat clusters, resolution = 0.6”). c. Pellet morphology for continuous (7,238 cells) and discontinuous (10,396 cells) treatment cells. Cells derived from discontinuous dosage have a larger and darker (more pigmented) pellet. This suggests that during discontinuous dosage, melanocytic cells (which are pigmented in nature) proliferate. d. On the UMAP, we recolored each cell by its expression for a select subset of genes that were identified as differentially expressed in drug resistant cells via the Seurat pipeline. e. UMAP of all barcoded cells. Cells are colored by type of dosage. f. UMAPs of representative twin clones (sharing the same barcode) that arise during discontinuous drug treatment. The twins largely end up with the same transcriptional fate type and have varying proliferative capacities. g. In discontinuous dosage, 68% of clones having high MLANA expression (log2 Expression > 2, in at least 50% of cells in a given clone) are proliferative (i.e. have clone size > 1). In continuous dosage, only 20% of clones having high MLANA expression are proliferative. h. (left) Total number of cells analyzed consisted of 60.5% discontinuous dosage samples and 39.5% continuous dosage samples. (right) The number of unique barcodes (i.e. resistant clones) displays a 3.6 fold increase in discontinuous dosage sample as compared to the continuous dosage sample. i. UMAPs of representative twin clones across the two splits of continuous and discontinuous dosing. Some twins end up in the similar transcriptional fate type while others tend to switch fate type.
Extended Data Fig. 9 Changing the therapy type to trametinib eliminates an additional resistant fate type present in the vemurafenib treatment.
a. UMAP where the resistant cells are colored by the associated therapy drug type, with dark blue representing vemurafenib (9,457 cells) and light blue representing trametinib (8,569 cells). Arrows represent UMAP regions that are present only in vemurafenib or trametinib. b. UMAP is split by each drug type, with colors representing clusters determined using Seurat’s FindClusters command(“Seurat clusters, resolution = 0.5”). Arrows represent UMAP regions that are present only in vemurafenib or trametinib. c. Painting of singletons and colonies onto the UMAP, colored by the condition, demonstrated that singletons largely belong to vemurafenib and are present predominantly in the MLANA-high cluster. Colonies are dispersed more across the UMAP with no particular region enriched for either condition except for the NGFR-high cluster. d. Imaging of nuclei (DAPI-stained) of resistant colonies emerging from treatment of WM989 A6-G3 cells to either vemurafenib or trametinib. The number of singletons in trametinib treated cells appear to be much less than those treated with vemurafenib, consistent with the sequencing data from FateMap. e. Quantification of the total number of colonies and singletons from each drug type of imaging data across biological replicates. This analysis demonstrated that while the total number of colonies are similar across the two drug types, there is a relative increase (~2.45-fold; n = 3 biological replicates) in the number of singletons in the case of vemurafenib. Error bars represent standard error of the mean. f. UMAPs are recolored for each cell by its expression for gene MLANA, which is a marker for cluster 3 relatively enriched in vemurafenib (as shown with arrows in A and B). g. A pie chart to demonstrate that of all the clones (barcodes) present in vemurafenib-treated split in cluster 3, only 4.8% were also present in the trametinib-treated split. h. A cumulative density contour plot capturing the types of fate switches that the MLANA-high cluster 3 clones from the vemurafenib-treated split adopt in the trametinib-treated split. i. Three representative examples of UMAP regions where twins from the MLANA-high cluster 3 in the vemurafenib-treated split adopt in the trametinib-treated split. j. UMAPs are recolored for each cell by its expression for gene NGFR, which is a marker for cluster 4 relatively enriched in trametinib (as shown with arrows in A and B). k. Composition of clones of different sizes within NGFR-high cluster 4 for both trametinib- and vemurafenib-treated splits. l. A pie chart to demonstrate that of all the clones (barcodes) present in the trametinib-treated split in cluster 4, 20.7% were also present in the vemurafenib-treated split. m. A cumulative density contour plot capturing the types of fate switches that the NGFR-high cluster 4 clones from the vemurafenib-treated split adopt in the trametinib-treated split. n. Two representative examples of UMAP regions where twins from the NGFR-high cluster 4 in trametinib-treated split adopt in the vemurafenib-treated split. o. UMAP for combined vemurafenib and trametinib treatment conditions recolored for each cell by its expression of the gene VCAM1, which is enriched in cluster 6. p. Painting of singletons and colonies onto the UMAP for the NGFR-high cluster 4, colored by the condition, showing a relative enrichment of cells from trametinib as compared to vemurafenib. This panel also demonstrates that both singletons and colonies occupy cluster 4 from each of the two conditions. q. We performed antibody stainings for NGFR on colonies emerging from treatment of the same number of starting melanoma cells with either vemurafenib or trametinib. Consistent with FateMap, we found an increased number of NGFR-positive resistant cells in trametinib treated cells as compared to the vemurafenib treatment. r. UMAP split by each drug condition (trametinib (8,569 cells) or vemurafenib and trametinib (7,023 cells)), with colors representing clusters determined using Seurat’s FindClusters command at a resolution of 0.5 (i.e. “Seurat clusters, resolution = 0.5”). s. UMAP recolored for combined resistant cells from trametinib (light blue) and vemurafenib and trametinib (dark blue). The cells from two conditions are interspersed into each other on the UMAP.
Extended Data Fig. 10 Inhibition of histone methyltransferase DOT1L results in the emergence of additional resistant proliferative clones and a reduction in singletons.
a. For each barcode identified by sequencing, we plotted its abundance in corresponding splits A (DMSO control) and B (DOT1L inhibition). Those present in both control and DOT1L splits are colored in dark blue, and those present only in either A (control) and B (DOT1L) are colored in cyan. Those present in both (dark blue; 171) exhibited a strong correlation, suggesting that their ability to survive and become resistant is invariant of drug dose. For those present only in either (cyan), we found them to be much more abundant in DOT1L (B, 43 barcodes) than DMSO control (A, 7 barcodes), suggesting that new barcodes, otherwise unable to survive in the control condition, become drug-resistant in the DOT1L inhibited condition. A total of one biological replicate. b. (left) Combined resistant cells in the control (9,343 cells) and DOT1L (7,044 cells) conditions obtained from UMAP applied to the first 50 principal components. Cells are colored by clusters determined using Seurat’s FindClusters command(“Seurat clusters, resolution = 0.8”). (right) UMAP is split by each condition. c. UMAP where the resistant cells are colored by the associated condition (control vs DOT1L). The arrow represents the UMAP region present predominantly in the control region and missing from the DOT1L-associated UMAP region. d. Quantification of singletons and colonies showed that while the number of resistant colonies is higher in DOT1L, it is accompanied by a reduced number of singletons cells compared to control. e. Painting of singletons and colonies onto the UMAP, colored by the condition, demonstrated that singletons largely belong to the control condition and are present predominantly in cluster 2 (MLANA-high). Colonies are dispersed more across the UMAP with no particular region enriched for either condition. f. Imaging of the nuclei (DAPI-stained) of resistant colonies emerging from vemurafenib treatment of WM989 A6-G3 cells, either for control or cells lacking DOT1L. g. Quantification of the total number of colonies and singletons from each fate type across n = 3 biological replicates demonstrated a relative increase (3.65-fold; n = 3 biological replicates) in total colonies and reduction in total singletons in the DOT1L and control conditions, respectively. Error bars represent standard error of the mean. h. UMAP is recolored for each cell by its expression for the gene MLANA, a marker for cluster 2, which is relatively enriched in control (as shown with an arrow). i. A pie chart to demonstrate that of all the clones (barcodes) present in the control condition split, only 3.1% were also present in the DOT1L inhibitor pretreatment split. j. Two representative examples of UMAP regions where twins from the MLANA-high cluster in the control condition go in the DOT1L condition. A cumulative density contour plot capturing the types of fate switches that MLANA-high cluster clones from control adopt in the DOT1L inhibitor-pretreated condition. k. A cumulative density contour plot capturing the types of fate switches that the MLANA-high cluster 2 clones from the control condition split adopt in the DOT1L inhibitor pretreatment split. l. Distribution of cells across clusters for control (top) and DOT1L inhibitor-pretreated (bottom) conditions for clone size>2.
Supplementary information
Supplementary Information
This file contains Supplementary discussion and Supplementary Figs. 1–17.
Supplementary Tables
This file contains Supplementary Tables 1–11.
Supplementary Video 1
Time-lapse imaging of single-cell derived WM989 A6-G3 cells exposed to a 1 µM dose of vemurafenib. The time stamp is provided in top left, and time 0 represents the time at which the drug was added.
Supplementary Video 2
Time-lapse imaging of single-cell derived WM989 A6-G3 cells exposed to a 100 nM dose of vemurafenib. The time stamp is provided in top left, and time 0 represents the time at which the drug was added.
Supplementary Video 3
Time-lapse imaging of single-cell derived WM989 A6-G3 cells exposed to a 5 nM dose of trametinib. The time stamp is provided in top left, and time 0 represents the time at which the drug was added.
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Goyal, Y., Busch, G.T., Pillai, M. et al. Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells. Nature 620, 651–659 (2023). https://doi.org/10.1038/s41586-023-06342-8
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DOI: https://doi.org/10.1038/s41586-023-06342-8
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