Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning

Area of Science:

• * Computational biology
• * Image analysis
• * Microscopy

Background:

• * Phenotypic profiling of large 3D microscopy datasets is limited by cell segmentation and feature selection challenges.
• * Computational demands hinder automated analysis of difficult-to-segment images, such as neurons and organoids.
• * Existing methods struggle with the complexity and heterogeneity of 3D biological data.

Purpose of the Study:

• * To introduce a comprehensive shallow-learning framework for automated quantitative phenotyping of 3D image data.
• * To enable computationally facile classification, clustering, and advanced data visualization of complex 3D microscopy images.
• * To address limitations in analyzing hard-to-segment biological structures.

Main Methods:

• * Development of a shallow-learning framework utilizing unsupervised, data-driven voxel-based feature learning.
• * Implementation of novel image analysis algorithms within the Phindr3D software.
• * Application to analyze phenotypic alterations in neurons and human mammary gland acinar organoids.

Main Results:

• * Demonstrated successful automated quantitative phenotyping of complex 3D image data.
• * Enabled computationally facile classification, clustering, and visualization of biological heterogeneity.
• * Validated the framework on neuronal responses to apoptosis and organoid morphogenesis.

Conclusions:

• * The Phindr3D framework offers a practical advance for 3D high-content analysis.
• * Data-driven voxel-based feature learning effectively captures biological insights from complex 3D images.
• * The approach preserves data heterogeneity, providing a more comprehensive understanding of biological phenotypes.