Three-Dimensional Morphological Characterisation of Human Cortical Organoids Using a Customised Image Analysis Workflow

• Published in: Organoids 2025-01, Vol.4 (1), p.1
• Main Authors: Handcock, Sarah, Richards, Kay, Karle, Timothy J., Kairath, Pamela, Soch, Alita, Chavez, Carolina A., Petrou, Steven, Maljevic, Snezana
• Format: Article
• Language: English
• ISSN: 2674-1172; 2674-1172
• DOI: 10.3390/organoids4010001

Summary Statement: A tailored image analysis workflow was applied to quantify cortical organoid health, development, morphology and cellular composition over time. The assessment of cellular composition and viability of stem cell-derived organoid models is a complex but essential approach to understanding the mechanisms of human development and disease. Aim: Our study was motivated by the need for an image-analysis workflow, including high-cell content, high-throughput methods, to measure the architectural features of developing organoids. We assessed stem cell-derived cortical organoids at 4 and 6 months post-induction using immunohistochemistry-labelled sections as the analysis testbed. The workflow leveraged fluorescence imaging tailored to classify cells as viable and dying or non-viable and assign neuronal and astrocytic perinuclear markers to count cells. Results/Outcomes: Image acquisition was accelerated by capturing the organoid slice in 3D using widefield-fluorescence microscopy. This method used computational clearing to resolve nuclear and perinuclear markers and retain their spatial information within the organoid’s heterogeneous structure. The customised workflow analysed over 1.5 million cells using DAPI-stained nuclei, filtering and quantifying viable and non-viable cells and the necrotic-core regions. Temporal analyses of neuronal cell number derived from perinuclear labelling were consistent with organoid maturation from 4 to 6 months of in vitro differentiation. Overall: We have provided a comprehensive and enhanced image analysis workflow for organoid structural evaluation, creating the ability to gather cellular-level statistics in control and disease models.