Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma

Abstract

Melanoma cells can switch between a melanocytic and a mesenchymal-like state. Scattered evidence indicates that additional intermediate state(s) may exist. Here, to search for such states and decipher their underlying gene regulatory network (GRN), we studied 10 melanoma cultures using single-cell RNA sequencing (RNA-seq) as well as 26 additional cultures using bulk RNA-seq. Although each culture exhibited a unique transcriptome, we identified shared GRNs that underlie the extreme melanocytic and mesenchymal states and the intermediate state. This intermediate state is corroborated by a distinct chromatin landscape and is governed by the transcription factors SOX6, NFATC2, EGR3, ELF1 and ETV4. Single-cell migration assays confirmed the intermediate migratory phenotype of this state. Using time-series sampling of single cells after knockdown of SOX10, we unravelled the sequential and recurrent arrangement of GRNs during phenotype switching. Taken together, these analyses indicate that an intermediate state exists and is driven by a distinct and stable ‘mixed’ GRN rather than being a symbiotic heterogeneous mix of cells.

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Fig. 1: Melanoma cultures exhibit distinct cell states.
Fig. 2: Transcriptional state predicts migratory capacity.
Fig. 3: Single-cell network inference reveals candidate regulators of the intermediate state.
Fig. 4: Results of SCENIC runs on multiple datasets pruned to a high-confidence GRN.
Fig. 5: Validation of cell states in a larger cohort of cultures and biopsies.
Fig. 6: SOX10 perturbation leads to common state transitions.
Fig. 7: Network inference reveals the recurrent dynamic gene regulatory changes during phenotype switching.

Data availability

A SCope instance73 containing these data is available online (http://scope.aertslab.org/#/Wouters_Human_Melanoma). A UCSC hub with BigWig and BED files of our ATAC-seq data (in hg19 and hg38) is also available online (the following URL should be used to connect to the track hub via the UCSC Genome Browser, http://ucsctracks.aertslab.org/papers/wouters_human_melanoma/hub.txt). The scRNA-seq and ATAC-seq data have also been deposited in GEO under the accession number GSE134432. Raw images, tracking information and videos for the single-cell migration experiments, and raw data, images and videos for the bulk migration experiments, are publicly available on the Open Science Framework (OSF) of the Center for Open Science (COS) at http://osf.io (https://doi.org/10.17605/OSF.IO/E6AHM). All of the networks in the manuscript are available at the Network Data Exchange NDEx: global melanoma GRN (https://doi.org/10.18119/N99C71), intermediate GRN (https://doi.org/10.18119/N95P54) and a highly refined SOX10 regulon (https://doi.org/10.18119/N91W2T). A list of the public data used in this study is provided in Supplementary Table 5. A list of public gene signatures used in this study is provided in Supplementary Tables 6 and 7. Information relating to the patient-derived melanoma cultures used in this study is provided in Supplementary Table 8 for (all tumours were obtained before any treatment). Source data are provided with this paper.

Code availability

We have made the scripts for the most important analyses, including SCENIC and the pseudotime analyses, available from the laboratory’s GitHub website (https://github.com/aertslab/singlecellRNA_melanoma_paper). A detailed outline of the applied SCENIC analyses is also available as Docker and Singularity containers, and Nextflow pipeline management (https://github.com/aertslab/SCENICprotocol)72.

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Acknowledgements

We thank J. Poźniak for help with the Leeds Melanoma Cohort, and T. Stakenborgs and E. Mathieu (IMEC) for help with the single-cell migration and lens-free imaging setup. This research was funded by an ERC Consolidator Grant to S.A. (no. 724226_cis-CONTROL), and by the KU Leuven (grant no. C14/18/092 to S.A.), the Harry J. Lloyd Charitable Trust, the Foundation Against Cancer (grant no, 2016-070 to S.A.), PhD fellowships from the FWO (no. 1S03317N, to L.M. and no. 11F1519N, to C.B.G.-B.) and a postdoctoral research fellowship from Kom op tegen Kanker (Stand up to Cancer), the Flemish Cancer Society and from Stichting tegen Kanker (Foundation against Cancer), and the Belgian Cancer Society (to J.W). Computing was performed at the Vlaamse Supercomputer Center Leuven and high-throughput sequencing was performed at the Genomics Core Leuven. Single-cell infrastructure was funded by the Hercules Foundation (no. AKUL/13/41). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The results of this publication are based in part on data generated by the University of Leeds in connection with the project ‘The Leeds Melanoma Cohort’; otherwise known as Melanoma Follow-up and Case-Control Family Study (REC reference no. 01/03/057). These data are presently held within the European Genome-phenome Archive at the European Bioinformatics Institute (EGAS00001002922). The generation of this data was funded by Cancer Research UK (nos. C588/A19167, C8216/A6129 and C588/A10721) and with the support of the National Institutes of Health (no. CA83115) and the European Commission Horizon 2020 Research and Innovation Programme (no. 641458).

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J.W., Z.K.-A. and S.A. conceived the study. J.W. performed the experimental research for the 10x scRNA-seq datasets with the help of V.C., S.M. and S.P.; J.W. performed the experimental research for the Drop-seq datasets with the help of K.D. and V.C.; J.W. and L.M. performed the experimental research for the Omni-ATAC datasets with the help of D.M. and V.C.; L.M. performed the experimental research and analysis of the single-cell migration assays with the help of F.C. and S.P.; D.P. performed the experimental research and analysis of the bulk migration assays with the help of J.W.; J.W., Z.K.-A. and K.I.S. performed all bioinformatics analyses with the help of G.H., M.D.W., K.D. and C.B.G.-B.; A.N. and G.G. provided melanoma cultures. M.D., F.R. and J.-C.M. provided materials and contributed to the manuscript. J.W., Z.K.-A., K.I.S. and S.A. wrote the manuscript.

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Correspondence to Stein Aerts.

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Extended data

Extended Data Fig. 1 Phenotypic and functional characterization of 10 melanoma cultures.

a, Nine patient-derived MM lines and the cell line A375 were multiplexed into a single 10x Chromium lane, followed by computational demultiplexing using SNPs. b, Pie charts representing the fraction of cells within each cell cycle phase, shown for each baseline melanoma culture. c, t-SNE displaying the cells clustering according to their cell line origin (left). Each culture has a subpopulation of cells with high cell cycle activity, shown in a t-SNE plot coloured according to G2M checkpoint gene signature activity (Hallmark; middle). t-SNE coloured according to TFAP2A and TFAP2B expression shows that all MM lines express TFAP2A whereas the A375 cell line expresses TFAP2B (right). d, The heat map shows the activity of gene signatures (rows) in each cell (columns), measured by AUCell. Unsupervised hierarchical clustering demonstrates that two groups are formed based on contrasting activity of melanocytic and pigmentation-related signatures vs. mesenchymal-like (de-differentiated, neural crest-like, immune-like) and resistance-related signatures. n=4,322. Source data

Extended Data Fig. 2 Correlation analysis of single-cell and bulk migration.

a, Scatter plots of single-cell (three variables on x axis) and bulk (y axis) migration data showing significant correlation. Dotted line indicates fitted linear regression line. Shaded region displays 0.95 confidence interval (n=9). Source data

Extended Data Fig. 3 Regulons identified by GRN inference.

a, The top 20 regulons for each state were extracted and AUCell values (Z-score) are plotted in box plots. HES6 regulon for the melanocytic state; NFATC2, EGR3, ETV4, ELF1 and SOX6 regulons for the intermediate state; and FOSL2, JUN regulons for the mesenchymal-like state have distinct AUCell values across the different states. Box plots display the median as centre line and the upper and lower quartiles as box limits (n=100 runs; dots represent the filtered regulons). b, Scatter plots of AUCell values for corresponding motif- and track-based regulons for each cell and the resulting Spearman correlation coefficient, for the 21 recurrently-observed transcription factors (n=4,322). c, Scatter plots of AUCell values for corresponding track- and motif-based regulons for each cell and the resulting Spearman correlation coefficient for the remaining transcription factors (n=4,322). d, Violin plots for motif- and track-based MITF regulon activity are shown for 10 melanoma cultures demonstrating a gradual decrease from extreme melanocytic to intermediate cultures. Violin plots display cell densities as shape and cells as dots (n=4,322). Source data

Extended Data Fig. 4 Chromatin landscape of melanoma cultures.

a, Normalized ATAC-seq signal in melanocytic regions (n=6,669) and mesenchymal-like regions (n=13,453) as previously identified4 shows higher chromatin accessibility in melanocytic regions in melanocytic and intermediate cultures and lower chromatin accessibility in mesenchymal-like regions, and vice versa. b, Normalized ATAC-seq signal in SOX10-, FOS- and JUND-bound regions, as previously identified by ChIP-seq, shows contrasting chromatin accessibility of SOX10-bound regions between mesenchymal-like and melanocytic cultures, and a gradual decreased accessibility in AP-1-associated regions (FOS and JUND) going from mesenchymal-like, over intermediate to melanocytic cultures. c, Normalized ATAC-seq signal at IRF2 (top left), FN1 (top right), SOX9 (bottom left) and NFATC2 (bottom right) gene loci shows higher accessibility in intermediate and mesenchymal-like cultures compared to the extreme melanocytic cultures. Source data

Extended Data Fig. 5 Melanoma states in scRNA-seq cohort of biopsies.

a, Visualization of the AUCell binarization procedure showing t-SNE coloured with AUCell score of the regulon (top), AUCell histogram with the threshold indicated as a red line (middle) and t-SNE coloured after AUCell binarization (bottom). Malignant cells are encircled in the top left t-SNE. b, Visualization of the AUCell binarization procedure showing t-SNE coloured with SOX11 and TFAP2B regulons. Grey arrows indicate clusters that have high AUCell scores for both regulons. c, Visualization of the AUCell binarization for a higher MITF threshold (motif- and track- based regulons). Cells with the highest MITF regulon activity correspond with cells that have high HES6 regulon activity (see panel a). Source data

Extended Data Fig. 6 Extended analyses of melanoma biopsies scRNA-seq cohort.

a, Scatter plots of the AUCell values for the intermediate regulons are plotted against each other in melanoma biopsies of Jerby-Arnon et al. (2018)46. The blue line indicates the fitted linear regression line. b, Box plots of the Pearson’s correlation coefficients between real regulons, compared to random regulons, for samples in this biopsy cohort. Correlations between real regulons are indicated with a red dot. Box plots display the median as centre line and the upper and lower quartiles as box limits c, GSEA for intermediate cells compared to the other malignant cells in these melanoma biopsies. Genes were ranked according to the log2-transformed fold-change values after differential gene expression analysis of cells with high vs low intermediate regulon activity (based on joined SOX6, NFATC2 and EGR3 regulons). Geneset name, normalized enrichment score (NES) and FDR BH-adjusted p-value are shown for each geneset. n=2,018. Source data

Extended Data Fig. 7 Phenotypic and functional characterization of the transcriptome after KD of SOX10.

a, Experimental setup of scRNA-seq after KD of SOX10. b, Western blot for SOX10, and GAPDH as control, after KD of SOX10 (the same time points) in the same three MM lines (MM074, MM087 and MM057; WB performed once). c, Pie charts showing fractions of cells in different cell cycle phases for MM074, MM087 and MM057 at baseline and control KD conditions together with different time points after KD of SOX10. d, Comparative alignment of transition trajectories using different methods. Trajectories predicted by DiffusionMap, Scorpius and Monocle-2 were aligned by applying dynamic time warping, using cellAlign, and the predicted optimal alignment is shown with a white line. The concordance between different methods is high. Source data

Extended Data Fig. 8 Dynamic melanoma GRNs after KD of SOX10.

The melanoma GRN (Fig. 4) now coloured by expression of MM074, MM087 and MM057 for various time points after KD of SOX10 (z-score aggregate counts; TF labels shown in Fig. 4). Source data

Extended Data Fig. 9 Recurrent transition trajectory across melanoma cultures.

a, Interpolated and scaled gene expression after KD of SOX10 for each culture along pseudo time shows a collapse of the transcriptome. b, Heat map with the activity of gene signatures (rows) in each cell (columns), measured by AUCell for all melanoma cultures after KD of SOX10 (for both 10x and Drop-seq scRNA-seq technologies), indicating the recurrent downregulation of cell cycle and melanocytic transcriptional programs, and upregulation of cellular migration, EMT, cancer metastasis, immune cell activation, angiogenesis, and melanoma-specific gene sets such as mesenchymal-like signatures, the melanoma TNF response, the AXL program signature and the signature for acquired resistance to BRAF inhibition. Comparison between 10x and Drop-seq scRNA-seq modalities demonstrates the consistency of the observed transcriptional changes. c, d, Ternary plots for gene expression (c) and gene signature activity (d) after KD of SOX10 indicating very high transcriptional concordance between melanoma cultures of various relevant down- and upregulated processes. Source data

Extended Data Fig. 10 Transcriptional response after inhibition of CDK7 by THZ2.

a, Heat map for 104 of the 114 genes that are reported to be downregulated after THZ1 also show downregulation after THZ2 (compared to DMSO treatment; remaining ten genes were not in matrix after filtering). b, Violin plots showing changes in activity of direct CDK7 targets for MM074, MM087 and MM057. Violin plots display cell densities as shape (n=27,163). Source data

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Supplementary Tables 1–9.

Supplementary Data

Supplementary File 1: all unfiltered regulons as identified by SCENIC.

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Wouters, J., Kalender-Atak, Z., Minnoye, L. et al. Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma. Nat Cell Biol 22, 986–998 (2020). https://doi.org/10.1038/s41556-020-0547-3

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