Biomedicine Seminar

Abstract: 
A snapshot of gene expression at RNA or protein levels can serve as a footprint for projecting cell functions and states. Transcriptome profiling by high throughput RNA sequencing has thus become a cornerstone of functional genomics, cell biology, understanding of disease pathology, and biomedical discovery. Bulk RNA sequencing offers the throughput and affordability needed to profile large human cohorts and longitudinal animal studies, yet averages away cellular heterogeneity. To overcome this, single-cell and single-nucleus RNA sequencing have enabled resolving individual cell types and states from rare to disease-driving populations, but at higher cost and limited scale. In tissues, all cells have evolved to be functionally and spatially organized. To resolve single-cell transcriptome signatures without losing their spatial contexts, spatial OMICS technologies at RNA, protein, and metabolite levels now allow mapping how cell states are organized within lesions, niches and microenvironments. 

In this seminar, I will present our group’s strategy for combining these complementary resolutions and throughputs to investigate degenerative disease across animal models and human cohorts. Using examples from our work, including pig models, multi-tissue single-cell atlases, and spatially resolved profiling of degenerating and regenerating tissues, I will illustrate how we match technology to question: bulk transcriptomics to screen cohorts and detect disease signatures, single-cell sequencing to pinpoint the cell types and states that carry those signatures, and spatial approaches to place them back into the anatomy of disease. Finally, I will illustrate how the systemic generation of these large-scale multi-OMICS data could potentially lead to the generation of AI model for biomedical research.