BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

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, frequ...

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Bibliographic Details
Published in:	Nature communications Vol. 15; no. 1; p. 509
Main Authors:	Fu, Xiaohang, Lin, Yingxin, Lin, David M., Mechtersheimer, Daniel, Wang, Chuhan, Ameen, Farhan, Ghazanfar, Shila, Patrick, Ellis, Kim, Jinman, Yang, Jean Y. H.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 13-01-2024 Nature Publishing Group Nature Portfolio
Subjects:	631/114/1305 631/114/1564 631/114/2397 631/114/2401 631/61/514/1949 Benchmarking Cell morphology Cytology Deep learning Erythrocytes, Abnormal Gene expression Gene Expression Profiling Gene mapping Histocompatibility Testing Humanities and Social Sciences Morphology multidisciplinary Platforms Science Science (multidisciplinary) Segmentation Self-supervised learning Spatial analysis Spatial data Supervised Machine Learning Transcriptomics
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-023-44560-w