Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
Open accessAbstract
Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.
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Multiplexing reduces technical effects due to separate sample processing.
“suggesting multiplexing reduces technical effects due to separate sample processing22, 23.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [23]
The Introduction distinguishes pooled screens from separately assessed, arrayed screens, and the Results report that Perturb-seq steps were performed in a single pool. Our copy does not state or test whether pooling reduces technical effects caused by separate sample processing, so that attributed benefit remains unsupported. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThese foundation models had shown strong performance in post-perturbation RNA-seq prediction tasks using Perturb-seq-based genetic perturbation benchmarks.
“These models had shown strong performance in post-perturbation RNA-seq prediction tasks using Perturb-seq-based genetic perturbation benchmarks [12].”
From Benchmarking foundation cell models for post-perturbation RNA-seq prediction (2025) · cites this paper as [12]
The Results section, “Perturb-seq: pooled, combinatorial CRISPR screens with scRNA-seq readout” (page unavailable), describes genetic perturbation experiments with RNA-seq readouts. The paper does not report foundation-model performance or evaluate those models on post-perturbation prediction benchmarks; its predictive results concern its own linear model. (LLM, conf=0.82)
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 [33]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredMeasurements can be coupled with systematic perturbations.
“along with the ability to couple these measurements with systematic perturbations.27–29”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [27]
Five methods commonly in use for low-MOI single-cell CRISPR screen association testing are Seurat-Wilcox, MIMOSCA, t-test, MAST, and KS test.
“identified five methods commonly in use: the default Seurat [19] FindMarkers() function based on the Wilcoxon test (Seurat-Wilcox), MIMOSCA [1], a t-test on the library-size-normalized expressions [10], MAST [20], and a Kolmogorov-Smirnov (KS) test on the library-size-normalized expressions [21]”
From Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection (2024) · cites this paper as [1]
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 [15]
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 [33]
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 [14]
SenCID can be used in high-throughput single-cell CRISPR screens to identify genome-wide senescence trigger and suppressor genes.
“Next, we investigate whether SenCID can be used in high-throughput single-cell CRISPR screens, such as CROP-seq117,118 or Perturb-seq119,120 to identify genome-wide senescence trigger and suppressor genes.”
From Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators (2024) · cites this paper as [119]
Detection of a sgRNA in a cell does not necessarily mean that the cell is perturbed.
“As we previously showed1, detection of a sgRNA in a cell does not necessarily mean that this cell is perturbed, either because the sgRNA has not perturbed the gene, or the perturbation does not have an impact.”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [1]
Perturbations altered expression of the ICR program in both co-culture and IFNγ treatment.
“The perturbations also altered the expression of the ICR program, which we originally defined in patients1, in both co-culture (Fig. 4e) and in response to IFN γ treatment (Fig. 4f).”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [1]
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 [4]
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 [4]
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 [4]
The cost of Perturb-seq experiments is prohibitive even for screens that are not genome-wide.
“First, costs are prohibitive even for non-genome scale screens4.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [4]
Other studies mapped cells to known reference cell types or states, using only marker genes.
“Alternatively cells were mapped to known reference cell (sub)types or states4,5,11,12, making effective use only of cell-state or cell-type specific marker genes.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · 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 [5]
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 [5]
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 [5]
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 [5]
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 Dixit 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 [1]
Cell-based statistical measures can identify successfully perturbed cells but currently do not quantify similarity of perturbation effects.
“Cell-based statistical measures can be used to identify successfully perturbed cells but do not currently quantify similarity of perturbation effects2,9.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [2]
Perturb-seq and CRISP-seq are highly parallelised single-cell technologies built on CRISPR-pooled screens.
“Some initial groundbreaking technologies in this field include Perturb-seq [7] and CRISP-seq [8], which are highly parallelised single-cell technologies built on CRISPR-pooled screens.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [7]
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 [7]
A split experimental and computational approach, in which a subset of interactions are sampled and the remainder are predicted computationally, is a possible strategy to address the scale of genetic interactions.
“One possible strategy to address the scale of genetic interactions is to pursue a split experimental/computational approach, in which only a subset of interactions are sampled (either randomly or through “compressed” experimental designs (28)) and the remainder are predicted computationally (Fig. 6A) (7, 29, 30).”
From Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019) · cites this paper as [7]
Perturb-seq provides a scalable means of constraining searches for genetic interactions, enabling exploration of the genetic interaction landscape.
“Perturb-seq provides a scalable means of constraining these types of searches (7), enabling exploration of the GI landscape.”
From Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019) · cites this paper as [7]
The approach is complementary to efforts based on composite measurements, compressed sensing, and rich readouts to predict unmeasured genetic interactions.
“This provides a complementary approach to efforts based on composite measurements, compressed sensing, and rich readouts to predict unmeasured GIs (7, 28).”
From Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019) · cites this paper as [7]
Functionally related genes interact similarly, making the genetic interaction manifold highly constrained and enabling imputation strategies.
“We and others have established that functionally related genes interact similarly—in geometric terms the GI manifold is therefore highly constrained, enabling imputation strategies (1, 2, 7, 12, 29).”
From Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019) · cites this paper as [7]
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 Dixit 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 Dixit et al., 2016
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 Dixit 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 [3]
Dixit et al. has 24 transcription factors perturbed in dendritic cells and 25 cell cycle genes targeted in K562 cells, while Papalexi et al. has 25 PD-L1 regulators perturbed in THP-1 cells.
“Dixit et al. has 24 transcription factors perturbed in dendritic cells and 25 cell cycle genes targeted in K562 cells, while Papalexi et al. has 25 PD-L1 regulators perturbed in THP-1 cells.”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [32]
For the Perturb-seq validation, datasets from Dixit et al. and Papalexi et al. were used.
“For the Perturb-seq validation, we used datasets from Dixit et al.32 and Papalexi et al.31”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [32]
A permutation test with 100 permutations was used to generate a null distribution for the perturbation effect of each guide on each gene.
“we employed a permutation test as in Dixit et al.32”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [32]
Pooled optical screens can assess complex morphological phenotypes that FACS cannot detect but fluorescence microscopy can.
“Complex morphological phenotypes that cannot be detected by FACS, but are apparent by fluorescence microscopy, can be evaluated in pooled optical screens78,79.”
From CRISPR-based functional genomics for neurological disease (2020) · cites this paper as [79]
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 [89]
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 [89]
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 [89]
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 [89]
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 [89]
CRISPR-Cas gene editing coupled with single-cell transcriptome readouts enables pooled genetic perturbation experiments in vivo.
“Furthermore, CRISPR–Cas gene editing coupled with single-cell transcriptome readouts15–17 enables pooled genetic perturbation experiments in vivo18.”
From Inferring and perturbing cell fate regulomes in human brain organoids (2023) · cites this paper as [15]
Combining functional methods with complex readouts can generate empirical truth data that supports in silico models predicting causal variants, effector transcripts, and cellular effects.
“Combined with complex readouts–including high-content imaging128 and single-cell transcriptomics and epigenomics129,130–these methods can generate empirical ‘truth’ data, supporting the development of in silico models to predict causal variants, effector transcripts126 and cellular effects.”
From A brief history of human disease genetics (2020) · cites this paper as [129]
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 [8]
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 Dixit et al, 2016
Perturb-seq analyses thousands of CRISPR-mediated perturbations and mRNA profiles in a single experiment of 200,000 cells.
“Perturb-seq is one of these methods, in which thousands of CRISPR-mediated perturbations and mRNA profiles are analysed in a single experiment of 200,000 cells82.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [82]
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- Platt RJ Chen S Zhou Y Yim MJ Swiech L Kempton HR Dahlman JE Parnas O Eisenhaure TM Jovanovic M CRISPR-Cas9 knockin mice for genome editing and cancer modeling Cell 2014b 159 440 455 25263330 PMC4265475
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- Authors
- Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., Adamson, B., Norman, T. M., Lander, E. S., Weissman, J. S., Friedman, N., & Regev, A.
- Affiliations
- Broad Institute (US)
- QB3 (US)
- Corresponding author
- Regev, Aviv
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 167
- Issue
- 7
- Pages
- 1853-1866.e17
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5181115
- OpenAlex
- W2561754210
- Cited by
- 1,863
- References
- 64
- Citations by year
- 2026: 324 · 2025: 279 · 2024: 229 · 2023: 195 · 2022: 180 · 2021: 169 · 2020: 159 · 2019: 126 · 2018: 107 · 2017: 87 · 2016: 2 · 2013: 1 · 2012: 1
- FWCI
- 55.46
- 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)
- Funders
- NDSEG Fellowship
- NIH (T32GM87232)
- Klarman Cell Observatory
- NHGRI (RM1HG006193)
- HHMI
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Perturb-seq, single-cell RNA sequencing, gene expression profiling, gene regulatory networks, transcription factor regulation, dendritic cells, lipopolysaccharide response, immune activation, genetic interactions, mitochondrial function, antiviral response
- MeSH terms
- Animals, Cell Cycle, Feedback, Humans, Mice, Transgenic, Transcription Factors, Sequence Analysis, RNA, K562 Cells, Gene Expression Profiling, Mice, Gene Knockdown Techniques, Single-Cell Analysis, Clustered Regularly Interspaced Short Palindromic Repeats
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