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- Title
BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.
- Authors
Fu, Xiaohang; Lin, Yingxin; Lin, David M.; Mechtersheimer, Daniel; Wang, Chuhan; Ameen, Farhan; Ghazanfar, Shila; Patrick, Ellis; Kim, Jinman; Yang, Jean Y. H.
- Abstract
Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery. Subcellular in situ spatial transcriptomics offers the promise to address biological problems that were previously inaccessible but requires accurate cell segmentation to uncover insights. Here, authors present BIDCell, a biologically informed, deep learning-based cell segmentation framework.
- Subjects
TRANSCRIPTOMES; DEEP learning; SUPERVISED learning; GENE expression; GENE mapping; CELL morphology
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-44560-w