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 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 inform

SUBMITTER: Benjamin K 

PROVIDER: S-EPMC11208150 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature