While there is strong evidence supporting the role of the anterior cingulate cortex, basolateral amygdala, and
the hippocampus (ACC, BLA, HIPP) as a key neural network regulating mood, and therefore central to the
pathophysiology of major depressive disorder (MDD), much remains unknown, including which gene pathways
and which specific cell types play a primary causal role mediating alterations in this circuit, and what cell-type
connections, within and between these regions, are particularly altered in depressive states. The overall
objective of this application is to generate single-cell transcriptomic profiles to study molecular changes,
including those specific to genetic ancestry and sex, associated with MDD in the mood regulation circuit. While
disease burden is greater in African Americans, the impact of genetic ancestry remains unknown as most
genomic studies in MDD so far have been limited to subjects of European descent. In addition, previous
studies revealed that transcriptomic changes associated with MDD are sex-specific, and gene networks are
differentially dysregulated between sexes. The applicants’ recent single-cell brain study revealed cell-specific
contributions to transcriptomic changes associated with MDD. The proposed project is a large-scale,
systematic investigation in the ACC, BLA, and HIPP to interrogate the transcriptome at single-nucleus
resolution in an unprecedently large and representative sample of MDD. The specific aims are to: 1) Identify, at
the single-cell level transcriptomic changes associated with MDD in 800 subjects across three linked brain
regions: ACC, BLA, and HIPP; 1b) Study the impact of genetic ancestry and sex; 2) Define cell networks
associated with mood regulation using machine learning approaches; and 3) Identify cell-specific expression
Quantitative Trait Loci (eQTLs) colocalizing with genome-wide significant SNPs identified in MDD GWAS
analyses. A large cohort (N=800) of human post-mortem samples obtained from subjects with MDD will be
compared to psychiatrically-healthy controls. The sample (~20% African American and ~30% female) will allow
for studying the impact of genetic ancestry and sex. Droplet-based single-nucleus RNA sequencing will be
applied to generate transcriptomic profiles. Deep learning approaches will be used to identify and annotate the
cell types and gene networks associated MDD. The latest GWAS data in MDD will be leveraged to fine map
genetic loci with cellular and regional resolution. The proposed research is innovative because it is the first
large-scale investigation of the ACC-BLA-HIPP circuit in humans and will represent the largest single-cell
transcriptional resource of the human brain. It will identify gene and cellular networks associated with sex or
genetic ancestry, and will also generate a vast amount of transcriptomic data on neurotypical brains. This
research is significant because it will greatly advance our understanding of the cellular and molecular pathways
involved in mood regulation and MDD. Through a better understanding of the mechanisms of depressive
illness, we may be one step closer to developing novel treatment strategies and personalize interventions.
