Author
Listed:
- 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
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
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
Broad Institute of MIT and Harvard)
- Samantha Bender
(Broad Institute of MIT and Harvard)
- Brian M. Wolpin
(Harvard Medical School
Brigham and Women’s Hospital
Dana Farber Cancer Institute)
- Mahmoud Ghandi
(Broad Institute of MIT and Harvard)
- Itay Tirosh
(Broad Institute of MIT and Harvard
Weizmann Institute of Science)
- Orit Rozenblatt-Rosen
(Broad Institute of MIT and Harvard
Broad Institute of MIT and Harvard)
- Jennifer A. Roth
(Broad Institute of MIT and Harvard)
- Todd R. Golub
(Broad Institute of MIT and Harvard
Harvard Medical School
Dana Farber Cancer Institute
Howard Hughes Medical Institute)
- Aviv Regev
(Broad Institute of MIT and Harvard
Broad Institute of MIT and Harvard
Howard Hughes Medical Institute
Koch Institute of Integrative Cancer Research)
- Andrew J. Aguirre
(Broad Institute of MIT and Harvard
Harvard Medical School
Brigham and Women’s Hospital
Dana Farber Cancer Institute)
- Francisca Vazquez
(Broad Institute of MIT and Harvard)
- Aviad Tsherniak
(Broad Institute of MIT and Harvard)
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.
Suggested Citation
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 , 2020.
"Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action,"
Nature Communications, Nature, vol. 11(1), pages 1-15, December.
Handle:
RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17440-w
DOI: 10.1038/s41467-020-17440-w
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Citations
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Cited by:
- L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022.
"Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets,"
Nature Communications, Nature, vol. 13(1), pages 1-15, December.
- Michal Kabza & Alexander Ritter & Ashley Byrne & Kostianna Sereti & Daniel Le & William Stephenson & Timothy Sterne-Weiler, 2024.
"Accurate long-read transcript discovery and quantification at single-cell, pseudo-bulk and bulk resolution with Isosceles,"
Nature Communications, Nature, vol. 15(1), pages 1-12, December.
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