Dr. Jonathan Liu received degrees in mechanical engineering at Princeton (B.S.E., 1999) and Stanford (M.S., 2000 & Ph.D., 2005). He was a postdoctoral fellow in the department of electrical engineering (Ginzton Labs) and the Molecular Imaging Program at Stanford (2005-2009), and was later appointed as an instructor within the Stanford University School of Medicine (2009-2010). After spending four years as an assistant professor in the biomedical engineering department at SUNY Stony Brook (2010-2014), Dr. Liu is now the Bryan McMinn endowed associate professor of mechanical engineering at the University of Washington, with an adjunct appointment in the department of pathology. Dr. Liu’s work has been funded by the NIH (NCI, NIDCR, NIBIB), NSF, Department of Defense, Fred Hutchinson Cancer Research Center, and other agencies/industry. Dr. Liu’s molecular biophotonics laboratory (www.me.washington.edu/liu) develops optical strategies for improving the detection, diagnosis, and treatment of diseases. Dr. Liu is a cofounder and senior scientific advisor for Lightspeed Microscopy Inc. (lightspeedmicro.com).
TITLE: “Nondestructive 3D pathology with open-top light-sheet microscopy for precision medicine”
The diagnostic gold standard of histology relies upon analog technologies that have changed little over the past century. These dated techniques contribute to large inter-pathologist variability, poor prognostication/prediction of tumor behavior in individual patients, and consequently, treatment that is often not optimized for an individual’s particular malignancy. We believe that in order to catalyze a digital pathology transformation, a technological approach is needed that offers significant advantages over traditional histopathology in terms of simplicity/cost, speed, accuracy, comprehensiveness of sampling, and superior sample preservation (including nucleic acids). Towards this end, we have developed an “open-top” light-sheet (OTLS) microscopy platform for rapid slide-free 3D pathology of surgical and biopsy specimens [Glaser et al, Nat. Biomed. Eng., 2017]. Our most-recent efforts have focused on optimizing a suite of technologies to demonstrate the feasibility of 3D OTLS microscopy to visualize structural and molecular biomarkers within large numbers of core-needle biopsies in toto, and ultimately to better predict patient outcomes (indolent vs. aggressive disease). These nondestructive and comprehensive digital pathology methods are synergistic with the rapidly growing fields of genomics and machine learning, which collectively have the potential to significantly improve patient prognostication and treatment stratification.