SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations

Ho, Yu-Jui, Anaparthy, Naishitha, Molik, David, Aicher, Toby, Patel, Ami, Hicks, James, Hammell, Molly (December 2017) SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations. bioRxiv. (Unpublished)

PDF 2017.Ho.Melanoma.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (23MB)

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 including metrics for: the determination of the number of clusters present, the likelihood that each cell belongs to a given cluster, and the association of each gene marker in determining cluster membership. 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 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. In addition, we compare integrated genomic and transcriptomic markers to show that resistance can arise stochastically within multiple distinct clonal populations.

Item Type:	Paper
Subjects:	diseases & disorders > cancer > cancer types > melanomas Investigative techniques and equipment > assays > Single cell sequencing
CSHL Authors:	Ho, Yu-Jui Hammell, Molly
Communities:	CSHL labs > Hammell M. lab School of Biological Sciences > Publications
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	24 December 2017
Date Deposited:	21 May 2021 18:53
Last Modified:	29 Feb 2024 21:07
URI:	https://repository.cshl.edu/id/eprint/40121

Actions (login required)

Administrator's edit/view item