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

• DOI: 10.1101/gr.234062.117
• Publication Date: 2018-07-30T20:15:19Z
• Authors: Yu-Jui Ho; Naishitha Anaparthy; David Molik; Grinu Mathew; Toby Aicher; Ami Patel; James Hicks; Molly Gale Hammell
• Keywords: RNA-Seq; Single-Cell Analysis

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