Computational Analysis of Cell Type-Specific Transcriptional Dynamics in Major Depressive Disorder - Zhang, Elaine (Transactions on Materials, Biotechnology and Life Sciences, 2024)
Depression, also known as major depressive disorder (MDD), is a complex mental health condition that affects millions of people worldwide. Since disease treatments often depend on levels of understanding, this project strives to learn more about the transcriptional components of MDD to delineate the transcriptomic dynamics in MDD. The sample includes the single-cell transcriptomic profiles in the dorsolateral prefrontal cortex (dlPFC) of 34 postmortem patients. This data was acquired from publicly available datasets and run through R-Studio code. Cell clusters were classified into cell types using cell-type-specific signature markers in the original resource datasets. After analyzing immediate early genes (IEGs), MDD-associated genes, and differentially expressed genes, the trend showed broadly lowered expression levels and frequency in MDD patients than in healthy control with some exceptions. After studying 10 different IEGs, the trend showed a lowered gene expression frequency in MDD patients than in control. For MDD-associated genes, a similar trend showed lower frequency and expression levels in the MDD sample, apart from GAD1 and RELN, which had a higher expression level in the MDD sample. Then, by analyzing the top differentially expressed genes in each cell type, most cells showed a lowered frequency in MDD patients besides macrophage/microglial, which had higher expression frequency in IGHG1 and RP11-315A16.1 for the control sample. With each analytical finding, the underlying elements of MDD may be better understood, leading to more precise wet lab experiments and the eventual treatment of MDD.