A random-sampling approach to track cell divisions in time-lapse fluorescence microscopy

Saoirse Amarteifio,Giovanni Sena,Todd Fallesen,Gunnar Pruessner

doi:10.1186/s13007-021-00723-8

Saoirse Amarteifio, Giovanni Sena + Show 2 more

Open Access

https://doi.org/10.1186/s13007-021-00723-8

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Abstract

BackgroundParticle-tracking in 3D is an indispensable computational tool to extract critical information on dynamical processes from raw time-lapse imaging. This is particularly true with in vivo time-lapse fluorescence imaging in cell and developmental biology, where complex dynamics are observed at high temporal resolution. Common tracking algorithms used with time-lapse data in fluorescence microscopy typically assume a continuous signal where background, recognisable keypoints and independently moving objects of interest are permanently visible. Under these conditions, simple registration and identity management algorithms can track the objects of interest over time. In contrast, here we consider the case of transient signals and objects whose movements are constrained within a tissue, where standard algorithms fail to provide robust tracking.ResultsTo optimize 3D tracking in these conditions, we propose the merging of registration and tracking tasks into a registration algorithm that uses random sampling to solve the identity management problem. We describe the design and application of such an algorithm, illustrated in the domain of plant biology, and make it available as an open-source software implementation. The algorithm is tested on mitotic events in 4D data-sets obtained with light-sheet fluorescence microscopy on growing Arabidopsis thaliana roots expressing CYCB::GFP. We validate the method by comparing the algorithm performance against both surrogate data and manual tracking.ConclusionThis method fills a gap in existing tracking techniques, following mitotic events in challenging data-sets using transient fluorescent markers in unregistered images.

Highlights

Particle-tracking in 3D is an indispensable computational tool to extract critical information on dynamical processes from raw time-lapse imaging
It is significantly more challenging to track an intermittent or transient signal, as in the case of cell divisions or other short-lived events [19,20,21], or a signal distributed in 3D space like in confocal or light-sheet microscopy stacks
The resulting fluorescent reporter CYCB1::GFP accumulates in cells transitioning between G2 and M phases of the cell cycle, and is quickly degraded after entering mitosis [33] and for this reasons it is widely adopted as a reliable live marker for mitotic events

Summary

Introduction

Particle-tracking in 3D is an indispensable computational tool to extract critical information on dynamical processes from raw time-lapse imaging. This is true with in vivo time-lapse fluorescence imaging in cell and developmental biology, where complex dynamics are observed at high temporal resolution. Common tracking algorithms used with time-lapse data in fluorescence microscopy typically assume a continuous signal where background, recognisable keypoints and independently moving objects of interest are permanently visible. It is generally understood that automated imaging and tracking methods can exceed manual tracking of objects in large data-sets These solutions are of growing importance in life science applications, where time-lapse microscopy is a powerful and popular tool for capturing dynamics at cellular and sub-cellular scales [1]. It is significantly more challenging to track an intermittent or transient signal, as in the case of cell divisions or other short-lived events [19,20,21], or a signal distributed in 3D space like in confocal or light-sheet microscopy stacks.

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