Catherine Kaczorowski, Ph.D., is a professor at The Jackson Laboratory (JAX) and the Evnin Family Endowed Chair for Alzheimer’s Research. She is a neurophysiologist, an expert in the systems genetics of ‘normal’ nonpathological aging and pathogenesis of Alzheimer’s disease. Kaczorowski has been a driving force in uncovering and describing the phenomenon of cognitive resilience in the context of ‘normal’ nonpathological aging and Alzheimer’s. She is a recognized authority in the development and application of genetically diverse mouse models for studies on aging and age-related neurodegenerative disorders, having pioneered the generation of the first translationally relevant polygenic model of human cognitive aging (B6-BXD) and Alzheimer’s (AD-BXDs), which was published in Neuron. Her research program entails several collaborative, multi-site projects, and leverages the innovative, translational integration of multi-scale data (genetics, omics, imaging, behavior) from genetically diverse mouse strain and human patients to identify genetic mechanisms that promote cognitive resilience to normal brain aging, Alzheimer’s and other age-related neurodegenerative diseases. Her tools permit dissection of aging-specific genetic mechanisms from those controlling the clinical manifestations resulting from disease-specific neuropathologies, which is impossible in human populations. Her recent publications demonstrate the strength of her lab’s mouse-to-human research translational workflow that continues to transform the field’s ability to model resilience to normal age-related cognitive decline and Alzheimer’s. Taken together, these collaborative works set the foundation of the mouse genetic reference panel, the behavioral and electrophysiological assays for cognitive resilience, the systems genetics and cross-species computational analysis pipeline, and cell type-specific and regional signatures of resilience that are integral to this proposal.
Integration of electrophysiological and transcriptomic signatures of cognitive resilience in the hippocampus using patch-seq