Author:
Wong Kin Sun,Zhong Xueying,Low Christine Siok Lan,Kanchanawong Pakorn
Abstract
AbstractCell 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.
Funder
Ministry of Education Research Scholarship Block
Mechanobiology Institute Graduate Scholarship
Ministry of Education - Singapore
Ministry of Education Academic Research Fund Tier 2
Ministry of Education Academic Research Fund Tier 3
Publisher
Springer Science and Business Media LLC
Cited by
5 articles.
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