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4D light sheet imaging, computational reconstruction, and cell tracking in mouse embryos.

Martin H Dominguez1, Jonathon M Muncie-Vasic2, Benoit G Bruneau3

  • 1Gladstone Institutes, San Francisco, CA, USA; Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA; Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA.

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|January 4, 2025
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Summary

This study presents a workflow for analyzing time-lapse light sheet fluorescence microscopy (LSFM) data from mouse embryos. The method enables detailed cellular-level understanding of developmental processes using open-source tools.

Keywords:
computer sciencesdevelopmental biologymicroscopy

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Area of Science:

  • Developmental Biology
  • Microscopy Techniques
  • Computational Biology

Background:

  • Light Sheet Fluorescence Microscopy (LSFM) is increasingly accessible for biological research.
  • Time-lapse imaging is crucial for understanding dynamic cellular processes.
  • Analyzing complex, large-scale imaging data presents a significant challenge.

Purpose of the Study:

  • To establish a comprehensive workflow for in toto imaging, processing, and analysis of multi-view LSFM data.
  • To utilize the ex vivo mouse embryo as a model system for developmental studies.
  • To enable quantification of cellular migration and morphodynamics.

Main Methods:

  • Imaging of ex vivo mouse embryos using a commercial LSFM instrument.
  • Computational analysis of image data in discrete segments.
  • Utilizing open-source software for data processing and analysis.

Main Results:

  • A complete protocol for multi-view LSFM data acquisition and processing is detailed.
  • The workflow facilitates the analysis of developmental processes at cellular resolution.
  • Quantification of cellular migration and morphodynamics is demonstrated.

Conclusions:

  • The presented workflow provides a robust method for analyzing complex LSFM time-lapse data.
  • This approach enhances the understanding of developmental biology using advanced microscopy.
  • Open-source tools make advanced LSFM analysis more accessible to researchers.