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MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules

Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch, Simone Reber
doi: https://doi.org/10.1101/368191
Varun Kapoor
1Imaging Lab, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
2Quantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
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William G. Hirst
2Quantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
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Christoph Hentschel
2Quantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
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Stephan Preibisch
1Imaging Lab, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
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  • For correspondence: stephan.preibisch@mdc-berlin.de simone.reber@iri-lifesciences.de
Simone Reber
2Quantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
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  • For correspondence: stephan.preibisch@mdc-berlin.de simone.reber@iri-lifesciences.de
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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.

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Posted July 13, 2018.
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MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules
Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch, Simone Reber
bioRxiv 368191; doi: https://doi.org/10.1101/368191
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MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules
Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch, Simone Reber
bioRxiv 368191; doi: https://doi.org/10.1101/368191

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