OrgDyn: feature- and model-based characterization of spatial and temporal organoid dynamics

Area of Science:

• Biotechnology
• Developmental Biology
• Computational Biology

Background:

• Organoid model systems mimic mammalian tissues, facilitating high-throughput experiments.
• Current phenotypic analysis methods are often manual, non-standardized, static, or qualitative, limiting experimental impact.

Purpose of the Study:

• To develop an open-source, modular pipeline (OrgDyn) for quantitative analysis of organoid shape dynamics.
• To enable standardized and high-throughput phenotypic analysis of organoid development and disease.

Main Methods:

• OrgDyn employs feature- and model-based approaches on time-series 2D organoid contour images.
• Key steps include geometrical/signal processing feature extraction, dimensionality reduction, time-series clustering, and dynamical modeling using point distribution models.

Main Results:

• The pipeline quantifies organoid shape dynamics, differentiating unique dynamical paths.
• OrgDyn successfully characterizes, clusters, and models diverse temporal shape variations.
• This enables the identification of distinct organoid dynamical behaviors leading to varied final shapes.

Conclusions:

• OrgDyn provides a quantitative framework for analyzing organoid shape dynamics, overcoming limitations of manual analysis.
• The pipeline facilitates a deeper understanding of the molecular basis underlying tissue development and disease progression.
• This tool supports standardized, high-throughput research in organoid biology.