Unraveling the transcriptomic signatures of Parkinson's disease and major depression using single-cell and bulk data.

Background: Motor symptoms are well-characterized in Parkinson's disease (PD). However, non-motor symptoms, such as depression, are commonly observed and can appear up to 10 years before motor features, resulting in one-third of individuals being misdiagnosed with a neuropsychiatric disorder. Thus, identifying diagnostic biomarkers is crucial for accurate PD diagnosis during its prodromal or early stages. Methods: We employed an integrative approach, combining single nucleus RNA and bulk mRNA transcriptomics to perform comparative molecular signatures analysis between PD and major depressive disorder (MDD). We examined 39,834 nuclei from PD (GSE202210) and 32,707 nuclei from MDD (GSE144136) in the dorsolateral prefrontal cortex (dlPFC) of Brodmann area 9. Additionally, we analyzed bulk mRNA peripheral blood samples from PD compared to controls (GSE49126, GSE72267), as well as MDD compared to controls (GSE39653). Results: Our findings show a higher proportion of astrocytes, and oligodendrocyte cells in the dlPFC of individuals with PD vs. MDD. The excitatory to inhibitory neurons (E/I) ratio analysis indicates that MDD has a ratio close to normal 80/20, while PD has a ratio of 62/38, indicating increased inhibition in the dlPFC. Microglia displayed the most pronounced differences in gene expression profiles between the two conditions. In PD, microglia display a pro-inflammatory phenotype, while in MDD, they regulate synaptic transmission through oligodendrocyte-microglia crosstalk. Analysis of bulk mRNA blood samples revealed that the Discussion: This study demonstrates that bulk peripheral immune cells play a role in both conditions, but neuroinflammation in the dlPFC specifically manifests in PD as evidenced by the analysis of single nucleus dlPFC datasets. Integrating these two omics levels offers a better understanding of the shared and distinct molecular pathophysiology of PD and MDD in both the periphery and the brain. These findings could lead to potential diagnostic biomarkers, improving accuracy and guiding pharmacological treatments.