ArticleScientific reports2022
Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 6 citations in OpenAlex.
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- Adipose-mimetic granular hydrogels uncover biophysical cues driving breast cancer invasion.bioRxiv : the preprint server for biology · 2025Article
- Machine learning-based approach for automated classification of cell and extracellular matrix using nanomechanical properties.Materials today. Bio · 2024Article
- From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.Molecular biology of the cell · 2024Article
- Constructing Tumor Organoid-like Tissue for Reliable Drug Screening Using Liver-Decellularized Extracellular Matrix Scaffolds.ACS omega · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell morphology is profoundly influenced by cellular interactions with microenvironmental factors such as the extracellular matrix (ECM). Upon adhesion to specific ECM, various cell types are known to exhibit different but distinctive morphologies, suggesting that ECM-dependent cell morphological responses may harbour rich information on cellular signalling states. However, the inherent morphological complexity of cellular and subcellular structures has posed an ongoing challenge for automated quantitative analysis. Since multi-channel fluorescence microscopy provides robust molecular specificity important for the biological interpretations of observed cellular architecture, here we develop a deep learning-based analysis pipeline for the classification of cell morphometric phenotypes from multi-channel fluorescence micrographs, termed SE-RNN (residual neural network with squeeze-and-excite blocks). We demonstrate SERNN-based classification of distinct morphological signatures observed when fibroblasts or epithelial cells are presented with different ECM. Our results underscore how cell shapes are non-random and established the framework for classifying cell shapes into distinct morphological signature in a cell-type and ECM-specific manner.
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