A Spatial Profiling Approach to Study Neurodegenerative Disease
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The brain is highly heterogeneous and the diverse cell types and brain regions show selective vulnerabilities to age-related neurodegenerative disease that can be a challenge to study with high precision. Join us for this first webinar of Brain Week to hear Caitlin S. Latimer, MD, PhD from the University of Washington discuss how she and her colleagues leveraged the spatial-preservation technology of the GeoMx® Digital Spatial Profiler (DSP) to generate highly quantitative spatial proteomic data from specific structures and cell types in the human brain for two research projects. The first project focuses on two different types of genetic Alzheimer’s disease, profiling amyloid plaques and microglia to determine if there are differences in the proteomic profile across cases. The second project is a study of polyproteinopathy in the aged brain, leveraging the use of cell-type specific morphology markers to profile neurons and glial in the setting of sporadic late-onset Alzheimer’s disease with and without comorbid TDP-43 pathology.
Speaker
Caitlin S. Latimer, MD, PhD
Assistant Professor, Division of Neuropathology, Department of Laboratory Medicine and Pathology, University of Washington Medical Center
Dr. Latimer is a neuropathologist and assistant professor in the Department of Laboratory Medicine and Pathology at the University of Washington Medical Center. She is co-leader of the University of Washington Alzheimer’s Disease Research Center Neuropathology Core and the University of Washington Biorepository and Integrated Neuropathology Lab. Her research focuses on the pathophysiology of age-related neurodegenerative diseases, particularly the mechanisms of interaction between multiple pathologies co-occurring in the aging brain. As a neuropathologist, her examination of human brain tissue drives hypotheses about the mechanisms that underly the observed pathology, which she then tests in experimental systems, integrating the relevance of human disease with the function consequences in a model system.
