Multiscale topology classifies cells in subcellular spatial transcriptomics
成果类型：
Article
署名作者：
Benjamin, Katherine; Bhandari, Aneesha; Kepple, Jessica D.; Qi, Rui; Shang, Zhouchun; Xing, Yanan; An, Yanru; Zhang, Nannan; Hou, Yong; Crockford, Tanya L.; McCallion, Oliver; Issa, Fadi; Hester, Joanna; Tillmann, Ulrike; Harrington, Heather A.; Bull, Katherine R.
署名单位：
University of Oxford; University of Oxford; Wellcome Centre for Human Genetics; University of Oxford; University of Oxford; Chinese Academy of Medical Sciences - Peking Union Medical College; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Oxford; University of Cambridge; Max Planck Society; Technische Universitat Dresden
刊物名称：
Nature
ISSN/ISSBN：
0028-4711
DOI：
10.1038/s41586-024-07563-1
发表日期：
2024-06-27
页码：
943-+
关键词：
expression
seq
摘要：
Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue(1), hitherto with some trade-off between transcriptome depth, spatial resolution and sample size(2). 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 samples(3-6). 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 homology(7-9), 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.