biostudies-literatureMcFarland JMHoward Hughes Medical InstituteNCI NIH HHS4296https://www.ebi.ac.uk/biostudies/studies/S-EPMC7453022biostudies-literatureUnknown11(1)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 coNature communicationsMultiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.PMC7453022U01 CA250549K08 CA218420P50 CA127003U01 CA224146Shibue TKuksenko OTsherniak ARozenblatt-Rosen OChambers EJones APaolella BRBender SGeiger-Schuller KRothberg MWolpin BMWarren ARegev AVazquez FDionne DColgan WNTirosh IMcFarland JMRoth JAGolub TRAguirre AJGhandi MfalseMultiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.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 co2020-01-01T00:00:00Z2020 Aug2026-05-02T20:07:17.956Z2025-05-18T12:17:46.507ZS-EPMC74530223285538710.1038/s41467-020-17440-w