PT  - JOURNAL ARTICLE
AU  - Varun Kapoor
AU  - William G. Hirst
AU  - Christoph Hentschel
AU  - Stephan Preibisch
AU  - Simone Reber
TI  - MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules
AID  - 10.1101/368191
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 368191
4099  - http://biorxiv.org/content/early/2018/07/13/368191.short
4100  - http://biorxiv.org/content/early/2018/07/13/368191.full
AB  - Microtubules are polar, dynamic filaments fundamental to many cellular processes. In vitro reconstitution approaches with purified tubulin are essential to elucidate different aspects of microtubule behavior. To date, deriving data from fluorescence microscopy images by manually creating and analyzing kymographs is still commonplace. Here, we present MTrack, implemented as a plug-in for the open-source platform Fiji, which automatically identifies and tracks dynamic microtubules with sub-pixel resolution using advanced objection recognition. MTrack provides automatic data interpretation yielding relevant parameters of microtubule dynamic instability together with population statistics. The application of our software produces unbiased and comparable quantitative datasets in a fully automated fashion. This helps the experimentalist to achieve higher reproducibility at higher throughput on a user-friendly platform. We use simulated data and real data to benchmark our algorithm and show that it reliably detects, tracks, and analyzes dynamic microtubules and achieves sub-pixel precision even at low signal-to-noise ratios.