Cell2location maps fine-grained cell types in spatial transcriptomics
Abstract
Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.
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Loki Decompose was assessed on an in-house TNBC dataset, a human colorectal cancer dataset and a brain dataset.
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From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [50]
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- Authors
- Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., Jain, M. S., Park, J. S., Ramona, L., Tuck, E., Arutyunyan, A., Vento-Tormo, R., Gerstung, M., James, L., Stegle, O., & Bayraktar, O. A.
- Affiliations
- Wellcome Sanger Institute (GB)
- Lomonosov Moscow State University (RU)
- Queen Mary University of London (GB)
- European Bioinformatics Institute (GB)
- University of California, Berkeley (US)
- University of Cambridge (GB)
- German Cancer Research Center (DE)
- Corresponding author
- Stegle, Oliver
- Bayraktar, Omer Ali
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2022-01-13
- Volume
- 40
- Issue
- 5
- Pages
- 661-671
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W4205817967
- Cited by
- 1,279
- References
- 67
- Citations by year
- 2026: 403 · 2025: 316 · 2024: 313 · 2023: 160 · 2022: 85 · 2021: 1
- FWCI
- 73.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- Wellcome Trust
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, cell type mapping, Bayesian model, astrocyte subtypes, lymph nodes, gut immune cells, lymphoid follicles, mouse brain, human tissue
- MeSH terms
- Animals, Bayes Theorem, Mice, Single-Cell Analysis, Transcriptome
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