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Peer-reviewedCC BY 4.0eLife

A quantitative pipeline for whole-mount deep imaging and multiscale analysis of gastruloids

  • Alice Gros1
  • Jules Vanaret1,2,3
  • Valentin Dunsing1
  • Agathe Rostan1
  • Philippe Roudot4,2,3
  • Pierre‐François Lenne1
  • Léo Guignard1
  • Sham Tlili1
4 affiliations
  1. 1Aix Marseille Univ, CNRS, IBDM (UMR 7288), Turing Centre for Living systems, Marseille, France
  2. 2Aix Marseille University, CNRS, Centrale Marseille, I2M (UMR 7373), Turing Centre for Living Systems, Marseille, France
  3. 3Aix Marseille University, CNRS, Centrale Marseille, IM (UMR 7373), Turing Centre for Living Systems, Marseille, France
  4. 4Aix Marseille University, CNRS, Centrale Marseille, FRESNEL (UMR 7249), Turing Centre for Living systems, Marseille, France
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Abstract

Whole-mount 3D imaging at the cellular scale is a powerful tool for exploring the complex processes of organoid morphogenesis, as it allows examining tissue architecture, cell types, and morphology simultaneously in 3D models. To address the challenges of whole-mount imaging in dense and diffusive tissues, we focused on gastruloids—3D embryonic organoids derived from mouse embryonic stem cells that replicate key aspects of in vivo organogenesis and present significant imaging challenges. We developed an integrated two-photon microscopy pipeline to image immunostained and cleared gastruloids in toto , achieving detailed visualization at both cellular and whole-mount scale. Our pipeline, implemented in Python with a Napari interface, includes image preprocessing to correct optical artifacts and segment individual cell nuclei. This enables comprehensive quantitative analysis of gene expression patterns, cellular events, and morphologies across multiple spatial scales using an interactive post-processing pipeline. This approach not only advances our understanding of gastruloids development but also lays the groundwork for applying these methods to other organoid and tumoroid systems, enhancing our ability to study a wide range of 3D biological models.

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Cite

BibTeXRIS
BibTeX
@article{gros2026quantitative,
  title = {{A quantitative pipeline for whole-mount deep imaging and multiscale analysis of gastruloids}},
  author = {Alice Gros and Jules Vanaret and Valentin Dunsing and Agathe Rostan and Philippe Roudot and Pierre‐François Lenne and Léo Guignard and Sham Tlili},
  journal = {eLife},
  year = {2026},
  month = may,
  doi = {10.7554/elife.107154.3},
  url = {https://hal.science/hal-04799747v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A quantitative pipeline for whole-mount deep imaging and multiscale analysis of gastruloids
AU  - Alice Gros
AU  - Jules Vanaret
AU  - Valentin Dunsing
AU  - Agathe Rostan
AU  - Philippe Roudot
AU  - Pierre‐François Lenne
AU  - Léo Guignard
AU  - Sham Tlili
PY  - 2026
DA  - 2026/05/22
JO  - eLife
DO  - 10.7554/elife.107154.3
UR  - https://hal.science/hal-04799747v1
AB  - Whole-mount 3D imaging at the cellular scale is a powerful tool for exploring the complex processes of organoid morphogenesis, as it allows examining tissue architecture, cell types, and morphology simultaneously in 3D models. To address the challenges of whole-mount imaging in dense and diffusive tissues, we focused on gastruloids—3D embryonic organoids derived from mouse embryonic stem cells that replicate key aspects of in vivo organogenesis and present significant imaging challenges. We developed an integrated two-photon microscopy pipeline to image immunostained and cleared gastruloids in toto , achieving detailed visualization at both cellular and whole-mount scale. Our pipeline, implemented in Python with a Napari interface, includes image preprocessing to correct optical artifacts and segment individual cell nuclei. This enables comprehensive quantitative analysis of gene expression patterns, cellular events, and morphologies across multiple spatial scales using an interactive post-processing pipeline. This approach not only advances our understanding of gastruloids development but also lays the groundwork for applying these methods to other organoid and tumoroid systems, enhancing our ability to study a wide range of 3D biological models.
SN  - 2050-084X
ER  -