December 10 2021
In this webinar, Michael Surace will review the factors which determine how stringent a validation should be, including the assay, the platform, the signal of interest, the reference signal, the degree of precision/ dimensions/ depth required in the analysis, the application, the purpose, the context, and the criteria for success. The standard development workflow for a 6-plex panel will be dissected and described in detail. The critical components of the low plex validation, including development tasks, pressure tests, and the formal verification, will be redeployed in a manner appropriate for a 40 plex iterative mIF assay, including caveats, potential artifacts, and criteria for success.
Dec 10, 2021
Dr. Michael Surace
Michael Surace received a BS in Biology from James Madison University in 2004 before joining the Medical Automation Research Center in the Department of Pathology at the University of Virginia. In 2006 he began graduate studies in the laboratory of Dr. Liwu Li, working in nuclear receptor and TLR signaling network crosstalk and its role in the polarization of macrophage activation phenotypes (M1/M2). He received his Ph.D. from Virginia Tech in 2010. Postdocs were at the Medical College of Virginia at Virginia Commonwealth University in Richmond, Virginia, in the departments of Anatomy and Neurobiology, investigating the cellular mechanisms of microglial activation in response to toxic insult in Parkinson’s Disease, then in Molecular Biology and Biochemistry, working on astrocytes as inflammatory immune cells in multiple sclerosis. In 2015 he joined STCube Pharmaceuticals as a research scientist characterizing novel immune checkpoint inhibitor antibodies with multiplex immunofluorescence and digital pathology image analysis to support mechanistic action research. In 2017 he joined Medimmune/ AstraZeneca as a scientist developing and validating Multiplex IF panels. Since then, he has continued developing the mIF platform inside Translational Medicine Oncology, focusing on validation of panels, reproducibility across sites, and improving the image and data analysis pipelines for research and clinical trials to support the development of predictive and prognostic models incorporating multiple markers and spatial information.