Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action
| Autor: | Francisca Vazquez, Kathryn Geiger-Schuller, Danielle Dionne, Tsukasa Shibue, Samantha Bender, Todd R. Golub, Aviad Tsherniak, Andrew Jones, Orit Rozenblatt-Rosen, Andrew J. Aguirre, Mahmoud Ghandi, Brenton R. Paolella, James M. McFarland, Aviv Regev, Brian M. Wolpin, Allison Warren, Jennifer Roth, Emily Chambers, Michael V. Rothberg, Itay Tirosh, Olena Kuksenko |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Cell Computational biology Biology Marker gene Phenotype 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Mechanism of action 030220 oncology & carcinogenesis Cancer cell medicine SNP Multiplex Viability assay medicine.symptom 030304 developmental biology |
| DOI: | 10.1101/868752 |
| Popis: | Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed 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, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple 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 can be used to predict long-term cell viability from short-term transcriptional responses to treatment. |
| Databáze: | OpenAIRE |
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