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

Arkaprabha Basu; Manash K Paul; Mitchel Alioscha-Perez; Anna Grosberg; Hichem Sahli; Steven M Dubinett; Shimon Weiss

doi:10.1038/s42003-022-03358-0

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

Commun Biol. 2022 May 2;5(1):407. doi: 10.1038/s42003-022-03358-0.

Authors

Arkaprabha Basu¹, Manash K Paul^{2

3}, Mitchel Alioscha-Perez^{4

5}, Anna Grosberg^{6

7}, Hichem Sahli^{4

5}, Steven M Dubinett^{2

3

8

9

10

11}, Shimon Weiss^{12

13

14}

Affiliations

¹ Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA.
² Department of Medicine, University of California Los Angeles, Los Angles, CA, USA.
³ Division of Pulmonary and Critical Care Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁴ Electronics and Informatics Department, Vrije Universiteit Brussel, Brussels, Belgium.
⁵ Interuniversity Microelectronics Centre, Heverlee, Belgium.
⁶ Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA.
⁷ The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California Irvine, Irvine, CA, USA.
⁸ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
⁹ Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
¹⁰ California NanoSystems Institute, Los Angeles, CA, USA.
¹¹ VA Greater Los Angeles Health Care System, Los Angeles, CA, USA.
¹² Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA. sweiss@chem.ucla.edu.
¹³ California NanoSystems Institute, Los Angeles, CA, USA. sweiss@chem.ucla.edu.
¹⁴ Department of Physiology, University of California Los Angeles, Los Angeles, CA, USA. sweiss@chem.ucla.edu.

Abstract

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.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't

MeSH terms

Cytoskeleton
Epithelial-Mesenchymal Transition* / physiology
Humans
Lung Neoplasms* / genetics
Microtubules
Phenotype