Living cells respond to external stimuli through the reorganization of the actin cytoskeleton, and the actin cytoskeleton significantly affects the cellular mechanical behavior. However, due to the lack of approaches to actin cytoskeleton quantification, the dynamics of mechanotransduction is still poorly understood. In this study, we propose an image recognition-based quantification (IRQ) approach to actin cytoskeleton quantification. IRQ quantifies the actin cytoskeleton through three parameters: the partial actin-cytoskeletal deviation (PAD), the total actin-cytoskeletal deviation (TAD) and the average actin-cytoskeletal intensity (AAI). First, Canny and Sobel edge detectors are applied to skeletonize the actin cytoskeleton images, then PAD and TAD are quantified using the direction of lines detected by Hough transform, and AAI is calculated through the summational brightness over the detected cell area. For validation, six different actin cytoskeleton meshwork models were generated to verify the quantification accuracy of IRQ. The average error for both the quantified PAD and TAD was less than 1.22°. Then IRQ was implemented to quantify the actin cytoskeleton of NIH/3T3 cells treated with an F-actin inhibitor. The quantification results suggest that the local and total actin-cytoskeletal organization of treated cells were more disordered than untreated cells, and the quantity of the actin cytoskeleton decreased significantly after the F-actin treatment.
- Dynamic Systems and Control Division
Modeling and Control of Dynamic Cellular Mechanotransduction: Part I — Actin Cytoskeleton Quantification
- Views Icon Views
- Share Icon Share
- Search Site
Liu, Y, & Ren, J. "Modeling and Control of Dynamic Cellular Mechanotransduction: Part I — Actin Cytoskeleton Quantification." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T11A006. ASME. https://doi.org/10.1115/DSCC2018-9180
Download citation file: