Multiscale topology classifies cells in subcellular spatial transcriptomics

Katherine Benjamin, Aneesha Bhandari, Jessica D. Kepple, Rui Qi, Zhouchun Shang, Yanan Xing, Yanru An, Nannan Zhang, Yong Hou, Tanya L. Crockford, Oliver McCallion, Fadi Issa, Joanna Hester, Ulrike Tillmann, Heather A. Harrington () and Katherine R. Bull ()
Additional contact information
Katherine Benjamin: University of Oxford
Aneesha Bhandari: University of Oxford
Jessica D. Kepple: University of Oxford
Rui Qi: University of Oxford
Zhouchun Shang: BGI Research
Yanan Xing: BGI Research
Yanru An: BGI Research
Nannan Zhang: BGI Research
Yong Hou: BGI Research
Tanya L. Crockford: University of Oxford
Oliver McCallion: University of Oxford
Fadi Issa: University of Oxford
Joanna Hester: University of Oxford
Ulrike Tillmann: University of Oxford
Heather A. Harrington: University of Oxford
Katherine R. Bull: University of Oxford

Nature, 2024, vol. 630, issue 8018, 943-949

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41586-024-07563-1

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