Kapoor, VarunHirst, WilliamHentschel, ChristophPreibisch, StephanReber, Simone2020-02-182020-02-182045-2322http://hdl.handle.net/1885/201739Microtubules 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.V.K. was supported by the IRI Life Sciences postdoc fellowship in the labs of S.R. and S.P. C.H. and S.R. acknowledge funding by the IRI Life Sciences (Humboldt-Universität zu Berlin, Excellence Initiative/DFG). W.H. was supported by the Alliance Berlin Canberra “Crossing Boundaries: Molecular Interactions in Malaria”, which is co-funded by a grant from the Deutsche Forschungsgemeinschaf (DFG) for the International Research Training Group (IRTG) 2290 and the Australian National University. S.P. was supported by the MDC Berlin.application/pdfen-AU© The Author(s) 2019http://creativecommons.org/licenses/by/4.0/201910.1038/s41598-018-37767-12019-11-25Creative Commons Attribution 4.0 International License