Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action
Name
s41467-020-17440-w.pdf
Description
Published version
Size
7.54 MB
Format
Unknown
Checksum (MD5)
006fef1b5186eadbf940db411a97cbeb
Author(s)
McFarland, James M.
Paolella, Brenton R.
Warren, Allison
Geiger-Schuller, Kathryn
Shibue, Tsukasa
Rothberg, Michael
Kuksenko, Olena
Colgan, William N.
Jones, Andrew
Chambers, Emily
Date Issued
August 2020
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). 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.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-020-17440-w
