Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations

Abstract

Single-cell RNA-seq's (scRNA-seq) unprecedented cellular resolution at a genome wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts.However, scRNA-seq datasets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present SAKE (Single-cell RNA-seq Analysis and Klustering Evaluation): a robust method for scRNA-seq analysis that provides quantitative statistical metrics at each step of the scRNA-seq analysis pipeline.Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors. Single-cell RNA-seq Cold Spring Harbor Laboratory Press on June 7, 2019 -Published by genome.cshlp.org Downloaded from data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. These experimentally validated markers of BRAFi resistance share overlap with previous analysis in different melanoma cell lines, demonstrating the generality of these findings and highlighting the utility of single-cell analysis to elucidate mechanisms of BRAFi resistance.

Failed apoptosis enhances melanoma cancer cells aggressiveness

“…A known example of ASR is the epithelial-mesenchymal transition (EMT), conferring drug resistance and stem-like features to the highly invasive cancer cells 36 . Several studies link the tyrosine kinase receptor AXL with the ASR of breast cancer or melanoma cells conferring resistance to targeted therapies, while interestingly, our study highlighted AXL as transcriptionally up-regulated in melanoma cells undergoing failed apoptosis 26, 37 . Moreover, the transcription factor ATF3, at the apex of our transcriptional regulation network characterizing failed apoptosis in melanoma, was also described as the central hub of the ASR 36 .…”

Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia

“…Transcriptome De Novo Cluster Identification-The number of clusters in the ALS datasets was determined using the SAKE software suite (Ho et al, 2018). Briefly, a variance stabilizing transformation was performed on the raw counts data using DESeq2.…”

TooManyCells identifies and visualizes relationships of single-cell clades

“…Clustering algorithms have been proposed to partition scRNA-seq data to identify groups of cells with related transcriptional programs 1 , 6 – 10 . In most scRNA-seq analyses, the identified cell clusters are visualized using dimensionality reduction algorithms such as t-SNE or UMAP 11 – 13 .…”