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Abstract
Single particle tracking is a compelling technique for investigating the dynamics of nanoparticles and biological molecules in a broad range of research fields. In particular, recent advances in fluorescence microscopy have made single molecule tracking a prevalent method for studying biomolecules with a high spatial and temporal precision. Particle tracking algorithms have matured over the past three decades into more easily accessible platforms. However, there is an inherent difficulty in tracing particles that have a low signal-to-noise ratio and/or heterogeneous subpopulations. Here, we present a new MATLAB based tracking program which combines the benefits of manual and automatic tracking methods. The program prompts the user to manually locate a particle when an ambiguous situation occurs during automatic tracking. We demonstrate the utility of this program by tracking the movement of β-actin mRNA in the dendrites of cultured hippocampal neurons. We show that the diffusion coefficient of β-actin mRNA decreases upon neuronal stimulation by bicuculline treatment. This tracking method enables an efficient dissection of the dynamic regulation of biological molecules in highly complex intracellular environments.
Highlights
Single molecule techniques using bright fluorophores have been indispensable for studying complex biological systems over the last 20 years
Users need to use their best knowledge to determine which software tool is appropriate for their specific biological problems and should be especially careful when the signal-to-noise ratio (SNR) of their data is lower than 44,7,8
For low-SNR data, more complicated automatic tracking methods such as multi-frame optimization and well-tuned motion models perform better than simpler methods using two-frame linking and nearest-neighbor search[4]
Summary
Single molecule techniques using bright fluorophores have been indispensable for studying complex biological systems over the last 20 years. A number of SPT software packages have been developed using particle detection approaches such as simple thresholding, centroid estimation, and Gaussian model fitting, in combination with various particle linking methods such as simple nearest-neighbor, multiple hypothesis tracking, Kalman filtering, and combinatorial optimization[4,6]. Those automatic software tools often give different tracking results on the same dataset depending on the algorithms and parameter settings, making it challenging to choose the correct tracking result.
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