An Automated Analysis Of Single Particle Tracking Data For Proteins That Exhibit Multi Component Motion.

• Main Author: Ali, Rehan
• Format: Others
• Language: en
• Published: ScholarWorks @ UVM 2018
• Subjects: Data Analysis; Kinesin Motors; Matlab; Particle Motion; Single Particle Tracking; Tau Protein; Neuroscience and Neurobiology
• Online Access: https://scholarworks.uvm.edu/graddis/870; https://scholarworks.uvm.edu/cgi/viewcontent.cgi?article=1870&context=graddis

Neurons are polarized cells with dendrites and an axon projecting from their cell body. Due to this polarized structure a major challenge for neurons is the transport of material to and from the cell body. The transport that occurs between the cell body and axons is called Axonal transport. Axonal transport has three major components: molecular motors which act as vehicles, microtubules which serve as tracks on which these motors move and microtubule associated proteins which regulate the transport of material. Axonal transport maintains the integrity of a neuron and its dysfunction is linked to neurodegenerative diseases such as, Alzheimer’s disease, Frontotemporal dementia linked to chromosome 17 and Pick’s disease. Therefore, understanding the process of axonal transport is extremely important. Single particle tracking is one method in which axonal transport is studied. This involves fluorescent labelling of molecular motors and microtubule associated proteins and tracking their position in time. Single particle tracking has shown that both, molecular motors and microtubule associated proteins exhibit motion with multiple components. These components are directed, where motion is in one direction, diffusive, where motion is random, and static, where there is no motion. Moreover, molecular motors and microtubule associated proteins also switch between these different components in a single instance of motion. We have developed a MATLAB program, called MixMAs, which specializes in analyzing the data provided by single particle tracking. MixMAs uses a sliding window approach to analyze trajectories of motion. It is capable of distinguishing between different components of motion that are exhibited by molecular motors and microtubule associated proteins. It also identifies transitions that take place between different components of motion. Most importantly, it is not limited by the number of transitions and the number of components present in a single trajectory. The analysis results provided by MixMAs include all the necessary parameters required for a complete characterization of a particle’s motion. These parameters are the number of different transitions that take place between different components of motion, the dwell times of different components of motion, velocity for directed component of motion and diffusion coefficient for diffusive component of motion. We have validated the working of MixMAs by simulating motion of particles which show all three components of motion with all the possible transitions that can take place between them. The simulations are performed for different values of error in localizing the position of a particle. The simulations confirm that MixMAs accurately calculates parameters of motion for a range of localization errors. Finally, we show an application of MixMAs on experimentally obtained single particle data of Kinesin-3 motor.