Multiplexed 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 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.
|Year of Publication||
2020 08 27
|PubMed Central ID||
K08 CA218420 / CA / NCI NIH HHS / United States
P50 CA127003 / CA / NCI NIH HHS / United States
U01 CA224146 / CA / NCI NIH HHS / United States
HHMI / Howard Hughes Medical Institute / United States