Cytoskeletal filaments interacting with molecular motors play a crucial role in understanding various physiological processes within cellular and molecular medicine. However, in vitro motility (IVM) assays, a key technique for this purpose, often face the challenge of accurately and rapidly analyzing filament motion from video recordings. This is where an innovative tool called Filament comes into play, offering a Python-based automated solution for high-throughput analysis.
Developed by Professor Carol Gregorio, Ryan Bowser, and Dr. Gerrie Farman at the University of Arizona, Philament is a filament tracking program designed to significantly improve the efficiency and accuracy of IVM assay analysis. Their work, published in the journal Biophysical Reports, presents a novel approach to data mining that reduces individual bias and allows for rapid and comprehensive analysis.
“The key advantage of Filament lies in its ability to automate the entire process, from video preprocessing to data extraction, making it a powerful tool for researchers studying actomyosin interactions,” said Professor Gregorio. “The program’s use of open-source Python packages ensures that it remains up-to-date and accessible for future developments.”
IVM assays typically involve examining the motion of fluorescently labeled filaments, such as F-actin or microtubules, over surfaces coated with motor proteins such as myosin or kinesin. While traditional analysis methods often require manual tracking, Philament automates this process, extracting data on instantaneous and average velocities, filament lengths, and smoothness of motion. By converting images to binary scale and employing centroid tracking algorithms, Philament provides detailed analysis of filament motion, even in high-throughput settings.
One of the standout features of Philament is its ability to handle overlapping filaments without losing tracking data, a common problem with older software. This ensures that critical information is not discarded, leading to more reliable and complete results. “Our program can track the movement of filaments even when they temporarily overlap or momentarily disappear from view, resuming tracking accurately once the filament reappears,” Professor Gregorio explained.
The researchers highlight the importance of Filament in advancing cardiovascular mechanics studies, as it simplifies entry into this field by reducing the learning curve associated with coding and complex image analysis software. “Filament’s automation capabilities enable high-throughput analysis of IVM data, which is crucial for large-scale studies investigating the effects of various physiological conditions, such as disease, exercise, and fatigue,” added Professor Gregorio.
In their study, the team validated Philament's performance by comparing its result with manual tracking methods and other semi-automated programs. They found that Philament not only matched the accuracy of manual measurements, but also outperformed existing software in terms of speed and number of objects tracked. “Philament accelerates analysis by a factor of 10 compared to previous programs, allowing for faster and more efficient data collection and analysis,” said Professor Gregorio.
Philament's potential applications go beyond basic research and provide valuable information for drug discovery and development. By enabling high-throughput screening of compounds that affect actin-myosin interactions, Philament may facilitate the identification of new therapeutic targets and the evaluation of drug efficacy.
As the scientific community continues to explore the intricate dynamics of cytoskeletal filaments and motor proteins, tools like Filament will play a crucial role in advancing our understanding and uncovering new possibilities for medical and scientific breakthroughs. With its easy-to-use interface and robust data analysis capabilities, Filament is a testament to the power of automation in modern scientific research. Professor Gregorio and her team have set a new standard for how we approach and analyze filament-motor interactions, paving the way for future innovations.
Journal reference
Bowser, RM, Farman, GP and Gregorio, CC (2024). Filament: A filament tracking program to quickly and accurately analyze in vitro motility assays. Biophysical Reports, 4, 100147. DOI: https://doi.org/10.1016/j.bpr.2024.100147
About the authors
I am currently a research scientist at the University of Arizona examining the role of interactions between myofilament proteins in healthy and diseased tissues. I examine how changes in protein structure through mutations, either hypertrophic or dilated cardiomyopathy, and phosphorylation (post-translational modifications) have on these interactions. To do this, I use numerous techniques, such as single cell and fiber bundle mechanics, to examine the tissue's response to stretch and calcium, the main ion used to regulate muscle contractility. I also examine how these proteins interact, either at the single molecule level, using in vitro motility (IVM) and rotational stiffness, a means of examining the innate stiffness of myosin (the muscle motor molecule) under different physiological conditions, or by X-ray diffraction. X-ray diffraction allows us to examine the structure of the muscle, down to the nanometer scale, under various conditions, allowing us to examine how the numerous proteins in the muscle framework interact.
In addition to that, I have mentored many students and postdocs in numerous laboratories and have passed on this acquired knowledge to others. Outside of the lab, I enjoy reading and biking around the Tucson area to explore the natural beauty of the city and its surroundings.
I am an Accelerated Master's student at the University of Arizona studying cardiac protein regulatory interactions in the Gregorio lab. My projects focus on better understanding the functions of Leiomodin (Lmod) and adenylyl cyclase-associated protein 2 (CAP2). I am self-taught in Python, which I learned when I first worked with Dr. Gregorio and Dr. Farman, and I deeply enjoy the creativity and problem-solving of programming.
In the lab, I develop automated data analysis methods to streamline research, such as our Philament software for in vitro motility (IVM), as well as several other scripts for single cell mechanics and sinusoidal perturbations. In addition to creating data analysis tools, I also perform IVM and single cell mechanics experiments for my research projects.
Outside of the laboratory, I am actively involved in science education. I have been featured on KXCI 91.3's “Thesis Thursdays” segment, mentor high school students as a coordinator in the STAR Lab, and love talking about science with students from kindergarten through high school.
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