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
Every source the paper cites for this claim was checked and supports it.
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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In the paper
“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 the paper
“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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In the paper
“EcoSSB similarly improved specificity of cytosolic protein staining (Extended Data Fig. 1d).”
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In the paper
“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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In the paper
“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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In the paper
“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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“Within these correlated gene sets were genes known to be enriched or functionally important in the memory T cell subset driven by the TF in question, such as IL4R for GATA3 and CTLA4 for both Helios and FOXP324.”
What the sources say
- Not checked yetcites [24] Schmiedel, B. J. et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701–1715.e16 (2018). source
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In the paper
“Accessibility at the RORγT and Tbet genes were strongly correlated with measurements of downstream regulatory events, suggesting that these TFs are regulated transcriptionally (Fig. 2c, Extended Data Fig. 5a,b).”
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In the paper
“We focused on rs62088464, a SNP located within a GATA motif site and for which our donor was heterozygous for the risk allele.”
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“We similarly identified genes with RNA expression significantly correlated with protein levels of each TF (Extended Data Fig. 7b).”
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In the paper
“We focused on rs62088464, a SNP located within a GATA motif site and for which our donor was heterozygous for the risk allele.”
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“The final TF in our panel, GATA3, showed clear discordance between RNA expression and protein levels across cells (Fig. 2c).”
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In the paper
“Our paired RNA and protein measurements also allowed us to identify candidate post-transcriptional regulators of GATA3 by performing differential expression analysis between cells expressing high GATA3 RNA but low protein vs cells expressing both high GATA3 RNA and protein (Fig. 2d).”
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In the paper
“For RORgT, Tbet, and GATA3, the correlated scATAC-seq peaks were enriched for the corresponding TF motif (Extended Data Fig. 7a).”
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In the paper
“Protein abundance is often challenging to estimate via scRNA-seq data due to post-transcriptional regulation and relatively low RNA capture rates10,11.”
What the sources say
- Not checked yetcites [10] Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014). source
- Not checked yetcites [11] Marinov, G. K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014). source
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In the paper
“Directly measuring single-cell protein levels of TFs, which are often much more abundant than their encoding transcripts12, can link individual TFs to regulated enhancers and target genes by correlating changes in TF protein levels to changes in regulatory element accessibility and expression of nearby genes.”
What the sources say
- Not checked yetcites [12] Gillespie, M. A. et al. Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis. Mol. Cell 78, 960–974.e11 (2020). source
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In the paper
“Because cell fixation increases the doublet rate in droplet-based single cell technologies4, we assayed a mixture of human K562 and mouse ESCs stained using antibodies against mutually exclusive TFs and antibody-linked hashtag oligos14 (HTOs) to assess both doublet identification and specificity of nuclear protein abundance measurements.”
What the sources say
- Not checked yetcites [4] Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00927-2 (2021). source
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In the paper
“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.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Since TFs can bind directly to enhancer elements to modulate target gene expression9, measuring their abundance provides critical insight into how these proteins drive gene regulatory states.”
What the sources say
- Not checked yetcites [9] Spitz, F. & Furlong, E. E. M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012). source
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In the paper
“The lack of concordance between FOXP3 expression and motif accessibility is consistent with previous studies showing that FOXP3 binds to pre-existing accessible enhancers to drive Treg fate19.”
What the sources say
- Not checked yetcites [19] Samstein, R. M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012). source
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In the paper
“Whereas previous studies investigating dosage-dependent effects of TFs often required building cell lines with a combination of hypomorphic and null alleles30,31 or inducible expression systems32, we demonstrate that NEAT-seq can measure the molecular consequences of continuous changes in TF levels in a biologically relevant setting for a panel of proteins simultaneously.”
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In the paper
“Due to a low observed enrichment of SOX2 in mESCs vs K562, we tested specificity of the conjugated SOX2 antibody by western blot and observed specific, if relatively weak, SOX2 binding (Extended Data Fig 2i).”
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In the paper
“Due to discordance between GATA3 protein and RNA levels, we verified specificity of our GATA3 antibody on GATA3-overexpressing cells (Extended Data Fig. 6a).”
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In the paper
“is associated with decreased pulmonary function26”
What the sources say
- Not checked yetcites [26] Alkes Group, UKBB summary statistics, https://alkesgroup.broadinstitute.org/ (2020).
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In the paper
“GATA3 translation is regulated by PI3K signaling through mTOR22”
What the sources say
- Not checked yetcites [22] Cook, K. D. & Miller, J. TCR-dependent translational control of GATA-3 enhances Th2 differentiation. J. Immunol. 185, 3209–3216 (2010). source
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“deviations from even mixtures will increase the inferred doublet rate relative to the observed doublet rate”
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In the paper
“We chose the NPC normalization method because it was more robust than CLR transformation in cases where cells are primarily positive for only one antibody in the panel, as was the case for the CD4 memory T cells.”
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In the paper
“NEAT-seq provides a new avenue for studying the quantitative effects of epigenetic regulator abundance on both chromatin and gene expression state in primary human samples.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“These results suggest upregulation of genes that promote translation may play a role in driving GATA3 protein production in the Th2 subset of memory T cells.”
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- Rests on an assumption: GAB2 is a direct activator of PI3K.
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In the paper
“The uncoupling of TF protein expression and motif accessibility highlights the caveats of using motif accessibility alone to infer TF activity.”
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“For perfectly even mixtures of mouse and human cells, the inferred doublet rate will be twice the observed doublet rate”
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“Both antibody concentrations showed specific staining of the targeted TF in the appropriate cell type, as shown in Extended Data Fig. 4b-c.”
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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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In the paper
“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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In the paper
“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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In the paper
“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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In the paper
“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).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
plaincorrelationalSingle case- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observed very similar results when normalizing to total ADT counts or just using raw ADT counts (Extended Data Fig S4a).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The ADT levels, but not RNA levels, were correlated with global changes in GATA3 motif accessibility.”
plaincorrelational- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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).”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
plain- 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.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For RNA-seq reads we observed greater cross-cell read contamination, particularly from mouse transcripts which had high abundance in non-cell droplets.”
plaincorrelational- 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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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
hedgedcorrelational- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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).”
plaincorrelational- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaindirect- 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.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that these TFs are regulated transcriptionally”
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
plaincorrelational- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
hedgedcorrelationalSingle case- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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).”
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Abstract · hedged
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
Twelve years of SAMtools and BCFtools
Danecek, P., Bonfield, J., Liddle, J., Marshall, J., Ohan, V., Pollard, M., Whitwham, A., Keane, T., McCarthy, S., Davies, R. M., et al. (2021) · GigaScience · doi:10.1093/gigascience/giab008 · Cited by 16,298
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
Two subsets of memory T lymphocytes with distinct homing potentials and effector functions
Sallusto, F., Lenig, D., Förster, R., Lipp, M., & Lanzavecchia, A. (1999) · Nature · doi:10.1038/44385 · Cited by 5,846
Regulation of Translation Initiation in Eukaryotes: Mechanisms and Biological Targets
Sonenberg, N., & Hinnebusch, A. G. (2009) · Cell · doi:10.1016/j.cell.2009.01.042 · Cited by 3,304
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098
Transcription factors: from enhancer binding to developmental control
Spitz, F., & Furlong, E. E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3207 · Cited by 2,227
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
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Weirauch, M. T., Yang, A. W. H., Albu, M., Coté, A. G., Montenegro‐Montero, A., Drewe, P., Najafabadi, H. S., Lambert, S. A., Mann, I. K., Cook, K. B., et al. (2014) · Cell · doi:10.1016/j.cell.2014.08.009 · Cited by 1,749
Cited by
Sorted by citations
Dictionary learning for integrative, multimodal and scalable single-cell analysis
Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631
Methods 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
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
Spatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang, D., Deng, Y., Kukanja, P., Agirre, E., Bartosovic, M., Dong, M., Ma, C., Ma, S., Su, G., Bao, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05795-1 · Cited by 483
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
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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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