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A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response
Open accessAbstract
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.
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In the citing paper
“Our analysis also delineated the canonical branches of the cellular stress response into the unfolded protein response (UPR), activated by the loss of ER-resident chaperones and translocation machinery, and integrated stress response (ISR), activated by loss of mitochondrial proteins, aminoacyl-tRNA synthetases, and translation initiation factors”
From Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (2022) · cites this paper as Adamson et al., 2016
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, single-cell RNA-sequencing pooled CRISPR screens can simultaneously sequence transcriptomes and sgRNAs from individual cells, enabling a comprehensive analysis of sgRNA-specific effects on genetic regulatory networks at single-cell resolution32–34.”
From Large-scale CRISPR screening in primary human 3D gastric organoids enables comprehensive dissection of gene-drug interactions (2025) · cites this paper as [32]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Our internal normalization approach is similar to the one described in Adamson et al. (2016).”
From Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (2022) · cites this paper as Adamson et al. (2016)
The cited source says what the claim attributes to it.
In the citing paper
“We found that individual ICs corresponded to recognizable biological phenomena (Figure 1D and Figure S2C–H)(Adamson et al., 2016), in contrast to results from principal components analysis (Figure S2A).”
From Molecular diversity and specializations among the cells of the adult mouse brain (2018) · cites this paper as Adamson et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“two alternative methods - Perturb-seq and CRISP-seq were published that use transcribed barcodes for single-cell CRISPR screening 14-16 .”
From Pooled CRISPR screening with single-cell transcriptome readout. (2017) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“Perturb-seq is a multiplexed lentiviral platform combining CRISPR-based genetic perturbations and single-cell RNA-seq (scRNA-seq) to accurately identify transcriptional profiles and single-cell states affected by individual genetic perturbations, extending beyond simple bulk cellular growth phenotypes32–34 (Fig. 4A).”
From Large-scale CRISPR screening in primary human 3D gastric organoids enables comprehensive dissection of gene-drug interactions (2025) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Moreover, high sgRNA and transcript capture rates indicated a comparable quality in organoids to previous datasets in various cell lines32,50 (Fig. S5D, E).”
From Large-scale CRISPR screening in primary human 3D gastric organoids enables comprehensive dissection of gene-drug interactions (2025) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Although CRISPR screening coupled with single-cell transcriptomic readout provides unprecedented resolution for phenotypic characterization14–16, such approaches have not been fully explored in organoids.”
From Single-cell brain organoid screening identifies developmental defects in autism (2023) · cites this paper as [16]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“IFN-β stimulation induces widespread transcriptomic changes observed as a shift in the t-SNE projections of singlets24”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [24]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“IFN-β, a potent cytokine that induces genome-scale changes in the transcriptional profiles of immune cells24, 25.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [24]
Experimental — accuracy not yet measuredIn the citing paper
“Each of these libraries encodes guide RNAs programmed with 32 unique guide RNA targeting regions: 30 which target genes whose depletion was previously shown to activate the unfolded protein response (UPR) by GBC Perturb-seq4 and 2 non-targeting controls (sgNegCtrl2 and sgNegCtrl3).”
From Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing (2020) · cites this paper as [4]
In the citing paper
“For GBC Perturb-seq, we did this using a threshold that separates the bimodal distribution of GBC coverage (reads per UMI) as previously described4.”
From Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing (2020) · cites this paper as [4]
In the citing paper
“In recent years, CRISPR screens have been combined with single-cell RNA-sequencing to measure the effects of genetic perturbations on gene expression across the transcriptome2–5.”
From Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens (2021) · cites this paper as [3]
In the citing paper
“differential gene expression testing was restricted to pre-defined candidate genes7,9,10 or genes were grouped into gene signatures3,4,6,8.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [8]
In the citing paper
“Second, lowly expressed genes and small effects are not measured efficiently4,7–9.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [8]
In the citing paper
“This risk was noted by Adamson et al.4 and Dixit et al.5.”
From On the design of CRISPR-based single-cell molecular screens (2018) · cites this paper as [4]
In the citing paper
“the implications of template switching at longer distances (e.g. the 2.5 kb+ separation between sgRNAs and barcodes in the pLGB-scKO, CRISP-seq, Perturb-seq, and Mosaic-seq vectors3–6), as well as for scRNA-seq study designs specifically, remain unexplored.”
From On the design of CRISPR-based single-cell molecular screens (2018) · cites this paper as [4]
In the citing paper
“CROP-seq7 differs from the other methods3–6 in not relying on pairing of sgRNAs and barcodes. Instead, the sgRNA itself serves as a barcode, as part of an overlapping Pol II transcript.”
From On the design of CRISPR-based single-cell molecular screens (2018) · cites this paper as [4]
In the citing paper
“It was altogether avoided by Adamson et al.4 through arrayed lentiviral production, but pooled lentiviral production was performed in some or all experiments of the other reports3,5,6.”
From On the design of CRISPR-based single-cell molecular screens (2018) · cites this paper as [4]
In the citing paper
“With 50% swapping, we observe a 4.8-fold decrease in the number of DEG (Figure 1f).”
From On the design of CRISPR-based single-cell molecular screens (2018) · cites this paper as [4]
In the citing paper
“Similar advances in the ability to measure transcriptomes in single cells have recently allowed high-throughput genetic screens coupled with simultaneous transcriptome phenotyping (Dixit et al., 2016; Adamson et al., 2016; Jaitin et al., 2016; Datlinger et al., 2017).”
From Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks (2019) · cites this paper as Adamson et al., 2016
In the citing paper
“A particularly scalable approach to differential expression is Perturb-Seq, which combines single-cell RNA sequencing with a high-throughput CRISPR screen1-4.”
From Transcriptome-wide analysis of differential expression in perturbation atlases (2025) · cites this paper as [2]
In the citing paper
“Some studies carry out pseudo-bulk calculations by combining all cells in a given perturbation setting1,7. This means losing information about the cell-to-cell variation in response.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [7]
In the citing paper
“In particular, the ability to perform combinatorial perturbations represents an opportunity to decode complex regulatory networks, with pioneering work demonstrating the ability to identify epistasis and other genetic interactions5–7.”
From Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq (2023) · cites this paper as [5]
In the citing paper
“For example, Perturb-seq pairs CRISPR-based screens with single-cell RNA sequencing (5–8). Each individual cell is in effect an independent experiment connecting a genetic perturbation to its transcriptional consequences, allowing hundreds of thousands of parallel measurements (9, 10).”
From Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019) · cites this paper as [6]
In the citing paper
“Mitochondria are a major site of cellular iron storage and utilization in processes such as heme synthesis and iron-sulfur cluster biogenesis59.”
From Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis (2021) · cites this paper as [59]
In the citing paper
“we made each vector individually, and subsequently pooled them in equimolar ratio, as this eliminates the possibility of swapping (Adamson et al., 2016).”
From Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens (2018) · cites this paper as Adamson et al., 2016
In the citing paper
“Recently, CRISPR screens have been coupled with single cell RNA sequencing (scRNA-seq), and vector-encoded RNA used as a barcode”
From Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens (2018) · cites this paper as Adamson et al., 2016
In the citing paper
“Several groups have incorporated scRNA-seq into pooled screens to obtain more comprehensive phenotyping than previously possible, and to achieve single cell resolution (Adamson et al., 2016; Datlinger et al., 2017; Dixit et al., 2016; Jaitin et al., 2016).”
From Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens (2018) · cites this paper as Adamson et al., 2016
In the citing paper
“There are a number of existing methods that perform perturbation-effect analysis in single-cell omics data, but none of them achieve guaranteed confounder identification, which is a necessary condition for interpretable causal-effect estimation.”
From Causal identification of single-cell experimental perturbation effects with CINEMA-OT (2023) · cites this paper as [3]
In the citing paper
“Coupling pooled CRISPR gene knockouts with single cell RNA sequencing provides a powerful approach to assess the effect of each gene perturbation on cell state (Adamson et al., 2016; Datlinger et al., 2017; Dixit et al., 2016; Jaitin et al., 2016).”
From Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies (2020) · cites this paper as Adamson et al., 2016
In the citing paper
“For example, single-cell RNA sequencing (scRNA-seq) coupled with pooled CRISPR screens offers much promise for analysing the genetic regulation of cell identity2–4, but cannot be readily used in many biological contexts.”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [2]
In the citing paper
“As previous work has shown (11–13, 19), focusing on gene modules instead of individual genes provides more statistical power to detect biologically-meaningful perturbation effects using fewer cells than would be required for single gene-level analysis, and can capture diversity both within and across cell types.”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [11]
In the citing paper
“Several methods have been developed using pooled CRISPR/CAS9 genome editing to introduce a large amount of perturbations in a population of cells, the effects of which can subsequently be measured using single-cell transcriptomics. These methods include CRISP-seq [88], Perturb-seq [89, 90], CROP-seq [91] and Mosaic-seq [92].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [90]
In the citing paper
“A companion paper of Perturb-seq uses CRISPRi to study the epistatic effects of three key regulators of independent pathways involved in unfolded protein response.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [90]
In the citing paper
“Similarly, CRISP-seq and Perturb-seq were also applied in a knockout context focused on identifying key regulators of the immune response and their combinatorial effects.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [90]
In the citing paper
“In Perturb-seq, a computational method is developed based on a linear model (Multi-Input-Multi-Output-Single-Cell-Analysis, MIMOSCA), which allows studying the impact of individual perturbations on gene expression and the marginal contributions of the genetic interactions.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [90]
In the citing paper
“They present interesting proof of principle applications, exploring the effect of a few dozen perturbations per experiment (typically targeting 10–30 genes, TFs or enhancers), and all expect that the number of elements screened will increase in future applications.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [90]
In the citing paper
“To identify which perturbation is applied to each cell, CRISP-seq, Perturb-seq and Mosaic-seq use poly-A barcodes, which are then computationally linked to the gRNA.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [90]
In the citing paper
“massively parallel scRNA-seq methods, such as Drop-seq7 and related methods8–10 can be readily applied at scale11 in a cost-effective manner12, but require intact single cell suspension as input.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [9]
In the citing paper
“One possible solution to this problem is the combination of single‐cell RNA‐seq with targeted CRISPR screens to produce more impactful perturbations at high throughput (Adamson et al, 2016; Dixit et al, 2016; Jaitin et al, 2016; Datlinger et al, 2017).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Adamson et al, 2016
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Citations
Cites
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799
Massively parallel digital transcriptional profiling of single cells
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Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationRobust principal component analysis?
Candès, E. J., Li, X., Ma, Y., & Wright, J. N. (2011) · Journal of the ACM · doi:10.1145/1970392.1970395 · Cited by 7,048
The Unfolded Protein Response: From Stress Pathway to Homeostatic Regulation
Walter, P., & Ron, D. (2011) · Science · doi:10.1126/science.1209038 · Cited by 5,897
Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A., & Liu, D. R. (2016) · Nature · doi:10.1038/nature17946 · Cited by 5,437
Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression
Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013) · Cell · doi:10.1016/j.cell.2013.02.022 · Cited by 5,016 · cited 6×
“coli MG1655-derived strain by lambda Red recombinase-mediated recombineering as described previously (Qi et al., 2013), and moved from the MG1655-derived strain into the dCas9expressing BW25113 strain by P1 transduction and selection on kanamycin following established protocols.”
MethodInfluentialSelf-citationMutation of the mouse klotho gene leads to a syndrome resembling ageing
Kuro-o, M., Matsumura, Y., Aizawa, H., Kawaguchi, H., Suga, T., Utsugi, T., Ohyama, Y., Kurabayashi, M., Kaname, T., Kume, E., et al. (1997) · Nature · doi:10.1038/36285 · Cited by 3,837
CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes
Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., Stern-Ginossar, N., Brandman, O., Whitehead, E. H., Doudna, J. A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.044 · Cited by 3,696 · cited 6×
“…as activity against genomically integrated GFP (using sgGFP, an sgRNA programmed with the previously validated EGFP-NT2 protospacer (Table S1) (Gilbert et al., 2013)) was robust and comparable to that from a previously validated sgRNA expression construct (95.4% and 96.2% repression of GFP…”
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Tuano, N., Beesley, J., Manning, M., Shi, W., Malaver-Ortega, L. F., Paynter, J. M., Black, D. M., Civitarese, A., McCue, K., Hatzipantelis, A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.07.459221
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Liu, R., Liang, L., Habib, M., Freed, E. F., & Eckert, C. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824469-2.00029-4
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Rosenbluh, J., Tuano, N., Beesley, J., Manning, M., Shi, W., Malaver-Ortega, L. F., Paynter, J. M., Black, D. M., Civitarese, A., McCue, K., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1223972/v1
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An immune world model for multiscale forecasting and therapeutic hypothesis generation
Cui, T., Wang, X., Li, Z., Ding, J., You, L., Xu, Y., Xu, W., Wu, F., Ying, K., Ouyang, W., et al. (2026) · arXiv · doi:10.48550/arxiv.2609.14709
Metadata
- Authors
- Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., Pak, R. A., Gray, A. N., Gross, C. A., Dixit, A., Parnas, O., Regev, A., & Weissman, J. S.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of California, San Francisco (US)
- Broad Institute (US)
- Corresponding author
- Weissman, Jonathan S.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 167
- Issue
- 7
- Pages
- 1867-1882.e21
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5315571
- OpenAlex
- W2562947047
- Semantic Scholar
- 119a59578a114c6274fc863d4a3212b0357b365d
- Cited by
- 1,193
- References
- 46
- Citations by year
- 2026: 201 · 2025: 157 · 2024: 125 · 2023: 103 · 2022: 122 · 2021: 111 · 2020: 102 · 2019: 104 · 2018: 94 · 2017: 71 · 2016: 2
- FWCI
- 38.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- PMC
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (5rm1hg006193-10)
- National Institute on Aging (R01AG041826)
- National Institute on Drug Abuse (R01DA036858)
- National Human Genome Research Institute (RM1‐HG006193, P50HG006193)
- National Cancer Institute (K99CA204602, U01CA168370, R00CA204602)
- National Institute of General Medical Sciences (F32GM116331, T32GM007618, R01GM102790, P50GM102706, R35GM118061)
- National Institute of Biomedical Imaging and Bioengineering (T32EB009383)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Endoplasmic Reticulum Stress and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Keywords
- unfolded protein response, Perturb-seq, CRISPR screen, CRISPR interference, endoplasmic reticulum homeostasis, IRE1α, translocon, feedback loops
- MeSH terms
- RNA, Guide, CRISPR-Cas Systems, Animals, Endoribonucleases, Feedback, Humans, Models, Molecular, Transcription, Genetic, Protein Serine-Threonine Kinases, Sequence Analysis, RNA, Unfolded Protein Response, Single-Cell Analysis, Clustered Regularly Interspaced Short Palindromic Repeats
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