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Peer-reviewedCC BY-NCJournal of microscopy

Reviewing reproducibility and replicability in 'Deconstructing gastrulation at single-cell resolution' by Stern et al. (2022).

  • Gabriel G Martins1
  • Haydee O Hernández2
  • Lachlan Whitehead3,4
  • Pradeep Rajasekhar3,4
4 affiliations
  1. 1GIMM - Gulbenkian Institute for Molecular Medicine, Oeiras, Portugal.
  2. 2Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, Mexico.
  3. 3Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
  4. 4Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia.
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Abstract

Quantitative analysis of embryonic morphogenesis often requires handling terabyte-scale 4D microscopy datasets, posing major computational and methodological challenges. Such analyses involve complex workflows for image restoration, segmentation, and tracking of thousands of cells in 3D, sometimes assembled from multiple generic tools and custom scripts. In their study 'Deconstructing gastrulation at single-cell resolution,' the authors present an integrated computational framework for cell segmentation, tracking and modelling of cell-cell interactions during Drosophila gastrulation. Their approach combines both generic tools such as ilastik, FIJI, Cellpose, MATLAB code and tools such as ImSAnE to reconstruct whole-embryo, single-cell morphogenetic atlas with unprecedented detail. While the methodology is transparent and well-documented, its implementation relies on multiple software environments and some non-open-source components, limiting full reproducibility and generalisability. Nevertheless, their 'computational framework' exemplifies rigorous data sharing, clear documentation, and methodological transparency aligned with several community guidelines for reproducible image analysis. We discuss this work in the context of reproducibility, replicability, generalisability, transparency, validity, and robustness, emphasising how emerging open-source and popular solutions could improve accessibility and replicability of similar large-scale bioimage analysis workflows in developmental biology.

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Cite

BibTeXRIS
BibTeX
@article{martins2026reviewing,
  title = {{Reviewing reproducibility and replicability in 'Deconstructing gastrulation at single-cell resolution' by Stern et al. (2022).}},
  author = {Gabriel G Martins and Haydee O Hernández and Lachlan Whitehead and Pradeep Rajasekhar},
  journal = {Journal of microscopy},
  year = {2026},
  month = oct,
  doi = {10.1111/jmi.70132},
  url = {https://europepmc.org/article/MED/42867836},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Reviewing reproducibility and replicability in 'Deconstructing gastrulation at single-cell resolution' by Stern et al. (2022).
AU  - Gabriel G Martins
AU  - Haydee O Hernández
AU  - Lachlan Whitehead
AU  - Pradeep Rajasekhar
PY  - 2026
DA  - 2026/10/13
JO  - Journal of microscopy
DO  - 10.1111/jmi.70132
UR  - https://europepmc.org/article/MED/42867836
AB  - Quantitative analysis of embryonic morphogenesis often requires handling terabyte-scale 4D microscopy datasets, posing major computational and methodological challenges. Such analyses involve complex workflows for image restoration, segmentation, and tracking of thousands of cells in 3D, sometimes assembled from multiple generic tools and custom scripts. In their study 'Deconstructing gastrulation at single-cell resolution,' the authors present an integrated computational framework for cell segmentation, tracking and modelling of cell-cell interactions during Drosophila gastrulation. Their approach combines both generic tools such as ilastik, FIJI, Cellpose, MATLAB code and tools such as ImSAnE to reconstruct whole-embryo, single-cell morphogenetic atlas with unprecedented detail. While the methodology is transparent and well-documented, its implementation relies on multiple software environments and some non-open-source components, limiting full reproducibility and generalisability. Nevertheless, their 'computational framework' exemplifies rigorous data sharing, clear documentation, and methodological transparency aligned with several community guidelines for reproducible image analysis. We discuss this work in the context of reproducibility, replicability, generalisability, transparency, validity, and robustness, emphasising how emerging open-source and popular solutions could improve accessibility and replicability of similar large-scale bioimage analysis workflows in developmental biology.
SN  - 0022-2720
SN  - 1365-2818
ER  -