Single-cell chromatin state analysis with Signac.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
17
11
2020
accepted:
27
08
2021
pubmed:
3
11
2021
medline:
29
12
2021
entrez:
2
11
2021
Statut:
ppublish
Résumé
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.
Identifiants
pubmed: 34725479
doi: 10.1038/s41592-021-01282-5
pii: 10.1038/s41592-021-01282-5
pmc: PMC9255697
mid: NIHMS1817977
doi:
Substances chimiques
Chromatin
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1333-1341Subventions
Organisme : NHGRI NIH HHS
ID : DP2 HG009623
Pays : United States
Organisme : NHGRI NIH HHS
ID : K99 HG011489
Pays : United States
Organisme : NIH HHS
ID : OT2 OD026673
Pays : United States
Organisme : NHGRI NIH HHS
ID : RM1 HG011014
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Ai, S. et al. Profiling chromatin states using single-cell itChIP-seq. Nat. Cell Biol. 21, 1164–1172 (2019).
pubmed: 31481796
doi: 10.1038/s41556-019-0383-5
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
pubmed: 26083756
pmcid: 4685948
doi: 10.1038/nature14590
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: 31431618
pmcid: 6702168
doi: 10.1038/s41467-019-11559-1
Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
pubmed: 25953818
pmcid: 4836442
doi: 10.1126/science.aab1601
Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).
pubmed: 31036827
pmcid: 6488672
doi: 10.1038/s41467-019-09982-5
Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell https://doi.org/10.1016/j.molcel.2019.07.015 (2019).
Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).
pubmed: 30923384
pmcid: 7187538
doi: 10.1038/s41592-019-0361-7
Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0147-6 (2019).
Luo, C. et al. Robust single-cell DNA methylome profiling with snmc-seq2. Nat. Commun. 9, 3824 (2018).
pubmed: 30237449
pmcid: 6147798
doi: 10.1038/s41467-018-06355-2
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).
pubmed: 31375813
pmcid: 7299161
doi: 10.1038/s41587-019-0206-z
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
pubmed: 25042786
pmcid: 4117646
doi: 10.1038/nmeth.3035
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science https://doi.org/10.1126/science.aau0730 (2018).
Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0290-0 (2019).
Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).
pubmed: 29472610
pmcid: 5823944
doi: 10.1038/s41467-018-03149-4
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and Single-Cell genomics. Cell https://doi.org/10.1016/j.cell.2019.01.022 (2019).
Lareau, C. A. et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0645-6 (2021).
Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).
pubmed: 31695190
pmcid: 7231560
doi: 10.1038/s41594-019-0323-x
Xing, Q. R. et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res. 30, 1027–1039 (2020).
pubmed: 32699019
pmcid: 7397874
doi: 10.1101/gr.257840.119
Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019).
pubmed: 30692544
pmcid: 6349937
doi: 10.1038/s41467-018-08205-7
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell https://doi.org/10.1016/j.cell.2020.09.056 (2020).
Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00927-2 (2021).
Fiskin, E., Lareau, C. A., Eraslan, G., Ludwig, L. S. & Regev, A. Single-cell multimodal profiling of proteins and chromatin accessibility using PHAGE-ATAC. Preprint at BioRxiv https://doi.org/10.1101/2020.10.01.322420 (2020).
Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. eLife 10, e63632 (2021).
pubmed: 33835024
pmcid: 8034981
doi: 10.7554/eLife.63632
Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376 (2019).
doi: 10.1016/j.cell.2018.11.022
Pierce, S. E., Granja, J. M. & Greenleaf, W. J. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat. Commun. 12, 2969 (2021).
pubmed: 34016988
pmcid: 8137922
doi: 10.1038/s41467-021-23213-w
Thornton, C. A. et al. Spatially mapped single-cell chromatin accessibility. Nat. Commun. 12, 1274 (2021).
pubmed: 33627658
pmcid: 7904839
doi: 10.1038/s41467-021-21515-7
Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. https://doi.org/10.1038/s41576-019-0093-7 (2019).
Bravo González-Blas, C. et al. cistopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods https://doi.org/10.1038/s41592-019-0367-1 (2019).
Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).
doi: 10.1016/j.cell.2018.06.052
Xiong, L. et al. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat. Commun. 10, 4576 (2019).
pubmed: 31594952
pmcid: 6783552
doi: 10.1038/s41467-019-12630-7
Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from Single-Cell chromatin accessibility data. Mol. Cell 71, 858–871.e8 (2018).
pubmed: 30078726
pmcid: 6582963
doi: 10.1016/j.molcel.2018.06.044
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
pubmed: 28825706
pmcid: 5623146
doi: 10.1038/nmeth.4401
Danese, A. et al. EpiScanpy: integrated single-cell epigenomic analysis. Nat.Commun. https://doi.org/10.1038/s41467-021-25131-3 (2021).
Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021).
pubmed: 33637727
pmcid: 7910485
doi: 10.1038/s41467-021-21583-9
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. https://doi.org/10.1038/s41588-021-00790-6 (2021).
Ji, Z., Zhou, W. & Ji, H. Single-cell regulome data analysis by SCRAT. Bioinformatics 33, 2930–2932 (2017).
pubmed: 28505247
pmcid: 5870556
doi: 10.1093/bioinformatics/btx315
Baker, S. M., Rogerson, C., Hayes, A., Sharrocks, A. D. & Rattray, M. Classifying cells with scasat, a single-cell ATAC-seq analysis tool. Nucleic Acids Res. 47, e10 (2019).
pubmed: 30335168
doi: 10.1093/nar/gky950
Zhao, C., Hu, S., Huo, X. & Zhang, Y. Dr.seq2: a quality control and analysis pipeline for parallel single cell transcriptome and epigenome data. PLoS ONE 12, e0180583 (2017).
pubmed: 28671995
pmcid: 5495495
doi: 10.1371/journal.pone.0180583
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
pubmed: 25867923
pmcid: 4430369
doi: 10.1038/nbt.3192
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. https://doi.org/10.1038/nbt.4096 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
doi: 10.1016/j.cell.2019.05.031
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
doi: 10.1016/j.cell.2021.04.048
Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife https://doi.org/10.7554/eLife.45105 (2019).
Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).
pubmed: 21208982
pmcid: 3042176
doi: 10.1093/bioinformatics/btq671
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K. & Harshman, R. Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391–407 (1990).
doi: 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arXiv.org/abs/1802.03426 (2018).
Pearce, E. L. et al. Control of effector CD8
pubmed: 14605368
doi: 10.1126/science.1090148
Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science https://doi.org/10.1126/science.aav1898 (2018).
GTEx Consortium. The GTEx Consortium Atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
doi: 10.1126/science.aaz1776
Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019).
pubmed: 31739806
pmcid: 6859644
doi: 10.1186/s13059-019-1854-5
Li, Y. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Preprint at bioRxiv https://doi.org/10.1101/2020.05.10.087585 (2020).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature https://doi.org/10.1038/s41586-019-0969-x (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat. Methods https://doi.org/10.1038/s41592-019-0619-0 (2019).
Brenner, S. Sequences and consequences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 207–212 (2010).
pubmed: 20008397
pmcid: 2842711
doi: 10.1098/rstb.2009.0221
Richmond, T. J. & Davey, C. A. The structure of DNA in the nucleosome core. Nature 423, 145–150 (2003).
pubmed: 12736678
doi: 10.1038/nature01595
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
pubmed: 24097267
pmcid: 3959825
doi: 10.1038/nmeth.2688
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Baglama, J. & Reichel, L. Augmented implicitly restarted Lanczos bidiagonalization methods. SIAM J. Sci. Comput. 27, 19–42 (2005).
doi: 10.1137/04060593X
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
pubmed: 31249361
pmcid: 6597582
doi: 10.1038/s41598-019-45839-z
Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).
doi: 10.1140/epjb/e2013-40829-0
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R. J. 8, 289–317 (2016).
pubmed: 27818791
pmcid: 5096736
doi: 10.32614/RJ-2016-021
Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).
pubmed: 16096348
doi: 10.1093/bioinformatics/bti623
Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2020).
pubmed: 31701148
Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198, 497–508 (2014).
pubmed: 25104515
pmcid: 4196608
doi: 10.1534/genetics.114.167908
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).
pubmed: 29991676
pmcid: 6039488
doi: 10.1038/s41467-018-05083-x
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).
pubmed: 30902100
pmcid: 6431044
doi: 10.1186/s13059-019-1662-y
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://arxiv.org/abs/1303.3997 (2013).