Author(s): Alex Everett
Mentor(s): Kevin Johnston
Institution UTech
Major depressive disorder (MDD), or clinical depression, is a neuropsychiatric condition defined by states of depressed mood and other negative symptoms. MDD is influenced by complex genetic and environmental factors which create variability in patient symptoms and outcomes. Patients diagnosed with MDD have higher risks of suicide, yet the biological mechanisms differentiating MDD patients with and without suicidal ideations remain unclear. This study aims to determine if gene expression of the different cell types in the brain will show distinct differences between MDD patients with suicidality and those without. In this study, we profile 28 human prefrontal cortex (PFC) samples, using single-nucleus RNA-seq via the 10x platform. This results in 299,601 cellular transcriptomic profiles, evenly divided between healthy control, MDD-no suicide, MDD-suicide, and suicide (no MDD diagnosis) conditions. So far as we are aware, this is the largest single-cell study analyzing depression and suicide in the human brain. Our study has identified differentially expressed genes on both suicide and MDD axes, across multiple cell types in the brain. Ontology analysis has identified multiple common features unique to suicidality in the brains of MDD patients. Some common features included upregulation of apoptotic processes, and downregulation of cytoplasmic protein translation and cellular respiration in MDD-suicide patients compared with MDD-no suicide patients. Analysis of inferred cell-cell communication using CellChat identified significant increases in overall signal interaction strength between MDD-suicide and MDD-no suicide patients, with upregulation occurring in all cell types except microglia. Analysis of the most prominent pathway alterations identified increases in NRG3-ERBB4 signaling, which is associated with anxiety, and NRXN1-NLGN1 signaling which modulates neurotransmitter communication across the synapse. Overall, this study will provide novel insights into the impacts of depression on human neurons, and potential factors associated with suicidality.