Single-cell multi-region transcriptional and epigenomic dissection of VCID. - Abstract Alzheimer’s disease (AD) and AD-related dementias (ADRDs) are major drivers of mortality, morbidity, and health care costs for patients and their loved ones, due to the aging population, lack of predictive diagnosis, and lack of effective treatments or prevention. Vascular contributions to cognitive impairment and dementia (VCIDs) are key contributors to AD and ADRDs and manifest through diverse cerebrovascular lesions, including atherosclerosis, microinfarcts, and small vessel strokes. VCIDs include cerebral small vessel disease (CSVD), cerebral amyloid angiopathy (CAA), and a monogenic familial form of CSVD (CADASIL, Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). Understanding the mechanisms and drivers of VCID will enable new biomarkers and therapeutics, similar to the success of addressing cardiovascular disease and hypertension in heart disease. To understand the cellular mechanisms underlying VCID across brain regions, cell types, pathology, and molecular pathways, we perform high- resolution profiling of epigenomic and transcriptional alterations in post-mortem CNS samples from both sporadic and genetic VCID patients. Aim 1: We profile single-nucleus RNA-sequencing (snRNA-seq) and DNA accessibility (snATAC-seq) to create a transcriptional and epigenomic atlas of VCID across diagnoses, brain regions, cell types, sexes, and individuals. Aim 2: We create an atlas of SVD-associated changes in genes, modules, pathways, and cell-cell interactions. Aim 3: We predict candidate driver genes, regulators, and pathways using causality analyses across temporal and genetic models, and we validate our results experimentally using imaging studies. The successful execution of our studies will delineate clinically-relevant VCID biomarkers and therapeutics across sporadic and genetic VCID, enabling us to dissect their common and distinct molecular circuits, across four affected CNS region and all major cell types within them, and capturing an unprecedented level of complexity and enabling rich computational comparisons. The datasets generated and the computational analyses will provide invaluable insights for addressing the pressing medical need of VCIDs, their temporal, region-specific, and cell-type-specific changes, which can help guide new therapeutics.
