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Characterizes fibroblast activation and identifies subpopulations in cancerous conditions, suggesting unique cellular behavior.
Fibroblasts undergo significant morphological and functional changes in response to specific environmental cues, adopting a novel phenotype when sustaining transformative activation in wound healing and cancerous processes. The aim of this project is to characterize cytoskeletal reorganization in both control fibroblasts’ (CFs) and cancer-associated fibroblasts’ (CAFs) activation. To achieve this, we propose a novel method based on the extraction of biophysical biomarkers from epifluorescence images of the cytoskeleton of individual fibroblasts, obtained from a patient with lung cancer and activated using TGF-β. These biophysical outputs were also used to identify CAFs subpopulations. While non-tumoral fibroblasts experience larger morphological changes characterized by an increase in area and the acquisition of a robust network of actin fibers, CAFs exhibited sustained larger areas throughout the process regardless of TGF- β administration, amongst other cytoskeletal transformations. The application of logistic regression has allowed for a classification between CFs and CAFs of 81% accuracy, highlighting the differences in the cytoskeleton of these cell types. Furthermore, the intragroup analysis provided by unsupervised clustering has enabled the identification of five clusters for non- activated CAFs, which converge at 72 hours post-activation into two clusters.
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L. et al. (2026) studied this question.
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