Statistical parametrization of cell cytoskeleton reveals lung cancer cytoskeletal phenotype with partial EMT signature

A computational method for automated quantification of actin stress fiber alignment in fluorescence images of cultured cells is presented, used to detect changes in stress fiber organization during EMT, with pathways regulating actin dynamics manipulated leading to the discovery of a cytoskeletal phenotype. Epithelial-mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, developing and using an image quantification tool, Statistical Parametrization of Cell Cytoskeleton (SPOCC), we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify their cytoskeletal response to drugs. SPOCC has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and by inference invasiveness) of the intermediate EMT phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for future treatment strategies that combat metastasis by specifically targeting the fiber alignment process.

Automated summary

This study presents a computational method for quantifying actin stress fiber alignment in fluorescence images of cultured cells, which can detect changes in stress fiber organization during EMT. The study also identifies an intermediate EMT state with a specific cytoskeletal signature and partitions EMT into two steps. Additionally, the study introduces a figure of merit, the Orientational Order Parameter (OOP), to track EMT progression and characterize the cytoskeletal response to drugs in live cells. The developed image quantification tool, SPOCC, has improved throughput and non-destructiveness, making it suitable for studying various biological processes.