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CShaperApp: Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development. In this study, we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during C. elegans embryogenesis. Based on the previously proposed framework CShaper, CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house dataset. Experimental results show that it takes about 30 min to process a three-dimensional time-lapse (4D) dataset, which consists of 150 image stacks at a ∼1.5-min interval and covers C. elegans embryogenesis from the 4-cell to 350-cell stages. The robustness of CShaperApp is also validated with the datasets from different laboratories. Furthermore, modularized implementation increases the flexibility in multi-task applications and promotes its flexibility for future enhancements. As cell morphology over development has emerged as a focus of interest in developmental biology, CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data collection. The software can be freely downloaded from the website of Github (cao13jf/CShaperApp) and is executable on Windows, macOS, and Linux operating systems. © 2024 The Authors. Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.
Original languageEnglish
Pages (from-to)329-334
JournalQuantitative Biology
Volume12
Issue number3
Online published16 May 2024
DOIs
Publication statusPublished - Sept 2024

Funding

National Natural Science Foundation of China,Grant/Award Numbers: 12090053, 32088101;Hong Kong Innovation and Technology Fund,Grant/Award Numbers: GHP/176/21SZ,InnoHK Project CIMDA; Hong Kong ResearchGrants Council, Grant/Award Numbers:11204821, HKBU12101323, HKBU12101520,HKBU12101522, N_HKBU201/18

Research Keywords

  • cellular segmentation
  • C. elegans embryogenesis
  • Deep learning
  • desktop software
  • cellular morphology

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

  • RGC-funded

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