McFarland, James MPaolella, Brenton RWarren, AllisonGeiger-Schuller, KathrynShibue, TsukasaRothberg, MichaelKuksenko, OlenaColgan, William NJones, AndrewChambers, EmilyDionne, DanielleBender, SamanthaWolpin, Brian MGhandi, MahmoudTirosh, ItayRozenblatt-Rosen, OritRoth, Jennifer AGolub, Todd RRegev, AvivAguirre, Andrew JVazquez, FranciscaTsherniak, AviadengK08 CA218420/CA/NCI NIH HHS/P50 CA127003/CA/NCI NIH HHS/U01 CA224146/CA/NCI NIH HHS/HHMI/Howard Hughes Medical Institute/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tEnglandNat Commun. 2020 Aug 27;11(1):4296. doi: 10.1038/s41467-020-17440-w.
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.