Dataset Information

SpheresDT/Mpacts-PiCS: Cell Tracking and Shape Retrieval in Membrane-labeled Embryos.

ABSTRACT: Uncovering the cellular and mechanical processes that drive embryo formation requires an accurate read out of cell geometries over time. However, automated extraction of 3D cell shapes from time lapse microscopy remains challenging, especially when only membranes are labeled. We present an image analysis framework for automated tracking and three-dimensional cell segmentation in confocal time lapses. A sphere clustering approach allows for local thresholding and application of logical rules to facilitate tracking and unseeded segmentation of variable cell shapes. Next, the segmentation is refined by a discrete element method simulation where cell shapes are constrained by a biomechanical cell shape model. We apply the framework on C. elegans embryos in various stages of early development and analyse the geometry of the - and 8-cell stage embryo, looking at volume, contact area and shape over time. The Python code for the algorithm and for measuring performance, along with all data needed to recreate the results is freely available at 10.5281/zenodo.5108416 and 10.5281/zenodo.4540092. The most recent version of the software is maintained at https://bitbucket.org/pgmsembryogenesis/sdt-pics. Supplementary data are available at Bioinformatics online.

SUBMITTER: Thiels W

PROVIDER: S-EPMC8665764 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

spheresDT/Mpacts-PiCS: cell tracking and shape retrieval in membrane-labeled embryos.

Thiels Wim W Smeets Bart B Cuvelier Maxim M Caroti Francesca F Jelier Rob R

Bioinformatics (Oxford, England) 20211201 24

<h4>Motivation</h4>Uncovering the cellular and mechanical processes that drive embryo formation requires an accurate read out of cell geometries over time. However, automated extraction of 3D cell shapes from time-lapse microscopy remains challenging, especially when only membranes are labeled.<h4>Results</h4>We present an image analysis framework for automated tracking and three-dimensional cell segmentation in confocal time lapses. A sphere clustering approach allows for local thresholding and ...[more]

PMID: 34329378

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Project description:BackgroundRecent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments.ResultsOur new framework BCOMS (Biologically Constrained Optimization based cell Membrane Segmentation) automates the extraction of the cell shape of C. elegans embryos. Both the segmentation and evaluation processes are automated. To automate the evaluation, we solve an optimization problem under biological constraints. The performance of BCOMS was validated against a manually created ground truth of the 24-cell stage embryo. The average deviation of 25 cell shape features was 5.6%. The deviation was mainly caused by membranes parallel to the focal planes, which either contact the surfaces of adjacent cells or make no contact with other cells. Because segmentation of these membranes was difficult even by manual inspection, the automated segmentation was sufficiently accurate for cell shape analysis. As the number of manually created ground truths is necessarily limited, we compared the segmentation results between two adjacent time points. Across all cells and all cell cycles, the average deviation of the 25 cell shape features was 4.3%, smaller than that between the automated segmentation result and ground truth.ConclusionsBCOMS automated the accurate extraction of cell shapes in developing C. elegans embryos. By replacing image processing parameters with easily adjustable biological constraints, BCOMS provides a user-friendly framework. The framework is also applicable to other model organisms. Creating the biological constraints is a critical step requiring collaboration between an experimentalist and a software developer.

Dataset Information

SpheresDT/Mpacts-PiCS: Cell Tracking and Shape Retrieval in Membrane-labeled Embryos.

Publications

spheresDT/Mpacts-PiCS: cell tracking and shape retrieval in membrane-labeled embryos.

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