Single-cell protein-DNA interactomics and multiomics tools for deciphering genome regulation

Open Access

Review

Issue		Natl Sci Open Volume 2, Number 3, 2023


Article Number		20220057
Number of page(s)		19
Section		Life Sciences and Medicine
DOI		https://doi.org/10.1360/nso/20220057
Published online		30 March 2023

Lemaire P. Unfolding a chordate developmental program, one cell at a time: invariant cell lineages, short-range inductions and evolutionary plasticity in ascidians. Dev Biol 2009; 332: 48-60. [Article] [CrossRef] [PubMed] [Google Scholar]
Sulston JE, Schierenberg E, White JG, et al. The embryonic cell lineage of the nematode Caenorhabditis elegans. Dev Biol 1983; 100: 64-119. [Article] [Google Scholar]
Kretzschmar K, Watt FM. Lineage tracing. Cell 2012; 148: 33-45. [Article] [Google Scholar]
Gómez-Villafuertes R, Paniagua-Herranz L, Gascon S, et al. Live imaging followed by single cell tracking to monitor cell biology and the lineage progression of multiple neural populations. J Vis Exp 2017; [Article] [Google Scholar]
Zovein AC, Hofmann JJ, Lynch M, et al. Fate tracing reveals the endothelial origin of hematopoietic stem cells. Cell Stem Cell 2008; 3: 625-636. [Article] [Google Scholar]
Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009; 6: 377-382. [Article] [CrossRef] [PubMed] [Google Scholar]
Picelli S, Faridani OR, Björklund ÅK, et al. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014; 9: 171-181. [Article] [CrossRef] [PubMed] [Google Scholar]
Hashimshony T, Wagner F, Sher N, et al. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2012; 2: 666-673. [Article] [Google Scholar]
Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015; 161: 1202-1214. [Article] [Google Scholar]
Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015; 161: 1187-1201. [Article] [Google Scholar]
Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014; 343: 776-779. [Article] [CrossRef] [PubMed] [Google Scholar]
Sheng K, Cao W, Niu Y, et al. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat Methods 2017; 14: 267-270. [Article] [CrossRef] [PubMed] [Google Scholar]
Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet 2003; 33: 245-254. [Article] [CrossRef] [PubMed] [Google Scholar]
Handy DE, Castro R, Loscalzo J. Epigenetic modifications. Circulation 2011; 123: 2145-2156. [Article] [Google Scholar]
Buenrostro JD, Wu B, Litzenburger UM, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 2015; 523: 486-490. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Meissner A. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 2005; 33: 5868-5877. [Article] [CrossRef] [Google Scholar]
Guo H, Zhu P, Wu X, et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res 2013; 23: 2126-2135. [Article] [Google Scholar]
Guo H, Zhu P, Guo F, et al. Profiling DNA methylome landscapes of mammalian cells with single-cell reduced-representation bisulfite sequencing. Nat Protoc 2015; 10: 645-659. [Article] [CrossRef] [PubMed] [Google Scholar]
Cao J, Packer JS, Ramani V, et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 2017; 357: 661-667. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Rosenberg AB, Roco CM, Muscat RA, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 2018; 360: 176-182. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Cusanovich DA, Hill AJ, Aghamirzaie D, et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 2018; 174: 1309-1324. [Article] [Google Scholar]
Lareau CA, Duarte FM, Chew JG, et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat Biotechnol 2019; 37: 916-924. [Article] [Google Scholar]
Johnson DS, Mortazavi A, Myers RM, et al. Genome-wide mapping of in vivo protein-DNA interactions. Science 2007; 316: 1497-1502. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Barski A, Cuddapah S, Cui K, et al. High-resolution profiling of histone methylations in the human genome. Cell 2007; 129: 823-837. [Article] [CrossRef] [Google Scholar]
Park PJ. ChIP-seq: Advantages and challenges of a maturing technology. Nat Rev Genet 2009; 10: 669-680. [Article] [CrossRef] [Google Scholar]
Zheng H, Huang B, Zhang B, et al. Resetting epigenetic memory by reprogramming of histone modifications in mammals. Mol Cell 2016; 63: 1066-1079. [Article] [CrossRef] [PubMed] [Google Scholar]
Skene PJ, Henikoff JG, Henikoff S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat Protoc 2018; 13: 1006-1019. [Article] [CrossRef] [PubMed] [Google Scholar]
Adli M, Bernstein BE. Whole-genome chromatin profiling from limited numbers of cells using nano-ChIP-seq. Nat Protoc 2011; 6: 1656-1668. [Article] [CrossRef] [PubMed] [Google Scholar]
Brind′Amour J, Liu S, Hudson M, et al. An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat Commun 2015; 6: 6033. [Article] [CrossRef] [PubMed] [Google Scholar]
Cao Z, Chen C, He B, et al. A microfluidic device for epigenomic profiling using 100 cells. Nat Methods 2015; 12: 959-962. [Article] [CrossRef] [PubMed] [Google Scholar]
Harada A, Maehara K, Handa T, et al. A chromatin integration labelling method enables epigenomic profiling with lower input. Nat Cell Biol 2019; 21: 287-296. [Article] [CrossRef] [PubMed] [Google Scholar]
Rotem A, Ram O, Shoresh N, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol 2015; 33: 1165-1172. [Article] [CrossRef] [PubMed] [Google Scholar]
Wang Q, Xiong H, Ai S, et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol Cell 2019; 76: 206-216. [Article] [CrossRef] [PubMed] [Google Scholar]
Kaya-Okur HS, Wu SJ, Codomo CA, et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun 2019; 10: 1930. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Ku WL, Nakamura K, Gao W, et al. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat Methods 2019; 16: 323-325. [Article] [CrossRef] [PubMed] [Google Scholar]
Hainer SJ, Bošković A, McCannell KN, et al. Profiling of pluripotency factors in single cells and early embryos. Cell 2019; 177: 1319-1329. [Article] [Google Scholar]
Ku WL, Pan L, Cao Y, et al. Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing. Genome Res 2021; 31: 1831-1842. [Article] [CrossRef] [PubMed] [Google Scholar]
Bartosovic M, Kabbe M, Castelo-Branco G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat Biotechnol 2021; 39: 825-835. [Article] [CrossRef] [PubMed] [Google Scholar]
Wu SJ, Furlan SN, Mihalas AB, et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat Biotechnol 2021; 39: 819-824. [Article] [CrossRef] [PubMed] [Google Scholar]
Ai S, Xiong H, Li CC, et al. Profiling chromatin states using single-cell itChIP-seq. Nat Cell Biol 2019; 21: 1164-1172. [Article] [CrossRef] [PubMed] [Google Scholar]
Kind J, Pagie L, de Vries SS, et al. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 2015; 163: 134-147. [Article] [Google Scholar]
Rang FJ, de Luca KL, de Vries SS, et al. Single-cell profiling of transcriptome and histone modifications with EpiDamID. Mol Cell 2022; 82: 1956-1970. [Article] [CrossRef] [PubMed] [Google Scholar]
Gopalan S, Wang Y, Harper NW, et al. Simultaneous profiling of multiple chromatin proteins in the same cells. Mol Cell 2021; 81: 4736-4746. [Article] [CrossRef] [PubMed] [Google Scholar]
Meers MP, Llagas G, Janssens DH, et al. Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag. Nat Biotechnol 2022; [Article] [PubMed] [Google Scholar]
Hu Y, Huang K, An Q, et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol 2016; 17: 88. [Article] [Google Scholar]
Hou Y, Guo H, Cao C, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 2016; 26: 304-319. [Article] [Google Scholar]
Bian S, Hou Y, Zhou X, et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 2018; 362: 1060-1063. [Article] [Google Scholar]
Macaulay IC, Haerty W, Kumar P, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods 2015; 12: 519-522. [Article] [CrossRef] [PubMed] [Google Scholar]
Pott S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 2017; 6: e23203. [Article] [Google Scholar]
Li L, Guo F, Gao Y, et al. Single-cell multi-omics sequencing of human early embryos. Nat Cell Biol 2018; 20: 847-858. [Article] [CrossRef] [PubMed] [Google Scholar]
Dey SS, Kester L, Spanjaard B, et al. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol 2015; 33: 285-289. [Article] [Google Scholar]
Xu W, Yang W, Zhang Y, et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods 2022; 19: 1243-1249. [Article] [CrossRef] [PubMed] [Google Scholar]
Liu L, Liu C, Quintero A, et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat Commun 2019; 10: 470. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Chen S, Lake BB, Zhang K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat Biotechnol 2019; 37: 1452-1457. [Article] [CrossRef] [PubMed] [Google Scholar]
Cao J, Cusanovich DA, Ramani V, et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 2018; 361: 1380-1385. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Zhu C, Yu M, Huang H, et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat Struct Mol Biol 2019; 26: 1063-1070. [Article] [CrossRef] [PubMed] [Google Scholar]
Ma S, Zhang B, LaFave LM, et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 2020; 183: 1103-1116. [Article] [Google Scholar]
Xiong H, Luo Y, Wang Q, et al. Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions. Nat Methods 2021; 18: 652-660. [Article] [CrossRef] [PubMed] [Google Scholar]
Zhu C, Zhang Y, Li YE, et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat Methods 2021; 18: 283-292. [Article] [CrossRef] [PubMed] [Google Scholar]
Stoeckius M, Hafemeister C, Stephenson W, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 2017; 14: 865-868. [Article] [CrossRef] [PubMed] [Google Scholar]
Lareau CA, Ludwig LS, Muus C, et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat Biotechnol 2021; 39: 451-461. [Article] [CrossRef] [PubMed] [Google Scholar]
Mimitou EP, Lareau CA, Chen KY, et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol 2021; 39: 1246-1258. [Article] [CrossRef] [PubMed] [Google Scholar]
Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell 2021; 184: 3573-3587. [Article] [Google Scholar]
Clark SJ, Argelaguet R, Kapourani CA, et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun 2018; 9: 781. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Efremova M, Teichmann SA. Computational methods for single-cell omics across modalities. Nat Methods 2020; 17: 14-17. [Article] [CrossRef] [PubMed] [Google Scholar]
Baker SM, Rogerson C, Hayes A, et al. Classifying cells with Scasat, a single-cell ATAC-seq analysis tool. Nucleic Acids Res 2019; 47: e10. [Article] [CrossRef] [PubMed] [Google Scholar]
Fang R, Preissl S, Li Y, et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat Commun 2021; 12: 1337. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Bravo González-Blas C, Minnoye L, Papasokrati D, et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat Methods 2019; 16: 397-400. [Article] [CrossRef] [PubMed] [Google Scholar]
Pliner HA, Packer JS, McFaline-Figueroa JL, et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol Cell 2018; 71: 858-871. [Article] [CrossRef] [PubMed] [Google Scholar]
Granja JM, Corces MR, Pierce SE, et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet 2021; 53: 403-411. [Article] [CrossRef] [PubMed] [Google Scholar]
Schep AN, Wu B, Buenrostro JD, et al. chromVAR: Inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat Methods 2017; 14: 975-978. [Article] [CrossRef] [PubMed] [Google Scholar]
Stuart T, Srivastava A, Madad S, et al. Single-cell chromatin state analysis with Signac. Nat Methods 2021; 18: 1333-1341. [Article] [CrossRef] [PubMed] [Google Scholar]
Yu W, Uzun Y, Zhu Q, et al. scATAC-pro: A comprehensive workbench for single-cell chromatin accessibility sequencing data. Genome Biol 2020; 21: 94. [Article] [Google Scholar]
Xiong L, Xu K, Tian K, et al. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat Commun 2019; 10: 4576. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Haghverdi L, Lun ATL, Morgan MD, et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol 2018; 36: 421-427. [Article] [CrossRef] [PubMed] [Google Scholar]
Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018; 36: 411-420. [Article] [CrossRef] [PubMed] [Google Scholar]
Stuart T, Satija R. Integrative single-cell analysis. Nat Rev Genet 2019; 20: 257-272. [Article] [CrossRef] [PubMed] [Google Scholar]
Welch JD, Hartemink AJ, Prins JF. MATCHER: Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol 2017; 18: 138. [Article] [CrossRef] [PubMed] [Google Scholar]
Zhang B, Srivastava A, Mimitou E, et al. Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nat Biotechnol 2022; 40: 1220-1230. [Article] [CrossRef] [PubMed] [Google Scholar]
Argelaguet R, Velten B, Arnol D, et al. Multi-Omics factor analysis—A framework for unsupervised integration of multi‐omics data sets. Mol Syst Biol 2018; 14: e8124. [Article] [CrossRef] [PubMed] [Google Scholar]
Argelaguet R, Clark SJ, Mohammed H, et al. Multi-Omics profiling of mouse gastrulation at single-cell resolution. Nature 2019; 576: 487-491. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Jin S, Zhang L, Nie Q. scAI: An unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles. Genome Biol 2020; 21: 25. [Article] [Google Scholar]
Gayoso A, Steier Z, Lopez R, et al. Joint probabilistic modeling of single-cell multi-Omic data with totalVI. Nat Methods 2021; 18: 272-282. [Article] [CrossRef] [PubMed] [Google Scholar]
Ernst J, Kellis M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotechnol 2015; 33: 364-376. [Article] [CrossRef] [PubMed] [Google Scholar]
Angermueller C, Lee HJ, Reik W, et al. DeepCpG: Accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol 2017; 18: 67. [Article] [Google Scholar]
Kapourani CA, Sanguinetti G. Melissa: Bayesian clustering and imputation of single-cell methylomes. Genome Biol 2019; 20: 61. [Article] [Google Scholar]
Farlik M, Sheffield NC, Nuzzo A, et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 2015; 10: 1386-1397. [Article] [Google Scholar]
Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell 2019; 177: 1888-1902. [Article] [Google Scholar]
Mulqueen RM, Pokholok D, O’Connell BL, et al. High-content single-cell combinatorial indexing. Nat Biotechnol 2021; 39: 1574-1580. [Article] [CrossRef] [PubMed] [Google Scholar]
Longo SK, Guo MG, Ji AL, et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 2021; 22: 627-644. [Article] [CrossRef] [PubMed] [Google Scholar]
Wei X, Fu S, Li H, et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 2022; 377: eabp9444. [Article] [CrossRef] [PubMed] [Google Scholar]
Femino AM, Fay FS, Fogarty K, et al. Visualization of single RNA transcripts in situ. Science 1998; 280: 585-590. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Chen KH, Boettiger AN, Moffitt JR, et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015; 348: aaa6090. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019; 363: 1463-1467. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Shah S, Lubeck E, Zhou W, et al. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 2016; 92: 342-357. [Article] [Google Scholar]
Kruse F, Junker JP, van Oudenaarden A, et al. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol 2016; 135: 299–307 [CrossRef] [PubMed] [Google Scholar]
Deng Y, Bartosovic M, Kukanja P, et al. Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level. Science 2022; 375: 681-686. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Deng Y, Bartosovic M, Ma S, et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 2022; 609: 375-383. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Thornton CA, Mulqueen RM, Torkenczy KA, et al. Spatially mapped single-cell chromatin accessibility. Nat Commun 2021; 12: 1274. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Sagar, Grün D. Deciphering cell fate decision by integrated single-cell sequencing analysis. Annu Rev Biomed Data Sci 2020; 3: 1-22. [Article] [Google Scholar]
Tedesco M, Giannese F, Lazarević D, et al. Chromatin velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nat Biotechnol 2022; 40: 235-244. [Article] [CrossRef] [PubMed] [Google Scholar]
Yue Y, Zong W, Li X, et al. Long-term, in toto live imaging of cardiomyocyte behaviour during mouse ventricle chamber formation at single-cell resolution. Nat Cell Biol 2020; 22: 332-340. [Article] [CrossRef] [PubMed] [Google Scholar]
Keller PJ, Schmidt AD, Santella A, et al. Fast, high-contrast imaging of animal development with scanned light sheet-based structured-illumination microscopy. Nat Methods 2010; 7: 637-642. [Article] [CrossRef] [PubMed] [Google Scholar]
Keller PJ, Schmidt AD, Wittbrodt J, et al. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 2008; 322: 1065-1069. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Dixit A, Parnas O, Li B, et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 2016; 167: 1853-1866. [Article] [Google Scholar]
Adamson B, Norman TM, Jost M, et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 2016; 167: 1867-1882. [Article] [Google Scholar]
Datlinger P, Rendeiro AF, Schmidl C, et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods 2017; 14: 297-301. [Article] [CrossRef] [PubMed] [Google Scholar]
Jaitin DA, Weiner A, Yofe I, et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 2016; 167: 1883-1896. [Article] [Google Scholar]
Rubin AJ, Parker KR, Satpathy AT, et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 2019; 176: 361-376. [Article] [Google Scholar]
Pierce SE, Granja JM, Greenleaf WJ. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat Commun 2021; 12: 2969. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Shema E, Bernstein BE, Buenrostro JD. Single-cell and single-molecule epigenomics to uncover genome regulation at unprecedented resolution. Nat Genet 2019; 51: 19-25. [Article] [CrossRef] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.