A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response
Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to...
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| Published in | Cell Vol. 167; no. 7; pp. 1867 - 1882.e21 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
15.12.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses.
[Display omitted]
•Perturb-seq allows parallel screening with rich phenotypic output from single cells•Simultaneous delivery and identification of up to three CRISPR perturbations•Genome-scale screens dissect the mammalian unfolded protein response•Analytical methods separate perturbation responses from confounding effects
A strategy for barcoding CRISPR-mediated perturbations allows pooled expression profiling via single-cell RNA sequencing. Application to the mammalian unfolded protein response then enabled systematic delineation of the transcriptional arms of the response and functional clustering of genes affecting ER homeostasis. |
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| AbstractList | Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses. Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses. [Display omitted] •Perturb-seq allows parallel screening with rich phenotypic output from single cells•Simultaneous delivery and identification of up to three CRISPR perturbations•Genome-scale screens dissect the mammalian unfolded protein response•Analytical methods separate perturbation responses from confounding effects A strategy for barcoding CRISPR-mediated perturbations allows pooled expression profiling via single-cell RNA sequencing. Application to the mammalian unfolded protein response then enabled systematic delineation of the transcriptional arms of the response and functional clustering of genes affecting ER homeostasis. Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses.Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses. Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ~100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses. |
| Author | Jost, Marco Pak, Ryan A. Hein, Marco Y. Norman, Thomas M. Villalta, Jacqueline E. Gray, Andrew N. Regev, Aviv Gross, Carol A. Cho, Min Y. Horlbeck, Max A. Gilbert, Luke A. Weissman, Jonathan S. Nuñez, James K. Adamson, Britt Parnas, Oren Chen, Yuwen Dixit, Atray |
| AuthorAffiliation | 5 Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA 8 Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA 94158, USA 7 Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94158, USA 4 Center for RNA Systems Biology, University of California, San Francisco, San Francisco, CA 94158, USA 9 Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02142 10 Broad Institute of MIT and Harvard, Cambridge MA 02142, USA 3 California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA 94158, USA 2 Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA 1 Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA 6 Innovative Genomics Initiative, University of California, Berk |
| AuthorAffiliation_xml | – name: 6 Innovative Genomics Initiative, University of California, Berkeley, Berkeley, CA 94720, USA – name: 1 Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – name: 2 Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA – name: 7 Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94158, USA – name: 9 Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02142 – name: 10 Broad Institute of MIT and Harvard, Cambridge MA 02142, USA – name: 8 Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA 94158, USA – name: 3 California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA 94158, USA – name: 12 Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge MA 02140, USA – name: 4 Center for RNA Systems Biology, University of California, San Francisco, San Francisco, CA 94158, USA – name: 5 Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA |
| Author_xml | – sequence: 1 givenname: Britt surname: Adamson fullname: Adamson, Britt organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 2 givenname: Thomas M. surname: Norman fullname: Norman, Thomas M. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 3 givenname: Marco surname: Jost fullname: Jost, Marco organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 4 givenname: Min Y. surname: Cho fullname: Cho, Min Y. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 5 givenname: James K. surname: Nuñez fullname: Nuñez, James K. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 6 givenname: Yuwen surname: Chen fullname: Chen, Yuwen organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 7 givenname: Jacqueline E. surname: Villalta fullname: Villalta, Jacqueline E. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 8 givenname: Luke A. surname: Gilbert fullname: Gilbert, Luke A. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 9 givenname: Max A. surname: Horlbeck fullname: Horlbeck, Max A. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 10 givenname: Marco Y. surname: Hein fullname: Hein, Marco Y. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 11 givenname: Ryan A. surname: Pak fullname: Pak, Ryan A. organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 12 givenname: Andrew N. surname: Gray fullname: Gray, Andrew N. organization: Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 13 givenname: Carol A. surname: Gross fullname: Gross, Carol A. organization: Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA – sequence: 14 givenname: Atray surname: Dixit fullname: Dixit, Atray organization: Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02142, USA – sequence: 15 givenname: Oren surname: Parnas fullname: Parnas, Oren organization: Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 16 givenname: Aviv surname: Regev fullname: Regev, Aviv organization: Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA – sequence: 17 givenname: Jonathan S. surname: Weissman fullname: Weissman, Jonathan S. email: jonathan.weissman@ucsf.edu organization: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27984733$$D View this record in MEDLINE/PubMed |
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| Keywords | CRISPR unfolded protein response Single-cell RNA-seq cell-to-cell heterogeneity single-cell genomics genome-scale screening CRIPSRi |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Present address: The Lautenberg Center for General and Tumor Immunology, The BioMedical Research Institute Israel Canada of the Faculty of Medicine (IMRIC), The Hebrew University Hadassah Medical School, 91120 Jerusalem, Israel Co-first author |
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| Snippet | Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq,... |
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| SubjectTerms | Animals cell-to-cell heterogeneity Clustered Regularly Interspaced Short Palindromic Repeats CRIPSRi CRISPR Endoribonucleases Feedback genome-scale screening Humans Models, Molecular Protein Serine-Threonine Kinases RNA, Guide, CRISPR-Cas Systems - metabolism Sequence Analysis, RNA - methods Single-Cell Analysis - methods single-cell genomics Single-cell RNA-seq Transcription, Genetic Unfolded Protein Response |
| Title | A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response |
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