Abstract
Deciphering cellular microenvironments at atlas scale remains challenging because molecular identity, spatial context, and platform heterogeneity are tightly coupled. Here we present CellNiche, a scalable contrastive-learning framework that identifies and characterizes cellular microenvironments from spatial omics data using cell-centric spatial-proximity subgraphs. CellNiche combines spatial co-localization and molecular co-expression cues to learn microenvironment-aware embeddings. Across spatial omics datasets from multiple platforms (>10 million cells in total), scaling experiments show improved representations with more training data and competitive clustering and embedding-quality performance with efficient computation. In a multi-sample human non-small-cell lung cancer (NSCLC) cohort, CellNiche identifies conserved and sample-specific tumor and immune microenvironments and captures localized spatial transitions. In four independent mouse brain atlases, CellNiche integrates 293 slices into a unified virtual brain map for cross-atlas annotation transfer and spatial refinement.
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Data availability
The osmFISH dataset of mouse somatosensory cortex is available at https://github.com/drieslab/spatial-datasets28. The mouse spleen CODEX dataset is available at https://data.mendeley.com/datasets/zjnpwh8m5b/130. The STARMap dataset of mouse brain is available at https://singlecell.broadinstitute.org/single_cell/study/SCP183032. The human CRC CODEX dataset is available at https://data.mendeley.com/datasets/mpjzbtfgfr/137. The NSCLC CosMx dataset is available at https://nanostring.com/products/cosmx-spatial-molecular-imager/nsclc-ffpe-dataset/27. The spatial transcriptomics atlases of mouse brain are available at https://singlecell.broadinstitute.org/single_cell/study/SCP1830 (Atlas 1)32, https://doi.brainimagelibrary.org/doi/10.35077/g.610 (Atlas 2)31, https://doi.brainimagelibrary.org/doi/10.35077/act-bag (Atlas 3)33, https://info.vizgen.com/mouse-brain-map (Atlas 4)34. The mouse E16.5 whole embryo Stereo-seq data is available at https://db.cngb.org/stomics/mosta/download/36. Source data are provided in this paper.
Code availability
The software package implementing the CellNiche algorithm has been deposited at GitHub https://github.com/Super-LzzZ/CellNiche under the MIT license. The version associated with this study has been archived at Zenodo (https://doi.org/10.5281/zenodo.19143524)65.
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Acknowledgements
This work was supported by the National Key Research and Development Program of China (2022YFA1004800), Strategic Priority Research Program of the Chinese Academy of Sciences (XDB1350000), the CAS Project for Young Scientists in Basic Research (YSBR-077), Zhejiang Province Vanguard Goose-Leading Initiative (no. 2025C01114), and the National Natural Science Foundation of China (12025107, 12571550, 12326610).
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Z.M.L. and S.P.L. conceived the study. Z.M.L. designed and developed CellNiche. Z.M.L. assembled the data and performed computational analyses. Z.M.L., Y.W., and S.P.L. designed analyses and supervised the work. B.X.Z. and M.Q.J. participated in the discussion and provided suggestions. Z.M.L., Y.W., and S.P.L. wrote the manuscript. All authors edited and approved the manuscript.
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Liang, Z., Zhong, B., Jiao, M. et al. CellNiche represents cellular microenvironments in atlas-scale spatial omics data with contrastive learning. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71759-4
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DOI: https://doi.org/10.1038/s41467-026-71759-4
