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Birk, S. ; Bonafonte Pardás, I. ; Feriz, A.M.* ; Boxall, A.* ; Agirre, E.* ; Memi, F.* ; Maguza, A.* ; Yadav, A.* ; Armingol, E.* ; Fan, R.* ; Castelo-Branco, G.* ; Theis, F.J. ; Bayraktar, O.A.* ; Talavera-López, C.* ; Lotfollahi, M.

Quantitative characterization of cell niches in spatially resolved omics data.

Nat. Genet. (2025)
Publ. Version/Full Text DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Spatial omics enable the characterization of colocalized cell communities that coordinate specific functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identification and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identification of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from different sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass' scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Common Coordinate Framework; Expression; Morphogenesis
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 1061-4036
e-ISSN 1546-1718
Journal Nature Genetics
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of AI for Health (AIH)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
G-540010-001
Grants Joint Federal and State Support Program for Young Academics (WISNA)
Faculty of Medicine of the Julius-Maximilian-Universitat Wuerzburg
Swedish Brain Foundation
Swedish Cancer Society (Cancerfonden)
Knut and Alice Wallenberg Foundation
Joachim Herz Stiftung
Helmholtz Association
Wellcome Trust
DOI 10.1038/s41588-025-02120-6
WOS ID 001448815000001
PubMed ID 40102688
Erfassungsdatum 2025-05-09
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