单细胞DNA甲基化测序数据处理流程与分析方法
收稿日期: 2024-05-30
修回日期: 2024-08-30
网络出版日期: 2024-09-12
基金资助
国家自然科学基金项目(82370148);农村和社会发展科技处/广州医科大学/广州医科大学附属第一医院重点研发计划(2024B03J0046);广州国家实验室专项项目(GZNL2023A02003);呼吸疾病国家重点实验室自主开放课题项目(J19112006202302);广州医科大学/附属肿瘤医院科研能力提升项目(02-410-2302244XM);呼吸疾病全国重点实验室自主课题基金(SKLRD-Z-202307)
Processing pipelines and analytical methods for single-cell DNA methylation sequencing data
Received date: 2024-05-30
Revised date: 2024-08-30
Online published: 2024-09-12
Supported by
National Natural Science Foundation of China(82370148);Rural and Social Development Science and Technology Office/Guangzhou Medical University/Guangzhou Medical University First Affiliated Hospital Key R&D Plan(2024B03J0046);Guangzhou National Laboratory Special Project(GZNL2023A02003);Independent Open Subject Project of the State Key Laboratory of Respiratory Diseases(J19112006202302);Guangzhou Medical University/Affiliated Cancer Hospital Scientific Research Capacity Improvement Project(02-410-2302244XM);Independent Research Fund of the State Key Laboratory of Respiratory Diseases(SKLRD-Z-202307)
单细胞DNA甲基化测序技术近年来取得了飞速发展，在揭示细胞间异质性及表观遗传学调控机制方面发挥着重要作用。随着测序技术的进步，单细胞甲基化数据的质量与数量也在不断提高，标准化的预处理流程与合适的分析方法对确保数据的可比性与结果的可靠性尤为关键。然而，目前尚未形成一套完整的数据分析流程来指导研究人员对现有数据进行挖掘。本文系统综述了单细胞甲基化数据预处理步骤和分析方法，简要介绍了相关算法和工具，并探讨了单细胞甲基化技术在脑科学、血细胞分化及癌症研究中的应用前景，旨在为研究人员分析数据时提供指导，推动单细胞甲基化测序技术的发展和应用。
关键词： 单细胞DNA甲基化测序; 表观遗传学; 预处理; 数据分析
王艳妮, 李佳 . 单细胞DNA甲基化测序数据处理流程与分析方法[J]. 遗传, 2024 , 46(10) : 807 -819 . DOI: 10.16288/j.yczz.24-154
Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.
| [1] | Gao Q, Lu SW, Wang YW, He LG, Wang MS, Jia RY, Chen S, Zhu DK, Liu MF, Zhao XX, Yang Q, Wu Y, Zhang SQ, Huang J, Mao S, Ou XM, Sun D, Tian B, Cheng AC. Bacterial DNA methyltransferase: a key to the epigenetic world with lessons learned from proteobacteria. Front Microbiol, 2023, 14: 1129437. |
| [2] | Wu TP, Wang T, Seetin MG, Lai YQ, Zhu SJ, Lin KX, Liu YF, Byrum SD, Mackintosh SG, Zhong M, Tackett A, Wang GL, Hon LS, Fang G, Swenberg JA, Xiao AZ. DNA methylation on N6-adenine in mammalian embryonic stem cells. Nature, 2016, 532(7599): 329-333. |
| [3] | Li E, Zhang Y. DNA Methylation in Mammals. Cold Spring Harb Perspect Biol, 2014, 6(5): a019133. |
| [4] | Greenberg MVC, Bourc’his D. The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol, 2019, 20(10): 590-607. |
| [5] | Nishiyama A, Nakanishi M. Navigating the DNA methylation landscape of cancer. Trends Genet, 2021, 37(11): 1012-1027. |
| [6] | J?hner D, Jaenisch R. DNA methylation in early mammalian development. Springer New York, 1984, 189-219. |
| [7] | Brockdorff N. Convergent Themes in X Chromosome Inactivation and Autosomal Imprinting. Oxford University Press, 1997, 191-210. |
| [8] | Surani MA. Imprinting and the initiation of gene silencing in the germ line. Cell, 1998, 93(3): 309-312. |
| [9] | Ng HH, Bird A. DNA methylation and chromatin modification. Curr Opin Genet Dev, 1999, 9(2): 158-163. |
| [10] | Groh S, Schotta G. Silencing of endogenous retroviruses by heterochromatin. Cell Mol Life Sci, 2017, 74(11): 2055-2065. |
| [11] | Okano M, Bell DW, Haber DA, Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for De Novo methylation and mammalian development. Cell, 1999, 99(3): 247-257. |
| [12] | Dor Y, Cedar H. Principles of DNA methylation and their implications for biology and medicine. Lancet, 2018, 392(10149): 777-786. |
| [13] | Singer BD. A practical guide to the measurement and analysis of DNA methylation. Am J Respir Cell Mol Biol, 2019, 61(4): 417-428. |
| [14] | Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, Andrews SR, Stegle O, Reik W, Kelsey G. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods, 2014, 11(8): 817-820. |
| [15] | Luo CY, Keown CL, Kurihara L, Zhou JT, He YP, Li JH, Castanon R, Lucero J, Nery JR, Sandoval JP, Bui B, Sejnowski TJ, Harkins TT, Mukamel EA, Behrens MM, Ecker JR. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science, 2017, 357(6351): 600-604. |
| [16] | Luo CY, Rivkin A, Zhou JT, Sandoval JP, Kurihara L, Lucero J, Castanon R, Nery JR, Pinto-Duarte A, Bui B, Fitzpatrick C, O’Connor C, Ruga S, Van Eden ME, Davis DA, Mash DC, Behrens MM, Ecker JR. Robust single-cell DNA methylome profiling with snmC-seq2. Nat Commun, 2018, 9(1): 3824. |
| [17] | Mulqueen RM, Pokholok D, Norberg SJ, Torkenczy KA, Fields AJ, Sun DC, Sinnamon JR, Shendure J, Trapnell C, O’Roak BJ, Xia Z, Steemers FJ, Adey AC. Highly scalable generation of DNA methylation profiles in single cells. Nat Biotechnol, 2018, 36(5): 428-431. |
| [18] | Nichols RV, O’Connell BL, Mulqueen RM, Thomas J, Woodfin AR, Acharya S, Mandel G, Pokholok D, Steemers FJ, Adey AC. High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nat Commun, 2022, 13(1): 7627. |
| [19] | Zhang Q, Ma S, Liu ZZ, Zhu BH, Zhou ZR, Li GS, Meana JJ, González-Maeso J, Lu C. Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes. Nat Commun, 2023, 14(1): 4672. |
| [20] | Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal, 2011, 17(1): 10-12. |
| [21] | Chen SF, Zhou YQ, Chen YR, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 2018, 34(17): i884-i890. |
| [22] | Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014, 30(15): 2114-2120. |
| [23] | Pedersen BS, Eyring KR, De S, Yang IV, Schwartz DA. Fast and accurate alignment of long bisulfite-seq reads. 2014. |
| [24] | Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 2011, 27(11): 1571-1572. |
| [25] | Guo WL, Fiziev P, Yan WH, Cokus S, Sun XG, Zhang MQ, Chen P-Y, Pellegrini M. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics, 2013, 14: 774. |
| [26] | Chen HF, Smith AD, Chen T. WALT: fast and accurate read mapping for bisulfite sequencing. Bioinformatics, 2016, 32(22): 3507-3509. |
| [27] | Zhou QW, Lim J-Q, Sung W-K, Li GL. An integrated package for bisulfite DNA methylation data analysis with Indel-sensitive mapping. BMC Bioinformatics, 2019, 20(1): 47. |
| [28] | Xi YX, Li W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics, 2009, 10(1): 232. |
| [29] | de Sena Brandine G, Smith AD. Fast and memory-efficient mapping of short bisulfite sequencing reads using a two-letter alphabet. NAR Genom Bioinform, 2021, 3(4): lqab115. |
| [30] | Farrell C, Thompson M, Tosevska A, Oyetunde A, Pellegrini M. BiSulfite Bolt: a bisulfite sequencing analysis platform. GigaScience, 2021, 10(5): giab033. |
| [31] | Zhang Y, Park C, Bennett C, Thornton M, Kim D. Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N. Genome Res, 2021, 31(7): 1290-1295. |
| [32] | Tran H, Porter J, Sun M-A, Xie HH, Zhang LQ. Objective and comprehensive evaluation of bisulfite short read mapping tools. Adv Bioinformatics, 2014, 2014: 472045. |
| [33] | Kunde-Ramamoorthy G, Coarfa C, Laritsky E, Kessler NJ, Harris RA, Xu MC, Chen R, Shen LL, Milosavljevic A, Waterland RA. Comparison and quantitative verification of mapping algorithms for whole-genome bisulfite sequencing. Nucleic Acids Res, 2014, 42(6): e43. |
| [34] | Gong WT, Pan XC, Xu DT, Ji GY, Wang YF, Tian YH, Cai JL, Li JQ, Zhang Z, Yuan XL. Benchmarking DNA methylation analysis of 14 alignment algorithms for whole genome bisulfite sequencing in mammals. Comput Struct Biotechnol J, 2022, 20: 4704-4716. |
| [35] | Lin SY, Linehan JA, Wilson TG, Hoon DSB. Emerging utility of urinary cell-free nucleic acid biomarkers for prostate, bladder, and renal cancers. Eur Urol Focus, 2017, 3(2-3): 265-272. |
| [36] | Merkle J, Daka A, Deppe AC, Wahlers T, Paunel-G?rgülü A. High levels of cell-free DNA accurately predict late acute kidney injury in patients after cardiac surgery. PLoS One, 2019, 14(6): e0218548. |
| [37] | Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV, CCGA Consortium. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol, 2020, 31(6): 745-759. |
| [38] | Li WY, Li QJ, Kang SL, Same M, Zhou YG, Sun C, Liu C-C, Matsuoka L, Sher L, Wong WH, Alber F, Zhou XJ. CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res, 2018, 46(15): e89. |
| [39] | Yin Y, Li J, Li J, Lee MJ, Zhao SB, Guo LL, Li JF, Zhang MT, Huang Y, Li X-N, Deng ZZ, Sun DQ. LiBis: an ultrasensitive alignment augmentation for low-input bisulfite sequencing. Brief Bioinform, 2021, 22(4): bbaa332. |
| [40] | Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. Twelve years of SAMtools and BCFtools. GigaScience, 2021, 10(2): giab008. |
| [41] | McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst, 2019, 8(4): 329-337.e4. |
| [42] | Iqbal W, Zhou WD. Computational methods for single-cell DNA methylome analysis. Genomics Proteomics Bioinformatics, 2023, 21(1): 48-66. |
| [43] | Guo SC, Diep D, Plongthongkum N, Fung H-L, Zhang K, Zhang K. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet, 2017, 49(4): 635-642. |
| [44] | Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol, 2017, 18(1): 67. |
| [45] | Kapourani C-A, Sanguinetti G. Melissa: bayesian clustering and imputation of single-cell methylomes. Genome Biol, 2019, 20(1): 61. |
| [46] | de Souza CP, Andronescu M, Masud T, Kabeer F, Biele J, Laks E, Lai D, Ye P, Brimhall J, Wang BX, Su E, Hui T, Cao Q, Wong M, Moksa M, Moore RA, Hirst M, Aparicio S, Shah SP. Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data. PLOS Comput Biol, 2020, 16(9): e1008270. |
| [47] | Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol, 2018, 19(1): 15. |
| [48] | Liu HQ, Zeng QR, Zhou JT, Bartlett A, Wang B-A, Berube P, Tian W, Kenworthy M, Altshul J, Nery JR, Chen HM, Castanon RG, Zu SP, Li YE, Lucero J, Osteen JK, Pinto-Duarte A, Lee J, Rink J, Cho S, Emerson N, Nunn M, O’Connor C, Wu ZH, Stoica I, Yao ZZ, Smith KA, Tasic B, Luo CY, Dixon JR, Zeng HK, Ren B, Behrens MM, Ecker JR. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature, 2023, 624(7991): 366-377. |
| [49] | Kapourani C-A, Argelaguet R, Sanguinetti G, Vallejos CA. scMET: bayesian modeling of DNA methylation heterogeneity at single-cell resolution. Genome Biol, 2021, 22(1): 114. |
| [50] | Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ, Hu TX, Krueger F, Smallwood S, Ponting CP, Voet T, Kelsey G, Stegle O, Reik W. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods, 2016, 13(3): 229-232. |
| [51] | Farlik M, Halbritter F, Müller F, Choudry FA, Ebert P, Klughammer J, Farrow S, Santoro A, Ciaurro V, Mathur A, Uppal R, Stunnenberg HG, Ouwehand WH, Laurenti E, Lengauer T, Frontini M, Bock C. DNA methylation dynamics of human hematopoietic stem cell differentiation. Cell Stem Cell, 2016, 19(6): 808-822. |
| [52] | Hui T, Cao Q, Wegrzyn-Woltosz J, O’Neill K, Hammond CA, Knapp DJHF, Laks E, Moksa M, Aparicio S, Eaves CJ, Karsan A, Hirst M. High-resolution single-cell DNA methylation measurements Reveal epigenetically distinct hematopoietic stem cell subpopulations. Stem Cell Reports, 2018, 11(2): 578-592. |
| [53] | Liu HQ, Zhou JT, Tian W, Luo CY, Bartlett A, Aldridge A, Lucero J, Osteen JK, Nery JR, Chen HM, Rivkin A, Castanon RG, Clock B, Li YE, Hou XM, Poirion OB, Preissl S, Pinto-Duarte A, O’Connor C, Boggeman L, Fitzpatrick C, Nunn M, Mukamel EA, Zhang ZZ, Callaway EM, Ren B, Dixon JR, Behrens MM, Ecker JR. DNA methylation atlas of the mouse brain at single-cell resolution. Nature, 2021, 598(7879): 120-128. |
| [54] | Schultz MD, He YP, Whitaker JW, Hariharan M, Mukamel EA, Leung D, Rajagopal N, Nery JR, Urich MA, Chen HM, Lin S, Lin Y, Jung I, Schmitt AD, Selvaraj S, Ren B, Sejnowski TJ, Wang W, Ecker JR. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature, 2015, 523(7559): 212-216. |
| [55] | Sun DQ, Xi YX, Rodriguez B, Park HJ, Tong P, Meong M, Goodell MA, Li W. MOABS: model based analysis of bisulfite sequencing data. Genome Biol, 2014, 15(2): R38. |
| [56] | Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics, 2011, 27(7): 1017-1018. |
| [57] | Johnson ND, Cutler DJ, Conneely KN. Investigating the potential of single-cell DNA methylation data to detect allele-specific methylation and imprinting. Am J Hum Genet, 2024, 111(4): 654-667. |
| [58] | Xu DT, Wang YF, Cai JL, Gong WT, Pan XC, Tian YH, Shen QP, Li JQ, Yuan XL. Study on detection of CNVs using human whole genome bisulfite sequencing data. Hereditas (Beijing), 2023, 45(4): 324-340. |
| 徐丹同, 王祎菲, 蔡佳丽, 龚文滔, 潘向春, 田雨晗, 沈箐鹏, 李加琪, 袁晓龙. 利用人类全基因组亚硫酸氢盐测序数据检测CNVs的研究. 遗传, 2023, 45(4): 324-340. | |
| [59] | Shi XH, Radhakrishnan S, Wen J, Chen JY, Chen JJ, Lam BA, Mills RE, Stranger BE, Lee C, Setlur SR. Association of CNVs with methylation variation. NPJ Genom Med, 2020, 5(1): 41. |
| [60] | Sun W, Bunn P, Jin C, Little P, Zhabotynsky V, Perou CM, Hayes DN, Chen M, Lin D-Y. The association between copy number aberration, DNA methylation and gene expression in tumor samples. Nucleic Acids Res, 2018, 46(6): 3009-3018. |
| [61] | Garvin T, Aboukhalil R, Kendall J, Baslan T, Atwal GS, Hicks J, Wigler M, Schatz MC. Interactive analysis and assessment of single-cell copy-number variations. Nat Methods, 2015, 12(11): 1058-1060. |
| [62] | Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet, 2023, 24(8): 494-515. |
| [63] | Landau DA, Clement K, Ziller MJ, Boyle P, Fan J, Gu HC, Stevenson K, Sougnez C, Wang LL, Li SQ, Kotliar D, Zhang WD, Ghandi M, Garraway L, Fernandes SM, Livak KJ, Gabriel S, Gnirke A, Lander ES, Brown JR, Neuberg D, Kharchenko PV, Hacohen N, Getz G, Meissner A, Wu CJ. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell, 2014, 26(6): 813-825. |
| [64] | Landan G, Cohen NM, Mukamel Z, Bar A, Molchadsky A, Brosh R, Horn-Saban S, Zalcenstein DA, Goldfinger N, Zundelevich A, Gal-Yam EN, Rotter V, Tanay A. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat Genet, 2012, 44(11): 1207-1214. |
| [65] | Xie HH, Wang M, de Andrade A, de F Bonaldo M, Galat V, Arndt K, Rajaram V, Goldman S, Tomita T, Soares MB. Genome-wide quantitative assessment of variation in DNA methylation patterns. Nucleic Acids Res, 2011, 39(10): 4099-4108. |
| [66] | Lee D, Koo B, Yang J, Kim S. Metheor: ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments. PLoS Comput Biol, 2023, 19(3): e1010946. |
| [67] | Scherer M, Nebel A, Franke A, Walter J, Lengauer T, Bock C, Müller F, List M. Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic Acids Research, 2020, 48(8): e46. |
| [68] | Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, Garg K, John S, Sandstrom R, Bates D, Boatman L, Canfield TK, Diegel M, Dunn D, Ebersol AK, Frum T, Giste E, Johnson AK, Johnson EM, Kutyavin T, Lajoie B, Lee B-K, Lee K, London D, Lotakis D, Neph S, Neri F, Nguyen ED, Qu HZ, Reynolds AP, Roach V, Safi A, Sanchez ME, Sanyal A, Shafer A, Simon JM, Song LY, Vong S, Weaver M, Yan YQ, Zhang ZC, Zhang ZZ, Lenhard B, Tewari M, Dorschner MO, Hansen RS, Navas PA, Stamatoyannopoulos G, Iyer VR, Lieb JD, Sunyaev SR, Akey JM, Sabo PJ, Kaul R, Furey TS, Dekker J, Crawford GE, Stamatoyannopoulos JA. The accessible chromatin landscape of the human genome. Nature, 2012, 489(7414): 75-82. |
| [69] | Hou Y, Guo HH, Cao C, Li XL, Hu BQ, Zhu P, Wu XL, Wen L, Tang FC, Huang YY, Peng JR. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res, 2016, 26(3): 304-319. |
| [70] | Clark SJ, Argelaguet R, Kapourani CA, Stubbs TM, Lee HJ, Alda-Catalinas C, Krueger F, Sanguinetti G, Kelsey G, Marioni JC, Stegle O, Reik W. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun, 2018, 9(1): 781. |
| [71] | Li L, Guo F, Gao Y, Ren YX, Yuan P, Yan LY, Li R, Lian Y, Li JY, Hu BQ, Gao JP, Wen L, Tang FC, Qiao J. Single-cell multi-omics sequencing of human early embryos. Nat Cell Biol, 2018, 20(7): 847-858. |
| [72] | Lin JL, Xue XH, Wang Y, Zhou Y, Wu J, Xie HL, Liu MY, Wen L, Tang FC. scNanoCOOL-seq: a long-read single-cell sequencing method for multi-omics profiling within individual cells. Cell Res, 2023, 33(11): 879-882. |
| [73] | Cao ZJ, Gao G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat Biotechnol, 2022, 40(10): 1458-1466. |
| [74] | He Z, Hu SF, Chen YW, An SJ, Zhou JH, Liu RY, Shi JF, Wang J, Dong GH, Shi JH, Zhao JX, Ou-Yang L, Zhu Y, Bo X, Ying XM. Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS. Nat Biotechnol, 2024. |
| [75] | Danese A, Richter ML, Chaichoompu K, Fischer DS, Theis FJ, Colomé-Tatché M. EpiScanpy: integrated single-cell epigenomic analysis. Nat Commun, 2021, 12(1): 5228. |
| [76] | Zong WT, Kang HE, Xiong Z, Ma YK, Jin T, Gong Z, Yi LZ, Zhang MC, Wu S, Wang GL, Bao YM, Li RJ. scMethBank: a database for single-cell whole genome DNA methylation maps. Nucleic Acids Res, 2022, 50(D1): D380-D386. |
| [77] | Lister R, Mukamel EA, Nery JR, Urich M, Puddifoot CA, Johnson ND, Lucero J, Huang Y, Dwork AJ, Schultz MD, Yu M, Tonti-Filippini J, Heyn H, Hu SJ, Wu JC, Rao A, Esteller M, He C, Haghighi FG, Sejnowski TJ, Behrens MM, Ecker JR. Global epigenomic reconfiguration during mammalian brain development. Science, 2013, 341(6146): 1237905. |
| [78] | Tian W, Zhou JT, Bartlett A, Zeng QR, Liu HQ, Castanon RG, Kenworthy M, Altshul J, Valadon C, Aldridge A, Nery JR, Chen HM, Xu JY, Johnson ND, Lucero J, Osteen JK, Emerson N, Rink J, Lee J, Li YE, Siletti K, Liem M, Claffey N, O’Connor C, Yanny AM, Nyhus J, Dee N, Casper T, Shapovalova N, Hirschstein D, Ding S-L, Hodge R, Levi BP, Keene CD, Linnarsson S, Lein E, Ren B, Behrens MM, Ecker JR. Single-cell DNA methylation and 3D genome architecture in the human brain. Science, 2023, 382(6667): eadf5357. |
| [79] | Zhang JW, Xu Q, Li GL. Epigenetics in the genesis and development of cancers. Hereditas (Beijing), 2019, 41(7): 567-581. |
| 张競文, 续倩, 李国亮. 癌症发生发展中的表观遗传学研究. 遗传, 2019, 41(7): 567-581. | |
| [80] | Johnson KC, Anderson KJ, Courtois ET, Gujar AD, Barthel FP, Varn FS, Luo D, Seignon M, Yi E, Kim H, Estecio MRH, Zhao DC, Tang M, Navin NE, Maurya R, Ngan CY, Verburg N, de Witt Hamer PC, Bulsara K, Samuels ML, Das S, Robson P, Verhaak RGW. Single-cell multimodal glioma analyses identify epigenetic regulators of cellular plasticity and environmental stress response. Nat Genet, 2021, 53(10): 1456-1468. |
| [81] | Bian SH, Wang YC, Zhou Y, Wang WD, Guo LM, Wen L, Fu W, Zhou X, Tang FC. Integrative single-cell multiomics analyses dissect molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer. Natl Sci Rev, 2023, 10(6): nwad094. |
| [82] | Fang J, Singh S, Cheng CD, Natarajan S, Sheppard H, Abu-Zaid A, Durbin AD, Lee HW, Wu Q, Steele J, Connelly JP, Jin HJ, Chen W, Fan YP, Pruett-Miller SM, Rehg JE, Koo SC, Santiago T, Emmons J, Cairo S, Wang RN, Glazer ES, Murphy AJ, Chen TS, Davidoff AM, Armengol C, Easton J, Chen X, Yang J. Genome-wide mapping of cancer dependency genes and genetic modifiers of chemotherapy in high-risk hepatoblastoma. Nat Commun, 2023, 14(1): 4003. |
| [83] | Liu YB, Siejka-Zielińska P, Velikova G, Bi Y, Yuan F, Tomkova M, Bai CS, Chen L, Schuster-B?ckler B, Song C-X. Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution. Nat Biotechnol, 2019, 37(4): 424-429. |
| [84] | Chatterton Z, Lamichhane P, Ahmadi Rastegar D, Fitzpatrick L, Lebhar H, Marquis C, Halliday G, Kwok JB. Single-cell DNA methylation sequencing by combinatorial indexing and enzymatic DNA methylation conversion. Cell Biosci, 2023, 13(1): 2. |
/
| 〈 |
|
〉 |
www.chinagene.cn
备案号：京ICP备09063187号-4
总访问:,今日访问:,当前在线:
