单细胞染色质转座酶可及性高通量测序技术及其应用

doi:10.13560/j.cnki.biotech.bull.1985.2023-0518

摘要/Abstract

摘要：

单细胞染色质转座酶可及性的高通量测序（single-cell assay for transposase-accessible chromatin with high-throughput sequencing, scATAC-seq）是利用转座酶研究单细胞染色质开放性的高通量测序技术，对于研究全基因组的表观遗传调控具有重要的意义。可从多个维度揭示有关染色质“组装”的重要信息，从而映射出细胞中的转录因子调控蛋白的结合区域和核小体定位等信息。目前，scATAC-seq技术已在生物和医学领域得到广泛应用，主要用于开放染色质图谱的绘制、细胞分化和发育、疾病致病机制以及肿瘤微环境的研究。本文阐述了scATAC-seq技术研究单细胞染色质开放区域的发展概况、数据分析和相关应用，期望对单细胞全基因组水平的染色质开放区域研究、顺式调控元件鉴定以及遗传调控网络的解析等提供借鉴，以期为今后更好的在生命科学研究中起到推动作用。

关键词: scATAC-seq, 数据分析, 应用

Abstract:

Single-cell assay for transposase-accessible chromatin with high-throughput sequencing（scATAC-seq）is a high-throughput sequencing technique that utilizes transposase to investigate the chromatin accessibility at the single-cell level, which is of great significance for investigating the epigenetic regulation of the whole genome. It can reveal important information about chromatin organization from multiple perspectives, mapping the binding regions of transcription factors and the positioning of nucleosomes in cells. Currently, scATAC-seq has been widely applied in the fields of biology and medicine, primarily for generating open chromatin profiles, studying cell differentiation and development, elucidating disease pathogenesis, and exploring the tumor microenvironment. This article provides an overview of the development, data analysis, and relevant applications of scATAC-seq technology in studying the open chromatin regions at the single-cell level, aiming to provide insights for the investigation of chromatin accessibility at the single-cell whole-genome level, identification of cis-regulatory elements, and deciphering genetic regulatory networks, thereby contributing to advancements in life science research.

Key words: scATAC-seq, data analysis, application

赵若含, 赵净颖, 柏毅承, 张瑞芳, 贾俊静, 豆腾飞. 单细胞染色质转座酶可及性高通量测序技术及其应用[J]. 生物技术通报, 2023, 39(12): 109-117.

ZHAO Ruo-han, ZHAO Jing-ying, BAI Yi-cheng, ZHANG Rui-fang, JIA Jun-jing, DOU Teng-fei. Single Cell Assay for Transposase Accessible Chromatin with High-throughput Sequencing Technology and Its Applications[J]. Biotechnology Bulletin, 2023, 39(12): 109-117.

图/表 3

参考文献 50

[1]	Bai YW, Zhou BR. Structures of native-like nucleosomes: one step closer toward understanding the structure and function of chromatin[J]. J Mol Biol, 2021, 433(6): 166648. doi: 10.1016/j.jmb.2020.09.007 URL
[2]	Tsompana M, Buck MJ. Chromatin accessibility: a window into the genome[J]. Epigenetics Chromatin, 2014, 7(1): 33. doi: 10.1186/1756-8935-7-33 pmid: 25473421
[3]	Grandi FC, Modi H, Kampman L, et al. Chromatin accessibility profiling by ATAC-seq[J]. Nat Protoc, 2022, 17(6): 1518-1552. doi: 10.1038/s41596-022-00692-9
[4]	Buenrostro JD, Giresi PG, Zaba LC, et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position[J]. Nat Methods, 2013, 10(12): 1213-1218. doi: 10.1038/nmeth.2688 pmid: 24097267
[5]	吴杰, 全建平, 叶勇, 等. 染色质转座酶可及性测序研究进展[J]. 遗传, 2020, 42(4): 333-346.
	Wu J, Quan JP, Ye Y, et al. Advances in assay for transposase-accessible chromatin with high-throughput sequencing[J]. Hereditas, 2020, 42(4): 333-346.
[6]	Cusanovich DA, Daza R, Adey A, et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing[J]. Science, 2015, 348(6237): 910-914. doi: 10.1126/science.aab1601 pmid: 25953818
[7]	Zhang K, Hocker JD, Miller M, et al. A single-cell atlas of chromatin accessibility in the human genome[J]. Cell, 2021, 184(24): 5985-6001.e19. doi: 10.1016/j.cell.2021.10.024 pmid: 34774128
[8]	Wen L, Tang FC. Recent advances in single-cell sequencing technologies[J]. Precis Clin Med, 2022, 5(1): pbac002. doi: 10.1093/pcmedi/pbac002 URL
[9]	Satpathy AT, Granja JM, Yost KE, et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion[J]. Nat Biotechnol, 2019, 37(8): 925-936. doi: 10.1038/s41587-019-0206-z pmid: 31375813
[10]	Yu YY, Wei XY, Deng QT, et al. Single-nucleus chromatin accessibility landscape reveals diversity in regulatory regions across distinct adult rat cortex[J]. Front Mol Neurosci, 2021, 14: 651355. doi: 10.3389/fnmol.2021.651355 URL
[11]	范海瑞. 基于单细胞转录组和染色质可及性图谱揭示猪外周血单个核细胞的免疫细胞异质性[D]. 扬州: 扬州大学, 2022.
	Fan HR. Single-cell transcriptomic and chromatin accessibility atlas of peripheral blood mononuclear cells reveal the immune cell heterogeneity of pigs[D]. Yangzhou: Yangzhou University, 2022.
[12]	Cusanovich DA, Reddington JP, Garfield DA, et al. The cis-regulatory dynamics of embryonic development at single-cell resolution[J]. Nature, 2018, 555(7697): 538-542. doi: 10.1038/nature25981 URL
[13]	Buenrostro JD, Wu B, Litzenburger UM, et al. Single-cell chromatin accessibility reveals principles of regulatory variation[J]. Nature, 2015, 523(7561): 486-490. doi: 10.1038/nature14590
[14]	Lake BB, Chen S, Sos BC, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain[J]. Nat Biotechnol, 2018, 36(1): 70-80. doi: 10.1038/nbt.4038 pmid: 29227469
[15]	Mezger A, Klemm S, Mann I, et al. High-throughput chromatin accessibility profiling at single-cell resolution[J]. Nat Commun, 2018, 9(1): 3647. doi: 10.1038/s41467-018-05887-x pmid: 30194434
[16]	Chen X, Miragaia RJ, Natarajan KN, et al. A rapid and robust method for single cell chromatin accessibility profiling[J]. Nat Commun, 2018, 9(1): 5345. doi: 10.1038/s41467-018-07771-0 pmid: 30559361
[17]	Lareau CA, Duarte FM, Chew JG, et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility[J]. Nat Biotechnol, 2019, 37(8): 916-924. doi: 10.1038/s41587-019-0147-6 pmid: 31235917
[18]	Cusanovich DA, Hill AJ, Aghamirzaie D, et al. A single-cell atlas of in vivo mammalian chromatin accessibility[J]. Cell, 2018, 174(5): 1309-1324.e18. doi: S0092-8674(18)30855-9 pmid: 30078704
[19]	Buenrostro JD, Corces MR, Lareau CA, et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation[J]. Cell, 2018, 173(6): 1535-1548.e16. doi: S0092-8674(18)30446-X pmid: 29706549
[20]	Sos BC, Fung HL, Gao DR, et al. Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing(THS-seq)assay[J]. Genome Biol, 2016, 17: 20. doi: 10.1186/s13059-016-0882-7 URL
[21]	Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data[J]. Bioinformatics, 2014, 30(15): 2114-2120. doi: 10.1093/bioinformatics/btu170 pmid: 24695404
[22]	Baek S, Lee I. Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation[J]. Comput Struct Biotechnol J, 2020, 18: 1429-1439.
[23]	Zhang Y, Liu T, Meyer CA, et al. Model-based analysis of ChIP-seq(MACS)[J]. Genome Biol, 2008, 9(9): R137. doi: 10.1186/gb-2008-9-9-r137
[24]	Becht E, McInnes L, Healy J, et al. Dimensionality reduction for visualizing single-cell data using UMAP[J]. Nat Biotechnol, 2019, 37(1): 38-44. doi: 10.1038/nbt.4314
[25]	Pliner HA, Packer JS, McFaline-Figueroa JL, et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data[J]. Mol Cell, 2018, 71(5): 858-871.e8. doi: S1097-2765(18)30547-1 pmid: 30078726
[26]	Schep AN, Wu BJ, Buenrostro JD, et al. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data[J]. Nat Methods, 2017, 14(10): 975-978. doi: 10.1038/nmeth.4401 pmid: 28825706
[27]	Zhang CT, Hong XP, Yu HY, et al. Gene regulatory network study of rheumatoid arthritis in single-cell chromatin landscapes of peripheral blood mononuclear cells[J]. Mod Rheumatol, 2023, 33(4): 739-750. doi: 10.1093/mr/roac072 URL
[28]	di Bella DJ, Habibi E, Stickels RR, et al. Molecular logic of cellular diversification in the mouse cerebral cortex[J]. Nature, 2021, 595(7868): 554-559. doi: 10.1038/s41586-021-03670-5
[29]	Morabito S, Miyoshi E, Michael N, et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease[J]. Nat Genet, 2021, 53(8): 1143-1155. doi: 10.1038/s41588-021-00894-z pmid: 34239132
[30]	Ziffra RS, Kim CN, Ross JM, et al. Single-cell epigenomics reveals mechanisms of human cortical development[J]. Nature, 2021, 598(7879): 205-213. doi: 10.1038/s41586-021-03209-8
[31]	Ruf-Zamojski F, Zhang ZD, Zamojski M, et al. Single nucleus multi-omics regulatory landscape of the murine pituitary[J]. Nat Commun, 2021, 12(1): 2677. doi: 10.1038/s41467-021-22859-w pmid: 33976139
[32]	Chiou J, Zeng C, et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk[J]. Nat Genet, 2021, 53(4): 455-466. doi: 10.1038/s41588-021-00823-0 pmid: 33795864
[33]	Philip M, Fairchild L, Sun LP, et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming[J]. Nature, 2017, 545(7655): 452-456. doi: 10.1038/nature22367 URL
[34]	Cheng P, Wirka RC, Shoa Clarke L, et al. ZEB2 shapes the epigenetic landscape of atherosclerosis[J]. Circulation, 2022, 145(6): 469-485. doi: 10.1161/CIRCULATIONAHA.121.057789 pmid: 34990206
[35]	Yu HY, Wu HW, Zheng FP, et al. Gene-regulatory network analysis of ankylosing spondylitis with a single-cell chromatin accessible assay[J]. Sci Rep, 2020, 10(1): 19411. doi: 10.1038/s41598-020-76574-5 pmid: 33173081
[36]	Yu HY, Hong XP, Wu HW, et al. The chromatin accessibility landscape of peripheral blood mononuclear cells in patients with systemic lupus erythematosus at single-cell resolution[J]. Front Immunol, 2021, 12: 641886. doi: 10.3389/fimmu.2021.641886 URL
[37]	de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth[J]. Cancer Cell, 2023, 41(3): 374-403. doi: 10.1016/j.ccell.2023.02.016 pmid: 36917948
[38]	Dong Y, Tu RF, Liu HD, et al. Regulation of cancer cell metabolism: oncogenic MYC in the driver's seat[J]. Signal Transduct Target Ther, 2020, 5(1): 124.
[39]	Becker WR, Nevins SA, Chen DC, et al. Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer[J]. Nat Genet, 2022, 54(7): 985-995. doi: 10.1038/s41588-022-01088-x pmid: 35726067
[40]	Alonso-Curbelo D, Ho YJ, Burdziak C, et al. A gene-environment-induced epigenetic program initiates tumorigenesis[J]. Nature, 2021, 590(7847): 642-648. doi: 10.1038/s41586-020-03147-x
[41]	LaFave LM, Kartha VK, Ma S, et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma[J]. Cancer Cell, 2020, 38(2): 212-228.e13. doi: S1535-6108(20)30310-X pmid: 32707078
[42]	Shu MY, Hong DN, Lin HL, et al. Single-cell chromatin accessibility identifies enhancer networks driving gene expression during spinal cord development in mouse[J]. Dev Cell, 2022, 57(24): 2761-2775.e6. doi: 10.1016/j.devcel.2022.11.011 pmid: 36495874
[43]	Norrie JL, Lupo MS, Xu BS, et al. Nucleome dynamics during retinal development[J]. Neuron, 2019, 104(3): 512-528.e11. doi: S0896-6273(19)30686-5 pmid: 31493975
[44]	Scott RW, Arostegui M, Schweitzer R, et al. Hic1 defines quiescent mesenchymal progenitor subpopulations with distinct functions and fates in skeletal muscle regeneration[J]. Cell Stem Cell, 2019, 25(6): 797-813.e9. doi: S1934-5909(19)30459-X pmid: 31809738
[45]	Abbasi S, Sinha S, Labit E, et al. Distinct regulatory programs control the latent regenerative potential of dermal fibroblasts during wound healing[J]. Cell Stem Cell, 2021, 28(3): 581-583. doi: 10.1016/j.stem.2021.02.004 pmid: 33667362
[46]	Pervolarakis N, Nguyen QH, Williams J, et al. Integrated single-cell transcriptomics and chromatin accessibility analysis reveals regulators of mammary epithelial cell identity[J]. Cell Rep, 2020, 33(3): 108273. doi: 10.1016/j.celrep.2020.108273 URL
[47]	Gao YH, Li JB, Cai GZ, et al. Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide[J]. BMC Genomics, 2022, 23(1): 338. doi: 10.1186/s12864-022-08562-0 pmid: 35501711
[48]	Mok GF, Folkes L, Weldon SA, et al. Characterising open chromatin in chick embryos identifies cis-regulatory elements important for paraxial mesoderm formation and axis extension[J]. Nat Commun, 2021, 12(1): 1157. doi: 10.1038/s41467-021-21426-7 pmid: 33608545
[49]	Cai CC, Wan P, Wang H, et al. Transcriptional and open chromatin analysis of bovine skeletal muscle development by single-cell sequencing[J]. Cell Prolif, 2023: e13430.
[50]	Cai SF, Hu B, Wang XY, et al. Integrative single-cell RNA-seq and ATAC-seq analysis of myogenic differentiation in pig[J]. BMC Biol, 2023, 21(1): 19. doi: 10.1186/s12915-023-01519-z pmid: 36726129

技术 Technology	检测的细胞数量 Number of detected cells	细胞类型 Cell type	目的 Objective	特点 Characteristic	参考文献 Reference
sci-ATAC	1 500个左右	任何细胞类型	获取染色质可及性信息	1.方法简单易行，适用于实验室； 2.测序覆盖范围相对较低； 3.随着板数量的增加，测序成本增加； 4.该技术尚未实现商业化，在实际操作中缺乏客观有效的操作流程，通常需要针对不同批次进行定制和微调。	[6,12]
Fluidigm C1	一次上样的细胞数为96个	直径在5-25 μm的细胞	获取染色质可及性信息	1.稳定的细胞捕获平台； 2.对操作者的技术要求较低； 3.实验周期短； 4.细胞通量低； 5.成本较高。	[13]
scTHS	60 000个	新鲜或冻存的组织样本	获取染色质可及性信息和提高了远端增强子信息	1.检测染色质可及性具有高灵敏度和特异性； 2.捕获转录起始位点附近和远端调控区域的较小峰值方面具有优势； 3.需定制Tn5转座酶； 4.对操作人员的技术要求较高； 5.成本相对较高。	[14]
uATAC	一次可以分离1 800个单细胞	适用于5-100 μm范围的活细胞	获取染色质可及性信息	1.实现无细胞大小偏好的分选； 2.单细胞反应孔可视化； 3.可筛选活的单细胞进行后续处理，可节约反应试剂并缩短数据分析时间； 4.细胞捕获效率低。	[15]
Plate	5 000-50 000个细胞	新鲜或冷冻保存的纯细胞	获取染色质可及性信息	1.无需昂贵的设备； 2.成本较低； 3.适用于分离培养的细胞； 4.细胞通量低。	[16]
dsci-ATAC	100 000个细胞	新鲜的组织样本	获取染色质可及性信息	1.该技术解决了快速分离细胞的挑战，显著提高了单细胞分析的速度； 2.细胞通量高； 3.高灵敏度； 4.成本相对较高。	[17]
10X	每次可处理8个样本，细胞通量高达80 000个以上	直径40 μm以下的活细胞	获取染色质可及性信息	1.操作流程自动化程度高； 2.细胞捕获效率高； 3.对细胞总量及活性要求较高，需仪器上门服务； 4.成本相对较低。	[9]

技术 Technology	检测的细胞数量 Number of detected cells	细胞类型 Cell type	目的 Objective	特点 Characteristic	参考文献 Reference
sci-ATAC	1 500个左右	任何细胞类型	获取染色质可及性信息	1.方法简单易行，适用于实验室； 2.测序覆盖范围相对较低； 3.随着板数量的增加，测序成本增加； 4.该技术尚未实现商业化，在实际操作中缺乏客观有效的操作流程，通常需要针对不同批次进行定制和微调。	[6,12]
Fluidigm C1	一次上样的细胞数为96个	直径在5-25 μm的细胞	获取染色质可及性信息	1.稳定的细胞捕获平台； 2.对操作者的技术要求较低； 3.实验周期短； 4.细胞通量低； 5.成本较高。	[13]
scTHS	60 000个	新鲜或冻存的组织样本	获取染色质可及性信息和提高了远端增强子信息	1.检测染色质可及性具有高灵敏度和特异性； 2.捕获转录起始位点附近和远端调控区域的较小峰值方面具有优势； 3.需定制Tn5转座酶； 4.对操作人员的技术要求较高； 5.成本相对较高。	[14]
uATAC	一次可以分离1 800个单细胞	适用于5-100 μm范围的活细胞	获取染色质可及性信息	1.实现无细胞大小偏好的分选； 2.单细胞反应孔可视化； 3.可筛选活的单细胞进行后续处理，可节约反应试剂并缩短数据分析时间； 4.细胞捕获效率低。	[15]
Plate	5 000-50 000个细胞	新鲜或冷冻保存的纯细胞	获取染色质可及性信息	1.无需昂贵的设备； 2.成本较低； 3.适用于分离培养的细胞； 4.细胞通量低。	[16]
dsci-ATAC	100 000个细胞	新鲜的组织样本	获取染色质可及性信息	1.该技术解决了快速分离细胞的挑战，显著提高了单细胞分析的速度； 2.细胞通量高； 3.高灵敏度； 4.成本相对较高。	[17]
10X	每次可处理8个样本，细胞通量高达80 000个以上	直径40 μm以下的活细胞	获取染色质可及性信息	1.操作流程自动化程度高； 2.细胞捕获效率高； 3.对细胞总量及活性要求较高，需仪器上门服务； 4.成本相对较低。	[9]