dc.contributor.author | Feldman, David | |
dc.contributor.author | Funk, Luke | |
dc.contributor.author | Le, Anna | |
dc.contributor.author | Carlson, Rebecca J | |
dc.contributor.author | Leiken, Michael D | |
dc.contributor.author | Tsai, FuNien | |
dc.contributor.author | Soong, Brian | |
dc.contributor.author | Singh, Avtar | |
dc.contributor.author | Blainey, Paul C | |
dc.date.accessioned | 2023-01-30T14:08:36Z | |
dc.date.available | 2023-01-30T14:08:36Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/147776 | |
dc.description.abstract | Discovery of the genetic components underpinning fundamental and disease-related processes is being rapidly accelerated by combining efficient, programmable genetic engineering with phenotypic readouts of high spatial, temporal and/or molecular resolution. Microscopy is a fundamental tool for studying cell biology, but its lack of high-throughput sequence readouts hinders integration in large-scale genetic screens. Optical pooled screens using in situ sequencing provide massively scalable integration of barcoded lentiviral libraries (e.g., CRISPR perturbation libraries) with high-content imaging assays, including dynamic processes in live cells. The protocol uses standard lentiviral vectors and molecular biology, providing single-cell resolution of phenotype and engineered genotype, scalability to millions of cells and accurate sequence reads sufficient to distinguish >106 perturbations. In situ amplification takes ~2 d, while sequencing can be performed in ~1.5 h per cycle. The image analysis pipeline provided enables fully parallel automated sequencing analysis using a cloud or cluster computing environment. | en_US |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | 10.1038/S41596-021-00653-8 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Nature | en_US |
dc.title | Pooled genetic perturbation screens with image-based phenotypes | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Feldman, David, Funk, Luke, Le, Anna, Carlson, Rebecca J, Leiken, Michael D et al. 2022. "Pooled genetic perturbation screens with image-based phenotypes." Nature Protocols, 17 (2). | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | en_US |
dc.relation.journal | Nature Protocols | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2023-01-30T14:03:28Z | |
dspace.orderedauthors | Feldman, D; Funk, L; Le, A; Carlson, RJ; Leiken, MD; Tsai, F; Soong, B; Singh, A; Blainey, PC | en_US |
dspace.date.submission | 2023-01-30T14:03:30Z | |
mit.journal.volume | 17 | en_US |
mit.journal.issue | 2 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |