Medical Engineering Special Seminar, Jina A. Ko

Due to an inherent biological heterogeneity across individuals and within a disease, it is extremely challenging to identify robust biomarkers that can accurately represent molecular status of the body for disease diagnostics. To solve this intractable problem, we have developed microfluidic platforms and molecular tools that enable high throughput, multiplexed profiling of biomarkers (e.g. cells, extracellular vesicles; EV). We achieved high throughput profiling by combining sequencing with parallelization of microchip technologies and droplet microfluidics. We overcame the variability of any individual biomarker between individual patients, by developing tools that can measure multiple markers and we applied machine learning to identify signatures that persist across this variability. To resolve cell and EV heterogeneity, we have recently developed an ultra-fast cycling method for single cell analysis and an ultra-high sensitive microfluidics that can achieve single particle detection sensitivity, enabling individual EV measurements.

Biography: Jina Ko is an Assistant Professor in the Departments of Pathology and Laboratory Medicine and Bioengineering at University of Pennsylvania. She focuses on developing single molecule detection from single extracellular vesicles (EV) and multiplexed molecular profiling to better diagnose diseases and monitor treatment efficacy. Jina graduated from Rice University with a B.S. in Bioengineering and a B.A. in French Studies in 2013 and she earned her Ph.D. in Bioengineering at the University of Pennsylvania in 2018. During her Ph.D., she developed machine learning-based microchip diagnostics that can detect blood-based biomarkers to diagnose pancreatic cancer and traumatic brain injury. For her postdoctoral training, she worked at Massachusetts General Hospital and the Wyss Institute at Harvard University as a Schmidt Science Fellow and a NIH K99/R00 award recipient. Jina developed new methods to profile single cells and single EV with high throughput and multiplexing. http://www.jinakolab.org