Basu, A., Paul, M. K., Alioscha-Perez, M., Anna , G., Sahli, H., Dubinett, S. M., & Weiss, S. (2022). Statistical Parametrization of Cell Cytoskeleton (SPOCC) reveals novel lung cancer cytoskeletal phenotype with partial EMT signature. Communications Biology, 5(407).

@article{030b93135c9540318312737faf779e52,
title = "Statistical Parametrization of Cell Cytoskeleton (SPOCC) reveals novel lung cancer cytoskeletal phenotype with partial EMT signature",
abstract = "Epithelial-mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, using a novel 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 new treatment strategies that combat metastasis by specifically targeting the fiber alignment process. ",
keywords = "cytoskeleton, image analysis, Epithelial-mesenchymal Transition",
author = "Arkaprabha Basu and Paul, {Manash K.} and Mitchel Alioscha-Perez and Grosberg Anna and Hichem Sahli and Dubinett, {Steven M.} and Shimon Weiss",
year = "2022",
language = "English",
volume = "5",
journal = "Communications Biology",
issn = "2399-3642",
publisher = "Nature Research Publishing",
number = "407",
}