BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience

Area of Science:

• Stem cell biology
• Neuroscience
• Bioinformatics

Background:

• Human-induced pluripotent stem cells (iPSCs) and derived organoids offer advanced models for biomedical research.
• Brain organoids (BO) are crucial for drug screening and disease modeling, necessitating accurate morphological assessment.
• Existing tools lack comprehensive, time-resolved analysis for BO morphology.

Purpose of the Study:

• Introduce BrAIn, a deep learning application for analyzing brain organoid developmental progression.
• Enable researchers to quantify BO morphology and identify key developmental features.
• Evaluate the impact of different culture conditions on BO development using BrAIn.

Main Methods:

• Developed BrAIn, a no-code deep learning application for BO morphological analysis.
• BrAIn tracks EB to BO evolution, quantifying parameters like area, diameter, and circularity.
• Classifies 3D organoid morphologies and detects neural rosette structures.

Main Results:

• BrAIn successfully quantifies BO morphology and developmental progression.
• Orbital shaker cultures yielded larger BOs; chip-based systems showed more homogeneous growth.
• Both dynamic culture methods enhanced morphological complexity compared to static cultures.

Conclusions:

• BrAIn is a robust, user-friendly tool for BO morphological analysis.
• The application facilitates the study of culture condition effects on BO development.
• BrAIn supports research into optimizing BO maturation for various applications.