Continuous neurogenesis in the mammalian hippocampus

作为我们目前R35资金的行政补充，我们建议延长我们的 对小鼠和人类诱导的海马神经发生进行了严格和多方面的分析， 多能干细胞模型用于死后成人神经发生的重点研究 从年龄匹配的神经型供体获得的海马组织和阿尔茨海默病 (AD)患者在AD的啮齿动物模型中，成年海马神经发生减少，而 增强成年海马神经发生足以挽救认知缺陷。之甚少 已知成人海马神经发生的分子特征， AD.我们最近完成了一项使用单核RNA-seq（snRNA-seq）的研究， 在海马体中产生人类未成熟神经元的转录特征， 寿命使用这个特征来描绘衰老大脑海马组织中的神经发生， 在一项初步研究中，我们观察到，在转录水平上， 与匹配的对照相比，来自AD患者的样本中的未成熟神经元。在此提出的 研究，我们将扩大我们的队列规模，严格测试我们的假设，定量和 AD背景下成人海马神经发生的质的变化。从技术上讲， 我们将利用单细胞方法建立我们的优势， 同一细胞内的染色质可及性。snRNA-seq和snATAC-seq的这种组合 将增强我们在神经原性轨迹中识别其他细胞类型的能力， 提供有关可能影响神经发生的AD表观遗传特征的额外信息。 我们还将利用我们基于机器学习的分析方法的优势。最后， 我们将采用综合生物信息学方法，将联合收割机单细胞分析、伪时间 分析以确定动态状态转换，以及基于配体-受体的细胞相互作用 表达水平。总之，我们的研究将从男性和女性中生成经验证的单细胞数据集。 女性AD患者和年龄匹配的对照，这将是AD的宝贵资源， 研究社区，并将产生关于AD病理生理学的机制假设， 潜在的治疗目标。

As an Administrative Supplement to our current R35 funding, we are proposing to extend our rigorous and multifaceted analyses of hippocampal neurogenesis in mice and human induced pluripotent stem cell models to a focused study of human adult neurogenesis in postmortem hippocampal tissue obtained from age-matched neurotypical donors and Alzheimer’s Disease (AD) patients. In rodent models of AD, adult hippocampal neurogenesis is reduced, whereas enhancing adult hippocampal neurogenesis is sufficient to rescue the cognitive deficits. Little is known about the molecular characteristics of adult hippocampal neurogenesis in humans and in AD. We have recently completed a study using single-nucleus RNA-seq (snRNA-seq) to generate a transcriptional signature of human immature neurons in the hippocampus across the lifespan. Using this signature to profile neurogenesis in hippocampal tissue of the aging brain, in a pilot study we observed potential differences in both the number and transcriptional profile of immature neurons in samples from AD patients compared to matched controls. In this proposed study, we will expand our cohort size to rigorously test our hypotheses of both quantitative and qualitative changes in adult human hippocampal neurogenesis in the context of AD. Technically, we will build on our strengths using single-cell approaches to profile both the transcriptome and chromatin accessibility within the same cell. This combination of snRNA-seq and snATAC-seq will enhance our ability to identify additional cell types within the neurogenic trajectory and provide additional information on the epigenetic signatures of AD that may impact neurogenesis. We will also build on the strength of our machine learning-based analytical approach. Finally, we will take an integrated bioinformatic approach to combine single-cell profiling, pseudotime analyses to identify dynamic state transitions, and cellular interactions based on ligand-receptor expression levels. Together, our study will generate validated single-cell datasets from male and female AD patients and age-matched controls that will be a valuable resource for the AD research community and will generate mechanistic hypothesis about AD pathophysiology and potential therapeutic targets.