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Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq

Nature Protocols volume 20, pages 2383–2417 (2025)Cite this article

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Abstract

The epigenome of a cell is tightly correlated with gene transcription, which controls cell identity and diverse biological activities. Recent advances in spatial technologies have improved our understanding of tissue heterogeneity by analyzing transcriptomics or epigenomics with spatial information preserved, but have been mainly restricted to one molecular layer at a time. Here we present procedures for two spatially resolved sequencing methods, spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq, that co-profile transcriptome and epigenome genome wide. In both methods, transcriptomic readouts are generated through tissue fixation, permeabilization and in situ reverse transcription. In spatial-ATAC-RNA-seq, Tn5 transposase is used to probe accessible chromatin, and in spatial-CUT&Tag-RNA-seq, the tissue is incubated with primary antibodies that target histone modifications, followed by Protein A-fused Tn5-induced tagmentation. Both methods leverage a microfluidic device that delivers two sets of oligonucleotide barcodes to generate a two-dimensional mosaic of tissue pixels at near single-cell resolution. A spatial-ATAC-RNA-seq or spatial-CUT&Tag-RNA-seq library can be generated in 3–5 d, allowing researchers to simultaneously investigate the transcriptomic landscape and epigenomic landscape of an intact tissue section. This protocol is an extension of our previous spatially resolved epigenome sequencing protocol and provides opportunities in multimodal profiling.

Key points

  • Spatially resolved concurrent profiling of both transcriptome and epigenome is essential for studying spatio-temporal regulation of gene expression, but the technical solutions permitting such analyses remain limited.

  • This protocol describes spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq, which profile genome-wide transcription jointly with open chromatin and histone modifications, respectively, on a tissue section.

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Fig. 1: Overview of the procedures for spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq.
Fig. 2: Library structure and oligonucleotide primer sequence design.
Fig. 3: Procedures for tissue fixation, permeabilization and Tn5 or pA-Tn5 tagmentation.
Fig. 4: Workflow of deterministic barcoding in tissue.
Fig. 5: Anticipated results of library visualization.
Fig. 6: Bioinformatics pipelines for analyzing spatially resolved epigenome-transcriptome data.
Fig. 7: Spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq data quality control.
Fig. 8: Evaluation and analysis of a spatial-ATAC-RNA-seq dataset.

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Data availability

Original data that are used to generate metrics plots in Figs. 7 and 8 are available in Source Data. Raw data for the illustrative results shown in this protocol are available in Zhang et al.26. Source data are provided with this paper.

Code availability

Codes for processing data of the associated publication26 are available at https://github.com/di-0579/Spatial_epigenome-transcriptome_co-sequencing.

References

  1. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    Article  PubMed  CAS  Google Scholar 

  2. Bannister, A. J. & Kouzarides, T. Regulation of chromatin by histone modifications. Cell Res. 21, 381–395 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Fleck, J. S. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 621, 365–372 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Li, H. et al. Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy. Cell Metab. 36, 1105–1125.e10 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Wang, G. et al. Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes. Nat. Genet. 55, 984–994 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).

    Article  PubMed  CAS  Google Scholar 

  9. Muto, Y., Li, H. & Humphreys, B. D. in Innovations in Nephrology: Breakthrough Technologies in Kidney Disease Care 87–102 (Springer, 2022); https://doi.org/10.1007/978-3-031-11570-7_5

  10. Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).

    Article  PubMed  CAS  Google Scholar 

  14. Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01676-0 (2023).

  15. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Wirth, J. et al. Spatial transcriptomics using multiplexed deterministic barcoding in tissue. Nat. Commun. 14, 1–15 (2023).

    Article  CAS  Google Scholar 

  17. Jiang, F. et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat. Methods 20, 1048–1057 (2023).

    Article  PubMed  CAS  Google Scholar 

  18. 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  PubMed  PubMed Central  CAS  Google Scholar 

  19. Xu, W. et al. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Nat. Protoc. 16, 4084–4107 (2021).

    Article  PubMed  CAS  Google Scholar 

  20. Grandi, F. C., Modi, H., Kampman, L. & Corces, M. R. Chromatin accessibility profiling by ATAC-seq. Nat. Protoc. 17, 1518–1552 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kaya-Okur, H. S., Janssens, D. H., Henikoff, J. G., Ahmad, K. & Henikoff, S. Efficient low-cost chromatin profiling with CUT&Tag. Nat. Protoc. 15, 3264–3283 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Farzad, N. et al. Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue. Nat. Protoc. 19, 3389–3425 (2024).

    Article  PubMed  CAS  Google Scholar 

  26. Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Wimmers, F. et al. Multi-omics analysis of mucosal and systemic immunity to SARS-CoV-2 after birth. Cell 186, 4632–4651.e23 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Terekhanova, N. V. et al. Epigenetic regulation during cancer transitions across 11 tumour types. Nature 623, 432–441 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Suppinger, S. et al. Multimodal characterization of murine gastruloid development. Cell Stem Cell 30, 867–884.e11 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Li, H. & Humphreys, B. D. Multimodal characterization of sexual dimorphism in the mammalian kidney. Kidney Int. 105, 653–655 (2024).

    Article  PubMed  CAS  Google Scholar 

  31. Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Fu, Z. et al. Cut&tag: a powerful epigenetic tool for chromatin profiling. Epigenetics 19, 2293411 (2024).

    Article  PubMed  Google Scholar 

  33. Wu, S. J. et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 39, 819–824 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Janssens, D. H. et al. Scalable single-cell profiling of chromatin modifications with sciCUT&Tag. Nat. Protoc. 19, 83–112 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Li, H., Li, D. & Humphreys, B. D. Chromatin conformation and histone modification profiling across human kidney anatomic regions. Sci. Data 11, 1–12 (2024).

    Google Scholar 

  36. Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Zhang, D. et al. Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping. Preprint at bioRxiv https://doi.org/10.1101/2024.07.28.605493 (2024).

  38. Lee, M. Y. Y., Kaestner, K. H. & Li, M. Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data. Genome Biol. 24, 244 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ledru, N. et al. Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing. Nat. Commun. 15, 1–19 (2024).

    Article  Google Scholar 

  40. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  PubMed  CAS  Google Scholar 

  41. Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Llorens-Bobadilla, E. et al. Solid-phase capture and profiling of open chromatin by spatial ATAC. Nat. Biotechnol. 41, 1085–1088 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Russell, A. J. C. et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625, 101–109 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Li, H. et al. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat. Commun. 14, 1548 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bai, Z. et al. Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues. Cell 187, 6760–6779.e24 (2024).

    Article  PubMed  CAS  Google Scholar 

  48. Kvastad, L. et al. The spatial RNA integrity number assay for in situ evaluation of transcriptome quality. Commun. Biol. 4, 57 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Adey, A. C. Tagmentation-based single-cell genomics. Genome Res. 31, 1693 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Li, H. & Humphreys, B. D. Protocol for multimodal profiling of human kidneys with simultaneous high-throughput ATAC and RNA expression with sequencing. STAR Protoc. 5, 103049 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Ramsköld, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wulf, M. G. et al. Non-templated addition and template switching by Moloney murine leukemia virus (MMLV)-based reverse transcriptases co-occur and compete with each other. J. Biol. Chem. 294, 18220 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Li, H. & Humphreys, B. D. Mouse kidney nuclear isolation and library preparation for single-cell combinatorial indexing RNA sequencing. STAR Protoc. 3, 101904 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Su, G. et al. Spatial multi-omics sequencing for fixed tissue via DBiT-seq. STAR Protoc. 2, 100532 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Navarro, J. F., Sjöstrand, J., Salmén, F., Lundeberg, J. & Ståhl, P. L. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics 33, 2591–2593 (2017).

    Article  PubMed  Google Scholar 

  58. 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  PubMed  PubMed Central  CAS  Google Scholar 

  59. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Zhang, K., Zemke, N. R., Armand, E. J. & Ren, B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat. Methods 21, 217–227 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458–1466 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. 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  PubMed  PubMed Central  CAS  Google Scholar 

  64. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Yale Center for Research Computing for guidance and use of the research computing infrastructure. The molds for microfluidic devices were fabricated at the Yale University School of Engineering and Applied Science Nanofabrication Center. Next-generation sequencing was conducted at the Yale Center for Genome Analysis, as well as at the Yale Stem Cell Center Genomics Core Facility, which was supported by the Connecticut Regenerative Medicine Research Fund and the Li Ka Shing Foundation. A service provided by the Genomics Core of Yale Cooperative Center of Excellence in Hematology (U54DK106857) was used. This research was supported by Packard Fellowship for Science and Engineering (to R.F.), Yale Stem Cell Center Chen Innovation Award (to R.F.), and grants from the US National Institutes of Health (U54AG076043, U54AG079759, UG3CA257393, UH3CA257393, R01CA245313, RF1MH128876, U54CA274509, U54CA268083 and U01CA294514 to R.F.). Illustrations were created with BioRender.com.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

    Haikuo Li, Shuozhen Bao, Negin Farzad, Xiaoyu Qin, Anthony A. Fung, Di Zhang, Zhiliang Bai & Rong Fan

  2. Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

    Bo Tao & Rong Fan

  3. Yale Stem Cell Center and Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA

    Rong Fan

  4. Yale Center for Research on Aging (Y-Age), Yale University School of Medicine, New Haven, CT, USA

    Rong Fan

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  1. Haikuo Li
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Contributions

H.L., N.F. and R.F. conceived, coordinated and designed the study. H.L., S.B., X.Q. and B.T. wrote the manuscript. H.L., S.B., A.A.F., D.Z., Z.B. and B.T. analyzed data and created figures. R.F. supervised the project and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rong Fan.

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Competing interests

R.F. is scientific founder of and advisor to IsoPlexis, Singleron Biotechnologies and AtlasXomics. The Yale University Provost’s Office reviewed and managed the interests of R.F. in accordance with the University’s conflict of interest policies. The other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Key references

Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023): https://doi.org/10.1038/s41586-023-05795-1

Farzad, N. et al. Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue. Nat. Protoc. 19, 3389–3425 (2024): https://doi.org/10.1038/s41596-024-01013-y

This protocol is an extension to: Nat. Protoc. 19, 3389–3425 (2024): https://doi.org/10.1038/s41596-024-01013-y

Extended data

Extended Data Fig. 1 Anticipated results of spatial-ATAC-seq library visualization.

TapeStation D5000 electropherogram (High-Sensitivity) showing fragment distribution of a spatial-ATAC-seq library generated from a human brain sample. The table on the right panel indicates the concentration, molarity and fractions of fragments in the select region (200-5,000 bp).

Supplementary information

Reporting Summary

Supplementary Table 1

Oligo sequence information.

Supplementary Data 1

Microfluidic CAD designs.

Supplementary Video 1

Video illustration for attaching PDMS reservoirs on the tissue.

Supplementary Video 2

Video illustration for ATAC-seq and CUT&Tag related procedures.

Supplementary Video 3

Video illustration for attaching PDMS chips on the tissue.

Supplementary Video 4

Video illustration for streptavidin-bead affinity pulldown.

Supplementary Video 5

Video illustration for PDMS associated equipment preparation.

Source data

Source Data Figs. 7 and 8

Original data used to generate metrics plots in Figs. 7 and 8.

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Li, H., Bao, S., Farzad, N. et al. Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq. Nat Protoc 20, 2383–2417 (2025). https://doi.org/10.1038/s41596-025-01145-9

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