Spatially resolved epigenomic profiling of single cells in complex tissues
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Abstract
The recent development of spatial omics methods has enabled single-cell profiling of the transcriptome and 3D genome organization with high spatial resolution. Expanding the repertoire of spatial omics tools, a spatially resolved single-cell epigenomics method will accelerate understanding of the spatial regulation of cell and tissue functions. Here, we report a method for spatially resolved epigenomic profiling of single cells using in situ tagmentation and transcription followed by multiplexed imaging. We demonstrated the ability to profile histone modifications marking active promoters, putative enhancers, and silent promoters in individual cells, and generated high-resolution spatial atlas of hundreds of active promoters and putative enhancers in embryonic and adult mouse brains. Our results suggested putative promoter-enhancer pairs and enhancer hubs regulating developmentally important genes. We envision this approach will be generally applicable to spatial profiling of epigenetic modifications and DNA-binding proteins, advancing our understanding of how gene expression is spatiotemporally regulated by the epigenome.
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High Cux2 GAS in clusters C9 and C6 indicates enrichment of excitatory neurons.
“Cux2 showed high GAS in cluster C9 (H3K27ac) and cluster C6 (H3K4me3), indicating enrichment of excitatory neurons5.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [5]
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High-throughput single-cell epigenomic profiling by targeted insertion of promoters (TIP-seq)
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Multiplexed single-cell profiling of chromatin states at genomic loci by expansion microscopy
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Spatial Epigenome Sequencing at Tissue Scale and Cellular LevelPreprint
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Spatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang, D., Deng, Y., Kukanja, P., Agirre, E., Bartosovic, M., Dong, M., Ma, C., Ma, S., Su, G., Bao, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05795-1 · Cited by 483 · see citing works
Exercise Metabolism and Adaptation in Skeletal Muscle
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From tissue architecture to clinical insights: Spatial transcriptomics in solid tumor studies
Sharma, A., Raut, S. S., Shukla, A., Gupta, S., Mishra, A., & Singh, A. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152389 · Cited by 29 · see citing works
Binan, L., Danquah, S., Valakh, V., Simonton, B., Bezney, J., Nehme, R., Cleary, B., & Farhi, S. L. (2023) · doi:10.1101/2023.11.30.569494 · Cited by 139 · see citing works
- Authors
- Lu, T., Ang, C. E., & Zhuang, X.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Zhuang, Xiaowei
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 185
- Issue
- 23
- Pages
- 4448-4464.e17
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC9691621
- OpenAlex
- W4307114720
- Cited by
- 197
- References
- 95
- Citations by year
- 2026: 43 · 2025: 59 · 2024: 43 · 2023: 43 · 2022: 9
- FWCI
- 12.51
- Type
- journal article
- Language
- en
- Update notices
- erratum (2023-05-11)
- Open access
- hybrid
- doi.org
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Howard Hughes Medical Institute
- Harvard University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- multiplexed imaging, histone modifications, mouse brain, spatial regulation of gene expression, DNA-binding proteins
- MeSH terms
- Animals, Chromatin, Enhancer Elements, Genetic, Promoter Regions, Genetic, Histone Code, Epigenesis, Genetic, Mice, Epigenomics, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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