Dr. Ramon Sun is a leading researcher in spatial biology, pioneering the development of next-generation spatial metabolomics to decode the biochemical basis of physiology and disease. His work integrates advanced mass spectrometry, imaging technologies, and artificial intelligence to map metabolic processes in space and time. Dr. Sun’s lab has developed a suite of AI-powered analytical platforms that transform spatial metabolomics data into mechanistic hypotheses, which are then systematically tested using human biospecimens and transgenic mouse models. These efforts have led to major discoveries in the molecular underpinnings of aging, cancer, and neurological disorders.

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At the University of Florida, Dr. Sun leads one of the most expansive NIH-funded research programs focused on metabolism and spatial biology. His laboratory is supported by four active R01 grants and one collaborative RM1 grant, and is home to more than 30 scientists, postdocs, graduate students, and technical specialists working across disciplines. The lab operates at the intersection of experimental and computational biology, using physiology-informed spatial omics to elucidate tissue-specific disease mechanisms.

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In addition to his role as a principal investigator, Dr. Sun is the founding Director of the UF Center for Advanced Spatial Biomolecule Research (CASBR) and serves as Associate Director of the McKnight Brain Institute. CASBR is the largest spatial metabolomics center in the United States by volume and scope, supporting over 150 research projects annually, processing more than 6,000 samples per year, and contributing to over 70 peer-reviewed publications to date. The center develops and disseminates cutting-edge technologies to the national research community, expanding the frontiers of spatial biology through innovation in instrumentation, software development, and high-throughput workflows.

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Dr. Sun’s program spans metabolic neuroscience, cancer biology, and systems physiology, aiming to uncover actionable mechanisms that drive disease. His integrative approach, combining AI, machine learning, computer vision, and custom software development, positions his team at the forefront of spatial systems biology. The work continues to inform both fundamental biology and translational research with the potential to accelerate therapeutic development.