Background: DNA methylation and chromatin accessibility are pivotal epigenetic regulators of gene expression and cellular identity, with significant implications in tumorigenesis and progression. Current single-cell multi-omics methods are limited in throughput and sensitivity, hindering comprehensive biomarker discovery.
Methods: We developed single-cell split-pool ligation-based multi-omics sequencing technology (SpliCOOL-seq), a high-throughput single-cell sequencing technology that simultaneously profiles whole-genome DNA methylation and chromatin accessibility in thousands of cells. By integrating in situ GpC methylation, universal Tn5 tagmentation, and split-pool combinatorial barcoding, SpliCOOL-seq achieves enhanced sensitivity and scalability.
Results: SpliCOOL-seq accurately distinguished lung cancer cell types based on genetic and multiple epigenetic modalities and revealed that the two DNA methyltransferase (DNMT) inhibitors, 5-Azacitidine and Decitabine, both cause large-scale demethylation but in distinct patterns. Applied to primary lung adenocarcinoma, SpliCOOL-seq identified tumour subclones within the tumour lesion and uncovered novel DNA methylation biomarkers (e.g., FAM124B, SFN, OR7E47P) associated with patient survival. Additionally, we demonstrated accelerated epigenetic ageing and mitotic activity in tumour subclones, providing new insights into tumorigenesis.
Conclusion: SpliCOOL-seq achieves parallel profiling of whole-genome DNA methylation and chromatin accessibility in the same individual cells in a high-throughput manner and is hopefully used to illustrate regulatory interactions under different cell states. SpliCOOL-seq enables high-resolution, multi-modal epigenetic profiling at single-cell resolution, offering a powerful platform for discovering cancer biomarkers. Its application reveals novel therapeutic targets and early-diagnostic markers, underscoring its potential in precision oncology.
| [1] |
Yousefi PD, Suderman M, Langdon R, et al. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022; 23: 369-383.
|
| [2] |
Chen Y, Liang R, Li Y, et al. Chromatin accessibility: biological functions, molecular mechanisms and therapeutic application. Sig Transduct Target Ther. 2024; 9: 340.
|
| [3] |
Smith ZD, Hetzel S, Meissner A. DNA methylation in mammalian development and disease. Nat Rev Genet. 2025; 26: 7-30.
|
| [4] |
Badia-i-Mompel P, Wessels L, Müller-Dott S, et al. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet. 2023; 24: 739-754.
|
| [5] |
Guo F, Li L, Li J, et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 2017; 27: 967-988.
|
| [6] |
Dario L, Rosa M, Mariela E, et al. Chromatin remodeling agents for cancer therapy. RRCT. 2008; 3: 192-203.
|
| [7] |
Hon GC, Hawkins RD, Caballero OL, et al. Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012; 22: 246-258.
|
| [8] |
Guo H, Hu B, Yan L, et al. DNA methylation and chromatin accessibility profiling of mouse and human fetal germ cells. Cell Res. 2017; 27: 165-183.
|
| [9] |
Pott S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife. 2017; 6:e23203.
|
| [10] |
Clark SJ, Argelaguet R, Kapourani C-A, et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun. 2018; 9: 781.
|
| [11] |
Gu C, Liu S, Wu Q, et al. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res. 2019; 29: 110-123.
|
| [12] |
Wang Y, Yuan P, Yan Z, et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat Commun. 2021; 12: 1247.
|
| [13] |
Yan R, Gu C, You D, et al. Decoding dynamic epigenetic landscapes in human oocytes using single-cell multi-omics sequencing. Cell Stem Cell. 2021; 28: 1641-1656. e7.
|
| [14] |
Luo C, Liu H, Xie F, et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genomics. 2022; 2:100107.
|
| [15] |
Nichols RV, Rylaarsdam LE, O'Connell BL, et al. Atlas-scale single-cell DNA methylation profiling with sciMETv3. Cell Genomics. 2025; 5:100726.
|
| [16] |
Nichols RV, O'Connell BL, Mulqueen RM, et al. High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nat Commun. 2022; 13: 7627.
|
| [17] |
Bai D, Zhang X, Xiang H, et al. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat Biotechnol. 2025; 43: 85-96.
|
| [18] |
Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011; 27: 1571-1572.
|
| [19] |
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9: 357-359.
|
| [20] |
Schultz MD, He Y, Whitaker JW, et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature. 2015; 523: 212-216.
|
| [21] |
Kent WJ, Zweig AS, Barber G, et al. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics. 2010; 26: 2204-2207.
|
| [22] |
Lin J, Xue X, Wang Y, et al. scNanoCOOL-seq: a long-read single-cell sequencing method for multi-omics profiling within individual cells. Cell Res. 2023; 33: 879-882.
|
| [23] |
Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods. 2020; 17: 261-272.
|
| [24] |
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26: 841-842.
|
| [25] |
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15: 550.
|
| [26] |
Traag VA, Waltman L, Van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019; 9: 5233.
|
| [27] |
Zhang K, Zemke NR, Armand EJ, Ren B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat Methods. 2024; 21: 217-227.
|
| [28] |
Zhang Y, Liu T, Meyer CA, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008; 9: R137.
|
| [29] |
Boeva V, Zinovyev A, Bleakley K, et al. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics. 2011; 27: 268-269.
|
| [30] |
Kremer LPM, Braun MM, Ovchinnikova S, et al. Analyzing single-cell bisulfite sequencing data with MethSCAn. Nat Methods. 2024; 21: 1616-1623.
|
| [31] |
Heinz S, Benner C, Spann N, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010; 38: 576-589.
|
| [32] |
Trapp A, Kerepesi C, Gladyshev VN. Profiling epigenetic age in single cells. Nat Aging. 2021; 1: 1189-1201.
|
| [33] |
De Waele G, Clauwaert J, Menschaert G, Waegeman W. CpG transformer for imputation of single-cell methylomes. Bioinformatics. 2022; 38: 597-603.
|
| [34] |
Xu S, Hu E, Cai Y, et al. Using clusterProfiler to characterize multiomics data. Nat Protoc. 2024; 19: 3292-3320.
|
| [35] |
Li S, Wan C, Zheng R, et al. Cistrome-GO: a web server for functional enrichment analysis of transcription factor ChIP-seq peaks. Nucleic Acids Research. 2019; 47: W206-W211.
|
| [36] |
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018; 34: i884.
|
| [37] |
Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29: 15-21.
|
| [38] |
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.
|
| [39] |
Mulqueen RM, Pokholok D, Norberg SJ, et al. Highly scalable generation of DNA methylation profiles in single cells. Nat Biotechnol. 2018; 36: 428-431.
|
| [40] |
Cao Y, Bai Y, Yuan T, et al. Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability. Proc Natl Acad Sci USA. 2023; 120:e2310367120.
|
| [41] |
Miura F, Enomoto Y, Dairiki R, Ito T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Research. 2012; 40: e136-e136.
|
| [42] |
Luo C, Keown CL, Kurihara L, et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science. 2017; 357: 600-604.
|
| [43] |
Zhang Q, Ma S, Liu Z, et al. Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes. Nat Commun. 2023; 14: 4672.
|
| [44] |
Smallwood SA, Lee HJ, Angermueller C, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014; 11: 817-820.
|
| [45] |
Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021; 184: 3573-3587. e29.
|
| [46] |
Liu H, Liu B, Zhang L, et al. An investigation to identify tumor microenvironment-related genes of prognostic value in lung squamous cell carcinoma based on The Cancer Genome Atlas. Transl Cancer Res TCR. 2021; 10: 1885-1899.
|
| [47] |
Fan X, Lu P, Wang H, et al. Integrated single-cell multiomics analysis reveals novel candidate markers for prognosis in human pancreatic ductal adenocarcinoma. Cell Discov. 2022; 8: 13.
|
| [48] |
Agrawal K, Das V, Vyas P, Hajdúch M. Nucleosidic DNA demethylating epigenetic drugs—A comprehensive review from discovery to clinic. Pharmacol Therap. 2018; 188: 45-79.
|
| [49] |
Diesch J, Zwick A, Garz A-K, et al. A clinical-molecular update on azanucleoside-based therapy for the treatment of hematologic cancers. Clin Epigenet. 2016; 8: 71.
|
| [50] |
Pan D, Rampal R, Mascarenhas J. Clinical developments in epigenetic-directed therapies in acute myeloid leukemia. Blood Advances. 2020; 4: 970-982.
|
| [51] |
Mehdipour P, Murphy T, De Carvalho DD. The role of DNA-demethylating agents in cancer therapy. Pharmacol Therap. 2020; 205:107416.
|
| [52] |
Mehdipour P, Murphy T, De Carvalho DD. The role of DNA-demethylating agents in cancer therapy. Pharmacol Therap. 2020; 205:107416.
|
| [53] |
Kagan AB, Garrison DA, Anders NM, et al. DNA methyltransferase inhibitor exposure–response: challenges and opportunities. Clin Transl Sci. 2023; 16: 1309-1322.
|
| [54] |
Zhao M, Sun J, Zhao Z. TSGene: a web resource for tumor suppressor genes. Nucleic Acids Res. 2013; 41: D970-D976.
|
| [55] |
Tang Y, Tian Y, Zhang C-X, Wang G-T. Olfactory receptors and tumorigenesis: implications for diagnosis and targeted therapy. Cell Biochem Biophys. 2024; 83: 295-305.
|
| [56] |
Martínez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nat Rev Cancer. 2021; 21: 669-680.
|
| [57] |
Chen P, Wang W, Liu R, et al. Olfactory sensory experience regulates gliomagenesis via neuronal IGF1. Nature. 2022; 606: 550-556.
|
| [58] |
Chen L, Liu S, Tao Y. Regulating tumor suppressor genes: post-translational modifications. Sig Transduct Target Ther. 2020; 5: 90.
|
| [59] |
Qin H, Ni H, Liu Y, et al. RNA-binding proteins in tumor progression. J Hematol Oncol. 2020; 13: 90.
|
| [60] |
Jia X, He X, Huang C, et al. Protein translation: biological processes and therapeutic strategies for human diseases. Sig Transduct Target Ther. 2024; 9: 44.
|
| [61] |
Hwang J-A, Kim Y, Hong S-H, et al. Epigenetic inactivation of heparan sulfate (glucosamine) 3-O-sulfotransferase 2 in lung cancer and its role in tumorigenesis. PLoS ONE. 2013; 8:e79634.
|
| [62] |
Ren Y, Zhang P, Li L, et al. Hyper-methylation and DNMT3A mediated LTC4S downregulation promoted lung adenocarcinoma tumorigenesis via mTORC1 signaling pathway. Heliyon. 2024; 10:e33203.
|
| [63] |
Li L, Lee K-M, Han W, et al. Estrogen and progesterone receptor status affect genome-wide DNA methylation profile in breast cancer. Human Molecular Genetics. 2010; 19: 4273-4277.
|
| [64] |
Yu X, Han Y, Liu S, et al. Analysis of genetic alterations related to DNA methylation in testicular germ cell tumors based on data mining. Cytogenet Genome Res. 2021; 161: 382-394.
|
| [65] |
Li R, Yan X, Zhong W, et al. Stratifin promotes the malignant progression of HCC via binding and hyperactivating AKT signaling. Cancer Letters. 2024; 592:216761.
|
| [66] |
Arakawa N, Ushiki A, Abe M, et al. Stratifin as a novel diagnostic biomarker in serum for diffuse alveolar damage. Nat Commun. 2022; 13: 5854.
|
| [67] |
Yoshida A, Hashimoto Y, Akane H, et al. Analysis of stratifin expression and proteome variation in a rat model of acute lung injury. J Proteome Res. 2025; 24: 1941-1955.
|
| [68] |
Shiba-Ishii A, Kim Y, Shiozawa T, et al. Stratifin accelerates progression of lung adenocarcinoma at an early stage. Mol Cancer. 2015; 14: 142.
|
| [69] |
Wu J, Li L, Zhang H, et al. A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene. 2021; 40: 4413-4424.
|
| [70] |
Zhao Y, Zhang H, Wu J, et al. Prediction of tumor microenvironment characteristics and treatment response in lung squamous cell carcinoma by pseudogene OR7E47P-related immune genes. Curr Med Sci. 2023; 43: 1133-1150.
|
| [71] |
Akl MR, Nagpal P, Ayoub NM, et al. Molecular and clinical significance of fibroblast growth factor 2 (FGF2 /bFGF) in malignancies of solid and hematological cancers for personalized therapies. Oncotarget. 2016; 7: 44735-44762.
|
| [72] |
Jovanovic D, Yan S, Baumgartner M. The molecular basis of the dichotomous functionality of MAP4K4 in proliferation and cell motility control in cancer. Front Oncol. 2022; 12:1059513.
|
| [73] |
Duan R, Fu Q, Sun Y, Li Q. Epigenetic clock: a promising biomarker and practical tool in aging. Age Res Rev. 2022; 81:101743.
|
| [74] |
Johnstone SE, Gladyshev VN, Aryee MJ, Bernstein BE. Epigenetic clocks, aging, and cancer. Science. 2022; 378: 1276-1277.
|
| [75] |
Margiotti K, Monaco F, Fabiani M, et al. Epigenetic clocks: in aging-related and complex diseases. Cytogenet Genome Res. 2023; 163: 247-256.
|
| [76] |
Teschendorff AE, Horvath S. Epigenetic ageing clocks: statistical methods and emerging computational challenges. Nat Rev Genet. 2025; 26: 350-368.
|
| [77] |
The Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, et al, The Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013; 45: 1113-1120.
|
| [78] |
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013; 14: 3156.
|
| [79] |
Hannum G, Guinney J, Zhao L, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013; 49: 359-367.
|
| [80] |
Yang Z, Wong A, Kuh D, et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 2016; 17: 205.
|
| [81] |
Bocklandt S, Lin W, Sehl ME, et al. Epigenetic predictor of age. PLoS ONE. 2011; 6:e14821.
|
| [82] |
Garagnani P, Bacalini MG, Pirazzini C, et al. Methylation of ELOVL 2 gene as a new epigenetic marker of age. Aging Cell. 2012; 11: 1132-1134.
|
| [83] |
Weidner CI, Wagner W. The epigenetic tracks of aging. Biol Chem. 2014; 395: 1307-1314.
|
| [84] |
Javed R, Chen W, Lin F, Liang H. Infant's DNA methylation age at birth and epigenetic aging accelerators. BioMed Res Int. 2016; 2016: 1-10.
|
| [85] |
Vidal-Bralo L, Lopez-Golan Y, Gonzalez A. Simplified assay for epigenetic age estimation in whole blood of adults. Front Genet. 2016; 7: 126.
|
| [86] |
Zhang Y, Wilson R, Heiss J, et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat Commun. 2017; 8:14617.
|
| [87] |
Horvath S, Oshima J, Martin GM, et al. Epigenetic clock for skin and blood cells applied to Hutchinson-Gilford progeria syndrome and ex vivo studies. Aging. 2018; 10: 1758-1775.
|
| [88] |
Levine ME, Lu AT, Quach A, et al An epigenetic biomarker of aging for lifespan and healthspan
|
| [89] |
Zheng Y, Joyce BT, Colicino E, et al. Blood epigenetic age may predict cancer incidence and mortality. EBioMedicine. 2016; 5: 68-73.
|
| [90] |
Pérez RF, Tejedor JR, Bayón GF, et al. Distinct chromatin signatures of DNA hypomethylation in aging and cancer. Aging Cell. 2018; 17:e12744.
|
| [91] |
Maugeri A, Barchitta M, Magnano San Lio R, et al. Epigenetic aging and colorectal cancer: state of the art and perspectives for future research. IJMS. 2020; 22: 200.
|
| [92] |
Tomasetti C, Vogelstein B. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science. 2015; 347: 78-81.
|
| [93] |
Klutstein M, Moss J, Kaplan T, Cedar H. Contribution of epigenetic mechanisms to variation in cancer risk among tissues. Proc Natl Acad Sci USA. 2017; 114: 2230-2234.
|
| [94] |
Yang Z, Wong A, Kuh D, et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 2016; 17: 205.
|
| [95] |
Youn A, Wang S. The MiAge calculator: a DNA methylation-based mitotic age calculator of human tissue types. Epigenetics. 2018; 13: 192-206.
|
| [96] |
Zhou W, Dinh HQ, Ramjan Z, et al. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nat Genet. 2018; 50: 591-602.
|
| [97] |
Teschendorff AE. A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Med. 2020; 12: 56.
|
| [98] |
Endicott JL, Nolte PA, Shen H, Laird PW. Cell division drives DNA methylation loss in late-replicating domains in primary human cells. Nat Commun. 2022; 13: 6659.
|
| [99] |
Zhu T, Tong H, Du Z, et al. An improved epigenetic counter to track mitotic age in normal and precancerous tissues. Nat Commun. 2024; 15: 4211.
|
| [100] |
Kondratskyi A (2013) Calcium-permeable ion channels in control of autophagy and cancer. Front Physiol 4:.
|
| [101] |
Espina JA, Cordeiro MH, Barriga EH (2023) Tissue interplay during morphogenesis. Seminars Cell Development Biol 147: 12-23.
|
| [102] |
Mancusi R, Monje M. The neuroscience of cancer. Nature. 2023; 618: 467-479.
|
| [103] |
Van Neerven SM, Vermeulen L. Cell competition in development, homeostasis and cancer. Nat Rev Mol Cell Biol. 2023; 24: 221-236.
|
| [104] |
De Luzy IR, Lee MK, Mobley WC, Studer L. Lessons from inducible pluripotent stem cell models on neuronal senescence in aging and neurodegeneration. Nat Aging. 2024; 4: 309-318.
|
| [105] |
Hall BM, Gleiberman AS, Strom E, et al. Immune checkpoint protein VSIG4 as a biomarker of aging in murine adipose tissue. Aging Cell. 2020; 19:e13219.
|
| [106] |
Khavinson VK, Kuznik BI, Tarnovskaia SI, Lin'kova NS. [Peptides and CCL11 and HMGB1 as molecular markers of aging: literature review and own data]. Adv Gerontol. 2014; 27: 399-406
|
| [107] |
Lin P, Guo Y, Shi L, et al. Development of a prognostic index based on an immunogenomic landscape analysis of papillary thyroid cancer. Aging. 2019; 11: 480-500.
|
| [108] |
Karmi O, Sohn Y-S, Zandalinas SI, et al. Disrupting CISD2 function in cancer cells primarily impacts mitochondrial labile iron levels and triggers TXNIP expression. Free Radical Biol Med. 2021; 176: 92-104.
|
| [109] |
Kim H Developing DNA methylation-based diagnostic biomarkers
|
| [110] |
Müller D. DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer. Cancer Diagnosis. 1877.
|
| [111] |
Feng Z, Kong X, Ma L. DNA methylation biomarkers in diabetic kidney disease: insights and implications. Mol Gene Genom. 2025; 300(1): 73
|
| [112] |
Luo H. Liquid biopsy of methylation biomarkers in cell-free DNA. Trends Mol Med
|
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2026 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.
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