Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action
© 2020, The Author(s). 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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/135406 |
| _version_ | 1826194027707367424 |
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| author | McFarland, James M Paolella, Brenton R Warren, Allison Geiger-Schuller, Kathryn Shibue, Tsukasa Rothberg, Michael Kuksenko, Olena Colgan, William N Jones, Andrew Chambers, Emily Dionne, Danielle Bender, Samantha Wolpin, Brian M Ghandi, Mahmoud Tirosh, Itay Rozenblatt-Rosen, Orit Roth, Jennifer A Golub, Todd R Regev, Aviv Aguirre, Andrew J Vazquez, Francisca Tsherniak, Aviad |
| author_facet | McFarland, James M Paolella, Brenton R Warren, Allison Geiger-Schuller, Kathryn Shibue, Tsukasa Rothberg, Michael Kuksenko, Olena Colgan, William N Jones, Andrew Chambers, Emily Dionne, Danielle Bender, Samantha Wolpin, Brian M Ghandi, Mahmoud Tirosh, Itay Rozenblatt-Rosen, Orit Roth, Jennifer A Golub, Todd R Regev, Aviv Aguirre, Andrew J Vazquez, Francisca Tsherniak, Aviad |
| author_sort | McFarland, James M |
| collection | MIT |
| description | © 2020, The Author(s). 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. |
| first_indexed | 2024-09-23T09:49:23Z |
| format | Article |
| id | mit-1721.1/135406 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:49:23Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1354062021-10-28T04:39:58Z Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action McFarland, James M Paolella, Brenton R Warren, Allison Geiger-Schuller, Kathryn Shibue, Tsukasa Rothberg, Michael Kuksenko, Olena Colgan, William N Jones, Andrew Chambers, Emily Dionne, Danielle Bender, Samantha Wolpin, Brian M Ghandi, Mahmoud Tirosh, Itay Rozenblatt-Rosen, Orit Roth, Jennifer A Golub, Todd R Regev, Aviv Aguirre, Andrew J Vazquez, Francisca Tsherniak, Aviad © 2020, The Author(s). 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. 2021-10-27T20:23:20Z 2021-10-27T20:23:20Z 2020 2021-07-22T16:24:23Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135406 en 10.1038/S41467-020-17440-W Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | McFarland, James M Paolella, Brenton R Warren, Allison Geiger-Schuller, Kathryn Shibue, Tsukasa Rothberg, Michael Kuksenko, Olena Colgan, William N Jones, Andrew Chambers, Emily Dionne, Danielle Bender, Samantha Wolpin, Brian M Ghandi, Mahmoud Tirosh, Itay Rozenblatt-Rosen, Orit Roth, Jennifer A Golub, Todd R Regev, Aviv Aguirre, Andrew J Vazquez, Francisca Tsherniak, Aviad Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| title | Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| title_full | Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| title_fullStr | Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| title_full_unstemmed | Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| title_short | Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| title_sort | multiplexed single cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action |
| url | https://hdl.handle.net/1721.1/135406 |
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