The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
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
“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]
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 [33]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
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 [15]
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 [33]
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 [14]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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 [4]
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 [4]
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 [4]
In the citing paper
“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]
In the citing paper
“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]
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 [5]
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 [5]
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 [5]
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 [5]
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 Dixit 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 [1]
In the citing paper
“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]
In the citing paper
“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]
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 [7]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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 Dixit 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 Dixit et al., 2016
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 Dixit 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 [3]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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 [89]
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 [89]
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 [89]
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 [89]
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 [89]
In the citing paper
“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]
In the citing paper
“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]
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 [8]
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 Dixit et al, 2016
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
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-citationLamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
Self-citationRepurposing 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
Self-citationMaps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Cited by
Sorted by citations
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849
Recovering 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
Hepatic stellate cells as key target in liver fibrosis
Higashi, T., Friedman, S. L., & Hoshida, Y. (2017) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2017.05.007 · Cited by 1,485
The next generation of CRISPR–Cas technologies and applications
Pickar-Oliver, A., & Gersbach, C. A. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0131-5 · Cited by 1,459
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Self-citationMultiplexed 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 1,101
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146
Self-citationMethods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244
Mechanisms and consequences of Jak–STAT signaling in the immune system
Villarino, A. V., Kanno, Y., & O'Shea, J. J. (2017) · Nature Immunology · doi:10.1038/ni.3691 · Cited by 1,221
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
Self-citationAdamson, 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., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193
Self-citationPooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096
“While this paper was in review 13 , two alternative methods – Perturb-seq and CRISP-seq – were published that use transcribed barcodes for single-cell CRISPR screening 14–16 .”
MethodBest practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
“3).”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
CRISPR technology: A decade of genome editing is only the beginning
Wang, J. Y., & Doudna, J. A. (2023) · Science · doi:10.1126/science.add8643 · Cited by 1,105
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037
Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2023) · Nature Methods · doi:10.1038/s41592-023-01943-7 · Cited by 867
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643
Self-citationMapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 795
“, Perturbseq, CROP-seq) (Adamson et al., 2016; Datlinger et al., 2017; Dixit et al., 2016; Jaitin et al., 2016; Replogle et al., 2020), technical advances have enabled their application to epigenetic (Rubin et al.”
Self-citationA brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697
Dissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Stringa, B., Hoffmann, C. M., Jindal, K., Solnica-Krezel, L., & Morris, S. A. (2023) · Nature · doi:10.1038/s41586-022-05688-9 · Cited by 677
MDSC: Markers, development, states, and unaddressed complexity
Hegde, S., Leader, A. M., & Mérad, M. (2021) · Immunity · doi:10.1016/j.immuni.2021.04.004 · Cited by 684
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 689
scGen 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
Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior
Yap, E.-L., & Greenberg, M. E. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.013 · Cited by 706
The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation
Wallrapp, A., Riesenfeld, S. J., Burkett, P. R., Abdulnour, R.-E. E., Nyman, J., Dionne, D. A., Hofree, M., Cuoco, M. S., Rodman, C., Farouq, D., et al. (2017) · Nature · doi:10.1038/nature24029 · Cited by 652
Self-citationLarge-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
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
Past, present, and future of CRISPR genome editing technologies
Pacesa, M., Pelea, O., & Jinek, M. (2024) · Cell · doi:10.1016/j.cell.2024.01.042 · Cited by 609
Cano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 613
The expanding vistas of spatial transcriptomics
Tian, L., Chen, F., & Macosko, E. Z. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01448-2 · Cited by 539
Bock, C., Datlinger, P., Chardon, F. M., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00093-4 · Cited by 641
Self-citationMimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529
Self-citationGenome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function
Shifrut, E., Carnevale, J., Tobin, V., Roth, T. L., Woo, J. M., Bui, C. T., Li, P. J., Diolaiti, M. E., Ashworth, A., & Marson, A. (2018) · Cell · doi:10.1016/j.cell.2018.10.024 · Cited by 573
Single-cell transcriptomics to explore the immune system in health and disease
Stubbington, M. J. T., Rozenblatt–Rosen, O., Regev, A., & Teichmann, S. A. (2017) · Science · doi:10.1126/science.aan6828 · Cited by 586
Self-citationLineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics
Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 554
Self-citationCRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons
Tian, R., Gachechiladze, M. A., Ludwig, C. H., Laurie, M. T., Hong, J. Y., Nathaniel, D., Prabhu, A. V., Fernandopulle, M. S., Patel, R., Abshari, M., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.014 · Cited by 543
The present and future of the Cancer Dependency Map
Arafeh, R., Shibue, T., Dempster, J. M., Hahn, W. C., & Vazquez, F. (2025) · Nature Reviews Cancer · doi:10.1038/s41568-024-00763-x · Cited by 454
What is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439
Synthetic lethality as an engine for cancer drug target discovery
Huang, A., Garraway, L. A., Ashworth, A., & Weber, B. L. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0046-z · Cited by 490
Gene regulatory network inference in the era of single-cell multi-omics
Badia-i-Mompel, P., Wessels, L., Müller‐Dott, S., Trimbour, R., Flores, R. O. R., Argelaguet, R., & Sáez-Rodríguez, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00618-5 · Cited by 507
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472
Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing
Replogle, J. M., Norman, T. M., Xu, A., Hussmann, J. A., Chen, J., Cogan, J. Z., Meer, E. J., Terry, J. M., Riordan, D. P., Srinivas, N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0470-y · Cited by 465
Self-citationMolecular logic of cellular diversification in the mouse cerebral cortex
Di Bella, D. J., Habibi, E., Stickels, R. R., Scalia, G., Brown, J., Yadollahpour, P., Yang, S. M., Abbate, C., Biancalani, T., Macosko, E. Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03670-5 · Cited by 464
Self-citationAm I ready for CRISPR? A user's guide to genetic screens
Doench, J. G. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.97 · Cited by 452
Mapping transcriptomic vector fields of single cells
Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Min, K. H., Wang, L., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.045 · Cited by 458
Self-citationApplications of single-cell RNA sequencing in drug discovery and development
de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 462
CRISPR in cancer biology and therapy
Katti, A., Diaz, B. J., Caragine, C. M., Sanjana, N. E., & Dow, L. E. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00441-w · Cited by 457
Synthetic lethal therapies for cancer: what’s next after PARP inhibitors?
Ashworth, A., & Lord, C. J. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-018-0055-6 · Cited by 434
Gubin, M. M., Esaulova, E., Ward, J. P., Malkova, O. N., Runci, D., Wong, P., Noguchi, T., Arthur, C. D., Meng, W., Alspach, E., et al. (2018) · Cell · doi:10.1016/j.cell.2018.09.030 · Cited by 424
Genome-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
Single-cell RNA sequencing: Technical advancements and biological applications
Hedlund, E., & Deng, Q. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.003 · Cited by 410
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393
Self-citationExploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392
Self-citationSingle-cell multimodal omics: the power of many
Zhu, C., Preissl, S., & Ren, B. (2020) · Nature Methods · doi:10.1038/s41592-019-0691-5 · Cited by 386
Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems
Zhang, X., Li, T., Liu, F., Chen, Y., Yao, J., Li, Z., Huang, Y., & Wang, J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.10.020 · Cited by 395
Unravelling biology and shifting paradigms in cancer with single-cell sequencing
Baslan, T., & Hicks, J. B. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.58 · Cited by 382
Variant interpretation: functional assays to the rescue
Starita, L. M., Ahituv, N., Dunham, M. J., Kitzman, J. O., Roth, F. P., Seelig, G., Shendure, J., & Fowler, D. M. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.07.014 · Cited by 381
Belk, J. A., Yao, W., Ly, N., Freitas, K. A., Chen, Y.-T., Shi, Q., Valencia, A. M., Shifrut, E., Kale, N., Yost, K. E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.06.001 · Cited by 382
CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine
Kampmann, M. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00657 · Cited by 387
Ketone Body Signaling Mediates Intestinal Stem Cell Homeostasis and Adaptation to Diet
Cheng, C., Biton, M., Haber, A. L., Gündüz, N., Eng, G. M., Gaynor, L. T., Tripathi, S. M., Calibasi‐Kocal, G., Rickelt, S., Butty, V. L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.048 · Cited by 379
Self-citationMultiplexed 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
Self-citationTranscriptional and epigenetic regulation of macrophages in atherosclerosis
Kuznetsova, T., Prange, K. H. M., Glass, C. K., & de Winther, M. P. J. (2019) · Nature Reviews Cardiology · doi:10.1038/s41569-019-0265-3 · Cited by 370
Predicting 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
Osorio, D., Zhong, Y., Li, G., Xu, Q., Yang, Y., Hillhouse, A., Chen, J., Davidson, L. A., Tian, Y., Chapkin, R. S., et al. (2021) · Patterns · doi:10.1016/j.patter.2022.100434 · Cited by 406
Self-citationOptical 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
Detection and removal of barcode swapping in single-cell RNA-seq data
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L., & Marioni, J. C. (2018) · Nature Communications · doi:10.1038/s41467-018-05083-x · Cited by 324
Massively parallel assessment of human variants with base editor screens
Hanna, R. E., Hegde, M., Fagre, C. R., DeWeirdt, P. C., Sangree, A. K., Szegletes, Z., Griffith, A., Feeley, M. N., Sanson, K. R., Baidi, Y., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.012 · Cited by 316
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 349
Rubin, A. J., Parker, K. R., Satpathy, A. T., Qi, Y., Wu, B., Ong, A. J., Mumbach, M. R., Ji, A. L., Kim, D. S., Cho, S. W., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.022 · Cited by 314
Spatial components of molecular tissue biology
Palla, G., Fischer, D. S., Regev, A., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01182-1 · Cited by 318
Self-citationHolland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323
CRISPR/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 338
Epigenetic regulation of T cell exhaustion
Belk, J. A., Dániel, B., & Satpathy, A. T. (2022) · Nature Immunology · doi:10.1038/s41590-022-01224-z · Cited by 346
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 335
Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307
In 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
Self-citationEmpowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315
Inferring and perturbing cell fate regulomes in human brain organoids
Fleck, J. S., Jansen, S. M. J., Wollny, D., Zenk, F., Seimiya, M., Jain, A., Okamoto, R., Santel, M., He, Z., Camp, J. G., et al. (2023) · Nature · doi:10.1038/s41586-022-05279-8 · Cited by 315
Cancer organoids 2.0: modelling the complexity of the tumour immune microenvironment
Polak, R., Zhang, E., & Kuo, C. J. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00706-6 · Cited by 347
Ma, P., Amemiya, H. M., He, L. L., Gandhi, S. J., Nicol, R., Bhattacharyya, R. P., Smillie, C. S., & Hung, D. T. (2023) · Cell · doi:10.1016/j.cell.2023.01.002 · Cited by 291
CRISPR/Cas9-based engineering of the epigenome
Pulecio, J., Verma, N., Mejía-Ramírez, E., Huangfu, D., & Raya, A. (2017) · Cell Stem Cell · doi:10.1016/j.stem.2017.09.006 · Cited by 300
High-speed fluorescence image–enabled cell sorting
Schraivogel, D., Kuhn, T. M., Rauscher, B., Rodríguez‐Martínez, M., Paulsen, M. S., Owsley, K., Middlebrook, A. J., Tischer, C., Ramasz, B., Ordoñez‐Rueda, D., et al. (2022) · Science · doi:10.1126/science.abj3013 · Cited by 287
Spatial CRISPR genomics identifies regulators of the tumor microenvironment
Dhainaut, M., Rose, S. A., Aktürk, G., Wroblewska, A., Nielsen, S. R., Park, E. S., Buckup, M., Roudko, V., Pia, L., Sweeney, R. E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.02.015 · Cited by 287
Single-cell brain organoid screening identifies developmental defects in autism
Li, C., Fleck, J. S., Martins-Costa, C., Burkard, T. R., Themann, J., Stuempflen, M., Peer, A. M., Vertesy, Á., Littleboy, J. B., Esk, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06473-y · Cited by 283
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281
Using single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250
Frangieh, C. J., Melms, J. C., Thakore, P. I., Geiger-Schuller, K. R., Ho, P., Luoma, A. M., Cleary, B., Jerby-Arnon, L., Malu, S., Cuoco, M. S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00779-1 · Cited by 276
Self-citationAn efficient KRAB domain for CRISPRi applications in human cells
Alerasool, N., Segal, D., Lee, H., & Taipale, M. (2020) · Nature Methods · doi:10.1038/s41592-020-0966-x · Cited by 277
Systems immunology: just getting started
Davis, M. M., Tato, C. M., & Furman, D. (2017) · Nature Immunology · doi:10.1038/ni.3768 · Cited by 275
Integrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 280
Datlinger, P., Rendeiro, A. F., Boenke, T., Senekowitsch, M., Krausgruber, T., Barreca, D., & Bock, C. (2021) · Nature Methods · doi:10.1038/s41592-021-01153-z · Cited by 266
Hotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
