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Cited by 152Single-cell and spatial transcriptomicsGenomics and Chromatin DynamicsGene Regulatory Network Analysis

NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells

Open access
Chen, Amy F., Parks, Benjamin, Kathiria, Arwa S., Ober-Reynolds, Benjamin, Goronzy, Jorg J., Greenleaf, William J.·2022·Nature Methods
DOI 10.1038/s41592-022-01461-yPMC PMC11192021

Published version of a preprint

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Abstract

In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation. NEAT-seq offers a tri-omics tool for profiling nuclear protein abundance along with ATAC-seq and RNA-seq in single cells.

AI snapshot

Written from the paper’s full text.

TL;DR

NEAT-seq enables simultaneous single-cell measurement of nuclear protein abundance, chromatin accessibility, and gene expression by combining oligo-conjugated antibodies with 10X Genomics Multiome. Applied to CD4 memory T cells, it reveals transcription, chromatin-binding, and translational modes of TF regulation and links a non-coding GWAS SNP to GATA3-dependent regulation of TSEN54.

Key findings

  • In a human K562/mouse ESC barnyard experiment, NEAT-seq detected median 13,496 and 16,883 RNA UMIs; 4,732 and 5,086 genes; and 26,762 and 26,929 ATAC fragments per cell, respectively, with >4-fold enrichment of GATA1 and OCT4 ADT counts and >2-fold enrichment of SOX2 ADT counts in their respective cell types.
  • HTO-based doublet detection enabled in silico removal of almost 50% of doublets, and NEAT-seq ADT data labeled CD4 memory T cell subsets more robustly than RNA data, which required smoothing due to dropout.
  • In CD4 memory T cells, the authors identified seven scATAC-seq clusters largely corresponding to scRNA-seq clusters and proposed three TF regulatory modes: transcriptional regulation (RORγT, Tbet), transcriptional expression but requirement of other TFs for chromatin binding (FOXP3), and translational regulation (GATA3).
  • GATA3 ADT levels, but not RNA levels, correlated with global GATA3 motif accessibility; candidate post-transcriptional regulators included EEF1G, RPL18, PABPC4, GAB2, and NIBAN1. TF-peak-gene linkages identified GATA3 targets CCR4, CCR8, and IL4R and the RORγT target CCR6, and the GWAS SNP rs62088464 in a GATA motif was linked to TSEN54, with most ATAC reads in high-GATA3-ADT cells mapping to the risk allele.

Methods

  • Antibodies were conjugated to barcoded oligos and pre-bound with E. coli ssDNA binding protein (EcoSSB) to improve nuclear protein staining; stained fixed/permeabilized cells were processed with the 10X Genomics Multiome kit for scATAC-seq and scRNA-seq, with HTOs for multiplexing and doublet detection.
  • The method was tested in a human K562/mouse ESC barnyard experiment and applied to primary human CD4+CD45RO+ memory T cells using an antibody panel against GATA3, Tbet, RORγT, FOXP3, and Helios.
  • Analysis used cellranger-ARC, ArchR, Seurat, chromVAR, Spearman correlations between TF ADT levels and peak accessibility or gene expression, TF-peak-gene linkage identification, GWAS SNP overlap, and allele-specific accessibility testing with a one-sided binomial test.

Limitations

  • The paper states that the CD4 memory T cell dataset showed a small reduction in unique ATAC and RNA reads relative to a standard 10X Multiome experiment, similar to other fixed single-cell assays.
  • The paper notes that cell fixation increases the doublet rate in droplet-based single-cell technologies, and that SOX2 antibody enrichment was low, with western blot showing specific but relatively weak binding.
  • The paper states that uncoupling of TF protein expression and motif accessibility highlights caveats of using motif accessibility alone to infer TF activity; evident from the text, the primary CD4 memory T cell analysis is limited to one TF panel and does not report replication across multiple donors.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41592-022-01461-y

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 19 cited sources checked · 0 supported · 0 flagged

  • Findingown data
    § Abstract

    Pre-incubating oligo-antibody with EcoSSB dramatically improved correlation between Cy5 antibody signal and GFP levels within the nucleus.

    “Pre-incubating the oligo-antibody with EcoSSB dramatically improved correlation between Cy5 antibody signal and GFP levels within the nucleus (Fig. 1a).”
    Quote in the paperSubstantial claim
  • Findingown data
    § Abstract

    NEAT-seq enables simultaneous quantification of nuclear protein epitope abundance, chromatin accessibility, and transcriptome in single cells.

    “In this work, we describe NEAT-seq (nuclear protein epitope abundance, chromatin accessibility, and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma.”
    • Also stated in: § Abstract · boosted
      “Together, these results demonstrate simultaneous quantification of endogenous nuclear protein abundance, chromatin accessibility, and gene expression in fixed single cells.”
    Quote in the paperSubstantial claim
  • Findingown data
    § Abstract

    EcoSSB improved specificity of cytosolic protein staining.

    “EcoSSB similarly improved specificity of cytosolic protein staining (Extended Data Fig. 1d).”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Abstract

    GATA1 staining is markedly increased in K562 cells relative to ESCs using a GATA1 antibody linked to a Cy5-modified oligo.

    “We observed a marked increase in GATA1 staining in K562 relative to ESCs using a GATA1 antibody linked to the Cy5-modified oligo (Extended Data Fig. 1c).”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Abstract

    Compared with inCITE-seq, the NEAT-seq approach showed a moderate improvement in staining that was most pronounced at lower protein levels.

    “In comparison with the inCITE-seq protocol8, our approach showed a moderate improvement in staining that was most pronounced at lower protein levels (Supplementary Note 2, Extended Data Fig 1e).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Chung, H. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods 18, 1204–1212 (2021). source

      Not checked in the free run.

  • May combine two claims. 24
  • Classified as probably not a substantial claim. 7
  • The quote only partly states this claim. 3

  • May combine two claims.

    Findingown data
    § Abstract

    HTO staining allowed clear separation between positive and negative HTO staining, enabling stringent cutoffs and identification of droplets positive for more than one hashing oligo, enabling in silico removal of almost 50% of doublets.

    “Clear separation between positive and negative HTO staining allowed us to set stringent cutoffs and identify droplets that were positive for more than one hashing oligo, enabling in silico removal of almost 50% of doublets (Fig. 1c-d, Extended Data Fig. 2a-d).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Antibody-based protein measurements for each transcription factor showed clear enrichment in the cell type that the transcription factor is known to drive and provided more robust detection of target transcription factors compared to RNA data.

    “Our antibody-based protein measurements for each TF showed clear enrichment in the cell type that the TF is known to drive and provided more robust detection of target TFs compared to our RNA data (Fig. 2c,d, Extended Data Fig. 4a-c)”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Fragment length distribution and average TSS enrichment were comparable to bulk ATAC-seq libraries.

    “The fragment length distribution and average TSS enrichment were also comparable to bulk ATAC-seq libraries (Extended Data Fig. 2e-f).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Seven clusters were identified in the CD4 memory T cell population using scATAC-seq, which largely corresponded to clusters identified using scRNA-seq.

    “We identified seven clusters in the population using scATAC-seq, which largely corresponded to clusters identified using scRNA-seq (Fig. 2a, Extended Data Fig. 3a).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Tn5 insertions in peaks were highly correlated with bulk data (r=0.90) and RNA-seq data was moderately correlated (r=0.67), with similar RNA correlation for standard 10X Multiome data.

    “Tn5 insertions in peaks were highly correlated with bulk data (r=0.90) and RNA-seq data was moderately correlated (r = 0.67), with a similar RNA correlation value observed for standard 10X Multiome data (Extended Data Fig. 2g-h).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    For human and mouse singlets, median RNA UMIs were 13,496 and 16,883; genes were 4,732 and 5,086; and ATAC fragments were 26,762 and 26,929 per cell, respectively.

    “For human and mouse singlets, we detected a median of 13,496 and 16,883 RNA UMIs; 4,732 and 5,086 genes; and 26,762 and 26,929 ATAC fragments per cell, respectively (Fig. 1e-g).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    GATA1 and OCT4 ADT counts were >4-fold enriched and SOX2 ADT counts were >2-fold enriched in their respective cell types.

    “Importantly, we observed >4-fold enrichment of GATA1 and OCT4 antibody-derived tag (ADT) counts and >2-fold enrichment of SOX2 ADT counts in their respective cell types (Fig. 1h).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Abstract

    In the CD4 memory T cell dataset, there was a small reduction in unique ATAC and RNA reads detected relative to a standard 10X Multiome experiment, similarly observed in other fixed single cell assays.

    “In this dataset, we observed a small reduction in unique ATAC and RNA reads detected relative to a standard 10X Multiome experiment, which was similarly observed in other fixed single cell assays (Supplementary Tables 1-3).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    For FOXP3 and Helios, motifs were not enriched in correlated peaks, consistent with earlier observations that these transcription factors are not correlated with global changes in motif accessibility.

    “For FOXP3 and Helios, motifs were not enriched in correlated peaks, consistent with our earlier observations that these TFs are not correlated with global changes in motif accessibility (Fig. 2c).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Most ATAC-seq reads in cells expressing high GATA3 ADT levels mapped to the risk allele, while little difference was observed in cells with lower GATA3 ADT levels.

    “Indeed, we observed that most ATAC-seq reads in cells expressing high GATA3 ADT levels mapped to the risk allele, while little difference was observed in cells with lower GATA3 ADT levels (Fig. 3j).”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data
    § Abstract

    In the CD4 memory T cell experiment, normalizing TF ADT counts to total ADT counts or using raw ADT counts produced results very similar to NPC normalization.

    “We observed very similar results when normalizing to total ADT counts or just using raw ADT counts (Extended Data Fig S4a).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    GATA3 ADT levels, but not RNA levels, were correlated with global changes in GATA3 motif accessibility.

    “The ADT levels, but not RNA levels, were correlated with global changes in GATA3 motif accessibility.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Smoothing of signal across neighboring cells in the UMAP was necessary for identification of cell types using RNA-seq data due to high dropout rates, while unsmoothed ADT data were sufficient to clearly label cell types.

    “smoothing of signal across neighboring cells in the UMAP was necessary for identification of cell types using RNA-seq data due to high dropout rates, while unsmoothed ADT data were sufficient to clearly label cell types (Extended Data Fig. 4a, d-e).”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data
    § Abstract

    Among the top upregulated genes were EEF1G, RPL18, PABPC4, GAB2, and NIBAN1.

    “Among the top upregulated genes were several core translation regulators, including the elongation factor EEF1G, large ribosome subunit RPL18, and poly-A binding protein PABPC4, along with more indirect regulators such as GAB2 and NIBAN121 (Fig. 2e, Extended Data fig. 6b).”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data
    § Abstract

    In the barnyard experiment, RNA-seq reads showed greater cross-cell read contamination than ATAC-seq reads, particularly from mouse transcripts that were highly abundant in non-cell droplets.

    “For RNA-seq reads we observed greater cross-cell read contamination, particularly from mouse transcripts which had high abundance in non-cell droplets.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    FOXP3 and Helios exhibited strong correlation between gene accessibility, RNA, and protein abundance but had differing patterns of motif accessibility.

    “FOXP3 and Helios exhibited strong correlation between gene accessibility, RNA, and protein abundance but had differing patterns of motif accessibility, suggesting that their expression is regulated transcriptionally but presence of the protein does not result in increased chromatin accessibility at motif sites.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    The risk allele is associated with increased TSEN54 expression in GTEx data and TSEN54 was the gene most strongly associated with the risk allele in various tissues.

    “Similarly, the risk allele is associated with increased TSEN54 expression in GTEx data and TSEN54 was the gene most strongly associated with the risk allele in various tissues (Extended Data Fig. 9).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Candidate direct target genes were significantly enriched for GO terms related to T cell function and included canonical surface markers for the corresponding cell type: among GATA3 targets were Th2 markers CCR4, CCR8, and IL4R, and among RORγT targets was the Th17 marker CCR6.

    “The candidate direct target genes were significantly enriched for GO terms related to T cell function and included canonical surface markers for the corresponding cell type: Among the GATA3 targets were Th2 markers CCR4, CCR8, and IL4R, and among RORγT targets was the Th17 marker, CCR6 (Fig. 3c-f, Extended Data Fig. 7c-e, 8).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § Abstract

    Tbet, GATA3, RORγT, FOXP3, and Helios drive Th1, Th2, Th17, and Treg cell fate respectively.

    “Our antibody panel targeted TFs that drive Th1 (Tbet), Th2 (GATA3), Th17 (RORγT), and Treg (FOXP3 and Helios) cell fate17.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Fang, D. & Zhu, J. Dynamic balance between master transcription factors determines the fates and functions of CD4 T cell and innate lymphoid cell subsets. J. Exp. Med. 214, 1861–1876 (2017). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Abstract

    NEAT-seq data internally validate GATA3-specific regulation of a non-coding GWAS SNP within a GATA motif and link it to a putative target gene.

    “We also link a non-coding GWAS SNP within a GATA motif to a putative target gene, using NEAT-seq data to internally validate GATA3-specific regulation of SNP impact.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § Abstract

    Primary human CD4 memory T cells are composed of distinct T cell subsets driven by known master transcription factors.

    “We next applied NEAT-seq to profile primary human CD4 memory T cells composed of distinct T cell subsets driven by known master TFs, providing a diverse system for dissecting the regulatory mechanisms upstream and downstream of these TFs16.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [16] Sallusto, F. & Lanzavecchia, A. Heterogeneity of CD4+ memory T cells: functional modules for tailored immunity. Eur. J. Immunol. 39, 2076–2082 (2009). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Abstract

    The observation of high accessibility and low RNA at cytokine loci is suggestive of epigenetic priming, consistent with the primed status of memory T cells.

    “This observation is suggestive of epigenetic priming, where transcription is absent but the gene locus is accessible and poised for transcriptional activation, and is consistent with the primed status of memory T cells18.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Barski, A. et al. Rapid recall ability of memory T cells is encoded in their epigenome. Sci. Rep. 7, 39785 (2017). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Abstract

    RORγT and Tbet are regulated transcriptionally.

    “suggesting that these TFs are regulated transcriptionally”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation
    § Abstract

    The gene linked to the peak containing rs62088464 encodes TSEN54, a gene with significantly enriched expression in the sputum of patients with type-2 airway inflammation.

    “The gene linked to the peak containing this SNP encodes TSEN54, a gene with significantly enriched expression in the sputum of patients with type-2 airway inflammation28,29 (Fig. 3g-i).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [28] Peters, M. C. et al. A transcriptomic method to determine airway immune dysfunction in T2-high and T2-low asthma. Am. J. Respir. Crit. Care Med. 199, 465–477 (2019). source

      Not checked in the free run.

    • cites [29] Singh, D. et al. COPD patients with chronic bronchitis and higher sputum eosinophil counts show increased type-2 and PDE4 gene expression in sputum. J. Cell. Mol. Med. 25, 905–918 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning
    § Abstract

    TF-peak-gene linkages could be used to interpret the effects of non-coding GWAS SNPs on transcription factor activity and connect the SNPs to putative target genes.

    “We also reasoned that these TF-peak-gene linkages could be used to interpret the effects of non-coding GWAS SNPs on TF activity and connect the SNPs to putative target genes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Abstract

    NIBAN1 phosphorylates EIF4EBP1 to allow assembly of the initiation complex.

    “which, like NIBAN1, phosphorylates EIF4EBP1 to allow assembly of the initiation complex23”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [23] Sonenberg, N. & Hinnebusch, A. G. Regulation of translation initiation in eukaryotes: mechanisms and biological targets. Cell 136, 731–745 (2009). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Abstract

    The data suggest three distinct modes of regulation in the transcription factor panel: transcriptional regulation, transcriptional regulation of expression but requirement of other transcription factors for chromatin binding, and translational regulation.

    “By comparing the TF gene locus chromatin accessibility, RNA expression, protein abundance, and genome-wide TF binding motif accessibility across cells for each TF assayed, our data suggest three distinct modes of regulation in our TF panel.”
    • Also stated in: § Abstract · hedged
      “Together, our results suggest three regulatory mechanisms used to modulate activity of the TFs in our panel: transcriptional regulation, as demonstrated by concordant RNA, protein, and motif accessibility patterns (RORγT, and Tbet); transcriptional regulation of expression but requirement of other TFs for chromatin binding (FOXP3); and translational regulation (GATA3).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Abstract

    Decreased pulmonary function can result from inflammatory lung diseases associated with Th2 immune responses.

    “which can result from inflammatory lung diseases associated with Th2 immune responses27”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [27] Gieseck, R. L., Wilson, M. S. & Wynn, T. A. Type 2 immunity in tissue repair and fibrosis. Nat. Rev. Immunol. 18, 62–76 (2018). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Abstract

    TF activity can be predicted by measuring differences in TF motif accessibility across cells using chromVAR.

    “We can also predict TF activity by measuring differences in TF motif accessibility across cells using chromVAR39.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [39] Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Abstract

    GATA3 binds the risk allele sequence to activate the regulatory element and drive expression of TSEN54, and this binding is disrupted with the protective allele.

    “Together, these results suggest that GATA3 binds the risk allele sequence to activate the regulatory element and drive expression of TSEN54 and that this binding is disrupted with the protective allele.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data
    § Abstract

    These observations are consistent with post-transcriptional regulatory mechanisms restricting GATA3 protein expression in memory T cells, which could only be uncovered with the addition of protein quantification.

    “These observations are consistent with post-transcriptional regulatory mechanisms restricting GATA3 protein expression in memory T cells, which could only be uncovered with the addition of protein quantification.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data
    § Abstract

    FOXP3 and Helios expression is regulated transcriptionally but presence of the protein does not result in increased chromatin accessibility at motif sites.

    “suggesting that their expression is regulated transcriptionally but presence of the protein does not result in increased chromatin accessibility at motif sites”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § Abstract

    The capacity to link nuclear protein levels to epigenetic and transcriptional status provides a powerful approach for studying mechanisms of gene regulation.

    “Since nuclear proteins encompass many proteins involved in gene regulation, the capacity to link nuclear protein levels to epigenetic and transcriptional status provides a powerful approach for studying mechanisms of gene regulation.”
    Quote in the paperNot a substantial claim
33 of 33

  • The paper relies on this without support.

    Quantification of nuclear gene regulatory proteins along with chromatin accessibility profiling and RNA-seq has not been demonstrated.

    “However, quantification of nuclear gene regulatory proteins along with chromatin accessibility profiling and RNA-seq has not been demonstrated.”

  • The paper relies on this without support.

    GAB2 is a direct activator of PI3K.

    “while GAB2 is a direct activator of PI3K.”

  • The authors state this as an assumption.

    Levels of the nuclear pore complex are relatively constant across cells.

    “since we expected that levels of the nuclear pore complex should be relatively constant across cells”
3 of 3

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?

  • NEAT-seq co-profiles nuclear protein epitopes, chromatin accessibility and transcriptome in single cells.

    “Incorporating even more modalities is NEAT-seq80, which co-profiles the abundance of nuclear protein epitopes, chromatin accessibility and the transcriptome in single cells.”

    From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [80]

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Related

Authors
Chen, A. F., Parks, B., Kathiria, A. S., Ober-Reynolds, B., Goronzy, J. J., & Greenleaf, W. J.
Affiliations
  • Stanford Medicine (US)
  • Chan Zuckerberg Biohub San Francisco (US)
Corresponding author
Greenleaf, William J.
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2022-05
Volume
19
Issue
5
Pages
547-553
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-022-01461-y
PMC
PMC11192021
OpenAlex
W4225287808
Semantic Scholar
eb381b263ca20a3cb5aed48ee44dd0331fb26d6d
Cited by
139
References
60
Citations by year
2026: 22 · 2025: 30 · 2024: 31 · 2023: 39 · 2022: 14 · 2021: 1 · 2013: 1
FWCI
10.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Methods (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (P50HG007735, UM1HG009442)
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (F32GM135996)
  • United States Department of Commerce | National Institute of Standards and Technology
  • Division of Intramural Research, National Institute of Allergy and Infectious Diseases (U19AI057266)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
single-cell multi-omics, chromatin accessibility, single-cell transcriptome, memory CD4+ T cells, master transcription factors, transcriptional regulation, translational regulation, GATA3, GATA motif, single nucleotide polymorphisms, T cell differentiation
MeSH terms
Chromatin, Nuclear Proteins, Gene Expression Profiling, Genome-Wide Association Study, Transcriptome

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