Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses.

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

SUBMITTER: Wong KS 

PROVIDER: S-EPMC9468179 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature