Year: 2026
Type: article
Abstract: Organoids, 3D tissue cultures that mimic real organs, offer valuable models for research. Traditional culture methods rely on manual feeding and orbital shakers, making them labor-intensive and inconsistent. Microfluidic systems have shown their potential to improve reproducibility by controlling media exchange and culture conditions, yet most still require standard incubators, which limit continuous monitoring due to space and humidity constraints. To address this, we developed a modular platform that integrates automated feeding, real-time imaging, and environmental control, eliminating the need for a conventional incubator. A key feature is a vertically oriented PDMS/glass chip that supports precise media delivery and monitoring while preserving incubation conditions, making it ideal for morphological studies. We demonstrated the platform's ability to maintain metabolic stability and media distribution over time using cerebral organoids. This platform improves organoid research by combining microfluidics, automation, and imaging, enhancing disease modeling, drug testing, and regenerative medicine applications.
Source: Scientific Reports
Institution: University of California, Santa Cruz
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: NSF2026: EAGER: The evolution and diversity of the human brain, EFRI BEGIN OI: Reinforcement Learning for Scalable Biocomputing, Deep phenotyping of human brain organoid models of autism spectrum disorder to unravel disease heterogeneity and develop biomarkers and treatments, Defining Neurovascular Metabolism in Neurodevelopmental and Neuropsychiatric Disorders, RECODE-ing the Logic of Ectodermal Organoid Patterning via Combinatorial Morphogen Treatments and Longitudinal Monitoring, G6423641252, Unraveling disease pathways for autism spectrum disorder with phenotyping of human brain organoids., G8858024052, Center for Live Cell Genomics
OpenAlex ID: W7129250968
