Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells
Open accessAbstract
Multimodal single-cell assays provide high-resolution snapshots of complex cell populations, but are mostly limited to transcriptome plus an additional modality. Here, we describe expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell. We demonstrate application of ECCITE-seq to multimodal CRISPR screens with robust direct single-guide RNA capture and to clonotype-aware multimodal phenotyping of cancer samples. ECCITE-seq combines the single-cell analysis of multiple modalities, for example transcriptome, immune cell receptors, cell surface proteins and single-guide RNAs.
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- Findingcitation
CITE-seq antibodies against human or mouse CD29 labeled cells according to species of origin.
“CITE-seq antibodies directed against human or mouse CD29 label cells according to their species of origin [II], illustrating the ability of ECCITE-seq to detect differentially expressed proteins within a sample.”
Quote in the paperNot a substantial claim - Findingnone given
ECCITE-seq enables direct and robust capture of sgRNAs from existing guide libraries and commonly used pooled-cloning vectors for Perturb-seq, CRISPR-seq or CROP-seq experiments.
“Importantly, we further adapt the system to enable direct and robust capture of sgRNAs from existing guide libraries and commonly used vectors compatible with pooled cloning, for use in Perturb-seq / CRISPR-seq / CROP-seq type experiments.”
Quote in the paperSubstantial claim - Findingown data
Guide detection proportions in the K562 screen matched genomic DNA-based detection.
“We leveraged the Cell Hashing feature to remove cell doublets and observed very high rates of guide capture (confident detection of guide sequences in 98.3% of cells), in proportions consistent with genomic DNA-based detection (Fig. 1d and Supplementary Fig. 1d).”
Quote in the paperSubstantial claim - Findingown data
TCR α/β and TCR γ/δ clonotypes were detected in PBMC and lymphoma cell clusters.
“Clonotypes for TCR α/β (following 10× protocol) and TCR γ/δ (custom adaptation, see methods) were detected in the PBMC and lymphoma cell clusters [III].”
Quote in the paperSubstantial claim - Findingown data
Guide tags captured directly from sgRNA molecules were detected specifically and robustly only in mouse cells.
“Finally, guide tags, derived directly from sgRNA molecules were specifically and robustly detected only in mouse cells [IV].”
Quote in the paperSubstantial claim
- Findingown data
The proportions of sgRNAs detected in single cells matched those from genomic DNA-based detection in bulk cells.
“sgRNA capture was highly efficient, with sgRNAs detected in 93.5% of mouse cells (Fig.1d), in proportions consistent with genomic DNA-based detection from bulk cells (Supplementary Fig. 1c).”
Quote in the paperSubstantial claim - Findingown data
Combining Cell Hashing with sgRNA detection distinguished apparent doublets from two virus infections (n=325) from true doublets caused by co-encapsulation of two cells (n=65).
“Importantly, the use of Cell Hashing together with sgRNA detection allowed us to distinguish between apparent “doublets” where cells have been infected with two viruses (n=325), from doublets resulting from co-encapsulation of two cells in the same droplet (n=65) (Fig. 1d).”
Quote in the paperSubstantial claim - Findingown data
ECCITE-seq allows high-throughput profiling of at least five types of information from individual cells.
“Here, we describe Expanded CRISPR-compatible Cellular Indexing of Transcriptomes and Epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell.”
- Also stated in: § RESULTS · plain
“ECCITE-seq enables the detection of at least five modalities of cellular information from single cells”
- Also stated in: § RESULTS · plain
“To illustrate the detection of six modalities (transcriptome, T cell receptor (TCR α/β and TCR γ/δ), surface protein, sample identity by hashtags, and sgRNA) in a single experiment”
Quote in the paperSubstantial claim - Also stated in: § RESULTS · plain
- Findingcitation
ECCITE-seq works with 5' capture-based scRNA-seq methods such as the 10x Genomics 5P/V(D)J system, enabling surface protein measurement alongside transcriptome and clonotype readouts.
“Here, we extend the utility of CITE-seq and the related Cell Hashing method for multiplexing and doublet detection21 to 5’ capture-based scRNA-seq methods, exemplified by the 10× Genomics 5P / V(D)J system, allowing the detection of surface proteins together with the scRNA-seq and clonotype features currently offered by the 10× Genomics system22,23.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [21] Van Buggenum, J. A. G. L. et al. Sci. Rep. 6, 22675 (2016). source
Not checked in the free run.
- cites [22] Abrams, J. T. et al. J. Investig. Dermatol. 96, 31–37 (1991). source
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- cites [23] Kaltoft, K. et al. In Vitro Cell. Dev. Biol. 28A, 161–167 (1992). source
Not checked in the free run.
- Findingown data
In the K562 CRISPR screen, guide sequences were confidently detected in 98.3% of cells.
“We leveraged the Cell Hashing feature to remove cell doublets and observed very high rates of guide capture (confident detection of guide sequences in 98.3% of cells), in proportions consistent with genomic DNA-based detection (Fig. 1d and Supplementary Fig. 1d).”
Quote in the paperSubstantial claim - Findingown data
sgRNA capture was highly efficient, with sgRNAs detected in 93.5% of mouse cells.
“sgRNA capture was highly efficient, with sgRNAs detected in 93.5% of mouse cells (Fig.1d), in proportions consistent with genomic DNA-based detection from bulk cells (Supplementary Fig. 1c).”
Quote in the paperSubstantial claim - Findingown data
The analysis revealed a distinct gene-expression signature in malignant CTCL cells, consistent with earlier studies.
“revealing a distinct gene expression signature of the malignant CTCL cells, consistent with prior studies29, and illustrating the power of ECCITE-seq to combine immunophenotype, clonotype and transcriptome information.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29]
Not checked in the free run.
- Findingown data
In silico gating on CD3 and CD4 protein levels combined with clonotype information enabled differential expression analysis comparing monoclonal and polyclonal T cells from the patient and healthy donor.
“in silico gating based on CD3 and CD4 protein levels coupled with clonotypic information enabled differential gene expression analysis comparing monoclonal T cells with polyclonal T cells from both the patient and the healthy donor sample (Fig. 2b,c), revealing a distinct gene expression signature of the malignant CTCL cells, consistent with prior studies29, and illustrating the power of ECCITE-se”
Quote in the paperSubstantial claim - Findingcitation
TP53 transcript was poorly detected in K562 cells.
“TP53 transcript was poorly detected, consistent with K562 cells expressing a single allele of TP53 that is likely a substrate for nonsense-mediated decay27.”
Quote in the paperNot a substantial claim - Findingown data
TCR α/β and γ/δ clonotype information was recovered for both the control and CTCL samples.
“We additionally recovered TCR α/β and γ/δ clonotype information for both the control and CTCL samples.”
Quote in the paperSubstantial claim - Findingown data
CD46 mRNA reduction was also visible in cells with targeting sgRNAs, though less prominently.
“mRNA reduction is also apparent in cells with targeting sgRNAs, albeit less notably.”
Quote in the paperNot a substantial claim - Findingown data
In the CTCL sample, clonal expansion was evident: one TRB CDR3 sequence (CSARFLRGGYNEQFF) was present in 36% of cells with recovered clonotype information (1,390 of 3,857 barcodes).
“In contrast, in the CTCL sample, clonal expansion was readily apparent with a single TRB CDR3 sequence (CSARFLRGGYNEQFF) present in 36% of cells for which we recovered clonotype information (1,390 out of 3,857 barcodes).”
Quote in the paperSubstantial claim - Findingown data
After computational merging of cells from both samples, RNA-based and protein-based clustering agreed on most cell subpopulations and their gene-expression signatures.
“For further comparative analysis, cells from both samples were computationally merged28 and clustering based on either RNA or protein showed agreement in detecting most cell sub-populations and their gene-expression signatures (Supplementary Fig. 3).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28]
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- Findingown data
For CD46, the expression change caused by two of three sgRNAs (CD46.1 and CD46.3) was confidently detected only at the protein level, even using all cells assigned to those sgRNAs.
“Additionally, as exemplified by CD46, the gene expression change triggered by 2 out of 3 sgRNAs (CD46.1 and CD46.3) was confidently detected only at the level of protein, even when considering all cells assigned to these sgRNAs.”
Quote in the paperSubstantial claim - Interpretationreasoning
ECCITE-seq addresses limitations of earlier systems for CRISPR screens read out by scRNA-seq.
“ECCITE-seq overcomes the limitations of existing systems for CRISPR screens with scRNA-seq readout, while also demonstrating the power of combining protein detection with scRNA-seq as a readout for CRISPR screens.”
Quote in the paperSubstantial claim - Interpretationown data
Using protein detection as the readout markedly reduces the number of cells needed to detect the direct effect of a perturbation compared with mRNA readout.
“This analysis suggests that the number of cells needed to detect the direct consequence of a given perturbation is markedly reduced when using protein detection as a readout compared to mRNA, increasing the numbers of perturbations that can be assessed for a given number of cells.”
Quote in the paperSubstantial claim - Interpretationown data
Protein detection is more sensitive than mRNA detection.
“Many cells have undetectable levels of CD46 mRNA even in the absence of targeting guides, likely reflecting the high drop-out rates of scRNA-seq, and the increased sensitivity that comes with protein detection.”
- Also stated in: § CRISPR screens with single cell multimodal readout · hedged
“The low dropout of protein detection7,8 suggests that ECCITE-seq could be more sensitive in detecting expression phenotypes than scRNA-seq alone.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [7] Hill, A. J. et al. Nat. Methods 15, 271–274 (2018).
Not checked in the free run.
- cites [8] Stoeckius, M. et al. Nat. Methods 14, 865–868 (2017). source
Not checked in the free run.
- Rests on an assumption: scRNA-seq has high dropout rates, which cause many cells to have undetectable CD46 mRNA even without targeting guides.
- Also stated in: § CRISPR screens with single cell multimodal readout · hedged
- Interpretationown data
Combining clonotype information with surface protein marker expression allowed fine separation of cell populations of interest and careful determination of molecular phenotypes.
“The recovery of clonotype information together with surface protein marker expression allowed fine separation of specific cell populations of interest, enabling careful determination of molecular phenotypes.”
Quote in the paperSubstantial claim - Interpretationreasoning
ECCITE-seq can be applied to any sgRNA library that uses the Cas9 S. pyogenes scaffold sequence.
“ECCITE-seq is readily applicable to any sgRNA library with the Cas9 S. pyogenes scaffold sequence and, by allowing direct capture of sgRNA molecules, overcomes documented problems of barcode swapping events observed with Perturb-seq that have the potential to confound single cell perturbation screens17–20.”
Quote in the paperSubstantial claim - Interpretationreasoning
Direct capture also captures a highly abundant RNA polymerase III transcript, which contributes to the high guide recovery rates observed.
“Direct capture has the added benefit of capturing a highly abundant RNA polymerase III transcript, contributing to the observed high rates of guide recovery.”
Quote in the paperNot a substantial claim - Study design and conductown data
Running scRNA-seq alone on the same cell aliquot showed no reduction in transcripts per cell, indicating that capturing additional modalities did not harm transcript capture.
“In parallel, we performed scRNA-seq alone on the same aliquot of cells and confirmed no reduction in transcripts per cell (Supplementary Fig. 1e.), demonstrating no detrimental effect of capturing additional modalities on transcript capture.”
Quote in the paperSubstantial claim - Interpretationreasoning
ECCITE-seq enables detailed phenotypic and functional characterization of individual cells.
“The enhancements to the CITE-seq toolkit provided by ECCITE-seq enable detailed phenotypic and functional characterization of single cells.”
Quote in the paperSubstantial claim - Interpretationcitation
ECCITE-seq may detect expression phenotypes more sensitively than scRNA-seq alone.
“The low dropout of protein detection7,8 suggests that ECCITE-seq could be more sensitive in detecting expression phenotypes than scRNA-seq alone.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [7] Hill, A. J. et al. Nat. Methods 15, 271–274 (2018).
Not checked in the free run.
- cites [8] Stoeckius, M. et al. Nat. Methods 14, 865–868 (2017). source
Not checked in the free run.
- Interpretationcitation
Direct capture of sgRNA molecules lets ECCITE-seq avoid the barcode-swapping problems reported for Perturb-seq.
“ECCITE-seq is readily applicable to any sgRNA library with the Cas9 S. pyogenes scaffold sequence and, by allowing direct capture of sgRNA molecules, overcomes documented problems of barcode swapping events observed with Perturb-seq that have the potential to confound single cell perturbation screens17–20.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [17] Replogle, J. M. et al. Direct capture of CRISPR guides enables scalable, multiplexed, and multi-omic Perturb-seq. Preprint at https://doi.org/10.1101/503367 (2018). source
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- cites [18] Shen, J. P. et al. Nat. Methods 14, 573–576 (2017). source
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- cites [19] Najm, F. J. et al. Nat. Biotechnol. 36, 179–189 (2018). source
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- cites [20] Kalhor, R. et al. Science 361, 893 (2018). source
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- Premisecitation
sgRNAs have a structure suitable for direct capture: the variable targeting region is at the 5' end and the 3' end is an invariant scaffold.
“sgRNAs have a structure that lends themselves to direct capture: the variable region that guides Cas9 to its target site is at the 5’ end while the 3’ end is an invariant scaffold25,26.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [25] Patel, S. J. et al. Nature 548, 537–542 (2017). source
Not checked in the free run.
- cites [26]
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- May combine two claims. 10
- Classified as probably not a substantial claim. 1
May combine two claims.
Findingown dataHuman-specific cell hashtags separated the three human samples, and their distribution matched transcriptome-based clustering.
“Cell hashtags specific to human cells were used to distinguish the three human samples, and the hashtag distribution was consistent with transcriptome-based clustering (Fig.1c, [I]).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataA mixture of human and mouse cells carrying different non-targeting sgRNAs was clearly separated by transcriptome, surface protein and sgRNA measurements, showing the method's specificity.
“A mixture of human and mouse cells transduced with different sets of non-targeting sgRNAs was well resolved by transcriptome, surface protein and sgRNA content, demonstrating the specificity of this approach (Fig. 1b, Supplementary Fig. 1a, Supplementary Tables 1,2).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMost markers were enriched at both protein and RNA levels in expected clusters, matching earlier 3' CITE-seq results.
“The majority of markers showed enrichmentat the level of both protein and RNA (not shown) in expected clusters, consistent with our previous 3’ CITE-seq results7.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Hill, A. J. et al. Nat. Methods 15, 271–274 (2018).
Not checked in the free run.
May combine two claims.
Findingown dataThe control sample yielded 1,606 clonotypes from 2,796 barcodes, and its top CD4+ clonotype (TRB CDR3 CASSTLQGKETQYF) made up about 1% of clonotype-associated barcodes.
“The control sample had 1,606 detected clonotypes from 2,796 barcodes, with the top CD4+ clonotype (defined by TRB CDR3 sequence: CASSTLQGKETQYF) accounting for ~1% of recovered clonotype-associated barcodes.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMost cells had detectable CD46 protein, which collapsed in cells with detectable anti-CD46 sgRNAs but not in cells with non-targeting sgRNAs.
“Cellular perturbations measured at transcript and protein level by ECCITE-seq reveal important features to consider, exemplified by CD46: most cells have detectable levels of protein, which collapse in cells with detectable levels of sgRNAs directed against CD46, but not in cells with non-targeting sgRNAs (Supplementary Fig. 1f).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTarget gene expression loss was readily seen at both mRNA and protein levels for ITGB1 (CD29) and CD46, and at the mRNA level for JAK1.
“Loss of expression of target genes at the level of mRNA and protein was readily apparent for ITGB1 (the gene encoding CD29 protein) and CD46 (Fig. 1e), and similarly apparent at the mRNA level for JAK1.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataClustering cells assigned to one guide by sgRNA counts produced 13 distinct clusters, matching the 13 guides used.
“Clustering based on sgRNA counts of cells assigned to one guide revealed 13 distinct clusters, corresponding to the 13 guides in the experiment.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationK562 cells carry a single TP53 allele whose transcript is likely degraded by nonsense-mediated decay.
“TP53 transcript was poorly detected, consistent with K562 cells expressing a single allele of TP53 that is likely a substrate for nonsense-mediated decay27.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27]
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe modular design of ECCITE-seq allows readouts of screens to be tailored, potentially enabling panels of transcripts and proteins to be interrogated in response to perturbations instead of or in addition to the transcriptome.
“The modular nature of ECCITE-seq allows the tailoring of readouts of such screens, potentially allowing the investigator to interrogate panels of transcripts and proteins of interest in response to their perturbations in addition to, or instead of, the transcriptome.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningMany cells had undetectable CD46 mRNA even without targeting guides, probably because scRNA-seq has high dropout.
“Many cells have undetectable levels of CD46 mRNA even in the absence of targeting guides, likely reflecting the high drop-out rates of scRNA-seq, and the increased sensitivity that comes with protein detection.”
Quote in the paperNot a substantial claim- Rests on an assumption: scRNA-seq has high dropout rates, which cause many cells to have undetectable CD46 mRNA even without targeting guides.
The paper relies on this without support.
Protein levels are generally much higher than the corresponding mRNA levels, so antibody-based protein detection is a more robust measure of gene expression than mRNA.
“Given that protein levels are typically much higher than corresponding mRNAs, detection of proteins via antibody-derived tags (hereafter called protein tags) is a more robust measure of gene expression.”
The paper relies on this without support.
scRNA-seq has high dropout rates, which cause many cells to have undetectable CD46 mRNA even without targeting guides.
“Many cells have undetectable levels of CD46 mRNA even in the absence of targeting guides, likely reflecting the high drop-out rates of scRNA-seq, and the increased sensitivity that comes with protein detection.”
The paper relies on this without support.
The 10x Genomics 5P workflow adds the cell barcode through the template switch oligo using a generic soluble poly(dT) primer for reverse transcription, which permits custom RT primers to be added for sequences of interest.
“In contrast to commonly used 3’ tag scRNA-seq methods where cell barcode information is appended to transcripts through the use of barcoded RT oligos, the 10× Genomics 5P workflow appends the barcode via TSO, using a generic, soluble poly(dT) oligo to prime RT, opening up the possibility of adding custom RT primers to sequences of interest.”
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?
ECCITE-seq simultaneously measures transcriptomes, surface protein levels, and perturbations at single-cell resolution.
“We recently introduced expanded CRISPR-compatible CITE-seq (ECCITE-seq), which simultaneously measures transcriptomes, surface protein levels, and perturbations at singlecell resolution [18].”
From Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens (2021) · cites this paper as [18]
A proof-of-concept ECCITE-seq study detected protein, RNA, and guide RNAs in individual cells but was small-scale and lacked the analytical framework needed for large screens.
“A proof-of-concept study using ECCITE-seq5 demonstrated simultaneous detection of protein, RNA, and guide RNAs in individual cells and was applied at a small scale without the analytical framework required for large scale screens.”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [5]
32 datasets report scRNA-seq exclusively; the resource also includes scATAC-seq from three publications, one with simultaneous protein measurements.
“While 32 datasets report scRNA-seq exclusively, we also include scATAC-seq from three publications, one with simultaneous protein measurements39.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [39]
The authors included scATAC-seq data from three different sources: Spear-ATAC, CRISPR-sciATAC, and ASAP-seq.
“We included scATAC-seq data from three different sources: Spear-ATAC42, CRISPR-sciATAC59 and ASAP-seq39.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [39]
ECCITE-seq and Perturb-CITE-seq entail in tandem detection of transcripts, proteins, and sgRNAs.
“ECCITE-seq [15] and Perturb-CITE-seq [16] are two paradigmatic cases that entail in tandem detection of transcripts, proteins, and sgRNAs.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [15]
CRISPR platforms have produced much larger genetic interaction maps than RNA interference, including CRISPRa gain-of-function maps and maps combining gain- and loss-of-function perturbations.
“This approach was pioneered in mammalian cells using RNA interference80,81, but CRISPR-based platforms have enabled the generation of substantially larger genetic interaction maps82, including CRISPRa-based gain-of-function maps83 and maps that combine gain- and loss-of-function perturbations to map directional pathways84.”
From CRISPR-based functional genomics for neurological disease (2020) · cites this paper as [84]
ECCITE-seq incorporates CRISPR-compatible transcriptome, immunity repertoire and proteome indexing, including clonotype profiling and CRISPR-directed perturbations at high sensitivity and single-cell resolution.
“Finally, expanded CRISPR-compatible CITE-seq (ECCITE-seq) expanded the protein multi-omics field to incorporate CRISPR-compatible transcriptome, immunity repertoire and proteome indexing, including both clonotype profiling and CRISPR-directed perturbations at high sensitivity and single-cell resolution81.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [81]
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Flagged first, then by citations
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Integrated analysis of multimodal single-cell data
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Computational principles and challenges in single-cell data integration
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Single-cell multimodal omics: the power of many
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Single-cell proteomics enabled by next-generation sequencing or mass spectrometry
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High-Throughput Mapping of B Cell Receptor Sequences to Antigen Specificity
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“2C) [125, 126].”
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CRISPR-based functional genomics for neurological disease
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Joint single-cell measurements of nuclear proteins and RNA in vivo
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Clustering of single-cell multi-omics data with a multimodal deep learning method
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Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq
Wessels, H.-H., Méndez-Mancilla, A., Hao, Y., Papalexi, E., Mauck, W. M., Lu, L., Morris, J. A., Mimitou, E. P., Smibert, P., Sanjana, N. E., et al. (2023) · Nature Methods · doi:10.1038/s41592-022-01705-x · Cited by 79 · see citing works
Self-citationSurface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation
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Isolating salient variations of interest in single-cell data with contrastiveVI
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Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC
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Pooled genetic perturbation screens with image-based phenotypes
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- Authors
- Mimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., Satija, R., Sanjana, N. E., Koralov, S. B., & Smibert, P.
- Affiliations
- New York Genome Center (US)
- The Jackson Laboratory for Genomic Medicine (US)
- New York University (US)
- University of Connecticut (US)
- Corresponding author
- Smibert, Peter
- ORCID
- Venue
- Nature Methods
- Published
- 2019-04-22
- Volume
- 16
- Issue
- 5
- Pages
- 409-412
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC6557128
- OpenAlex
- W2939422961
- Semantic Scholar
- 89f6897236672fe80990d2692ea729d831e84d71
- Cited by
- 520
- References
- 45
- Citations by year
- 2026: 67 · 2025: 62 · 2024: 68 · 2023: 84 · 2022: 75 · 2021: 81 · 2020: 67 · 2019: 14
- FWCI
- 19.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- Hope Funds for Cancer Research
- Chan Zuckerberg Initiative
- New York Genome Center
- National Institutes of Health (1dp2hg009623-01)
- National Heart, Lung, and Blood Institute (R01HL125816)
- National Human Genome Research Institute (DP2-HG-009623, DP2HG010099, R21HG009748)
- National Cancer Institute (P30CA034196)
- National Institute of General Medical Sciences (R35GM124998)
- 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 › Genomics and Phylogenetic Studies
- Keywords
- CRISPR screen, cell surface proteins, transcriptome, single-cell proteomics, tumor cell populations
- MeSH terms
- RNA, Guide, CRISPR-Cas Systems, Animals, Humans, Leukocytes, Mononuclear, Proteins, Skin Neoplasms, Tumor Cells, Cultured, Lymphoma, T-Cell, Cutaneous, Sequence Analysis, RNA, Gene Expression Profiling, NIH 3T3 Cells, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-04.
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