Quantifying yeast microtubules and spindles using the Toolkit for Automated Microtubule Tracking (TAMiT)

Area of Science:

• Cell Biology
• Biophysics
• Microscopy and Imaging

Background:

• Accurate quantification of microtubule dynamics and organization is crucial for understanding cellular processes.
• Current image analysis methods for fluorescent microtubules in living cells are often manual and limited, especially at low signal-to-noise ratios.
• Automated analysis tools developed for in vitro studies do not always translate effectively to complex cellular environments.

Approach:

• We present the Toolkit for Automated Microtubule Tracking (TAMiT), a novel software for automatic detection, optimization, and tracking of fluorescent microtubules in yeast cells.
• TAMiT employs a geometrical scanning technique and non-linear least squares fitting in MATLAB to achieve sub-pixel accuracy for linear and curved polymers.
• The software is benchmarked using simulated images to ensure reliable detection even with low signal-to-noise ratios.

Key Points:

• TAMiT successfully measures microtubule bundle number, length, and lifetime in S. pombe mutants, revealing a role for CLASP/Cls1 in spindle microtubule bundling.
• Automated tracking of curved astral microtubules in S. cerevisiae allows for measurement of dynamic instability parameters.
• Results from TAMiT align with previous manual measurements, demonstrating its efficacy in cellular microtubule analysis.

Conclusions:

• TAMiT provides a robust and automated solution for analyzing microtubule dynamics and organization in living yeast cells.
• The software overcomes limitations of manual analysis and low signal-to-noise ratios inherent in cellular imaging.
• TAMiT facilitates high-throughput quantitative analysis, advancing research in microtubule-related cellular functions and dynamics.