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

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 analyzi...

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Published in:Scientific reports 2019-03, Vol.9 (1), p.3794-3794, Article 3794
Main Authors: Kapoor, Varun, Hirst, William G., Hentschel, Christoph, Preibisch, Stephan, Reber, Simone
Format: Article
Language:English
Subjects:
631/114/1564
631/553/794
Automation
Data interpretation
Data processing
Filaments
Fluorescence microscopy
Humanities and Social Sciences
Microtubules
multidisciplinary
Population statistics
Science
Science (multidisciplinary)
Statistical analysis
Tubulin
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cited_by cdi_FETCH-LOGICAL-c474t-be938866159f75591497ff04c256a133adcc8ee068457942cd07021f18d92a2f3
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creator Kapoor, Varun
Hirst, William G.
Hentschel, Christoph
Preibisch, Stephan
Reber, Simone
description 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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subjects 631/114/1564
631/553/794
Automation
Data interpretation
Data processing
Filaments
Fluorescence microscopy
Humanities and Social Sciences
Microtubules
multidisciplinary
Population statistics
Science
Science (multidisciplinary)
Statistical analysis
Tubulin
title MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules
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