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MTrack: automated detection, tracking, and analysis of dynamic microtubules

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Item Type:Article
Title:MTrack: automated detection, tracking, and analysis of dynamic microtubules
Creators Name:Kapoor, V. and Hirst, W.G. and Hentschel, C. and Preibisch, S. and Reber, S.
Abstract: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.
Source:Scientific Reports
ISSN:2045-2322
Publisher:Nature Publishing Group
Volume:9
Number:1
Page Range:3794
Date:7 March 2019
Official Publication:https://doi.org/10.1038/s41598-018-37767-1
PubMed:View item in PubMed
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https://edoc.mdc-berlin.de/17678/Preprint version

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