The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index; 1 of the 19 sources its claims cite were checked for support.
NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells
Open accessPublished version of a preprint
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.
Overview
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.
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Claims the paper makes
32 Own result · 16 Not checked yet · 16 Own interpretation · 3 Own method · 1 Supported · 1 No basis given
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In the paper
“For Helios, binding may result in chromatin compaction rather than accessibility, as was recently observed in mouse hematopoietic progenitor cells20.”
What the sources say
- Supportedcites [20] Cova, G. et al. Helios represses megakaryocyte priming in hematopoietic stem and progenitor cells. J. Exp. Med. 218, e20202317 (2021). source
The Results section reports that Helios binding coincided with chromatin closing in wild-type hematopoietic stem and progenitor cells, while Helios-knockout cells showed increased accessibility at those sites. It concludes that Helios promotes chromatin compaction at target genes, supporting the citing sentence’s hedged claim. (LLM, conf=0.93)
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“Pre-incubating the oligo-antibody with EcoSSB dramatically improved correlation between Cy5 antibody signal and GFP levels within the nucleus (Fig. 1a).”
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“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.”
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“EcoSSB similarly improved specificity of cytosolic protein staining (Extended Data Fig. 1d).”
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“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).”
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“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).”
What the sources say
- Not checked yetcites [8] Chung, H. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods 18, 1204–1212 (2021). source
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“These clusters also exhibited high chromatin accessibility at functionally relevant cytokine gene loci, but low or undetectable RNA expression (Extended Data Fig. 3c-d).”
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“We further confirmed that quantification of the conjugated oligo reflected GFP levels within the nucleus by sorting nuclei into 3 populations of increasing GFP abundance for qPCR targeting the conjugated oligo (Extended Data Fig. 1a-b).”
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“The risk allele, which preserves the GATA motif, is associated with decreased pulmonary function26, which can result from inflammatory lung diseases associated with Th2 immune responses27.”
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“We overlapped peaks in our TF-peak-gene linkages with candidate causal GWAS SNPs25 and identified rs62088464 located within a GATA motif sequence in a GATA3 ADT-associated peak.”
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In the paper
“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).”
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“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)”
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“The fragment length distribution and average TSS enrichment were also comparable to bulk ATAC-seq libraries (Extended Data Fig. 2e-f).”
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“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).”
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“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).”
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“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).”
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“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).”
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“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).”
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“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).”
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“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).”
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“We observed very similar results when normalizing to total ADT counts or just using raw ADT counts (Extended Data Fig S4a).”
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In the paper
“The ADT levels, but not RNA levels, were correlated with global changes in GATA3 motif accessibility.”
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In the paper
“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).”
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“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).”
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“For RNA-seq reads we observed greater cross-cell read contamination, particularly from mouse transcripts which had high abundance in non-cell droplets.”
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“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.”
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In the paper
“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).”
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“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).”
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In the paper
“Our antibody panel targeted TFs that drive Th1 (Tbet), Th2 (GATA3), Th17 (RORγT), and Treg (FOXP3 and Helios) cell fate17.”
What the sources say
- Not checked yetcites [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
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In the paper
“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.”
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In the paper
“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.”
What the sources say
- Not checked yetcites [16] Sallusto, F. & Lanzavecchia, A. Heterogeneity of CD4+ memory T cells: functional modules for tailored immunity. Eur. J. Immunol. 39, 2076–2082 (2009). source
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In the paper
“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.”
What the sources say
- Not checked yetcites [18] Barski, A. et al. Rapid recall ability of memory T cells is encoded in their epigenome. Sci. Rep. 7, 39785 (2017). source
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In the paper
“suggesting that these TFs are regulated transcriptionally”
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In the paper
“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).”
What the sources say
- Not checked yetcites [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 yetcites [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
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In the paper
“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.”
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In the paper
“which, like NIBAN1, phosphorylates EIF4EBP1 to allow assembly of the initiation complex23”
What the sources say
- Not checked yetcites [23] Sonenberg, N. & Hinnebusch, A. G. Regulation of translation initiation in eukaryotes: mechanisms and biological targets. Cell 136, 731–745 (2009). source
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“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).”
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In the paper
“which can result from inflammatory lung diseases associated with Th2 immune responses27”
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We can also predict TF activity by measuring differences in TF motif accessibility across cells using chromVAR39.”
What the sources say
- Not checked yetcites [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
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In the paper
“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.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“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.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“suggesting that their expression is regulated transcriptionally but presence of the protein does not result in increased chromatin accessibility at motif sites”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“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.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence.
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”
Claims other papers make about this one
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In the citing paper
“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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Metadata
- 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.
- Venue
- Nature Methods
- Published
- 2022-05
- Volume
- 19
- Issue
- 5
- Pages
- 547-553
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- 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
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- 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
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.
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