Pathways underlying selective neuronal vulnerability in Alzheimer's disease: contrasting the vulnerable locus coeruleus to the resilient substantia nigra

Identifying factors underlying selective neuronal vulnerability is crucial for understanding Alzheimer's disease (AD) pathophysiology. The Neuromodulatory Subcortical System (NSS) includes nuclei that exhibit early, but varied vulnerability to tau accumulation and neuronal loss. This varied vulnerability represents a valuable opportunity to explore the underlying mechanisms of AD. In this study, we investigated factors contributing to selective neuronal vulnerability by comparing transcriptomic profiles of two similar NSS nuclei with differing vulnerabilities to AD, the locus coeruleus and substantia nigra. Using paired samples from well-characterized postmortem human tissue from individuals in early Braak stages and free of comorbid neuropathologic diagnoses, we identified pathways related to cholesterol homeostasis and antioxidant pathways response as key potential drivers of vulnerability. The locus coeruleus and substantia nigra were sampled from human postmortem brains from Alzheimer's disease Braak stages 0-III. Samples were frozen in RNAlater, were sent to a vendor (Novogene Inc., Davis, CA) for RNA extraction and sequencing. Tissue homogenization and cell lysis was performed in TRIzol. After cell lysis, impurities removal, and inhibition of RNAse activity, total RNA was extracted by using phase separation method from cell debris. An Agilent bioanalyzer 2100 was used to measure RNA quality and concentration. Samples with an RNA integrity number (RIN) greater than 4 and at least 0.1µg of RNA available was passed to library preparation and sequencing. The quality of sequence files was assessed using the FastQC package before and after trimming steps. Trimmomatic (ILLUMINACLIP: TruSeq3-PE.fa:2:30:10:2:True, LEADING: 3, TRAILING: 3, SLIDINGWINDOW: 4:15, and MINLEN: 36) was used to remove adapter sequences and any sequences with low mean quality scores. Sequences were aligned to GRCh38 using STAR alignment and count matrices were generated using featureCounts. The count matrices were converted to counts per million (CPM) using the edgeR package.