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Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.

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Τίτλος: Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.
Συγγραφείς: Mao J; Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia., Choi J; Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia., Lê Cao KA; Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia. Electronic address: kimanh.lecao@unimelb.edu.au.
Πηγή: Cell reports methods [Cell Rep Methods] 2026 Aug 17; Vol. 6 (8), pp. 101483. Date of Electronic Publication: 2026 Jun 12.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918227360606676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2667-2375 (Electronic) Linking ISSN: 26672375 NLM ISO Abbreviation: Cell Rep Methods Subsets: MEDLINE
Imprint Name(s): Original Publication: [New York] : Elsevier Inc., [2021]-
Ιατρικοί όροι (MeSH): Spatial Transcriptomics*/methods , Transcriptome*/genetics, Humans ; Single-Cell Gene Expression Analysis
Περίληψη: We introduce Φ-Space ST, a platform-agnostic method to identify continuous cell states in spatial transcriptomics (ST) data using multiple scRNA-seq references. For ST with supercellular resolution, Φ-Space ST achieves interpretable cell-type deconvolution with significantly faster computation. For subcellular resolution, Φ-Space ST annotates cell states without cell segmentation, leading to highly insightful spatial niche identification. Φ-Space ST harmonizes annotations derived from multiple scRNA-seq references and provides interpretable characterizations of disease cell states by leveraging healthy references. We validate Φ-Space ST in four case studies involving CosMx, Visium, Xenium, and Stereo-seq platforms for various cancer tissues. Our method revealed niche-specific enriched cell types and distinct cell-type co-presence patterns that distinguish tumor from non-tumor tissue regions. These findings highlight the potential of Φ-Space ST as a robust and scalable tool for ST data analysis for understanding complex tissues and pathologies.
(Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of interests The authors declare they have no competing interests.
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Contributed Indexing: Keywords: CP: cancer biology; CP: computational biology; cell states; cell type deconvolution; spatial niches; spatial transcriptomics
Entry Date(s): Date Created: 20260612 Date Completed: 20260817 Latest Revision: 20260823
Update Code: 20260823
PubMed Central ID: PMC13494538
DOI: 10.1016/j.crmeth.2026.101483
PMID: 42285101
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2667-2375
DOI:10.1016/j.crmeth.2026.101483