Journal article
Multiscale topology classifies cells in subcellular spatial transcriptomics
- Abstract:
- Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 12.6MB, Terms of use)
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- Publisher copy:
- 10.1038/s41586-024-07563-1
Authors
- Publisher:
- Springer Nature
- Journal:
- Nature More from this journal
- Volume:
- 630
- Issue:
- 8018
- Pages:
- 943–949
- Place of publication:
- England
- Publication date:
- 2024-06-19
- Acceptance date:
- 2024-05-14
- DOI:
- EISSN:
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1476-4687
- ISSN:
-
0028-0836
- Pmid:
-
38898271
- Language:
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English
- Pubs id:
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2009370
- Local pid:
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pubs:2009370
- Deposit date:
-
2024-06-25
- ARK identifier:
Terms of use
- Copyright holder:
- Benjamin et al
- Copyright date:
- 2024
- Rights statement:
- © 2024 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Notes:
- This work is related to the thesis Topological data analysis for high-dimensional data in biology.
- Licence:
- CC Attribution (CC BY)
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