Bowser Ryan M, Farman Gerrie P, Gregorio Carol C
Department of Cellular and Molecular Medicine and Sarver Molecular Cardiovascular Research Program, The University of Arizona, Tucson, Arizona.
Cardiovascular Research Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Biophys Rep (N Y). 2024 Jan 30;4(1):100147. doi: 10.1016/j.bpr.2024.100147. eCollection 2024 Mar 13.
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 While programs have been created in the past to enable data extraction, many are now out of date or require the use of proprietary software.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.
体外运动性(IVM)分析可用于研究细胞骨架细丝与分子马达之间的基本相互作用,以及许多生理因素对这种相互作用的影响。可以研究的因素包括模拟疲劳的ADP和pH值变化、疾病可能导致的磷酸化改变以及引起疾病的肌丝蛋白突变。虽然IVM分析可以手动进行,但主要限制在于能否快速从收集的视频中无个体偏差地提取准确数据。虽然过去已经创建了一些程序来进行数据提取,但现在许多程序已经过时,或者需要使用专有软件。在此,我们报告了一个基于Python的跟踪程序Philament的生成,它可以自动提取瞬时速度和平均速度的数据,并允许对IVM记录进行全自动分析。生成的数据以易于访问的基于电子表格的逗号分隔值文件形式呈现。Philament还包含一种量化细丝运动平滑度的新方法。通过将曲线拟合到速度标准差和平均速度上,可以相互比较不同实验条件的影响。这种比较提供了蛋白质相互作用的定性度量,其中较陡的斜率表明肌丝内的相互作用更不稳定,而较浅的斜率表明相互作用更稳定。总体而言,Philament对IVM分析的自动化使得更容易进入心血管力学领域,并使用户能够创建真正的高通量实验数据分析。