Orateur
Description
Chromatin accessibility is a key indicator of the cell's regulatory state in normal and cancerous tissues (1). However, whether it is shaped by the local cellular context remains unexplored. Recent spatial multiomics technologies that simultaneously profile chromatin accessibility and gene expression while preserving tissue architecture now make it possible to address this question directly (2, 3).
Here we present a computational framework for the spatial analysis of paired ATAC-seq and RNA-seq data in tissue sections. As a proof of concept, we analyzed a published human melanoma dataset comprising spatially resolved paired snRNA-seq and snATAC-seq profiles (2). We characterized chromatin accessibility in 2,529 spatially resolved cells by defining gene-centric accessibility profiles from the ATAC-seq signal in promoter-proximal regions (±1,000 bp windows centered on transcription start sites of protein-coding genes). Unsupervised clustering of these profiles identified nine chromatin accessibility states (ATAC clusters) that partially, but not entirely, recapitulate RNA-based cell type annotations.
To assess whether these chromatin states exhibit spatial structure, we reconstructed the tissue's cellular network using tysserand (4) and applied mosna (5) to quantify spatial co-localization patterns. These analyses revealed that chromatin accessibility states are spatially organized. These results provide initial evidence that chromatin accessibility is not only linked to cell identity, but is also spatially structured within the tissue microenvironment
References
1. S. L. Klemm, Z. Shipony, W. J. Greenleaf, Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).
2. A. J. C. Russell, J. A. Weir, N. M. Nadaf, M. Shabet, V. Kumar, S. Kambhampati, R. Raichur, G. J. Marrero, S. Liu, K. S. Balderrama, C. R. Vanderburg, V. Shanmugam, L. Tian, J. B. Iorgulescu, C. H. Yoon, C. J. Wu, E. Z. Macosko, F. Chen, Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625, 101–109 (2024).
3. Y.-H. Huang, J. A. Belk, R. Zhang, N. E. Weiser, Z. Chiang, M. G. Jones, P. S. Mischel, J. D. Buenrostro, H. Y. Chang, Unified molecular approach for spatial epigenome, transcriptome, and cell lineages. Proc. Natl. Acad. Sci. 122, e2424070122 (2025).
4. A. Coullomb, V. Pancaldi, Tysserand—fast and accurate reconstruction of spatial networks from bioimages. Bioinformatics 37, 3989–3991 (2021).
5. A. Coullomb, C. Foulon, B. Van Haastrecht, P. Monsarrat, V. Pancaldi, mosna reveals different types of cellular interactions predictive of response to immunotherapies and survival in cancer. Mol. Cell. Proteomics, 101536 (2026).