Philament: A filament tracking program to quickly and accurately analyze in vitro motility assays.

Biophys Rep (N Y)

Department of Cellular and Molecular Medicine and Sarver Molecular Cardiovascular Research Program, The University of Arizona, Tucson, Arizona.

Published: March 2024

AI Article Synopsis

  • In vitro motility (IVM) assays help study the interactions between cytoskeletal filaments and molecular motors, focusing on factors like fatigue-related changes and disease mutations.
  • A limitation of traditional analysis is the difficulty in quickly and accurately extracting data from videos without bias.
  • The new Python-based program, Philament, automates data extraction and analysis, offering detailed insights into filament motion and allowing easier access to research in cardiovascular mechanics.

Article Abstract

In vitro motility (IVM) assays allow for the examination of the basic interaction between cytoskeletal filaments with molecular motors and the influence many physiological factors have on this interaction. Examples of factors that can be studied include changes in ADP and pH that emulate fatigue, altered phosphorylation that can occur with disease, and mutations within myofilament proteins that cause disease. While IVM assays can be analyzed manually, the main limitation is the ability to extract accurate data rapidly from videos collected without individual bias. While programs have been created in the past to enable data extraction, many are now out of date or require the use of proprietary software. Here, we report the generation of a Python-based tracking program, Philament, which automatically extracts data on instantaneous and average velocities, and allows for fully automated analysis of IVM recordings. The data generated are presented in an easily accessible spreadsheet-based, comma-separated values file. Philament also contains a novel method of quantifying the smoothness of filament motion. By fitting curves to standard deviations of velocity and average velocities, the influence of different experimental conditions can be compared relative to one another. This comparison provides a qualitative measure of protein interactions where steeper slopes indicate more unstable interactions and shallower slopes indicate more stable interactions within the myofilament. Overall, Philament's automation of IVM analysis provides easier entry into the field of cardiovascular mechanics and enables users to create a truly high-throughput experimental data analysis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10884813PMC
http://dx.doi.org/10.1016/j.bpr.2024.100147DOI Listing

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