Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action

James M. McFarland, Brenton R. Paolella, Allison Warren, Kathryn Geiger-Schuller, Tsukasa Shibue, Michael Rothberg, Olena Kuksenko, William N. Colgan, Andrew Jones, Emily Chambers, Danielle Dionne, Samantha Bender, Brian M. Wolpin, Mahmoud Ghandi, Itay Tirosh, Orit Rozenblatt-Rosen, Jennifer A. Roth, Todd R. Golub, Aviv Regev, Andrew J. Aguirre (), Francisca Vazquez () and Aviad Tsherniak ()
Additional contact information
James M. McFarland: Broad Institute of MIT and Harvard
Brenton R. Paolella: Broad Institute of MIT and Harvard
Allison Warren: Broad Institute of MIT and Harvard
Kathryn Geiger-Schuller: Broad Institute of MIT and Harvard
Tsukasa Shibue: Broad Institute of MIT and Harvard
Michael Rothberg: Broad Institute of MIT and Harvard
Olena Kuksenko: Broad Institute of MIT and Harvard
William N. Colgan: Broad Institute of MIT and Harvard
Andrew Jones: Broad Institute of MIT and Harvard
Emily Chambers: Broad Institute of MIT and Harvard
Danielle Dionne: Broad Institute of MIT and Harvard
Samantha Bender: Broad Institute of MIT and Harvard
Brian M. Wolpin: Harvard Medical School
Mahmoud Ghandi: Broad Institute of MIT and Harvard
Itay Tirosh: Broad Institute of MIT and Harvard
Orit Rozenblatt-Rosen: Broad Institute of MIT and Harvard
Jennifer A. Roth: Broad Institute of MIT and Harvard
Todd R. Golub: Broad Institute of MIT and Harvard
Aviv Regev: Broad Institute of MIT and Harvard
Andrew J. Aguirre: Broad Institute of MIT and Harvard
Francisca Vazquez: Broad Institute of MIT and Harvard
Aviad Tsherniak: Broad Institute of MIT and Harvard

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment.

Date: 2020
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DOI: 10.1038/s41467-020-17440-w

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