PROJECT SUMMARY
Although amyloid-β plaques and neurofibrillary tangles are the current criteria for pathologic diagnosis of
Alzheimer’s Diseases (AD), only 9% of clinically diagnosed AD patients have "pure" AD pathology and most AD
cases have mixed pathologies, which significantly increase the odds of dementia . Because diverse intra- and
extracellular pathologies and stressors contribute to AD progression, it is essential to track how they affect the
various cell types of the brain by cataloging cell-type-specific transcriptomic responses to both intra- and
extracellular pathologies in AD pathogenesis. Therefore, this proposal aims to measure the effects of multiple
pathologies on each cell type in their native environment, then make this information actionable by
computationally identifying the drivers of these effects and testing them in human cell models. To this end, we
propose two approaches to simultaneously measure the cell transcriptomes and multiple pathologies in
millions of individual cells in their native context. The first approach, “pathology-indexing scRNA-seq,” is
designed for intracellular pathologies. It combines single-cell RNA-seq (scRNA-seq) with a set of oligo-
barcoded antibodies against intracellular pathologies. This approach enables us to simultaneously measure
gene expression and multiple intracellular pathologies all in the same cell. The second approach, “pathology
spatial transcriptomics,” is designed for extracellular pathologies. It obtains gene expression of 1~10 cells (55-
μM resolution) in spatial registration with extracellular pathology. This enables us to quantify the effects of
extracellular pathologies and microenvironment on cell disease states. We will apply these two innovative
sequencing technologies to two brain regions, the dorsal lateral prefrontal cortex and hippocampus of
postmortem brains of deeply-phenotyped ROSMAP participants. Using univariate, systems biology, and deep
learning computational methods, we will identify candidate genes that drive cell-type-specific disease states.
To test predicted early driver genes and provide therapeutic targets, we will conduct CRISPR screens in
human cortical cell models derived from control and AD isogenic iPSC lines. Collectively, our study will shed
important light on the cell-type-specific driver genes in AD pathogenesis, define molecular pathways leading to
cell disease-states, and provide experimentally validated targets for preventing the disease-state transition
during early AD development.
