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Abstract The genetic architecture of Parkinson’s disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.

One inevitable consequence of aging is the gradual deterioration of physical function and exercise capacity, driven in part by the adverse effect of age on muscle tissue. We hypothesized that relationships exist between age-related differentially expressed genes (DEGs) in skeletal muscle and age-associated declines in physical function and exercise capacity. Previously, male C57BL/6mice (6m, months old, 24m, and 28m) were tested for physical function using a composite scoring system (comprehensive functional assessment battery, CFAB) comprised of five well-validated tests of physical function. In this study, total RNA was isolated from tibialis anterior samples (n = 8) randomly selected from each age group in the parent study. Using Next Generation Sequencing RNAseq to determine DEGs during aging (6m vs. 28m, and 6m vs. 24m), we found a greater than five-fold increase in DEGs in 28m compared to the 24m. Furthermore, regression of the normalized expression of each DEG with the CFAB score of the corresponding mouse revealed many more DEGs strongly associated (R ≥ |0.70|) with functional status in the older mice. Gene ontology results indicate highly enriched axon guidance and acetyl choline receptor gene sets, suggesting that denervation/reinnervation flux might potentially play a critical role in functional decline. We conclude that specific age-related DEG patterns are associated with declines in physical function, and the data suggest accelerated aging occurring between 24 and 28 months.

Urgent care (UC) is one of the fastest growing venues of health care delivery for nonemergent conditions. This study compared the patient experience during virtual and in-person urgent care visits. We conducted a cross-sectional study of patients with the same diagnosis during Virtual Urgent Care (VUC) and in-person UC visits with the same diagnosis with regard to time and cost over a period of one year. We recorded and analyzed 16 685 urgent care visits: In-person UC (n = 14 734), VUC (n = 1262). Significant differences were found in the average total time for a visit in an in-person UC (70.89 minutes), and VUC (9.38 minutes). The average total cost of VUC ($49) and in-person UC ($142.657) differed significantly. Significant difference was found between UC turnaround time and VC turnaround time (Dependent variable (DV): 53.77, P < .01). We found significant differences in cost and time between in favor of virtual visits. Our findings suggest additional policy reform to expand the use of virtual care among target populations to improve access, reduce costs, meet the needs of patients, and reduce emergency department visits.