MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch, Simone Reber

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. More... »

PAGES

3794

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-37767-1

DOI

http://dx.doi.org/10.1038/s41598-018-37767-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112604804

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30846705


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