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 grapple with the challenge of accurately and swiftly analyzing filament motion from video recordings. This is where a groundbreaking tool named Philament steps in, offering an automated, Python-based solution for high-throughput analysis.
Developed by Professor Carol Gregorio, Ryan Bowser, and Dr. Gerrie Farman from the University of Arizona, Philament is a filament tracking program designed to significantly enhance the efficiency and accuracy of IVM assay analysis. Their work, published in the journal Biophysical Reports, presents a novel approach to data extraction that reduces individual bias and enables rapid, comprehensive analysis.
“Philament’s main advantage 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 it remains up-to-date and accessible for future developments.”
IVM assays typically involve examining the movement of fluorescently labeled filaments, such as F-actin or microtubules, on surfaces coated with motor proteins like 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 motion smoothness. By converting images to binary scale and employing centroid tracking algorithms, Philament provides a 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 issue with older software. This ensures that critical information is not discarded, leading to more reliable and comprehensive results. “Our program can track the motion of filaments even when they temporarily overlap or are momentarily lost from view, resuming tracking accurately once the filament reappears,” explained Professor Gregorio.
The researchers highlight the significance of Philament in advancing cardiovascular mechanics studies, as it simplifies the entry into this field by reducing the learning curve associated with coding and complex image analysis software. “Philament’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 output 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 the number of objects tracked. “Philament speeds up analysis by a factor of 10 compared to previous programs, allowing for quicker and more efficient data collection and analysis,” noted Professor Gregorio.
The potential applications of Philament extend beyond basic research, offering valuable insights into drug discovery and development. By enabling high-throughput screening of compounds affecting actin-myosin interactions, Philament can facilitate the identification of new therapeutic targets and the evaluation of drug efficacy.
As the research community continues to explore the intricate dynamics of cytoskeletal filaments and motor proteins, tools like Philament will play a crucial role in advancing our understanding and uncovering new possibilities for medical and scientific breakthroughs. With its user-friendly interface and robust data analysis capabilities, Philament stands as 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, R. M., Farman, G. P., & Gregorio, C. C. (2024). Philament: 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 myofilament protein interactions in healthy and diseased tissue. I examine how changes in the protein structure via mutations, either hypertrophic or dilated cardiomyopathy and phosphorylation (post-translational modifications) have on these interactions. To do this I employ many numerous techniques such as single cell, and fiber bundle mechanics to examine the response of the tissue to stretch and calcium, the main ion used to regulate contractility of the muscle. I also examine how these proteins interact either on the single molecule level using in vitro motility (IVM) and rotational stiffness, a means of examining myosin’s (the motor molecule in muscle) innate stiffness under different physiological conditions or through 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 scrutinize how the many proteins in the muscle lattice interact.
Beyond that I’ve mentored many students and post-docs in numerous labs passing on this acquired knowledge to others. Outside of the lab I enjoy reading and riding my bike around the Tucson area exploring the natural beauty in and around the city.
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 roles of Leiomodin (Lmod) and adenylyl cyclase-associated protein 2 (CAP2). I am self-taught in Python, which I learned when first working with Dr. Gregorio & 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 software Philament for in vitro motility (IVM), as well as various other scripts for single-cell mechanics and sinusoidal perturbations. Besides creating data analysis tools, I also run IVM and single-cell mechanics experiments for my research projects.
Outside of the lab, I am active in scientific education. I appeared on KXCI 91.3’s “Thesis Thursday” segment, I mentor high school students as a coordinator in the STAR Lab, and I love getting to talk about science to students from kindergarten to high school!
