Multiplexed Single-Cell Transcriptional Response Profiling to Define Cancer
ARTICLE https://doi.org/10.1038/s41467-020-17440-w OPEN Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action James M. McFarland 1,11, Brenton R. Paolella 1,11, Allison Warren1, Kathryn Geiger-Schuller 1,2, Tsukasa Shibue1, Michael Rothberg1, Olena Kuksenko1,2, William N. Colgan 1, Andrew Jones1, Emily Chambers1, Danielle Dionne1,2, Samantha Bender1, Brian M. Wolpin3,4,5, Mahmoud Ghandi 1, Itay Tirosh2,6, Orit Rozenblatt-Rosen1,2, Jennifer A. Roth1, Todd R. Golub 1,3,7,8, Aviv Regev 1,2,8,9,10, ✉ ✉ ✉ Andrew J. Aguirre 1,3,4,5,12 , Francisca Vazquez 1,12 & Aviad Tsherniak 1,12 1234567890():,; 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.
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