# Protocol for quality control screening of brain organoid morphology

## TL;DR

This paper introduces a method for quality control of brain organoids using image analysis to assess their shape and structure.

## Contribution

A semi-automated, protocol-agnostic pipeline for organoid morphology-based quality control is presented.

## Key findings

- A reference dataset of brain organoids with complex morphology is provided.
- Unbiased sample selection is achieved using k-means clustering of morphological features.
- Steps for image analysis and clustering are detailed for reproducible quality screening.

## Abstract

Neural organoids can exhibit variability in both tissue shape and tissue identity. Here, we present a pipeline for rapid, protocol-agnostic quality control screening of brain organoids based on their overall gross morphology. We describe a semi-automated image analysis of organoid size, shape, and texture from 2D bright-field imaging. We provide a reference dataset of brain organoids with complex morphology. We show how to integrate input and reference organoids and perform the unbiased sample selection by k-means clustering.

For complete details on the use and execution of this protocol, please refer to Chiaradia et al.1

•Instructions for bright-field imaging of neural organoids for morphological analysis•Steps for semi-automated image analysis to select organoids based on morphology•Training data of organoids with good morphology from different protocols•Code for morphospace generation and supervised clustering analysis

Instructions for bright-field imaging of neural organoids for morphological analysis

Steps for semi-automated image analysis to select organoids based on morphology

Training data of organoids with good morphology from different protocols

Code for morphospace generation and supervised clustering analysis

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Neural organoids can exhibit variability in both tissue shape and tissue identity. Here, we present a pipeline for rapid, protocol-agnostic quality control screening of brain organoids based on their overall gross morphology. We describe a semi-automated image analysis of organoid size, shape, and texture from 2D bright-field imaging. We provide a reference dataset of brain organoids with complex morphology. We show how to integrate input and reference organoids and perform the unbiased sample selection by k-means clustering.