Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues

Abstract

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.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Single-cell profiling of several histone modifications in the mouse brain.
Fig. 2: De novo identification of cell types by cell-type-specific scCUT&Tag marker regions.
Fig. 3: Integration of scCUT&Tag data with single-cell gene expression.
Fig. 4: Spreading of H3K4me3 mark at promoters at single-cell resolution.
Fig. 5: scCUT&Tag analysis of transcription factor binding.
Fig. 6: Prediction of gene regulatory networks from scCUT&Tag data.

Data availability

Raw data are deposited in GEO under accession GSE163532. The scCUT&Tag dataset can be explored at web resources available at https://ki.se/en/mbb/oligointernode and https://mouse-brain-cutandtag.cells.ucsc.edu/. The following publicly available datasets were used in this study: GSE96107 (HiC of mESC and H3K27me3 ChIP–seq of cortical neurons), GSE124557 (scCUT&Tag in iCell8 platform), GSE117309 (scChIP–seq), GSE104435 (H3K27me3 ChIP–seq of microglia), SRP135960 (scRNA-seq of the adolescent mouse brain), GSE75330 (scRNA-seq of oligodendrocyte lineage) and GSE135296 (H3K27me3 CUT&Run of mouse NIH/3T3 cells). Data were also accessed for the Mouse Brain Atlas (https://storage.googleapis.com/linnarsson-lab-loom/l5_all.loom), 10x Genomics single-cell ATAC–seq of P50 mouse cortex (https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_adult_brain_fresh_5k) and ENCODE H3K27me3 ChIP–seq of Bruce4 mESCs (https://www.encodeproject.org/experiments/ENCSR000CFN/).

Code availability

Code is available at https://github.com/Castelo-Branco-lab/scCut-Tag_2020.

References

  1. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Lorthongpanich, C. et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science 341, 1110–1112 (2013).

    CAS  PubMed  Article  Google Scholar 

  5. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).

  6. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. Rotem, A. et al. Single-cell ChIP–seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Ai, S. et al. Profiling chromatin states using single-cell itChIP–seq. Nat. Cell Biol. 21, 1164–1172 (2019).

    CAS  PubMed  Article  Google Scholar 

  9. Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell 76, 206–216.e7 (2019).

    CAS  PubMed  Article  Google Scholar 

  10. Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC–seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Carter, B. et al. Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT–seq). Nat. Commun. 10, 3747 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  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).

    CAS  PubMed  Article  Google Scholar 

  13. Falcão, A. M. et al. Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis. Nat. Med. 24, 1837–1844 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. Marques, S. et al. Transcriptional convergence of oligodendrocyte lineage progenitors during development. Dev. Cell 46, 504–517.e7 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Jäkel, S. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Jung, M. et al. Lines of murine oligodendroglial precursor cells immortalized by an activated neu tyrosine kinase show distinct degrees of interaction with axons in vitro and in vivo. Eur. J. Neurosci. 7, 1245–1265 (1995).

    CAS  PubMed  Article  Google Scholar 

  18. Davis, C. A. et al. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    CAS  PubMed  Article  Google Scholar 

  19. Xiao, Y. et al. Hippo pathway deletion in adult resting cardiac fibroblasts initiates a cell state transition with spontaneous and self-sustaining fibrosis. Genes Dev. 33, 1491–1505 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Sousa, V. H., Miyoshi, G., Hjerling-Leffler, J., Karayannis, T. & Fishell, G. Characterization of Nkx6-2-derived neocortical interneuron lineages. Cereb. Cortex 19, i1–i10 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  21. Matsuoka, T. et al. Neural crest origins of the neck and shoulder. Nature 436, 347–355 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  23. Zhu, X. et al. Age-dependent fate and lineage restriction of single NG2 cells. Development 138, 745–753 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Zhu, X., Bergles, D. E. & Nishiyama, A. NG2 cells generate both oligodendrocytes and gray matter astrocytes. Development 135, 145–157 (2008).

    CAS  PubMed  Article  Google Scholar 

  25. Matsuda, T. et al. Pioneer factor NeuroD1 rearranges transcriptional and epigenetic profiles to execute microglia–neuron conversion. Neuron 101, 472–485.e7 (2019).

    CAS  PubMed  Article  Google Scholar 

  26. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Wang, W. et al. PRC2 acts as a critical timer that drives oligodendrocyte fate over astrocyte identity by repressing the notch pathway. Cell Rep 32, 108147 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Benayoun, B. A. et al. H3K4me3 breadth is linked to cell identity and transcriptional consistency. Cell 158, 673–688 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Li, Y. et al. The structural basis for cohesin–CTCF-anchored loops. Nature 578, 472–476 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Yu, Y. et al. Olig2 targets chromatin remodelers to enhancers to initiate oligodendrocyte differentiation. Cell 152, 248–261 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  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).

    PubMed  PubMed Central  Article  Google Scholar 

  36. Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e24 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871.e8 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Moore, J. E. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. Zhang, Y. et al. Model-based analysis of ChIP–Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).

    CAS  PubMed  Article  Google Scholar 

  45. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009); https://doi.org/10.1007/978-0-387-98141-3

  47. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    PubMed  Article  CAS  Google Scholar 

  48. Kaya-Okur, H. Bench top CUT&Tag. protocols.io https://doi.org/10.17504/protocols.io.bcuhiwt6 (2020).

  49. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Flyamer, I. M., Illingworth, R. S. & Bickmore, W. A. Coolpup.py: versatile pile-up analysis of Hi-C data. Bioinformatics 36, 2980–2985 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Abdennur, N. & Mirny, L. A. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics 36, 311–316 (2020).

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

We would like to thank T. Jimenez-Beristain for writing laboratory animal ethics permit 1995_2019 and assistance with animal experiments; L. Kirby, M. Meijer and P. Kukanja for assistance with animal experiments; F. Gabriel for setting up CUT&Run technology; S. Henikoff for providing protein A–Tn5 and protein A–MNAse, B. Hervé for setting up the Shiny web resource; M. Speir and M. Haeussler at the University of California Santa Cruz Genomics Institute for setting up the UCSC Cell Browser and UCSC Genome Browser resources; S. Elsässer and R. Sandberg for proofreading and providing critical comments on the manuscript; I. Johansson and E. Dunevall and the Karolinska Institute Protein Science facility for cloning the pA–Tn5 construct and the protein purification, M. Eriksson for performing the 10x ATAC–seq protocol, the Single Cell Genomics Facility and S. Elsässer, Karolinska Institutet, for providing mESCs; O. Fernandez Capetillo, Karolinska Institutet, for providing NIH-3T3 cells and the staff at Comparative Medicine-Biomedicum and Biomedicum flow cytometry core facility (BFC). Figure 1a was generated using BioRender. We acknowledge support from the National Genomics Infrastructure in Stockholm funded by the Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, and the Swedish National Infrastructure for Computing/Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. M.B. is funded by the Vinnova Seal of Excellence Marie-Sklodowska Curie Actions grant RNA-centric view on Oligodendrocyte lineage development (RODent). Work in G.C.-B’s research group was supported by the Swedish Research Council (grant nos. 2015-03558 and 2019-01360), the European Union (Horizon 2020 Research and Innovation Programme/European Research Council Consolidator Grant EPIScOPE, grant agreement no. 681893), the Swedish Brain Foundation (FO2017-0075 and FO2018-0162), the Swedish Cancer Society (Cancerfonden; 190394 Pj), the Knut and Alice Wallenberg Foundation (grants no. 2019-0107 and 2019-0089), The Swedish Society for Medical Research (SSMF, grant no. JUB2019), the Ming Wai Lau Center for Reparative Medicine and the Karolinska Institutet.

Author information

Authors and Affiliations

Authors

Contributions

M.B. and G.C.-B. conceived the study, designed the experiments and analysis and wrote the manuscript. M.B. optimized and performed the scCUT&Tag experiments and analyzed the scCUT&Tag data. M.K. and M.B. performed the HiChIP experiment. M.K. analyzed the HiChIP data and helped with generation of related figures. All authors contributed and approved the manuscript.

Corresponding authors

Correspondence to Marek Bartosovic or Gonçalo Castelo-Branco.

Ethics declarations

Competing interests

M.B. and M.K. have performed paid consultation for the company Abcam.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Optimization of tagmentation step in the scCUT&Tag protocol by addition of BSA.

a, Table depicting scCUT&Tag protocol steps (Primary Antibody, Secondary Antibody, Tn5 binding, Tagmentation) and whether BSA was included in the scCUT&Tag buffers during these steps. b, DAPI counterstaining of the nuclei after the scCUT&Tag procedure. Inclusion of BSA in the procedure substantially reduces clumping of the nuclei. c, Genome browser profiles of bulk CUT&tag experiment from the different BSA conditions (described in a). d, Gating strategy depicting sorting of GFP cells. P1 Depicts general gate for selection of cells, P2 gate selects for singlets, DAPI- gate selects only live cells and GFP+ gate selects cells that possess GFP signal (Sox10-Cre/GFP).

Extended Data Fig. 2 scCUT&Tag of mixture of mouse cell lines.

a, UMAP projection of H3K27me3 scCUT&Tag in two dimensions. Points are colored by assigned cell identity. N = 2 technical replicates b, Genome browser view of merged pseudobulk profiles of 5 scCUT&Tag clusters and bulk ChIP–seq or CUT&Run profiles of the respective cell lines. c, PCA analysis and Pearson correlation matrix of 5 scCUT&Tag clusters and bulk ChIP–seq or CUT&Run data. The PCA was performed on the top 150 most variable marker regions selected from the scCUT&Tag data. Heatmap shows Pearson’s correlation coefficient of signal in the same features. d, Scatterplot showing correlation of scCUT&Tag signal among clusters and technical replicates. e, Stacked barplot showing relative proportions of cell types identified using scCUT&Tag data. f,g, Metagene plot showing the distribution of ChIP–seq for mESCs (f) and 3T3 cells (g) and downscaled scCUT&Tag signal around peaks that were called from the bulk dataset.

Extended Data Fig. 3 Quality control of the scCUT&Tag data.

a, Merged pseudobulk profiles of scCUT&Tag with four antibodies against modified histones. b, Scatterplot of number of reads per cells (x axis) and fraction of reads originating from peak regions (y axis) that was used to set cutoffs for cells identification. Cutoffs were set after manual inspection of the plots and are depicted as horizontal and vertical lines overlaid over the plot. c, Histogram of number of features identified per cell for each antibody used in scCUT&Tag. d, Violin plots showing fraction of reads per cell that overlap peak regions that were called on merged bulk profiles using the same parameters for all compared samples. scCUT&Tag peak calling parameters are different from parameters used in Figure 1d. Point specifies mean of the distribution and lines standard error of mean. Number of cells per sample – H3K27me3_N1 3304, H3K27me3_N2 3090, H3K27me3_N3 5145, H3K27me3_N4 2393, H3K27me3_cell_lines_1 4872, H3K27me3_cell_lines_2 3873, K562_H3K4me2_iCell8 807, K562_H3K27me3_iCell8 1387, H1_H3K27me3_iCell8 486, Grosselin_1 2005, Grosselin_2 4122, Grosselin_3 960. e, Violin plot showing number of unique reads per cell. Point specifies mean of the distribution and lines standard error of mean. Number of cells per sample – H3K27me3_N1 n = 3304, H3K27me3_N2 n = 3090, H3K27me3_N3 n = 5145, H3K27me3_N4 n = 2393, H3K27me3_cell_lines_1 n = 4872, H3K27me3_cell_lines_2 n = 3873, K562_H3K4me2_iCell8 n = 807, K562_H3K27me3_iCell8 n = 1387, H1_H3K27me3_iCell8 n = 486, Grosselin_1 n = 2005, Grosselin_2 n = 4122, Grosselin_3 n = 960. f, Barplot depicting number of analyzed cells per experiment. g, Fingerprint plot representing relationship between cumulative signal of scCUT&Tag and scChIP–seq relative to fraction of genomic bins analyzed.

Extended Data Fig. 4 De novo identification of cell types by cell type specific marker regions.

Projection of gene activity scores of (a) H3K27ac and (b) H3K36me3 scCUT&Tag on the two-dimensional UMAP embedding. Gene name is depicted in the title and specific population is highlighted in the UMAP plot by labeling the cell type. c-d, Heatmap representation of the scCUT&Tag signal for (c) H3K27ac and (d) H3K36me3. X axis represents genomic region, each row in Y axis contains data from one cell. Cell correspondence to clusters is depicted by color bar on the right side of the heatmap and annotated with cell type. Signal is aggregated per 250 bp windows and binarized. e-h, Aggregated pseudobulk scCUT&Tag profiles for four histone modifications in all identified cell types around selected marker genes.

Extended Data Fig. 5 Summary of meta features of cells analyzed by scCUT&Tag.

a, Two-dimensional UMAP embedding of the scCUT&Tag data. Cells are colored by correspondence to GFP population, developmental age and biological replicate. b-c, Bar plot summary of the correspondence to the (b) GFP population and (c) developmental age per cell type identified from the H3K4me3 scCUT&Tag data.

Extended Data Fig. 6 Comparison of merged profiles of populations identified from scCUT&Tag data and corresponding bulk ChIP–seq or bulk Cut&Run data.

a, Genome browser view of a representative region harboring microglia-specific and neuron-specific H3K27me3 peak regions (highlighted in gray). b, PCA analysis and Pearson correlation matrix of merged scCUT&Tag profiles per cluster and bulk ChIP–seq and bulk CUT&Run data. PCA was performed on top 150 most variable marker regions selected from scCUT&Tag data. Heatmap shows Pearson’s correlation coefficient of signal in the same features. c, Relative cell type proportions identified from scCUT&Tag data and scRNA-seq data from biological replicates.

Extended Data Fig. 7 Metagene analysis of gene activity scores.

a-d, Metagene activity projection of scCUT&Tag data on the UMAP embeddings of four histone modification scCUT&Tag datasets. Metagenes are selected as top 100 most specifically expressed in the scRNA-seq data1.

Extended Data Fig. 8 Gene Ontology analysis of H3K4me3 scCUT&Tag marker genes.

a, Gene ontology analysis of the marker genes determined by gene activity scores from the H3K4me3 scCUT&Tag data. GO terms were manually selected from the list of all enriched GO terms in all populations.

Extended Data Fig. 9 scCUT&Tag of transcription factors.

a, Meta-region activity scores of marker regions determined from H3K4me3 scCUT&Tag data and specific for the respective population for (a) OLIG2 and (b) RAD21 scCUT&Tag data. c-d, Boxplot representation of a and b, single cell meta-region profiles aggregated per cell type. Lower and upper bound of boxplot specify 25th and 75th percentile and lower and upper whisker specifies minimum and maximum no further than 1.5 times of interquartile range. Outliers are not displayed. non_oligo cells n = 2877, oligo cells n = 1667. e-f, Co-embedding of (e) H3K27ac and OLIG2 and (f) H3K27ac and RAD21 in single two-dimensional UMAP space. g, Additional motifs identified using MEME from the merged pseudobulk profile of OLIG2 scCUT&Tag.

Extended Data Fig. 10 Benchmarking of loops predicted by the ABC model with scCUT&Tag 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. b, Venn diagram showing the overlap of loops predicted with scCUT&Tag data with loops predicted with ABC model with bulk CUT&Run data and Cicero. c, Scatterplot showing consistency of predictions of ABC model run with downscaled scCUT&Tag data. d, Boxplot representation of lengths of the loops predicted by various methods. Lower and upper bound of boxplot specify 25th and 75th percentile and lower and upper whisker specifies minimum and maximum no further than 1.5 times of interquartile range. Outliers are not displayed. mOL n = 1796, Astrocytes n = 1506, OEC n = 913, Unknown n = 160.

Supplementary information

Reporting Summary

Supplementary Table 1

List of marker regions (bins) defined by the scCUT&Tag data.

Supplementary Table 2

List of enhancer-promoter loops predicted by ABC model.

Supplementary Table 3

List of correlated H3K27ac peak regions predicted by Cicero.

Supplementary Table 4

Summary of number of analyzed cells in this study

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat Biotechnol 39, 825–835 (2021). https://doi.org/10.1038/s41587-021-00869-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-021-00869-9

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing