1/2 Large-scale, single-cell characterization of molecular and cellular networks of mood regulation circuitry in major depressive disorder

摘要 虽然有强有力的证据支持前扣带回皮质、基底外侧杏仁核和 海马体(ACC，BLA，HIPP)作为调节情绪的关键神经网络，因此对 重度抑郁障碍(MDD)的病理生理机制仍不清楚，包括哪些基因途径 以及哪些特定的细胞类型在这个回路中起主要的因果作用，以及什么类型的细胞 在抑郁状态下，这些区域内部和之间的联系尤其会发生变化。整体而言 该应用的目的是生成单细胞转录图谱以研究分子变化， 包括那些特定于遗传祖先和性别的基因，与情绪调节回路中的MDD相关。而当 非洲裔美国人的疾病负担更大，遗传祖先的影响仍然未知，因为大多数 到目前为止，MDD的基因组研究仅限于欧洲血统的受试者。此外，以前的 研究表明，与MDD相关的转录改变是性别特有的，而基因网络是 性别差异失调。申请者最近的单细胞大脑研究揭示了细胞特异性 对与MDD相关的转录改变的贡献。拟建项目规模较大， 用ACC、BLA和HIPP系统研究单核转录组 在MDD的一个空前大的和有代表性的样本中的分辨率。具体目标是：1)确定、 800名受试者中与MDD相关的单细胞水平转录水平的变化 地区：ACC、BLA和HIPP；1b)研究遗传祖先和性别的影响；2)定义细胞网络 与使用机器学习方法的情绪调节相关；以及3)识别特定细胞的表达 与全基因组显著SNPs共定位的数量性状基因座(EQTL) 分析。从患有MDD的受试者身上获得的大量(N=800)人类尸检样本将是 与精神健康的对照组相比。样本(约20%的非裔美国人和约30%的女性)将允许 研究遗传血统和性别的影响。基于液滴的单核RNA测序将被 应用于生成转录模板。将使用深度学习方法来识别和注释 与MDD相关的细胞类型和基因网络。将利用MDD中最新的Gwas数据来精细绘制地图 具有细胞和区域分辨率的遗传基因座。这项拟议的研究具有创新性，因为它是第一项 对人类ACC-BLA-HIPP回路的大规模研究将代表最大的单细胞 人脑的转录资源。它将识别与性或性有关的基因和细胞网络 基因祖先，还将产生大量关于神经典型大脑的转录数据。这 研究意义重大，因为它将极大地促进我们对细胞和分子途径的理解。 参与情绪调节和MDD。通过更好地了解抑郁的机制 在疾病方面，我们可能离开发新的治疗策略和个性化干预措施又近了一步。

SUMMARY 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.