The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index; 3 of the 22 sources its claims cite were checked for support.
Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues
Open accessAbstract
In contrast to single-cell approaches for measuring gene expression and DNA accessibility, single-cell methods for analyzing histone modifications are limited by low sensitivity and throughput. Here, we combine the CUT&Tag technology, developed to measure bulk histone modifications, with droplet-based single-cell library preparation to produce high-quality single-cell data on chromatin modifications. We apply single-cell CUT&Tag (scCUT&Tag) to tens of thousands of cells of the mouse central nervous system and probe histone modifications characteristic of active promoters, enhancers and gene bodies (H3K4me3, H3K27ac and H3K36me3) and inactive regions (H3K27me3). These scCUT&Tag profiles were sufficient to determine cell identity and deconvolute regulatory principles such as promoter bivalency, spreading of H3K4me3 and promoter–enhancer connectivity. We also used scCUT&Tag to investigate the single-cell chromatin occupancy of transcription factor OLIG2 and the cohesin complex component RAD21. Our results indicate that analysis of histone modifications and transcription factor occupancy at single-cell resolution provides unique insights into epigenomic landscapes in the central nervous system. An improved method for single-cell analysis of histone modifications is applied to the mouse brain.
Overview
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TL;DR
The paper develops single-cell CUT&Tag (scCUT&Tag) by coupling CUT&Tag with droplet-based 10x Genomics scATAC-seq, enabling high-throughput profiling of histone modifications and transcription factor occupancy in complex tissues. Applied to tens of thousands of mouse CNS cells, it resolves major brain cell types and reveals regulatory features such as promoter bivalency, H3K4me3 spreading, and enhancer-promoter connectivity.
Key findings
- scCUT&Tag generated profiles for 47,340 single cells across histone modifications, with median unique fragments per cell ranging from 98 (H3K36me3) to 453 (H3K27ac); 39.4%–85.6% of fragments fell within narrow peak regions.
- In a three-cell-line mixture, H3K27me3 scCUT&Tag yielded 4,872 and 3,873 single cells with 597 and 568 unique fragments per cell, respectively, and separated mESC, NIH-3T3, and Oli-neu populations.
- In mouse brain, scCUT&Tag identified major CNS populations including mature oligodendrocytes, astrocytes, OECs, vascular cells, OPC/COP/NFOL, neurons, and microglia; H3K4me3 breadth increased during oligodendrocyte differentiation, and H3K27me3 was enriched in oligodendrocytes, microglia, and a subset of neurons.
- TF scCUT&Tag recovered Olig2 and Rad21 binding with median 48 and 240 unique reads per cell, respectively; ABC modeling predicted ~200,000 enhancer-promoter loops, refined to 61,000 with H3K4me3 and ~5,000 with Rad21 on both sides, and Cicero found 14,322 correlated features with score >0.2 in mOLs.
Methods
- Design: CUT&Tag was combined with droplet-based 10x Genomics single-cell ATAC-seq library preparation; nuclei from mouse CNS or cell lines were barcoded after antibody-directed tagmentation.
- Sample/data: Mouse Sox10:Cre/RCE brains at P15 and P21–P25 were FACS-sorted into GFP+ and GFP− cells; antibodies targeted H3K4me3, H3K27ac, H3K36me3, H3K27me3, Olig2, and Rad21.
- Analysis: Cell-feature matrices used 5-kb or 50-kb bins; clustering used LSI, UMAP, SNN, and Leiden in Seurat/Signac; integration used CCA with scRNA-seq; enhancer-promoter predictions used the ABC model and Cicero, with HiChIP validation.
Limitations
- The paper states that scCUT&Tag currently distinguishes major cell types but fails to uncover subpopulation heterogeneity in an unsupervised manner; oligodendrocyte heterogeneity was only revealed at H3K4me3 level after integration with scRNA-seq.
- The paper states that TF scCUT&Tag data are sparser than histone scCUT&Tag, and Rad21 binding could not distinguish vascular cells and OPCs, the smallest populations sharing features with larger populations.
- The paper states that improving unique fragments per cell and introducing multi-omic approaches, such as simultaneous scCUT&Tag and RNA-seq from the same cell, would further enhance applications; evident from the text, TF profiles required annotation assumptions based on correlation with H3K4me3/H3K27ac rather than direct marker-based clustering.
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Claims the paper makes
72 Own result · 11 Own interpretation · 10 Not checked yet · 4 Own method · 4 No basis given · 1 Supported · 1 Partly supported · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Cohesin binding has been associated with loop formation38, so we further filtered the predictions using the presence of Rad21 binding on both sides of the loop (promoter and enhancer), which resulted in approximately 5,000 loops.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Prediction of enhancer-promoter interactions from scCut&Tag data
What the sources say
- Supportedcites [38] Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e24 (2017). source
The citation is attached to the background statement that cohesin binding is associated with loop formation, not to the citing authors’ filtering procedure or its approximately 5,000-loop result. In Results, under “Loop domains are rapidly lost after degradation of cohesin” (page unavailable), the paper says loop anchors typically bind cohesin and that loop-domain formation requires cohesin. (LLM, conf=0.92)
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- Supported
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In the paper
“We found several motifs enriched in the Olig2 scCUT&Tag, including motif CAGMTG, similar to the previously identified CAGMTG/CAGCTG motif specific for Olig2 (Figure 5h) in mouse33 and rat34 respectively.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
What the sources say
- Partly supportedcites [33] Yu, Y. et al. Olig2 targets chromatin remodelers to enhancers to initiate oligodendrocyte differentiation. Cell 152, 248–261 (2013). sourceExperimental — accuracy not yet measured
The Results section reports that Olig2 and Brg1 binding sites share an E-box consensus motif and identifies the broader E-box sequence as CANNTG. Our copy does not specify CAGMTG as the Olig2 motif, so it supports the motif similarity but not the precise sequence attributed to this source. The scCUT&Tag enrichment is the citing authors’ own finding, not a finding attributed to this paper. (LLM, conf=0.82)
- Supportedcites [34] Darr, A. J. et al. Identification of genome-wide targets of Olig2 in the adult mouse spinal cord using ChIP-Seq. PloS One 12, e0186091 (2017). source
The citation supports the previously identified mouse Olig2 motif, not the citing authors’ own scCUT&Tag enrichment result. In Results, “Comprehensive de novo mapping of Olig2 targets in vivo,” the paper reports an enriched CA(G/T)(C/A)TG(T/G)N motif at Olig2-binding sites; its CAG(C/A)TG sequence corresponds to CAGMTG. (LLM, conf=0.92)
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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
“These scCUT&Tag profiles were sufficient to determine cell identity and deconvolute regulatory principles such as promoter bivalency, spreading of H3K4me3, and promoter-enhancer connectivity.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Abstract
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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
“These scCUT&Tag profiles were sufficient to determine cell identity and deconvolute regulatory principles such as promoter bivalency, spreading of H3K4me3, and promoter-enhancer connectivity.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Abstract
boostedconditional- Asserted: passes — The authors put it forward as true.
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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
“These scCUT&Tag profiles were sufficient to determine cell identity and deconvolute regulatory principles such as promoter bivalency, spreading of H3K4me3, and promoter-enhancer connectivity.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Here we combine the CUT&Tag technology, developed to measure bulk histone modifications, with droplet-based single-cell library preparation to produce high-quality single-cell data on chromatin modifications.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“These scCUT&Tag profiles were sufficient to determine cell identity and deconvolute regulatory principles such as promoter bivalency, spreading of H3K4me3, and promoter-enhancer connectivity.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Abstract
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In the paper
“We found that addition of BSA reduced nuclei clumping (Extended Data Figure 1b) but did not substantially alter the efficiency of tagmentation nor the signal distribution for bulk Cut&Tag (Extended Data Figure 1c).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > CUT&Tag profiling of single cells
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In the paper
“In particular, H3K4me3 was mainly present at regions flanking transcription start sites, H3K27ac occupied both these regions and neighboring intergenic regions (most likely enhancers) and H3K36me3 was spread throughout the gene bodies”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
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In the paper
“The fragment length distribution was consistent with the capture of sub nucleosome fragments as well as mono-, di- and tri -nucleosomes for all modifications (Figure 1e).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
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In the paper
“In contrast, H3K27me3 was associated with genes where the other active markers were absent, or it coincided with H3K4me3.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
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In the paper
“we manually annotated the populations as mature oligodendrocytes (MOL, Mbp+, Mog+, Cldn11+), astrocytes (AST, Slc1a2+, Rfx4+, Aqp4+), olfactory ensheathing cells (OEC, Alx3+, Alx4+, Frzb+), vascular cells (VAS, Nes+, Tbx18+, Foxf2+) and cells within the spectrum of oligodendrocyte progenitors cells (OPCs), committed OPCs (COPs) and newly formed oligodendrocytes (NFOLs) (OPC, Pdgfra+, Neu4+, Gpr17+”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > scCUT&Tag of individual histone modifications allows identification of specific cell populations from the mouse brain
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Inspection of genome browser tracks revealed similarity between corresponding populations of bulk and single-cell tracks and lower level of background signal in scCUT&Tag data (Extended Data Figure 6a).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > scCUT&Tag of individual histone modifications allows identification of specific cell populations from the mouse brain
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“We found that addition of BSA reduced nuclei clumping (Extended Data Figure 1b) but did not substantially alter the efficiency of tagmentation nor the signal distribution for bulk Cut&Tag (Extended Data Figure 1c).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > CUT&Tag profiling of single cells
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Inspection of merged pseudo-bulk datasets including all cells revealed distinct profiles for the histone modifications highlighting the specificity of the technique (Extended Data Figure 3a).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
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In the paper
“Altogether we obtained scCut&Tag profiles of various histone modifications for 47340 single cells with median ranging between 98 (H3K36me3) and 453 (H3K27ac) unique fragments per cell (Figure 1c).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
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In the paper
“The clustering was highly reproducible among biological replicates and cells originating from P15/P25 age were well intermingled within the clusters (Extended Data Figure 5a–c).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > scCUT&Tag of individual histone modifications allows identification of specific cell populations from the mouse brain
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In the paper
“The cell states of OLG lineage reflect the mouse age with majority of OPCs originating from P15 and differentiated OLG coming from P25 (Extended Data Figure 5b, c).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > scCUT&Tag of individual histone modifications allows identification of specific cell populations from the mouse brain
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In the paper
“Between 39.4% and 85.6% of fragments fell within narrow peak regions (Figure 1d), indicating a low level of background.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
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In the paper
“Our scCUT&Tag data showed similar or higher specificity (as measured by fraction of fragments in peaks), equal or lower number of unique fragments per cell, but yielded more cells per experiment compared to the iCell8 scCUT&Tag6 and the latest scChIP-seq method12 (Extended Data Figures 3d-f).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
What the sources say
- Not checked yetcites [6] Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019). source
- Not checked yetcites [12] Grosselin, K. et al. High-throughput single-cell ChIP–seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060–1066 (2019). source
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In the paper
“Consistent with this, fingerprint plots showed that scCUT&Tag displayed higher specificity and better signal to noise ratio compared to scChIP-seq12 and similar level of specificity to the iCell8 scCUT&Tag (Extended Data Figure 3g).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
What the sources say
- Not checked yetcites [12] Grosselin, K. et al. High-throughput single-cell ChIP–seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060–1066 (2019). source
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In the paper
“Nevertheless, we were able to obtain in median 48 and 240 unique reads per cell in Olig2 and Rad21 scCUT&Tag respectively.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
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In the paper
“Interestingly, the low binder non-OLGs cells randomly co-embedded with the populations of OEC, Astrocytes and vascular cell clusters that were defined by H3K27ac, whereas OLG cluster specifically co-embeded with the OLG H3K27ac signal (Extended Data Figure 9e).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
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In the paper
“Olig2 dataset separated into 2 clusters based on depth (number of unique reads per cell), and we annotated the cluster with low number of unique reads as “low binders” (Figure 5a).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“These loops showed substantially higher specificity in the HiChIP pileup signal, compared to the original set of unfiltered loops (Figure 6c) and higher overlap between ABC predictions from bulk CUT&Run and scCUT&Tag (Extended Data Figure 10a).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Prediction of enhancer-promoter interactions from scCut&Tag data
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In the paper
“Strikingly, the obtained two-dimensional representation of the data recapitulated the original non-supervised clustering with great precision (Figure 3e) and the clusters annotated with the same cell type in different datasets co-occupied the same low dimensional space (Figure 3e).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Differential global and genome-wide patterns of histone modifications in the single-cell populations
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In the paper
“This finding is consistent with expression of Olig2 throughout the cell types, being highly expressed throughout the OLG lineage, whereas OECs and VLMCs do not express Olig2 and only small portion of astrocytes express Olig2 (Figure 5f).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
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In the paper
“For this purpose, we co-embedded the H3K4me3 scCUT&Tag and scRNA-seq using CCA, which showed good integration of the techniques, while the scRNA-seq clustering was retained (Figure 3b).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Integration of scCUT&Tag data with single-cell gene expression
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In the paper
“found GO terms such as astrocyte differentiation and activation (AST), myelination (OLG), regulation of myelination (OEC), cell migration involved in vasculogenesis (VAS), glial cell development (OPCs), neuron development, neuron maturation and axonogenesis (NEU) and microglial cell activation involved in immune response (MGL) (Extended Data Figure 8) to be specifically enriched in respective”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Integration of scCUT&Tag data with single-cell gene expression
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In the paper
“We also observed an increase in H3K4me3 breadth during the process of differentiation in the OLG lineage at unprecedented resolution, indicating that spreading of this modification can be used to delineate lineage progression.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Discussion
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In the paper
“We show that analysis of histone modifications at a single-cell level allows identification of distinct neural cell types, with the active marks H3K4me3 and H3K27ac and the repressive mark H3k27me3 being particularly useful for this purpose.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Discussion
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In the paper
“In the current state, scCUT&Tag is able to distinguish the major cell types but fails to uncover heterogeneity of the subpopulations in an unsupervised manner.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Discussion
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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
“While Olig2 preferentially binds oligodendrocyte lineage specific enhancers and promoters and binds only weakly to astrocyte-specific gene regulatory regions, Rad21, a ubiquitously expressed genome architecture factor, binds cell type–specific promoters and enhancers.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Discussion
plaincorrelational- 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.
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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
“Cicero predictions modestly overlapped with ABC model predictions (Extended Data Figure 10b) and the length of the predicted loops was substantially different between cicero and ABC model (Extended Data Figure 10d).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Prediction of enhancer-promoter interactions from scCut&Tag data
plaincorrelational- 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.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Breeding with males containing a hemizygous Cre allele in combination with the reporter allele to non-Cre carrier females resulted in early Cre activation in the whole animal and consequently avoided.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > Animals
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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 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
“scCUT&Tag allowed the deconvolution of potential H3K4me3/H3K27me3 bivalency, but also revealed a close epigenetic relationship between OPCs and astrocytes.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
“Additional motifs identified using MEME from the merged pseudobulk profile of Olig2 scCUT&Tag.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Extended Data
plainn = 1,667- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Nevertheless, both predictions methods successfully identified known Sox10 enhancers 40 and connected them to the Sox10 promoter (Figure 6e).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Prediction of enhancer-promoter interactions from scCut&Tag data
What the sources say
- Not checked yetcites [40] Werner, T., Hammer, A., Wahlbuhl, M., Bösl, M. R. & Wegner, M. Multiple conserved regulatory elements with overlapping functions determine Sox10 expression in mouse embryogenesis. Nucleic Acids Res. 35, 6526–6538 (2007). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Interestingly, we detected a major, likely transient population of astrocytes, in GFP+ fraction of the Sox10:Cre/RCE mouse (Extended Data Figure 2a, 2c), most probably derived from ventral regions22–24.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > scCUT&Tag of individual histone modifications allows identification of specific cell populations from the mouse brain
What the sources say
- Not checked yetcites [22] Huang, W. et al. Novel NG2-CreERT2 knock-in mice demonstrate heterogeneous differentiation potential of NG2 glia during development. Glia 62, 896–913 (2014). source
- Not checked yetcites [23] Zhu, X. et al. Age-dependent fate and lineage restriction of single NG2 cells. Development 138, 745–753 (2011). source
- Not checked yetcites [24] Zhu, X., Bergles, D. E. & Nishiyama, A. NG2 cells generate both oligodendrocytes and gray matter astrocytes. Development 135, 145–157 (2008). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“We found the motif of chromatin architecture factor, CTCF as the highest enriched in the Rad21 dataset (Figure 5g), which is consistent with the cooperativity between Ctcf and Cohesin32.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
What the sources say
- Not checked yetcites [32] Li, Y. et al. The structural basis for cohesin–CTCF-anchored loops. Nature 578, 472–476 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Consistently, it was recently reported that disruption of H3K27me3 impairs OPC differentiation to MOL, and triggers switch towards an astrocytic faith29.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Differential global and genome-wide patterns of histone modifications in the single-cell populations
What the sources say
- Not checked yetcites [29] Wang, J. et al. EED-mediated histone methylation is critical for CNS myelination and remyelination by inhibiting WNT, BMP, and senescence pathways. Sci. Adv. 6, eaaz6477 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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
“Therefore, it is unlikely that the signal heterogeneity is caused by differential tagmentation efficiency between cell types but rather by differential levels of modifications between the cell types.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Differential global and genome-wide patterns of histone modifications in the single-cell populations
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.
- Rests on an assumption: The number of unique reads per cell reflects the absolute amount of a histone modification in single cells.
In the paper
“Interestingly, we also found a motif similar to the motif of transcription factors from the Sox family (ACARWR, Extended Data Figure 9f), which is consistent with physical interaction and cooperativity between Olig2 and members of Sox family of transcription factors (Sox8, Sox10)35.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
What the sources say
- Not checked yetcites [35] Wißmüller, S., Kosian, T., Wolf, M., Finzsch, M. & Wegner, M. The high-mobility-group domain of Sox proteins interacts with DNA-binding domains of many transcription factors. Nucleic Acids Res. 34, 1735–1744 (2006). source
hedgedcorrelational- 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
“The OLG population in Olig2 scCUT&Tag is likely composed of both mature OLG and OPCs, which appear to form a subpopulation within the OLG cluster (Extended Data Figure 9a).”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single cell Cut&Tag of transcription factors
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.
- The authors state this as an assumption: Olig2 and Rad21 binding in specific cell types correlates with enhancer and promoter activity.
In the paper
“The breadth of the H3K4me3 mark has been previously linked to cell identity, gene expression and transcriptional consistency across a variety of cell types30.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Increase in breadth of H3K4me3 upon oligodendrocyte differentiation
What the sources say
- Not checked yetcites [30] Benayoun, B. A. et al. H3K4me3 breadth is linked to cell identity and transcriptional consistency. Cell 158, 673–688 (2014). 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. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The battery of screened infective agents met the standard health profile established in Karolinska Institutet animal housing facilities.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > Animals
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“HiChIP was performed in three biological replicates with at least 1 milion cells used as input.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > HiChIP
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. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Then, cells were counted, and ~7000 cells processed using chromium single cell 3’ reagent kit (10x Genomics) according to manufacturer’s instructions in two biological replicates.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > scRNA-seq
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All animals were free from the most common mouse viral pathogens, ectoparasites, endoparasites, and mouse bacteria pathogens harbored in research animals.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > Animals
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. It may state two separate claims as one.
The authors state this as an assumption.
Olig2 and Rad21 binding in specific cell types correlates with enhancer and promoter activity.
“we based cluster annotation on the assumption that Olig2/Rad21 binding in specific cell types is correlated with enhancer/promoter activity.”
The paper relies on this without support.
The number of unique reads per cell reflects the absolute amount of a histone modification in single cells.
“We used the number of unique reads per cell as a proxy for absolute amount of the histone modification in single 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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This method has been modified further by other groups to profile histone modifications at the single-cell level: methods such as scCUT&Tag48 and scCUT&Tag2for149 use tagmentation directed by antibodies to profile active and silenced regulatory elements genome-wide by targeting domains bound by, respectively, RNA polymerase II and Polycomb repressive complexes.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [48]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“single-cell CUT&Tag (scCUT&Tag) integrated CUT&Tag with 10× Genomics scATAC-seq protocol, enabling the investigation of histone modifications at single-cell resolution [120].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [120]
In the citing paper
“Integration and co-embedding of spatial CUT&Tag and scCUT&Tag data36,37 (Fig. 3d,e and Extended Data Fig. 7a) showed that the epigenetic states in our data for H3K27me3, H3K27ac and H3K4me3 agreed with the corresponding projection in scCUT&Tag.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > ABC model
“Regulatory loops with an ABC score > 0.2 were used for downstream analyses.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > Statistics
“ABC predictions with threshold score higher than 0.02 were selected as significant.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Extended Data
“a. Bar plot depicting fraction of loops predicted by the ABC model with scCUT&Tag data that overlap with loops predicted by ABC model with bulk CUT&Tag data.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Prediction of enhancer-promoter interactions from scCut&Tag data
“The predicted loops were consistent with loops predicted by the ABC model from bulk OLG Cut&Run data (Extended Data Figure 10a, b).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Online Methods > Animals
“Littermate animals were sacrificed either at postnatal (P4-P6) or at juvenile stages (P21-25) and both sexes were included in the study.”
§ Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues > Results > Single-cell profiles of several histone modifications in the mouse brain
“We sorted both GFP+ and GFP-cells from whole juvenile brain at postnatal day 15 (P15 timepoint).”
Citations
Cites
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
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
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
MEME SUITE: tools for motif discovery and searching
Bailey, T. L., Boden, M., Buske, F. A., Frith, M., Grant, C. E., Clementi, L., Ren, J., Li, W. W., & Noble, W. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp335 · Cited by 10,648
deepTools2: a next generation web server for deep-sequencing data analysis
Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw257 · Cited by 8,096
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
Self-citationSlingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E., & Dudoit, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4772-0 · Cited by 3,159
Snakemake—a scalable bioinformatics workflow engine
Köster, J., & Rahmann, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts480 · Cited by 2,780
HiC-Pro: an optimized and flexible pipeline for Hi-C data processing.
Servant, N., Varoquaux, N., Lajoie, B. R., Viara, E., Chen, C.-J., Vert, J.-P., Heard, E., Dekker, J., & Barillot, E. (2015) · Genome Biology · doi:10.1186/s13059-015-0831-x · Cited by 2,565
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
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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
Microglia states and nomenclature: A field at its crossroads
Paolicelli, R. C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.020 · Cited by 1,973
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
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616
Applications of single-cell sequencing in cancer research: progress and perspectives
Lei, Y., Tang, R., Xu, J., Wang, W., Zhang, B., Liu, J., Yu, X., & Shi, S. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01105-2 · Cited by 610
Chromatin accessibility profiling by ATAC-seq
Grandi, F. C., Modi, H., Kampman, L., & Corces, M. R. (2022) · Nature Protocols · doi:10.1038/s41596-022-00692-9 · Cited by 557
Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study
Wu, Y., Lin, Z., Li, C., Lin, X. J., Shan, S., Guo, B., Zheng, M.-H., Li, F., Yuan, L., & Li, Z. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01333-7 · Cited by 549
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
Self-citationGene 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
Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level
Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., & Fan, R. (2022) · Science · doi:10.1126/science.abg7216 · Cited by 393
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Bennett, H. M., Stephenson, W., Rose, C. M., & Darmanis, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01791-5 · Cited by 351
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Belk, J. A., Dániel, B., & Satpathy, A. T. (2022) · Nature Immunology · doi:10.1038/s41590-022-01224-z · Cited by 346
Characterizing cis-regulatory elements using single-cell epigenomics
Preissl, S., Gaulton, K. J., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00509-1 · Cited by 251
Crossing epigenetic frontiers: the intersection of novel histone modifications and diseases
Yao, W., Hu, X., & Wang, X. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01918-w · Cited by 257
Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2024) · Nature · doi:10.1038/s41586-023-06837-4 · Cited by 225
Functional inference of gene regulation using single-cell multi-omics
Kartha, V. K., Duarte, F. M., Hu, Y., Ma, S., Chew, J. G., Lareau, C. A., Earl, A., Burkett, Z. D., Kohlway, A. S., Lebofsky, R., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100166 · Cited by 224
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Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222
Spatially resolved epigenomic profiling of single cells in complex tissues
Lu, T., Ang, C. E., & Zhuang, X. (2022) · Cell · doi:10.1016/j.cell.2022.09.035 · Cited by 197
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 222
“On this basis, single-cell CUT&Tag (scCUT&Tag) integrated CUT&Tag with 10 × Genomics scATAC-seq protocol, enabling the investigation of histone modifications at single-cell resolution [120].”
MethodThe neuropathobiology of multiple sclerosis
Woo, M. S., Engler, J. B., & Friese, M. A. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00823-z · Cited by 205
Advances in single-cell omics and multiomics for high-resolution molecular profiling
Lim, J., Park, C., Kim, M., Kim, H., Kim, J., & Lee, D.-S. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01186-2 · Cited by 193
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Li, Z., Kuppe, C., Ziegler, S. M., Cheng, M., Kabgani, N., Menzel, S., Zenke, M., Kramann, R., & Costa, I. G. (2021) · Nature Communications · doi:10.1038/s41467-021-26530-2 · Cited by 125
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Chen, S., Zhu, B., Huang, S., Hickey, J. W., Lin, K. Z., Snyder, M., Greenleaf, W. J., Nolan, G. P., Zhang, N. R., & Ma, Z. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01943-0 · Cited by 119
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Casado-Peláez, M., Bueno-Costa, A., & Esteller, M. (2022) · Trends in cancer · doi:10.1016/j.trecan.2022.06.005 · Cited by 115
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Mehrmohamadi, M., Sepehri, M. H., Nazer, N., & Norouzi, M. R. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.714687 · Cited by 119
SEdb 2.0: a comprehensive super-enhancer database of human and mouse
Wang, Y., Song, C., Zhao, J., Zhang, Y., Zhao, X., Feng, C., Zhang, G., Zhu, J., Wang, F., Qian, F., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac968 · Cited by 111
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778
Simultaneous profiling of multiple chromatin proteins in the same cells
Gopalan, S., Wang, Y., Harper, N. W., Garber, M. G., & Fazzio, T. G. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.09.019 · Cited by 112
Mapping epigenetic modifications by sequencing technologies
Chen, X., Xu, H., Shu, X., & Song, C.-X. (2025) · Cell Death & Differentiation · doi:10.1038/s41418-023-01213-1 · Cited by 105
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104
Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag
Meers, M. P., Llagas, G., Janssens, D. H., Codomo, C. A., & Henikoff, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01522-9 · Cited by 105
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100
Park, H., Jo, S. H., Lee, R. H., Macks, C. P., Ku, T., Park, J., Lee, C. W., Hur, J. K., & Sohn, C. H. (2023) · Advanced Science · doi:10.1002/advs.202206939 · Cited by 107
Chemical reprogramming for cell fate manipulation: Methods, applications, and perspectives
Wang, J., Sun, S., & Deng, H. (2023) · Cell Stem Cell · doi:10.1016/j.stem.2023.08.001 · Cited by 91
Epigenomic heterogeneity as a source of tumour evolution
Laisné, M., Lupien, M., & Vallot, C. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00757-9 · Cited by 93
Linking environmental risk factors with epigenetic mechanisms in Parkinson’s disease
Tsalenchuk, M., Gentleman, S., & Marzi, S. J. (2023) · npj Parkinson s Disease · doi:10.1038/s41531-023-00568-z · Cited by 94
Bravo González-Blas, C., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-023-01316-4 · Cited by 85
Single-cell mapping of DNA G-quadruplex structures in human cancer cells
Hui, W. W. I., Simeone, A., Zyner, K. G., Tannahill, D., & Balasubramanian, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-02943-3 · Cited by 85
Ober-Reynolds, B., Wang, C., Ko, J. M., Rios, E. J., Aasi, S. Z., Davis, M. M., Oro, A. E., & Greenleaf, W. J. (2023) · Nature Genetics · doi:10.1038/s41588-023-01445-4 · Cited by 83
Methyl-CpG-binding 2 K271 lactylation-mediated M2 macrophage polarization inhibits atherosclerosis
Chen, L., Zhang, M., Yang, X., Wang, Y., Huang, T., Li, X., Ban, Y., Li, Q., Yang, Q., Zhang, Y., et al. (2024) · Theranostics · doi:10.7150/thno.94738 · Cited by 92
Transcriptional enhancers and their communication with gene promoters
Ray-Jones, H., & Spivakov, M. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03903-w · Cited by 81
Microfluidic Biochips for Single‐Cell Isolation and Single‐Cell Analysis of Multiomics and Exosomes
Wang, C., Qiu, J., Liu, M., Wang, Y., Yu, Y., Liu, H., Zhang, Y., & Han, L. G. (2024) · Advanced Science · doi:10.1002/advs.202401263 · Cited by 81
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 90
Guyer, R. A., Stavely, R. G., Robertson, K., Bhave, S. M., Mueller, J. L., Picard, N., Hotta, R., Kaltschmidt, J. A., & Goldstein, A. M. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112194 · Cited by 81
Exploring the untapped potential of single‐cell and spatial omics in plant biology
Nobori, T. (2025) · New Phytologist · doi:10.1111/nph.70220 · Cited by 88
Interpreting non-coding disease-associated human variants using single-cell epigenomics
Gaulton, K. J., Preißl, S., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00598-6 · Cited by 81
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 81
Benchmarking algorithms for single-cell multi-omics prediction and integration
Hu, Y., Wan, S., Luo, Y., Li, Y., Wu, T., Deng, W., Jiang, C., Jiang, S., Zhang, Y., Liu, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02429-w · Cited by 79
A guide to systems-level immunomics
Bonaguro, L., Schulte-Schrepping, J., Ulas, T., Aschenbrenner, A. C., Beyer, M., & Schultze, J. L. (2022) · Nature Immunology · doi:10.1038/s41590-022-01309-9 · Cited by 80
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Sharma, M., Sidhu, A. K., Samota, M. K., Gupta, M., Koli, P., & Choudhary, M. (2023) · Proteomes · doi:10.3390/proteomes11040038 · Cited by 76
Time-resolved single-cell RNA-seq using metabolic RNA labelling
Erhard, F., Saliba, A.-E., Lusser, A., Toussaint, C., Hennig, T., Prusty, B. K., Kirschenbaum, D., Abadie, K., Miska, E. A., Friedel, C. C., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00157-z · Cited by 73
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Mo, Y., & Jiao, Y. (2022) · The Plant Journal · doi:10.1111/tpj.15772 · Cited by 78
Variant to function mapping at single-cell resolution through network propagation
Yu, F., Cato, L. D., Weng, C., Liggett, L. A., Jeon, S., Xu, K., Chiang, C. W. K., Wiemels, J. L., Weissman, J. S., de Smith, A. J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01341-y · Cited by 80
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Xie, Y., Zhu, C., Wang, Z., Tastemel, M., Chang, L., Li, Y. E., & Ren, B. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01060-1 · Cited by 69
Advances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 70
Chromatin accessibility during human first-trimester neurodevelopment
Mannens, C. C. A., Hu, L., Lönnerberg, P., Schipper, M., Reagor, C. C., Li, X., He, X., Barker, R. A., Sundström, E., Posthuma, D., et al. (2024) · Nature · doi:10.1038/s41586-024-07234-1 · Cited by 69
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Zeller, P. J., Yeung, J., Gaza, H. V., de Barbanson, B., Bhardwaj, V., Florescu, M., van der Linden, R., & van Oudenaarden, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01260-3 · Cited by 67
Yu, H., Xiao, A., Wu, J., Li, H., Duan, Y., Chen, Q., Zhu, H., & Cao, Y. (2023) · The Plant Cell · doi:10.1093/plcell/koad129 · Cited by 64
Genome-coverage single-cell histone modifications for embryo lineage tracing
Liu, M., Yue, Y., Chen, X., Xian, K., Dong, C. F., Shi, M., Xiong, H., Tian, K., Li, Y., Zhang, Q. C., et al. (2025) · Nature · doi:10.1038/s41586-025-08656-1 · Cited by 63
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Coughlan, A. Y., & Testa, G. (2021) · International Journal of Cancer · doi:10.1002/ijc.33727 · Cited by 58
Janssens, D. H., Otto, D. J., Meers, M. P., Setty, M. N., Ahmad, K., & Henikoff, S. (2022) · Genome biology · doi:10.1186/s13059-022-02642-w · Cited by 58
Guan, X., Liu, R., Wang, B., Xiong, R., Cui, L., Liao, Y., Ruan, Y., Fang, L., Lü, X., Yu, X., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1692 · Cited by 58
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Harpaz, N., Mittelman, T., Beresh, O., Griess, O., Furth, N., Salame, T., Oren, R., Fellus‐Alyagor, L., Harmelin, A., Alexandrescu, S., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.023 · Cited by 61
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Xiong, H., Wang, Q., Li, C. C., & He, A. (2024) · Science Advances · doi:10.1126/sciadv.adi3664 · Cited by 58
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Li, M., Jiang, J., Li, J., Niu, H., Ying, Y., Tian, R., & Long, Y. (2024) · Nature Methods · doi:10.1038/s41592-024-02492-3 · Cited by 63
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Lancaster, M. A. (2024) · Cell · doi:10.1016/j.cell.2024.08.052 · Cited by 58
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Efe, G., Dunbar, K. J., Sugiura, K., Cunningham, K. M., Carcamo, S., Karaiskos, S., Tang, Q., Cruz‐Acuña, R., Resnick‐Silverman, L., Peura, J., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0601 · Cited by 52
Zenk, F. L., Fleck, J. S., Jansen, S. M. J., Kashanian, B., Eisinger, B., Santel, M., Dupré, J.-S., Camp, J. G., & Treutlein, B. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01652-0 · Cited by 59
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Balusu, S., Praschberger, R., Lauwers, E., Strooper, B. D., & Verstreken, P. (2023) · Neuron · doi:10.1016/j.neuron.2023.01.016 · Cited by 53
Cut&tag: a powerful epigenetic tool for chromatin profiling
Fu, Z., Jiang, S., Sun, Y., zheng, S., Zong, L., & Li, P. (2023) · Epigenetics · doi:10.1080/15592294.2023.2293411 · Cited by 55
Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies
Yang, J., Zhou, F., Luo, X., Fang, Y., Wang, X., Liu, X., Xiao, R., Jiang, D., Tang, Y., Yang, G., et al. (2025) · Cell Death Discovery · doi:10.1038/s41420-025-02366-3 · Cited by 56
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Grima-Terrén, M., Campanario, S., Ramírez‐Pardo, I., Cisneros, A., Hong, X., Perdiguero, E., Serrano, A. L., Isern, J., & Muñoz‐Cánoves, P. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101319 · Cited by 52
Hamashima, K., Wong, K. W., Sam, T. W., Teo, J. H. J., Taneja, R., Le, M. T. N., Li, Q.-J., Hanna, J. H., Li, H., & Loh, Y. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.08.010 · Cited by 47
High-throughput single-cell epigenomic profiling by targeted insertion of promoters (TIP-seq)
Bartlett, D. A., Dileep, V., Handa, T., Ohkawa, Y., Kimurâ, H., Henikoff, S., & Gilbert, D. M. (2021) · The Journal of Cell Biology · doi:10.1083/jcb.202103078 · Cited by 46
Roehrig, A., Hirsch, T. Z., Pire, A., Morcrette, G., Gupta, B., Marcaillou, C., Imbeaud, S., Chardot, C., Gonzales, E. M., Jacquemin, E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47280-x · Cited by 43
Johnson, A., Laterra, J., & López-Bertoni, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.995498 · Cited by 45
Li, H., Bao, S., Farzad, N., Qin, X., Fung, A. A., Zhang, D., Bai, Z., Tao, B., & Fan, R. (2025) · Nature Protocols · doi:10.1038/s41596-025-01145-9 · Cited by 44
Single‐cell omics: Overview, analysis, and application in biomedical science
Stein, C. M., Weiskirchen, R., Damm, F., & Strzelecka, P. M. (2021) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30134 · Cited by 43
Single-cell analysis of cis-regulatory elements
Marand, A. P., & Schmitz, R. J. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102094 · Cited by 41
Profiling plant histone modification at single‐cell resolution using snCUT&Tag
Ouyang, W., Luan, S., Xu, X., Guo, M., Zhang, Y., Li, G., & Li, X. (2021) · Plant Biotechnology Journal · doi:10.1111/pbi.13768 · Cited by 41
Flynn, E., Almonte-Loya, A., & Fragiadakis, G. K. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020422-050645 · Cited by 43
Opportunities and Challenges in Advancing Plant Research with Single-cell Omics
Rhaman, M. S., Ali, M., Ye, W., & Li, B. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae026 · Cited by 45
From morphology to single-cell molecules: high-resolution 3D histology in biomedicine
Xu, X., Su, J., Zhu, R., Li, K., Zhao, X., Fan, J., & Mao, F. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02240-x · Cited by 43
Wen, Q., Zhou, J., Tian, C., Li, X., Song, G., Gao, Y., Sun, Y., Ma, C., Yao, S., Liang, X., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01477-w · Cited by 41
Epigenetic control of microglial immune responses
Scholz, R., Brösamle, D., Yuan, X., Beyer, M., & Neher, J. J. (2024) · Immunological Reviews · doi:10.1111/imr.13317 · Cited by 39
Bonev, B., Castelo‐Branco, G., Chen, F., Codeluppi, S., Corces, M. R., Fan, J., Heiman, M., Harris, K., Inoue, F., Kellis, M., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01806-0 · Cited by 44
Self-citationQuantifying cell-state densities in single-cell phenotypic landscapes using Mellon
Otto, D. J., Jordan, C., Dury, B., Dien, C., & Setty, M. N. (2024) · Nature Methods · doi:10.1038/s41592-024-02302-w · Cited by 38
Exploring the complexity of MECP2 function in Rett syndrome
Liu, Y., Whitfield, T. W., Bell, G. W., Guo, R., Flamier, A., Young, R. A., & Jaenisch, R. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00926-1 · Cited by 39
Targeting the epigenome to reinvigorate T cells for cancer immunotherapy
Xiong, D., Zhang, L., & Sun, Z. (2023) · Military Medical Research · doi:10.1186/s40779-023-00496-2 · Cited by 36
Single-cell multi-omics and lineage tracing to dissect cell fate decision-making
Haghverdi, L., & Ludwig, L. S. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.12.003 · Cited by 37
Lobato-Moreno, S., Yildiz, U., Claringbould, A., Servaas, N. H., Vlachou, E. P., Arnold, C., Bauersachs, H. G., Campos-Fornés, V., Kim, M., Berest, I., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02700-8 · Cited by 35
Ramirez, M., Badayeva, Y., Yeung, J., Wu, J., Abdalla-Wyse, A., Yang, E., Trost, B., Scherer, S. W., & Goldowitz, D. (2022) · eLife · doi:10.7554/elife.74207 · Cited by 33
Loers, J. U., & Vermeirssen, V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae382 · Cited by 39
CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks
Abbasova, L., Urbanaviciute, P., Hu, D., Ismail, J. N., Schilder, B. M., Nott, A., Skene, N., & Marzi, S. J. (2025) · Nature Communications · doi:10.1038/s41467-025-58137-2 · Cited by 32
Deep Learning in Single-cell Analysis
Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 38
Metadata
- Authors
- Bartosovic, M., Kabbe, M., & Castelo-Branco, G.
- Affiliations
- Karolinska Institutet (SE)
- Venue
- Nature Biotechnology
- Published
- 2021-04-12
- Volume
- 39
- Issue
- 7
- Pages
- 825-835
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7611252
- OpenAlex
- W3156830838
- Semantic Scholar
- e3838c1afa483c16cb19e88f8243c4e159ab6149
- Cited by
- 483
- References
- 54
- Citations by year
- 2026: 96 · 2025: 94 · 2024: 106 · 2023: 93 · 2022: 63 · 2021: 30
- FWCI
- 23.47
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- histone modifications, chromatin accessibility, transcription factor occupancy, Olig2, RAD21, cohesin, H3K4me3, H3K27ac, H3K36me3, H3K27me3, central nervous system, mice, epigenomics
- MeSH terms
- Animals, Brain, Gene Expression Regulation, Histones, Humans, Transcription Factors, Cluster Analysis, Mice, Single-Cell Analysis
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