Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

Abstract

Melanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this "transition" state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.

Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning

“…The melanoma MM lines are derived from patient biopsies by the Laboratory of Oncology and Experimental Surgery (Prof. Dr. Ghanem Ghanem) at the Institut Jules Bordet, Brussels 8,12,39 . Cells were cultured in Ham’s F10 nutrient mix (ThermoFisher Scientific) supplemented with 10% fetal bovine serum (Invitrogen) and 100 µg/ml penicillin/streptomycin (ThermoFisher Scientific).…”

“…The alternative approach is to assess allelic imbalance at a heterozygous site itself by comparing the two alleles of the diploid genome against each other. This allele counting approach is extensively used with RNA-seq data to identify allele-specific expression variants 8 , but is also applicable to other types of functional genomics data 6 . Here, the strategy relies on finding the allelic origin of the signal observed in functional genomics data.…”

“…Each sample was sequenced to an average depth of 38X (except MM087 at 68X and MM099 at 133X). We also obtained chromatin accessibility profiles of the same melanoma lines with ATAC-seq 8 (9 samples were profiled with OmniATAC-seq and were obtained from Wouters et al 8 ; while ATAC-seq data for A375 was downloaded from GSE82330 9 ). For combining the personal diploid genomes with ATAC-seq, we used a modified alleleseq pipeline to obtain allele-specific chromatin accessibility variants (ASCAVs) 10 ( Figure 1a,b ) (see Materials and Methods ).…”

“…However, these generic models are usually trained on large collections of epigenomics data, allowing their employment to “any” cell type. In contrast, we follow a different strategy, specifically tailored towards the regulatory program of two melanoma cell states, namely the melanocytic state (MEL), and the mesenchymal-like state (MES) 8,12,25 . Particularly, in our companion paper (Minnoye & Taskiran et al, companion paper submitted ), we trained a deep learning model, called DeepMEL, on a cohort of melanoma ATAC-seq data.…”

“…OmniATAC-seq data for the human lines MM001, MM011, MM029, MM031, MM047, MM074, MM057, MM087 and MM099 are available at the Gene Expression Omnibus, accession number GSE134432 8 . The whole genome sequence data generated for this study is being deposited to European Genome-phenome Archive (EGA; http://www.ebi.ac.uk/ega/).…”