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.
- 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
“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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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 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.
- 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
“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.
- 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
“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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- 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
“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.
- 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.
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.
- 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
“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.
- 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
“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 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
“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]
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
Cited by
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Items 101–200 of 479
Identification of chromatin states during zebrafish gastrulation using CUT & RUN and CUT &Tag
Akdogan‐Ozdilek, B., Duval, K. L., Meng, F. W., Murphy, P. J., & Goll, M. G. (2021) · Developmental Dynamics · doi:10.1002/dvdy.430 · Cited by 32
Global approaches for profiling transcription initiation
Policastro, R. A., & Zentner, G. E. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100081 · Cited by 33
Tenney, A. P., Gioia, S. A. D., Webb, B. D., Chan, W. M., de Boer, E., Garnai, S. J., Barry, B. J., Ray, T., Kosicki, M., Robson, C. D., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01424-9 · Cited by 31
Scalable single-cell profiling of chromatin modifications with sciCUT&Tag
Janssens, D. H., Greene, J. E., Wu, S. J., Codomo, C. A., Minot, S. S., Furlan, S. N., Ahmad, K., & Henikoff, S. (2023) · Nature Protocols · doi:10.1038/s41596-023-00905-9 · Cited by 30
HiCuT: An efficient and low input method to identify protein-directed chromatin interactions
Sati, S., Jones, P., Kim, H. S., Zhou, L., Rapp-Reyes, E., & Leung, T. H. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010121 · Cited by 22
Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine
Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29
scChIX-seq infers dynamic relationships between histone modifications in single cells
Yeung, J., Florescu, M., Zeller, P. J., de Barbanson, B., Wellenstein, M. D., & van Oudenaarden, A. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01560-3 · Cited by 29
Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes
Zhang, Q., Ma, S., Liu, Z., Zhu, B., Zhou, Z., Li, G., Meana, J. J., González‐Maeso, J., & Lu, C. (2023) · Nature Communications · doi:10.1038/s41467-023-40411-w · Cited by 30
Genetic and epigenetic alterations in aging and rejuvenation of human
Park, K., Jeon, M. C., Lee, D., Kim, J., & Im, S.-W. (2024) · Molecules and Cells · doi:10.1016/j.mocell.2024.100137 · Cited by 30
Epigenetic regulation of vascular smooth muscle cell phenotypes in atherosclerosis
Lambert, J., & Jørgensen, H. F. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.119085 · Cited by 28
Epigenetic aging in adult neurogenesis
Zocher, S., & Toda, T. (2023) · Hippocampus · doi:10.1002/hipo.23494 · Cited by 29
Liquid biopsy: creating opportunities in brain space
Trivedi, R., & Bhat, K. P. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02446-0 · Cited by 27
Zhao, L., Xing, P., Polavarapu, V. K., Zhao, M., Valero-Martínez, B., Dang, Y., Maturi, N., Mathot, L., Neves, I., Yildirim, I., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab813 · Cited by 26
Characterizing crosstalk in epigenetic signaling to understand disease physiology
Lempiäinen, J. K., & Garcia, B. A. (2023) · Biochemical Journal · doi:10.1042/bcj20220550 · Cited by 29
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Collora, J. A., & Ho, Y. (2023) · Genome Research · doi:10.1101/gr.277698.123 · Cited by 26
scTrends: A living review of commercial single-cell and spatial 'omic technologies
Jonghe, J. D., Opzoomer, J. W., Vilas‐Zornoza, A., Nilges, B. S., Crane, P., Vicari, M., Lee, H., Lara‐Astiaso, D., Groß, T., Morf, J., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100723 · Cited by 27
Paul, S., Xie, S., Yao, X., & Dey, A. (2022) · Cells · doi:10.3390/cells11142225 · Cited by 25
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Peeters, F., Cappuyns, S., Piqué‐Gili, M., Phillips, G., Verslype, C., Lambrechts, D., & Dekervel, J. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101094 · Cited by 26
Recent advances in deciphering oligodendrocyte heterogeneity with single-cell transcriptomics
Valihrach, L., Matusova, Z., Žucha, D., Klassen, R., Benesova, S., Abaffy, P., Kubista, M., & Anděrová, M. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.1025012 · Cited by 26
Tang, L., Huang, Z., Mei, H., & Hu, Y. (2023) · Military Medical Research · doi:10.1186/s40779-023-00486-4 · Cited by 24
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Acke, A., Belle, S. V., Louis, B., Vitale, R., Rocha, S., Voet, T., Debyser, Z., & Hofkens, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac521 · Cited by 24
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Zhang, H., Mulqueen, R. M., Iannuzo, N., Farrera, D. O., Polverino, F., Galligan, J. J., Ledford, J. G., Adey, A., & Cusanovich, D. A. (2024) · Genome biology · doi:10.1186/s13059-023-03150-1 · Cited by 25
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Ahmed, N. I., Khandelwal, N., Anderson, A. G., Oh, E., Vollmer, R. M., Kulkarni, A., Gibson, J. R., & Konopka, G. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114257 · Cited by 23
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Aikawa, M., Sonawane, A. R., Chelvanambi, S., Asano, T., Halu, A., Matamalas, J. T., Singh, S. A., Uchida, S., Aïkawa, E., Arenas, À., et al. (2025) · Frontiers in Science · doi:10.3389/fsci.2025.1474469 · Cited by 24
Houser, A. E., Kazmi, A., Nair, A., & Ji, A. L. (2023) · JID Innovations · doi:10.1016/j.xjidi.2023.100198 · Cited by 23
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Barcenas-Walls, J. R., Ansaloni, F., Hervé, B., Strandback, E., Nyman, T., Castelo‐Branco, G., & Bartošovič, M. (2023) · Nature Protocols · doi:10.1038/s41596-023-00932-6 · Cited by 22
Self-citationGan, C., Yaqoob, U., Lu, J., Xie, M., Anwar, A., Jalan-Sakrikar, N., Jerez, S., Sehrawat, T. S., Navarro-Corcuera, A., Kostallari, E., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.174775 · Cited by 21
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Zhang, Q., Zhang, C., Kang, C., Zhu, J., He, Q., Li, H., Tong, Q., Wang, M., Zhang, L., Xiong, X., et al. (2024) · Advanced Science · doi:10.1002/advs.202405987 · Cited by 24
Xiong, X., Wang, X., Liu, C., Shao, Z.-M., & Yu, K. (2024) · Biomarker Research · doi:10.1186/s40364-024-00654-1 · Cited by 21
High-throughput single-cell omics using semipermeable capsules
Baronas, D., Žvirblytė, J., Norvaisis, S., Leonavičienė, G., Goda, K., Mikulenaite, V., Kašėta, V., Šablauskas, K., Griškevičius, L., Juzėnas, S., et al. (2025) · Science · doi:10.1126/science.ady7227 · Cited by 22
Hao, J., Huang, J., Hua, C., Zuo, Y., Yu, W., Wu, X., Li, L., Xue, G., Wan, X., Ru, L., et al. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002256 · Cited by 21
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Ray, A., Stelloh, C. T., Liu, Y., Meyer, A. E., Geurts, A. M., Cowley, A. W., Greene, A. S., Liang, M., & Rao, S. (2023) · Hypertension · doi:10.1161/hypertensionaha.123.21755 · Cited by 22
Leveraging zebrafish to investigate pancreatic development, regeneration, and diabetes
Mi, J., Ren, L., & Andersson, O. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.05.002 · Cited by 21
Perez, A. A., Goronzy, I. N., Blanco, M. R., Yeh, B. T., Guo, J. K., Lopes, C. S., Ettlin, O., Burr, A., & Guttman, M. (2024) · Nature Genetics · doi:10.1038/s41588-024-02000-5 · Cited by 22
Köhnke, T., Nuno, K. A., Alder, C. C., Gars, E. J., Phan, P., Fan, A. C., & Majeti, R. (2024) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-23-0235 · Cited by 23
Li, Q., Guo, Y., Wu, Z., Xu, X., Zhen-huan, J., Qi, S., Liu, Z., Wen, L., & Tang, F. (2024) · Nature Methods · doi:10.1038/s41592-024-02453-w · Cited by 20
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Zuo, J.-Y., Chen, H., Liu, Z.-G., Yang, Q., & He, G. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01365-y · Cited by 21
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Chehimi, S. N., Crist, R. C., & Reiner, B. C. (2023) · Genes · doi:10.3390/genes14030771 · Cited by 20
Chen, M., Jiang, J., & Hou, J. (2023) · Biomarker Research · doi:10.1186/s40364-023-00502-8 · Cited by 19
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Sun, K., Liu, X., Xu, R., Liu, C., Meng, A., & Lan, X. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01449-0 · Cited by 19
The role of single-cell genomics in human genetics
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Tao, X., Guan, X., Hong, G., He, Y., Li, S., Feng, S., Wang, J., Chen, G. J., Xu, F., Wang, J., et al. (2023) · Plant Biotechnology Journal · doi:10.1111/pbi.14029 · Cited by 18
Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy
Montagne, J. M., Jaffee, E. M., & Fertig, E. J. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2200660 · Cited by 18
de Mello, F. N., Tahira, A. C., Berzoti-Coelho, M. G., & Verjovski‐Almeida, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad538 · Cited by 18
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Li, T., Zeng, W., Zhu, F., & Lü, P. (2025) · aBIOTECH · doi:10.1007/s42994-025-00237-0 · Cited by 17
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Lv, J., Liu, X., Sun, Z., Gao, J., Yu, X., Zhang, M., Zhang, Z., Ren, S., & Zuo, Y. (2024) · Human Genetics · doi:10.1007/s00439-024-02646-5 · Cited by 17
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Shang, Y., Wang, Z., Xi, L., Wang, Y., Liu, M., Feng, Y., Wang, J., Wu, Q., Xiang, X., Chen, M., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108454 · Cited by 18
Wu, H., Wang, M., Zheng, Y., & Xie, X. S. (2025) · Cell Discovery · doi:10.1038/s41421-025-00770-8 · Cited by 16
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Elkin, E. R., Campbell, K. A., Lapehn, S., Harris, S. M., Padmanabhan, V., Bakulski, K. M., & Paquette, A. G. (2023) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2023.112066 · Cited by 15
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Greenstreet, L., Afanassiev, A., Kijima, Y., Heitz, M., Ishiguro, S., King, S. B., Yachie, N., & Schiebinger, G. R. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.005 · Cited by 15
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Maizels, R. J. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0049 · Cited by 17
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Sandovici, I., Morais, T., Constância, M., & Monteiro, M. P. (2025) · Journal of the Endocrine Society · doi:10.1210/jendso/bvaf129 · Cited by 16
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Fischer, D. K., Krick, K. S., Han, C., Woolf, M. T., & Heller, E. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19908-9 · Cited by 14
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McDonough, A., & Weinstein, J. R. (2024) · Glia · doi:10.1002/glia.24634 · Cited by 14
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Penkov, D., Zubkova, E., & Parfyonova, Y. V. (2023) · Methods and Protocols · doi:10.3390/mps6020024 · Cited by 14
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Cen, X., Huang, X., Deng, E., Gong, X., Tan, N., Ye, J. T., Wang, Y., Eils, R., Luo, Q., Li, Y., et al. (2026) · MedComm · doi:10.1002/mco2.70713 · Cited by 14
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Zhu, X., Ma, S., & Wong, W. H. (2024) · Genome biology · doi:10.1186/s13059-023-03142-1 · Cited by 15
Ye, F., Zhang, S., Fu, Y., Yang, L. X., Zhang, G., Wu, Y., Pan, J., Chen, H., Wang, X., Ma, L., et al. (2024) · Cell Discovery · doi:10.1038/s41421-023-00642-z · Cited by 13
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Ozier‐Lafontaine, A., Fourneaux, C., Durif, G., Arsenteva, P., Vallot, C., Gandrillon, O., Gonin-Giraud, S., Michel, B., & Picard, F. (2024) · Genome biology · doi:10.1186/s13059-024-03255-1 · Cited by 13
Landragin, C., Saichi, M., Laisné, M., Durand, A., Prompsy, P., Leclère, R., Mesple, J., Borgman, K. J. E., Trouchet, A., Faraldo, M. M., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02331-9 · Cited by 13
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Tuong, Z. K., Stewart, B. J., Guo, S. A., & Clatworthy, M. R. (2021) · Immunological Reviews · doi:10.1111/imr.13036 · Cited by 13
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Zhang, X., Qiu, H., Zhang, F., & Ding, S. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.883861 · Cited by 14
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Dai, X., Cai, L., & He, F. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac011 · Cited by 13
Sun, Y., Wang, H., Wang, W., Lu, J., Zhang, J., Luo, X., Luan, L., Wang, K., Jia, J., Yan, J., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.993955 · Cited by 13
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Stutt, N., Song, M., Wilson, M. D., & Scott, I. C. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.11.011 · Cited by 13
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Walls, A. W., & Rosenthal, A. Z. (2024) · Transcription · doi:10.1080/21541264.2024.2334110 · Cited by 13
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Lee, A. S., Ayers, L. J., Kosicki, M., Chan, W. M., Fozo, L., Pratt, B. M., Collins, T. E., Zhao, B., Rose, M. F., Sanchis‐Juan, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52463-7 · Cited by 12
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Lugenbühl, J. F., Viho, E. M. G., Binder, E. B., & Daskalakis, N. P. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.09.023 · Cited by 12
Kefalopoulou, S., Rullens, P. M. J., de Luca, K. L., de Vries, S. S., Korthout, T., van Oudenaarden, A., Zeller, P. J., & Kind, J. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01687-w · Cited by 12
Ditzer, N., Senoglu, E., Kolodziejczyk, A., Schütze, T. M., Nikolaidi, A., Küster, K., Sameith, K., Dietz, S., Derihaci, R. P., Birdir, C., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.06.016 · Cited by 13
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Guyer, R. A., Mueller, J. L., & Goldstein, A. M. (2022) · Biomolecules · doi:10.3390/biom12030452 · Cited by 12
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Wang, M., Li, Q., & Liu, L. (2023) · Biomolecules · doi:10.3390/biom13020304 · Cited by 15
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Samur, M., Szalat, R. E., & Munshi, N. C. (2023) · Blood · doi:10.1182/blood.2022017145 · Cited by 12
Nagy, D., Maude, H., Birdsey, G. M., RANDI, A. M., & Cebola, I. (2023) · Journal of Molecular Endocrinology · doi:10.1530/jme-23-0026 · Cited by 12
An oligodendrocyte silencer element underlies the pathogenic impact of lamin B1 structural variants
Nmezi, B., Bey, G. R., Oranburg, T. D., Dudnyk, K., Lardo, S. M., Herdman, N., Jacko, A. M., Rubio, S., Loeza‐Alcocer, E., Kofler, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56378-9 · Cited by 11
Li, G., Wang, Z. L., Wu, C., Wang, D., Han, I., Lee, J., Kaeli, D. R., Dy, J., Weinberger, K. Q., & Gu, A. Z. (2025) · ISME Communications · doi:10.1093/ismeco/ycaf015 · Cited by 11
Advances in Epigenomic Sequencing and Their Applications in Cancer Diagnostics
Lin, L., & Liu, Y. (2025) · Precision Chemistry · doi:10.1021/prechem.5c00014 · Cited by 11
Wang, Y., Li, J., Malcolm, A. A., Mansfield, W., Clark, S. J., Argelaguet, R., Biggins, L., Acton, R., Andrews, S., Reik, W., et al. (2025) · Science Advances · doi:10.1126/sciadv.adu3308 · Cited by 11
Jiang, A., Li, J., Wang, L., Liu, Y., Wu, Z., Wang, H., Wu, S., & Bao, W. (2025) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-025-01263-0 · Cited by 11
Multi-CUT&Tag to simultaneously profile multiple chromatin factors
Gopalan, S., & Fazzio, T. G. (2022) · STAR Protocols · doi:10.1016/j.xpro.2021.101100 · Cited by 11
Natural and Experimental Rewiring of Gene Regulatory Regions
Downes, D. J., & Hughes, J. R. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-112921-010715 · Cited by 12
A new "single" era of biomedicine and implications in disease research
Hu, C. (2023) · Journal of Bio-X Research · doi:10.1097/jbr.0000000000000140 · Cited by 11
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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