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Tri-omic single-cell mapping of the 3D epigenome and transcriptome in whole mouse brains throughout the lifespan

Nature Methods volume 22, pages 994–1007 (2025) Cite this article

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Abstract

Exploring the genomic basis of transcriptional programs has been a long-standing research focus. Here we report a single-cell method, ChAIR, to map chromatin accessibility, chromatin interactions and RNA expression simultaneously. After validating in cultured cells, we applied ChAIR to whole mouse brains and delineated the concerted dynamics of epigenome, three-dimensional (3D) genome and transcriptome during maturation and aging. In particular, gene-centric chromatin interactions and open chromatin states provided 3D epigenomic mechanism underlying cell-type-specific transcription and revealed spatially resolved specificity. Importantly, the composition of short-range and ultralong chromatin contacts in individual cells is remarkably correlated with transcriptional activity, open chromatin state and genome folding density. This genomic property, along with associated cellular properties, differs in neurons and non-neuronal cells across different anatomic regions throughout the lifespan, implying divergent nuclear mechano-genomic mechanisms at play in brain cells. Our results demonstrate ChAIR’s robustness in revealing single-cell 3D epigenomic states of cell-type-specific transcription in complex tissues.

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Fig. 1: ChAIR captures comprehensive 3D epigenome and transcriptome landscapes in single cells.
The alternative text for this image may have been generated using AI.
Fig. 2: Single-cell 3D epigenomic and transcriptomic landscapes of whole-brain cells during mouse maturation and aging.
The alternative text for this image may have been generated using AI.
Fig. 3: Regional-specific 3D epigenomic features in mouse brain cells revealed by ChAIR.
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Fig. 4: Chromatin architecture reorganization during cell differentiation.
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Fig. 5: The remodeling of chromatin folding in mouse brain cells throughout the lifespan.
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Fig. 6: Chromatin megacontacts in brain cells as a marker of aging.
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Data availability

All datasets generated in this study including ChAIR and ChIATAC data have been submitted to Genome Sequence Archive in National Genomics Data Center with accession number PRJCA024774. The histological staining results used in this study are available from the Allen Brain Atlas (atlas.brain-map.org). Source data are provided with this paper.

Code availability

The pipeline for processing ChAIR data (ChAIR-PIPE) is available via GitHub at https://github.com/fengchuiguo1994/ChAIR-PIPE. The ChAIR data visualization tool ChAIR-Viewer is available via Github at https://github.com/fengchuiguo1994/ChAIR-Viewer.

References

  1. Rowley, M. J. & Corces, V. G. Organizational principles of 3D genome architecture. Nat. Rev. Genet. 19, 789–800 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Schoenfelder, S. & Fraser, P. Long-range enhancer-promoter contacts in gene expression control. Nat. Rev. Genet. 20, 437–455 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Zheng, H. & Xie, W. The role of 3D genome organization in development and cell differentiation. Nat. Rev. Mol. Cell Biol. 20, 535–550 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e1120 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Xu, W. et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat. Methods 19, 1243–1249 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Liu, Z. et al. Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq. Science 380, 1070–1076 (2023).

    Article  CAS  PubMed  Google Scholar 

  8. Qu, J. et al. Simultaneous profiling of chromatin architecture and transcription in single cells. Nat. Struct. Mol. Biol. 30, 1393–1402 (2023).

    Article  CAS  PubMed  Google Scholar 

  9. Wu, H. et al. Simultaneous single-cell three-dimensional genome and gene expression profiling uncovers dynamic enhancer connectivity underlying olfactory receptor choice. Nat. Methods 21, 974–982 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhou, T. et al. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nat. Genetics 56, 1701–1711 (2024).

    Article  CAS  PubMed  Google Scholar 

  11. Chai, H. et al. ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs. Nat. Commun. 14, 213 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang, H. et al. Chromatin structure dynamics during the mitosis-to-G1 phase transition. Nature 576, 158–162 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gibcus, J. H. et al. A pathway for mitotic chromosome formation. Science 359, eaao6135 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lee, M. T. et al. Nanog, Pou5f1 and SoxB1 activate zygotic gene expression during the maternal-to-zygotic transition. Nature 503, 360–364 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bonora, G. et al. Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation. Genome Biol. 22, 279 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tan, L., Xing, D., Chang, C.-H., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 e1022 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tan, L. et al. Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development. Cell 184, 741–758 e717 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Graybuck, L. T. et al. Enhancer viruses for combinatorial cell-subclass-specific labeling. Neuron 109, 1449–1464 e1413 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mich, J. K. et al. Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex. Cell Rep 34, 108754 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mich, J. K. et al. Enhancer-AAVs allow genetic access to oligodendrocytes and diverse populations of astrocytes across species. Preprint at bioRxiv https://doi.org/10.1101/2023.09.20.558718 (2023).

  30. Hirai, H. et al. Cbln1 is essential for synaptic integrity and plasticity in the cerebellum. Nat. Neurosci. 8, 1534–1541 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Abe, H., Okazawa, M. & Nakanishi, S. The Etv1/Er81 transcription factor orchestrates activity-dependent gene regulation in the terminal maturation program of cerebellar granule cells. Proc. Natl Acad. Sci. USA 108, 12497–12502 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–D92 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tian, W. et al. Single-cell DNA methylation and 3D genome architecture in the human brain. Science 382, eadf5357 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Heffel, M. G. et al. Temporally distinct 3D multi-omic dynamics in the developing human brain. Nature 635, 481–489 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bedi, K. S., Hall, R., Davies, C. A. & Dobbing, J. A stereological analysis of the cerebellar granule and purkinje cells of 30-day-old and adult rats undernourished during early postnatal life. J. Comp. Neurol. 193, 863–870 (1980).

    Article  CAS  PubMed  Google Scholar 

  37. Bedi, K. S. Effects of undernutrition during early life on granule cell numbers in the rat dentate gyrus. J. Comp. Neurol. 311, 425–433 (1991).

    Article  CAS  PubMed  Google Scholar 

  38. Gagyi, E. et al. Decreased oligodendrocyte nuclear diameter in Alzheimer’s disease and Lewy body dementia. Brain Pathol. 22, 803–810 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Takei, Y. et al. Single-cell nuclear architecture across cell types in the mouse brain. Science 374, 586–594 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Liu, S. et al. Cell type–specific 3D-genome organization and transcription regulation in the brain. Sci. Adv. 11, eadv2067 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e1721 (2022).

    Article  CAS  PubMed  Google Scholar 

  42. Hochgerner, H., Zeisel, A., Lonnerberg, P. & Linnarsson, S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019).

    Article  PubMed  Google Scholar 

  44. Tan, L. et al. Lifelong restructuring of 3D genome architecture in cerebellar granule cells. Science 381, 1112–1119 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mao, S. et al. A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures. Genome Research 33, 1381–1394 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Stevens, S. R. et al. Ankyrin-R Links Kv3.3 to the spectrin cytoskeleton and is required for Purkinje neuron survival. J. Neurosci. 42, 2–15 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Steffens, D. C. et al. Genome-wide screen to identify genetic loci associated with cognitive decline in late-life depression. Int. Psychogeriatr. 36, P1021–1029 (2024).

    Article  Google Scholar 

  48. Wang, P. et al. Genome-wide association studies identify novel loci in rapidly progressive Alzheimer's disease. Alzheimers Dement. 20, 2034–2046 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e324 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yang, F., Babak, T., Shendure, J. & Disteche, C. M. Global survey of escape from X inactivation by RNA-sequencing in mouse. Genome Res. 20, 614–622 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lee, B. et al. ChIA-PIPE: a fully automated pipeline for comprehensive ChIA-PET data analysis and visualization. Sci. Adv. 6, eaay2078 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e3529 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e324 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Paulsen, J., Rødland, E. A., Holden, L., Holden, M. & Hovig, E. A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions. Nucleic Acids Res. 42, e143–e143 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Open2C et al. Cooltools: enabling high-resolution Hi-C analysis in Python. PLoS Comput. Biol. 20, e1012067 (2024).

  64. Wolff, J. et al. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Res. 48, W177–W184 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kruse, K., Hug, C. B. & Vaquerizas, J. M. FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data. Genome Biology 21, 303 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Stevens, T. J. et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  69. Collombet, S. et al. Parental-to-embryo switch of chromosome organization in early embryogenesis. Nature 580, 142–146 (2020).

    Article  CAS  PubMed  Google Scholar 

  70. Collombet, S. et al. RNA polymerase II depletion from the inactive X chromosome territory is not mediated by physical compartmentalization. Nat. Struct. Mol. Biol. 30, 1216–1223 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. He, Y. et al. Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome. Genome Res. 33, 1354–1368 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Zheng, Y., Shen, S. & Keleş, S. Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D. Genome Biol. 23, 222 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Dimmick, M. C., Lee, L. J. & Frey, B. J. HiCSR: a Hi-C super-resolution framework for producing highly realistic contact maps. Preprint at bioRxiv https://doi.org/10.1101/2020.02.24.961714 (2020).

  76. Reiff, S. B. et al. The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data. Nat. Commun. 13, 2365 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank S. Liu, Y. Bai and Z. Zhuang from BGI, Hangzhou, China for helpful discussions. We also extend our appreciation to P. Wang (Northwestern University, Chicago, USA), S.Z. Tian and M. Zheng (Southern University of Science and Technology, Shenzhen, China) for initial support. H.C. was supported by National Natural Science Foundation of China (grant no. 32400426). Y.R. was supported by National Natural Science Foundation of China (grant no. 32250710678). D.P. was supported by Polish National Science Centre (grant no. 2020/37/B/NZ2/03757). C.-L.W. was supported by NIH grant nos. U54-DK107967, UM1-HG009409, R01-GM127531, R01-HG011253, P30-CA034196 and R33-CA236681.

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Author notes

  1. These authors contributed equally: Haoxi Chai, Xingyu Huang.

Authors and Affiliations

  1. Life Sciences Institute and The Second Affiliated Hospital, Zhejiang University, Hangzhou, China

    Haoxi Chai, Xingyu Huang, Guangzhou Xiong, Jiaxiang Huang, Katarzyna Karolina Pels, Lingyun Meng, Jin Han, Dongmei Tang, Guanjing Pan, Liang Deng, Qin Xiao & Yijun Ruan

  2. Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China

    Xiaotao Wang

  3. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Meng Zhang & Chia-Lin Wei

  4. Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

    Krzysztof Banecki & Dariusz Plewczynski

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  1. Haoxi Chai
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Contributions

H.C. and Y.R. conceived the ChAIR strategy. H.C. performed experiments with the help from K.K.P., L.M., J.H., L.D., Q.X., M.Z. and C.-L.W. The data analysis was done by H.C., X.H., G.X., J.H. and Y.R. with the help from D.T., G.P., X.W., K.B. and D.P. The manuscript was written by H.C. and Y.R. with input from all other authors.

Corresponding author

Correspondence to Yijun Ruan.

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The authors declare no competing interests.

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Nature Methods thanks Sheng Zhong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Benchmark ChAIR data derived from K562 and Patski cells with related methods.

(a-d) Violin plots comparing ChAIR with other single-cell methods for the numbers of RNA unique molecular identifiers (UMIs) (a), counts of identified genes (b), numbers of captured chromatin fragments in ATAC-seq data (c), and numbers of ATAC-seq peaks (d). (e-f) Violin plots comparing ChAIR with Hi-C based single-cell methods in detecting chromatin contacts (e) and the ratio of contacts associated with ATAC peak sites (f). Medians for each data category and the total number of cells analyzed by different methods were provided. Center line indicates median, asterisk indicates average, box represents interquartile range (25th to 75th percentiles), and whiskers extend to 1.5× interquartile range.

Source data

Extended Data Fig. 2 Characterization of ChAIR data.

(a) Percentage of ChAIR-RNA reads that were mapped to exon, intron, and intergenic regions and metagene profiles (reads per kilobase per million mapped reads, RPKM) in K562 (top) and Patski cells (bottom) in reference with bulk RNA-seq data. (b) Signal enrichment of ChAIR-ATAC data, 10x ATAC, ChIATAC, bulk ATAC-seq, and sci-Hi-C data in K562 (top) and Patski (bottom) cells, at ATAC peak (left) and TSS sites (right). (c) Categories of ChromHMM defined chromatin states for open chromatin loci identified by bulk ATAC-seq data (n = 176,891) (left) and ChAIR-ATAC data (n = 201,540) (right) in K562. (d) Categories of ChromHMM defined chromatin states for chromatin loops in ChIATAC (n = 37,579) (left) and ChAIR-PET data (n = 232,988) (right). (e) Chromatin contact distance distribution of chromatin loops with different interaction frequencies in ChIATAC and ChAIR-PET data in K562 and Patski cells.

Source data

Extended Data Fig. 3 Cell cycle-specific gene expression and chromatin interactions.

(a) Scatter plots showing gene expression of marker genes specific to S and G2/M phases from ChAIR-RNA data in K562 and Patski cells. (b) PCA plots showing the distribution of individual K562 cells based on their cell-cycle marker genes (left) and further grouped into metacells based on cell-cycle pseudotime (right). (c) 2D contact heatmaps of ChAIR-PET data in K562 and Patski cells at G1, S, and G2/M stages. (d) Aggregate compartment analysis of ChAIR-PET data in K562 (top) and Patski (bottom). (e) Aggregate TAD analysis of ChAIR-PET data in K562 (top) and Patski (bottom). (f) Aggregate plots of chromatin contacts between promoters (TSS) and putative distal enhancer elements (E) for phase-specific marker genes. The intensity values of phase-specific TSS-E interactions were given for comparison.

Source data

Extended Data Fig. 4 Single-cell analysis of ChAIR data derived from mouse brain cells.

UMAP plots of ChAIR (P365) data from the following modalities: (a) Mono-modal ChAIR-RNA data, (b) Mono-modal ChAIR-ATAC data, (c) ChAIR-PET (all interactions) data, (d) ChAIR-PET (gene-centric interactions) data, and (e-g) the combination of different modalities. (h) Cell type-specific ChAIR-RNA profiles in matrix plot showing canonical marker gene expression profiles in 7 major brain cellular classes and in 121 cell types. (i) Cell type-specific ChAIR-ATAC profiles in matrix plot showing cell type-specific ATAC peak signals in 7 classes and in 121 cell types.

Extended Data Fig. 5 Characterization of cell type-specific enhancers identified by ChAIR.

(a) The matrix of ATAC signals at cell type-specific enhancer loci (n = 562) across 9 cell groups. (b) Genomic annotations. (c) Chromatin states annotated by ChromHMM. (d-e) Number of enhancers in different cell groups (d) and number of associated cell type-specific marker genes (e). (f) Distances from enhancers to their nearest genes and to the targeted marker genes. The P value was calculated using the two-sided Wilcoxon test. (g-h) Numbers of enhancers represented in each cell group (g) and associated with each target gene (h). (i-j) Mean distance of enhancer to target genes in different cell groups (i) and for individual genes (j). Center line in violin plot indicates median, asterisk indicates average, box represents interquartile range (25th to 75th percentiles), and whiskers extend to 1.5× interquartile range.

Source data

Extended Data Fig. 6 Validation of the correlation between megacontact, key cellular features, and nuclear volume using image-based measurements.

(a-d) The correlation between ChAIR data derived measurements (Y-axis) and nuclear volumes (X-axis), inferred from DAPI-stained imaging data. The ChAIR data derived measurements included: (a) megacontact ratio, (b) genome folding density inferred from 3D models, (c) global transcriptional activity, and (d) global chromatin accessibility. The fitted lines were smoothed by linear models with shading indicating the confidence interval. (e-g) P95 ChAIR data mapped megacontact ratio (e), global transcriptional activity (f), and chromatin accessibility (g) in 17 mouse brain cells types. Notably, megacontact was negatively correlated with global transcriptional activity (measured by RNA-MERFISH40), chromatin accessibility, and nuclear volume (inferred by DNA-MERFISH40). Center line denotes median, box represents interquartile range (25th–75th percentiles), and whiskers extend to 1.5× interquartile range.

Source data

Extended Data Fig. 7 Validation of integrating ChAIR and Stereo-seq data.

(a-c) Canonical marker gene expression profile in ChAIR-RNA (a) and Stereo-seq data (b-c). Stereo-seq data with cell type annotations (b) and anatomic region annotations (c) were shown. (d) Spatial visualization of cells from various cerebrum cortex layers and the gene expression of marker genes in Stereo-seq and Allen Brain Atlas in situ hybridization (ABA ISH) data (atlas.brain-map.org). (e) Cell type-specific feature (that is, transcription, chromatin loop, promoter ATAC, enhancer ATAC, gene activity, and the combination of these information) signal matrices calculated by different features across region-resolved cell types. Normalized signal intensities were provided. Group CIS was provided to measure the overall specificity of the feature examined across all cell types.

Extended Data Fig. 8 Examples of chromatin rewiring in neurons during differentiation.

(a) Contact distance spectrum of cerebellar ExNs (CBNBL2 and CBGRC) at P2 stage. Cells were arranged from the least extent of megacontact to the highest. Contact frequencies were normalized to 0-1. (b) UMAP plots of ChAIR data with RNA and chromatin megacontact pseudotime information in P2 ChAIR data. (c) 3D models of genome folding architectures reconstructed from ChAIR-PET data for CBNBL2 and CBGRC at P2 stage. (d) Gene expression profiles of CBNBL2-specific gene (Igfbpl1) and CBGRC-specific gene (Cbln1) along the megacontact pseudotime. The fitted curves were smoothed by generalized additive models with shading indicating the confidence interval. The analyses done in (a-d) were also applied to InhNs from OB in (e-g).

Extended Data Fig. 9 Nuclear volume dynamics of ExNs during ageing.

(a) Example views of 3D models of genome folding architectures in TEGLU and CBGRC cells across five age points. (b-d) Boxplots of megacontact percentage (b), genome folding density inferred from the 3D models of genome folding architectures (c), and the global transcriptional activity measured in UMI (d) for TEGLU (red) and CBGRC (blue) cells, based on ChAIR data. Center line in boxplot denotes median, box represents interquartile range (25th–75th percentiles), and whiskers extend to 1.5× interquartile range.

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Supplementary information

Supplementary Information (download PDF )

ChAIR step-by-step protocol and Figs. 1–22.

Reporting Summary (download PDF )

Supplementary Table 1 (download XLSX )

Datasets generated and used in this study.

Supplementary Table 2 (download XLSX )

Number of cells across different categories in the mouse brain ChAIR dataset.

Supplementary Table 3 (download XLSX )

Cell-type-specific enhancers identified by ChAIR.

Supplementary Table 4 (download XLSX )

Cell-type-specific marker genes used for cell differentiation analysis.

Supplementary Table 5 (download XLSX )

Age-related genes identified by ChAIR.

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Chai, H., Huang, X., Xiong, G. et al. Tri-omic single-cell mapping of the 3D epigenome and transcriptome in whole mouse brains throughout the lifespan. Nat Methods 22, 994–1007 (2025). https://doi.org/10.1038/s41592-025-02658-7

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