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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The analysis delineated the canonical branches of the cellular stress response into the unfolded protein response (UPR) and integrated stress response (ISR).
“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
The Results section (‘Systematic delineation of the three branches of the UPR’; page unavailable) says the study defined three overlapping UPR branches, and the Perturb-seq results link chaperone and translocon depletion to UPR activation. Our copy does not say the analysis delineated the cellular stress response into UPR and ISR; it delineated branches within the UPR. The ISR half of the attributed claim is therefore unsupported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSingle-cell RNA-sequencing pooled CRISPR screens can simultaneously sequence transcriptomes and sgRNAs from individual cells, enabling comprehensive analysis of sgRNA-specific effects on genetic regulatory networks at single-cell resolution.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredOur internal normalization approach is similar to the one described in Adamson et al. (2016).
“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)
Individual independent components corresponded to recognizable biological phenomena, in contrast to results from principal components analysis.
“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
Perturb-seq and CRISP-seq are alternative methods that use transcribed barcodes for single-cell CRISPR screening.
“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]
Perturb-seq is a multiplexed lentiviral platform combining CRISPR-based genetic perturbations and single-cell RNA-seq to accurately identify transcriptional profiles and single-cell states affected by individual genetic perturbations, extending beyond simple bulk cellular growth phenotypes.
“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]
High sgRNA and transcript capture rates indicated comparable quality in organoids to previous datasets in various cell lines.
“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]
CRISPR screening coupled with single-cell transcriptomic readout provides unprecedented resolution for phenotypic characterization.
“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]
IFN-β stimulation induces widespread transcriptomic changes observed as a shift in t-SNE projections of singlets.
“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]
The Results section on UPR branch delineation describes t-SNE clustering of cells by UPR perturbation, not a shift following IFN-β stimulation. Searches for IFN-β, interferon beta, and singlets found no passage in our copy addressing the claimed response; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIFN-β induces genome-scale changes in the transcriptional profiles of immune cells.
“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]
The Results section on UPR transcriptional programs describes responses to thapsigargin and tunicamycin, not IFN-β in immune cells. The Discussion mentions dendritic-cell activation only as a parallel study. Searches for IFN-β and interferon-related transcriptional responses found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredThe three UPR CRISPRi libraries each contain 32 guide RNA targeting regions: 30 target genes previously reported to activate the unfolded protein response upon depletion, and 2 are non-targeting controls.
“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]
For GBC Perturb-seq, a threshold separating the bimodal distribution of GBC coverage (reads per UMI) was used as previously described.
“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]
CRISPR screens combined with single-cell RNA-sequencing measure effects of genetic perturbations on gene expression across the transcriptome.
“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]
Earlier studies limited differential expression testing to predefined candidate genes or grouped genes into signatures.
“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]
Perturb-seq does not efficiently measure lowly expressed genes or small effects.
“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]
Adamson et al. and Dixit et al. had already pointed out this template-switching risk.
“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]
How template switching affects longer separations, including the >2.5 kb gaps between sgRNAs and barcodes in pLGB-scKO, CRISP-seq, Perturb-seq, and Mosaic-seq, and its consequences for scRNA-seq designs, had not been studied.
“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]
Unlike the other approaches, CROP-seq does not pair sgRNAs with separate barcodes; the sgRNA is itself the barcode within an overlapping Pol II transcript.
“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]
Adamson et al. eliminated this problem by producing lentivirus individually, while other studies used pooled production in at least some experiments.
“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]
Applying a 50% swap rate to Adamson et al.'s Perturb-seq data reduced the count of differentially expressed genes by 4.8-fold.
“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]
High-throughput genetic screens can be coupled with simultaneous transcriptome phenotyping in single cells.
“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
Perturb-Seq combines single-cell RNA sequencing with high-throughput CRISPR screens.
“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]
Some studies combine all cells in a perturbation setting into pseudo-bulk calculations, which loses information about cell-to-cell variation in response.
“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]
Combinatorial perturbations can reveal epistasis and other genetic interactions.
“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]
Perturb-seq pairs CRISPR-based screens with single-cell RNA sequencing, allowing each cell to be an independent experiment connecting a genetic perturbation to its transcriptional consequences.
“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]
Mitochondria are a major site of cellular iron storage and utilization in processes such as heme synthesis and iron-sulfur cluster biogenesis.
“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]
Making each vector individually and pooling them in equimolar ratio eliminates the possibility of barcode swapping.
“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
CRISPR screens coupled with single-cell RNA sequencing using vector-encoded RNA barcodes enabled higher-content, higher-resolution screens.
“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
Multiple groups have used scRNA-seq in pooled screens to gain more comprehensive phenotyping and single-cell resolution.
“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
Existing methods that perform perturbation-effect analysis in single-cell omics data do not achieve guaranteed confounder identification, which is a necessary condition for interpretable causal-effect estimation.
“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]
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.
“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
scRNA-seq coupled with pooled CRISPR screens offers promise for analysing the genetic regulation of cell identity but cannot be readily used in many biological contexts.
“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]
Focusing on gene modules instead of individual genes provides more statistical power to detect biologically meaningful perturbation effects with fewer cells and captures diversity within and across cell types.
“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]
CRISP-seq, Perturb-seq, CROP-seq and Mosaic-seq are methods that use pooled CRISPR/CAS9 genome editing to introduce a large amount of perturbations in a population of cells, with effects measured using single-cell transcriptomics.
“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]
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.
“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]
CRISP-seq and Perturb-seq were applied in a knockout context focused on identifying key regulators of the immune response and their combinatorial effects.
“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 Perturb-seq, a computational method based on a linear model (MIMOSCA) allows studying the impact of individual perturbations on gene expression and the marginal contributions of genetic interactions.
“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]
These four approaches present proof of principle applications exploring the effect of a few dozen perturbations per experiment, typically targeting 10-30 genes, TFs or enhancers.
“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]
To identify which perturbation is applied to each cell, CRISP-seq, Perturb-seq and Mosaic-seq use poly-A barcodes that are computationally linked to the gRNA.
“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]
Massively parallel scRNA-seq methods such as Drop-seq can be applied at scale cost-effectively but require intact single-cell suspensions.
“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]
Combining single-cell RNA-seq with targeted CRISPR screens can produce more impactful perturbations at high throughput.
“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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Choosing the Right Tool for the Job: RNAi, TALEN, or CRISPR
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ROBUST PRINCIPAL COMPONENT ANALYSIS
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The origin of genetic diversity in HIV-1
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A functional link between the co-translational protein translocation pathway and the UPR
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Self-citationSources of Error in Mammalian Genetic Screens
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Scrublet: computational identification of cell doublets in single-cell transcriptomic data
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Self-citationGenome-wide association studies
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Molecular diversity and specializations among the cells of the adult mouse brain
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“We found that individual ICs corresponded to recognizable biological phenomena (Figures 1D and S2C–S2H) (Adamson et al., 2016), in contrast to results from principal components analysis (Figure S2A).”
ResultRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
Integrative single-cell analysis
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Self-citationscGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,383 · see citing works
“For the attention-based target gene selection, we fine-tuned the scGPT blood model on the 756 Adamson perturbation dataset that consists of 87 CRISPR interference experiments on a leukemia 757 cell line [36].”
BackgroundMethodInfluentialMultiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
The chromatin accessibility landscape of primary human cancers
Corces, M. R., Granja, J. M., Shams, S., Louie, B. H., Seoane, J. A., Zhou, W., Silva, T. C., Groeneveld, C., Wong, C. K., Cho, S. W., et al. (2018) · Science · doi:10.1126/science.aav1898 · Cited by 1,230 · see citing works
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
Self-citationPooled CRISPR screening with single-cell transcriptome readout.
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A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
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Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing
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Self-citationMULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
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Self-citationMapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
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“124 Although Perturb-seq is compatible with a range of CRISPR-based perturbations including 125 knockout (10–12), knockdown (CRISPRi) (9), or activation (CRISPRa) (20), we elected to use 126 CRISPRi for several reasons.”
MethodSelf-citationMitochondrial stress is relayed to the cytosol by an OMA1–DELE1–HRI pathway
Guo, X., Aviles, G., Liu, Y., Tian, R., Unger, B. A., Lin, Y.-H. T., Wiita, A. P., Xu, K., Correia, M. A., & Kampmann, M. (2020) · Nature · doi:10.1038/s41586-020-2078-2 · Cited by 674 · see citing works
Dissecting cell identity via network inference and in silico gene perturbation
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Synthetic lethality and cancer
O’Neil, N. J., Bailey, M. L., & Hieter, P. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.47 · Cited by 658 · see citing works
Pervasive functional translation of noncanonical human open reading frames
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Self-citationscGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661 · see citing works
Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior
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Large-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628 · see citing works
“Using Perturb-seq 51,52 data resources to train models for modeling cellular response to perturbations is a key task of computational biology 53–55 .”
A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613 · see citing works
The expanding vistas of spatial transcriptomics
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Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function
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Multiplexed CRISPR technologies for gene editing and transcriptional regulation
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CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons
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Synthetic lethality as an engine for cancer drug target discovery
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Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing
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Self-citationPromoter of lncRNA Gene PVT1 Is a Tumor-Suppressor DNA Boundary Element
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Self-citationMapping transcriptomic vector fields of single cells
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Self-citationAm I ready for CRISPR? A user's guide to genetic screens
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Reshaping endoplasmic reticulum quality control through the unfolded protein response
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CRISPR technologies for precise epigenome editing
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Applications of single-cell RNA sequencing in drug discovery and development
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What is a cell type and how to define it?
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CRISPR in cancer biology and therapy
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Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
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Self-citationGenome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis
Tian, R., Abarientos, A., Hong, J., Hashemi, S. H., Yan, R., Dräger, N., Leng, K., Nalls, M. A., Singleton, A. B., Xu, K., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00862-0 · Cited by 414 · see citing works
Single-cell RNA sequencing: Technical advancements and biological applications
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Single-cell multimodal omics: the power of many
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Self-citationUnravelling biology and shifting paradigms in cancer with single-cell sequencing
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CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine
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Multiplexed Engineering and Analysis of Combinatorial Enhancer Activity in Single Cells
Xie, S., Duan, J., Li, B., Zhou, P., & Hon, G. C. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.03.007 · Cited by 334 · see citing works
Molecular recording of mammalian embryogenesis
Chan, M. M., Smith, Z. D., Grosswendt, S., Kretzmer, H., Norman, T. M., Adamson, B., Jost, M., Quinn, J. J., Yang, D., Jones, M. G., et al. (2019) · Nature · doi:10.1038/s41586-019-1184-5 · Cited by 364 · see citing works
Self-citationPredicting transcriptional outcomes of novel multigene perturbations with GEARS
Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355 · see citing works
Challenges and emerging directions in single-cell analysis
Yuan, G., Cai, L., Elowitz, M. B., Enver, T., Fan, G., Guo, G., Irizarry, R. A., Kharchenko, P. V., Kim, J., Orkin, S. H., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1218-y · Cited by 339 · see citing works
Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution
Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.015 · Cited by 333 · see citing works
Self-citationCRISPR/Cas9 for cancer research and therapy
Zhan, T., Rindtorff, N., Betge, J., Ebert, M., & Boutros, M. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.04.001 · Cited by 333 · see citing works
Epigenetic regulation of T cell exhaustion
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Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices
Prakadan, S. M., Shalek, A. K., & Weitz, D. A. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.15 · Cited by 327 · see citing works
Optical Pooled Screens in Human Cells
Feldman, D., Singh, A., Schmid-Burgk, J. L., Carlson, R. J., Mezger, A., Garrity, A. J., Zhang, F., & Blainey, P. C. (2019) · Cell · doi:10.1016/j.cell.2019.09.016 · Cited by 321 · see citing works
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Mapping the Genetic Landscape of Human Cells
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Self-citationIn vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes
Jin, X., Simmons, S. K., Guo, A., Shetty, A. S., Ko, M., Nguyen, L., Jokhi, V., Robinson, E., Oyler, P., Curry, N., et al. (2020) · Science · doi:10.1126/science.aaz6063 · Cited by 317 · see citing works
Self-citationNon-cell-autonomous cancer progression from chromosomal instability
Li, J., Hubisz, M. J., Earlie, E. M., Paez, M. A. D., Hong, C., Varela, A., Lettera, E., Deyell, M., Tavora, B., Havel, J. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06464-z · Cited by 321 · see citing works
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Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts
Quinn, J. J., Jones, M. G., Okimoto, R. A., Nanjo, S., Chan, M. M., Yosef, N., Bivona, T. G., & Weissman, J. S. (2021) · Science · doi:10.1126/science.abc1944 · Cited by 312 · see citing works
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Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens
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CRISPR/Cas9-based engineering of the epigenome
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Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities
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Spatial CRISPR genomics identifies regulators of the tumor microenvironment
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- 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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