Spencer Brown, Daniel Chao, Zhixiang Tong, Rishi Bedi, Justin Nicola, Anthony Batarse, Jordan M. Sorokin, Julia Bergamaschi, Kelly Li, Arden Piepho, Shiron Drusinsky, David Grayson, Austin McKay, Brenda Dang, Oliver Wueseke, Brian G. Rash, Matthew Schultz, Geffen Treiman, Carlos Castrillo, Alex Rogozhnikov, Pei-Ken Hsu, Andy Lash, Juliana Hilliard, Noah Young, Deborah Pascoe, Elliot Mount, Luigi Enriquez, Morgan M. Stanton, Patrick A. Taylor, G. Sean Escola, Saul Kato, Pavan Ramkumar, Ismael Oumzil, Cagsar Apaydin, Doug Flanzer, Kevan Shah, Jessica Sims, Robert Blattner, Gaia Skibinski, Justin Paek, Sean Poust, Alex Pollen, Daphne Quang, Ryan Jones, Chia-Yao Lee, Chili Johnson, and Anthony Bosshardt
Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/− forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.