Back

A phenotypic brain organoid atlas for neurodevelopmental disorders

Wang, L.; Nakamura, Y.; Li, J.; Sievert, D.; Liu, Y.; Nguyen, T.; Jetti, P. S.; Thai, E.; Zhou, R. Y.; Weng, J.; Meave, N.; Yadavilli, M.; Howarth, R.; Camey, K.; Banka, N.; Owusu-Hammond, C.; Barrows, C.; Kingsmore, S. F.; Zaki, M. S.; Mukamel, E. A.; Gleeson, J. G.

2025-09-13 neuroscience
10.1101/2025.09.12.675864 bioRxiv
Show abstract

Thousands of genes are associated with neurodevelopmental disorders (NDDs), yet mechanisms and targeted treatments remain elusive. To fill these gaps, we present a CIRM-initiated NDD biobank of 352 publicly-available genetically-diverse patient-derived iPSCs, along with clinical details, brain imaging and genomic data, representing four major categories of disease: microcephaly (MIC), polymicrogyria (PMG), epilepsy (EPI), and intellectual disability (ID). From 35 representative patients, we studied over 6000 brain organoids for histology and single cell transcriptomics. Compared with an organoid library from ten neurotypicals, patients showed distinct cellular defects linked to underlying clinical disease categories. MIC showed defects in cell survival and excessive TTR+ cells, PMG showed intermediate progenitor cell junction defects, EPI showed excessive astrogliosis, and ID showed excessive generation of TTR+ cells. Our organoid atlas demonstrates both conserved and divergent NDD category-specific phenotypes, bridging genotype and phenotype. This NDD iPSC biobank can support future disease modeling and therapeutic approaches. HIGHLIGHTSO_LIResource of 352 CIRM-funded genetically-diverse IPSC lines from patients with neurodevelopmental disorders (NDDs). C_LIO_LIGenome/exome and brain images available for these genetically-diverse IPSC lines. C_LIO_LIDerived human brain organoids (hBOs) show disease-specific histological and cellular phenotypes. C_LIO_LIhBO phenotypes show unanticipated differentiation towards non-neuronal cell fates in NDDs. C_LI

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.