Waisman investigators receive NIH funding to optimize the use of brain organoids in research
By Charlene N. Rivera-Bonet | Waisman Science Writer
A newly funded project will combine the expertise of four Waisman Center investigators to develop advanced machine learning methods for studying brain organoids, three-dimensional cell clusters grown from stem cells that closely mimic key aspects of human brain development. Funded by the National Institutes of Health (NIH), the project aims to improve how researchers analyze brain organoid data, leading to more accurate in-vitro models of brain development and disease that enable further investigating underlying cellular and molecular mechanisms.
Brain organoids have emerged as powerful tools for neuroscience research because they recreate many of the cellular interactions and developmental processes that occur in the brain. Derived from human pluripotent stem cells, these miniature brain-like structures offer unprecedented opportunities to study how the brain develops and what goes wrong in neurological and neurodevelopmental disorders. Although their ability to closely mimic human brain development opens up a plethora of new possibilities for research, organoids are relatively new and highly complex. Researchers still need better ways to evaluate how faithfully they reflect the biology of the developing human brain, and to analyze the vast amounts of data they generate.
The interdisciplinary nature of the project, requiring expertise in machine learning, stem cells and organoids, genomics, and neuroscience, demands collaboration. Waisman investigators Daifeng Wang, PhD, H.I. Romnes Associate Professor of biostatics and medical informatics, and computer sciences, Xinyu Zhao, PhD, Jenni & Kyle Professor in Novel Neurodevelopmental Diseases, André Sousa, PhD, associate professor of neuroscience, and Qiang Chang, PhD, professor of medical genetics and neurology and director of the Waisman Center are combining their expertise, serving as co-investigators with Wang and Zhao leading the project.
Their first aim is to create machine learning, artificial intelligence (AI) tools that will allow them to uncover the ways developmental stages are conserved or diverge between brain organoids and the human brain, leveraging emerging multimodal single-cell data. These tools will help researchers understand how reliably organoids model human brain development.
Their second aim looks to evaluate the fidelity of current organoid generation protocols through gene regulatory network prediction and analysis capturing how dynamically genes coordinate during development. As the use of brain organoids continues to expand, the lack of standardized methods has become a significant challenge. By assessing current protocols, the researchers hope to advance rigor, reproducibility, and consistency across the field.
Third, the team will develop open-source tools including web apps for comprehensive evaluation of users’ brain organoids that could also be used by other researchers and support the broader scientific community to accelerate discoveries in neuroscience.
Ultimately, this NIH-funded project may enhance how brain organoid data is integrated and analyzed to allow for a deeper understanding of brain cells and their function through development. The work has the potential to strengthen the use of organoids as models of human brain development and to expand their value in studying neurological and neurodevelopmental disorders.