The development of brain organoids represents a significant breakthrough in modeling human neurodevelopment and neurological disorders. However, their utility has been limited by variability between preparations and challenges in systematic characterization. Here, I discuss how automated analysis of single-cell RNA sequencing (scRNA-seq) and microelectrode array (MEA) recordings provides a crucial framework for improving brain organoid reproducibility and functional assessment. High-throughput single-cell transcriptomics combined with MEA recordings generates complementary datasets that capture both molecular states and network-level activity patterns. The integration of these data modalities enables the correlation of gene expression patterns with functional network properties, providing unprecedented insights into the relationship between molecular identity and circuit behavior. Automated analysis pipelines enable objective comparison between organoid preparations and with reference brain tissue, facilitating quality control and standardization. The ability to correlate gene expression programs with emergent network properties advances our understanding of how transcriptional states influence circuit formation and function. This systematic approach to organoid analysis, combining modern deep learning architectures with multiple data modalities, promises to accelerate their application in disease modeling, drug screening, and fundamental neuroscience research while ensuring reproducibility and physiological relevance.

Event Host: Jesus Gonzalez-Ferrer, Ph.D. Student, Biomolecular Engineering & Bioinformatics

Advisor: David Haussler