Abstract
The study of motor protein dynamics within cytoskeletal networks is of high interest to physicists and biologists to understand how the dynamics and properties of individual motors lead to cooperative effects and control of overall network behaviour. Here, we report a method to detect and track muscle myosin II filaments within an actin network tethered to supported lipid bilayers. Based on the characteristic shape of myosin II filaments, this automated tracking routine allowed us to follow the position and orientation of myosin II filaments over time, and to reliably classify their dynamics into segments of diffusive and processive motion based on the analysis of displacements and angular changes between time steps. This automated, high throughput method will allow scientists to efficiently analyse motor dynamics in different conditions, and will grant access to more detailed information than provided by common tracking methods, without any need for time consuming manual tracking or generation of kymographs.
Highlights
Molecular motors are important for many cellular processes such as cell cortex dynamics, cell migration, and the intracellular transport of vesicles
The computational methods described here were developed with the aim of analysing the motion of myosin II filaments containing multiple motor protein domains when bound to an underlying actin filament network
Splitting tracks into regions of diffusive or processive motion allows us to potentially probe the dynamics of different bound states for the myosin II filaments, which could be extended to the study of different biological systems
Summary
Molecular motors are important for many cellular processes such as cell cortex dynamics, cell migration, and the intracellular transport of vesicles. Tracks are generated by comparing the positions, orientations and detected areas of particles between subsequent frames We developed this routine to analyse data for muscle myosin II filament dynamics in a flat, lipid anchored actin filament network [8]. This Python based routine worked robustly in the detection of myosin II filaments in a dynamic and changing actin network, and made it possible to generate tracks of hundreds of myosin II filaments split into regions of diffusive and processive motion. This tracking routine that we have called myoSPT (code can be found at https://github.com/cmcb-warwick/myoSPT) could have wider applications in the tracking of other anisotropic objects such as actin filaments or bacteria, and could be used to characterise their motion after taking into account orientational fluctuations
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