Protocol for quality control screening of brain organoid morphology

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 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.