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CentTracker: a trainable, machine learning-based tool for large-scale analyses of C. elegans germline stem cell mitosis

M. Réda Zellag, Yifan Zhao, Vincent Poupart, View ORCID ProfileRamya Singh, Jean-Claude Labbé, View ORCID ProfileAbigail R. Gerhold
doi: https://doi.org/10.1101/2020.11.22.393272
M. Réda Zellag
1Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada
3Department of Biology, McGill University, 1205 avenue Docteur Penfield, Montréal, QC, H2A 1B1, Canada
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Yifan Zhao
3Department of Biology, McGill University, 1205 avenue Docteur Penfield, Montréal, QC, H2A 1B1, Canada
4Harvard-MIT Health Sciences and Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States
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Vincent Poupart
1Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada
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Ramya Singh
3Department of Biology, McGill University, 1205 avenue Docteur Penfield, Montréal, QC, H2A 1B1, Canada
1Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada
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Jean-Claude Labbé
1Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada
2Department of Pathology and Cell Biology, Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada
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Abigail R. Gerhold
3Department of Biology, McGill University, 1205 avenue Docteur Penfield, Montréal, QC, H2A 1B1, Canada
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  • ORCID record for Abigail R. Gerhold
  • For correspondence: abigail.gerhold@mcgill.ca jc.labbe@umontreal.ca
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Abstract

Investigating the complex interactions between stem cells and their native environment requires an efficient means to image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term image acquisition and analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable image analysis tool that uses machine learning to pair mitotic centrosomes and which can extract a variety of mitotic parameters rapidly from large-scale datasets. We employ CentTracker to assess a range of mitotic features in GSCs and show that subpopulations with distinct mitotic profiles are unlikely to exist within the stem cell pool. We further find evidence for spatial clustering of GSC mitoses within the germline tissue and for biases in mitotic spindle orientation relative to the germline’s distal-proximal axis, and thus the niche. The technical and analytical tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 22, 2020.
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CentTracker: a trainable, machine learning-based tool for large-scale analyses of C. elegans germline stem cell mitosis
M. Réda Zellag, Yifan Zhao, Vincent Poupart, Ramya Singh, Jean-Claude Labbé, Abigail R. Gerhold
bioRxiv 2020.11.22.393272; doi: https://doi.org/10.1101/2020.11.22.393272
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CentTracker: a trainable, machine learning-based tool for large-scale analyses of C. elegans germline stem cell mitosis
M. Réda Zellag, Yifan Zhao, Vincent Poupart, Ramya Singh, Jean-Claude Labbé, Abigail R. Gerhold
bioRxiv 2020.11.22.393272; doi: https://doi.org/10.1101/2020.11.22.393272

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