In vivo Perturb-seq to Study Brain Aging

Aging leads to neurodegenerative diseases and cognitive decline. Several studies observed changes in gene expression with age, but the causal role of genes driving brain aging has not been systematically examined. Discovering such genes will be critical for identifying new therapeutic targets to counteract age-related cognitive decline. My goal is to develop and use a scalable system to rapidly identify genes that cause cellular aging in the brain. Astrocytes are a particularly exciting target cell type for such a system: they are abundant in the brain, they play essential roles in promoting brain homeostasis and repair, and they interact with other cell types. My project will leverage an innovative approach that combines cutting-edge technologies from biology and machine learning. I will use the CRISPR genome-editing tool together with measurement of gene expression at single cell resolution to systematically perturb genes in astrocytes in old mice and assess the resulting effects on gene expression. I will then leverage my extensive training as a computational scientist and use artificial intelligence approaches (called “aging clocks”) to identify gene perturbations that make an old cell’s gene expression profile look older or younger, thus detecting genes that could serve as drug targets for maintaining brain resilience. Importantly, I will use spatial multimodal transcriptomics – an innovative technology that images tissue sections and records multiple cellular features - to map the effects of gene perturbations not only on gene expression but also on the morphology and protein landscape of aging astrocytes, and on the cells they interact with. Overall, my proposed work aims to design a system to study gene perturbations in the old brain at scale and to uncover molecular drivers of astrocyte aging and resilience. These experiments should identify new functional targets to modulate astrocyte function and promote healthy brain aging.