Continuous neurogenesis in the mammalian hippocampus

作为我们目前R35资金的行政补充，我们提议延长我们的 小鼠和人诱导的海马神经发生的严谨和多方面分析 多能干细胞模型在成人死后神经发生研究中的应用 年龄匹配的神经典型供者和阿尔茨海默病患者的海马区组织 (Ad)病人。在AD的啮齿动物模型中，成年海马神经发生减少，而 增强成年海马区的神经发生足以挽救认知缺陷。小才是 已知成人海马神经发生的分子特征 广告。我们最近完成了一项使用单核RNA-seq(snRNA-seq)的研究，以 在海马区生成人类未成熟神经元的转录签名 寿命。利用这一特征来描述老化大脑的海马区组织中的神经发生 在一项初步研究中，我们观察到在数量和转录图谱上的潜在差异 AD患者样本中未成熟神经元与匹配对照组的比较。在本建议中 研究中，我们将扩大我们的队列规模，以严格检验我们的假设 阿尔茨海默病背景下成人海马神经发生的质的变化。从技术上讲， 我们将利用我们的优势，使用单细胞方法来分析转录组和 同一细胞内染色质的可及性。SnRNA-seq和SnATAC-seq的这种组合 将增强我们在神经源性轨迹中识别其他细胞类型的能力 提供可能影响神经发生的AD表观遗传学特征的更多信息。 我们还将利用我们基于机器学习的分析方法。最后， 我们将采用一种集成的生物信息学方法来结合单细胞图谱、伪时间 基于配体-受体识别动态转换和细胞相互作用的分析 表达级别。总之，我们的研究将从男性和 女性AD患者和年龄匹配的对照组，这将是AD的宝贵资源 研究社区，并将产生关于AD病理生理学和 潜在的治疗靶点。

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.