Abstract

SummaryLive imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly.Availability and implementationNucliTrack is available as a free, cross platform application and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

  • Live imaging studies are allowing us to explore the previously shrouded world of mammalian cell signalling dynamics

  • Supplementary information: Supplementary data are available at Bioinformatics online

  • Implementations of global optimization approaches to tracking in cell biology research have generally been limited to standalone packages, meaning that segmentation, track inspection and track correction have to be performed in other software which is non-trivial for scientists with little programming expertise (Hilsenbeck et al, 2016)

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Summary

Introduction

Live imaging studies are allowing us to explore the previously shrouded world of mammalian cell signalling dynamics. Imaging populations of live single cells over several days has revealed how signalling dynamics can control key cell fate decisions (Cooper and Bakal, 2017). Hindering the ease, timeperiods and throughput with which live single-cell studies can be performed, are challenges in automatically tracking fluorescently labelled objects (cells, nuclei or organelles) accurately over time periods often exceeding several days, sometimes in highly motile cells (Cabantous et al, 2005).

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