A transcriptomic atlas of immune cells in a model of synucleinopathy

神经退行性疾病，如阿尔茨海默病(AD)、帕金森氏病伴痴呆(PDD)和 路易体痴呆(DLb)无法治愈，但这三种疾病都有α-突触核蛋白(α-SYN)病理和 神经炎的症状。尽管越来越多的证据支持这样的观点，即免疫细胞对 神经退行性变在上述疾病的发病机制中起积极作用，抗炎治疗 到目前为止，都没有在神经退行性疾病中提供任何有益的效果。我们假设，既然 免疫细胞表现出表型的异质性，有效的免疫反应调节疗法 神经退行性疾病需要靶向神经炎症的特定成分，而不是 对其信号的广泛抑制。这个项目的基本原理是，一旦区域和时间基因组 动物模型中枢神经系统内外免疫细胞的特征 联核病是已知的，有意义的生物标记物可以被识别，创新的治疗策略可以 被设计用于PDD、DLB，甚至可能是AD。因此，提出了以下两个目标。要定义 中枢神经系统内免疫细胞对α-SYN病理反应的异质性，在AIM1中，我们将定义大脑 转基因(TG)mThy1-α-SYN小鼠61系(公认模型)的免疫细胞亚群 应用新鲜提取的免疫液滴单细胞RNA测序(ScRNASeq) 来自不同脑区的细胞，从2个月(无/轻微神经炎)到14个月不等 (显性神经炎症)；非甘油三酯将用作对照。使用自适应的计算技术 对于低深度的单细胞rna-seq数据，我们将从不同的免疫细胞亚型中鉴定转录。 小鼠大脑区域；这包括集群稳健性的估计，细胞类型特定基因的特征， 以及标记基因的鉴定。在AIM1中，我们还将通过探测新的- 已定义的和已有的标记基因在原位。由于α-SYN病理也发生在中枢神经系统之外，在AIM2， 我们将确定血液、脾中对α-SYN病理的系统免疫细胞反应的表型多样性。 和来自TG线61的淋巴结(和非Tg产仔)在与AIM 1相同的选定时间点，并将 进行相同的scRNA-seq程序和细胞类型分析。然后我们将开发新的双 将中枢神经系统和外周免疫系统细胞的变化联系起来的隔室模型，目的是识别 推测在联体核病中这两组细胞之间的相互作用。圆满完成 拟议的研究将建立一份关于同种核病免疫细胞表型异质性的图谱 阐明了定位于中枢神经系统内外的免疫细胞对α-SYN反应的相互作用 病理学。这些发现将对这一研究领域产生重要的积极影响，因为它们将提供 生物标记物识别和治疗干预的机会，并将推动我们的机制 了解PDD和DLB，以及可能的AD。

Neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) have no cure, but all three share signs of α-synuclein (α-syn) pathology and of neuroinflammation. Although mounting evidence supports the notion that the immune cell response to neurodegeneration plays an active role in the pathogenesis of the above disorders, anti-inflammatory therapies have failed, thus far, to provide any beneficial effects in neurodegenerative diseases. We hypothesize that since immune cells exhibit a phenotypic heterogeneity, effective immune response-modifying therapy for neurodegenerative diseases requires the targeting of specific components of neuroinflammation rather than the broad inhibition of its signaling. The rationale for this project is that, once the regional and temporal genomic signatures of immune cells both inside and outside of the central nervous system (CNS) of animal models of synucleinopathies are known, meaningful biomarkers can be identified and innovative therapeutic strategies can be devised for PDD, DLB and even perhaps AD. Thus, the following two aims are proposed. To define the heterogeneity of the immune cell response to α-syn pathology within the CNS, in AIM 1, we will define brain immune cell subpopulations in transgenic (Tg) mThy1-α-syn mouse line 61 (a recognized model of synucleinopathies) by droplet-based single-cell RNA-sequencing (scRNASeq) using freshly extracted immune cells from different brain regions at time points ranging from 2 (no/minimal neuroinflammation) to 14 months (overt neuroinflammation); non-Tg littermates will be used as controls. Using computational techniques adapted to low-depth single-cell RNA-seq data, we will identify transcriptomic subtypes of immune cells from the different mouse brain regions; this includes estimates of cluster robustness, characterization of cell type-specific genes, and identification of marker genes. In AIM1, we will also validate the single-cell data by probing for sets of newly- defined and existing marker genes in situ. Since α-syn pathology also takes place outside of the CNS, in AIM 2, we will define the phenotypic diversity of the systemic immune cell response to α-syn pathology in blood, spleen and lymph nodes from Tg Line 61 (and non-Tg littermates) at the same selected time points as in AIM 1, and will be subjected to the same scRNA-seq procedure and cell type analyses. We will then develop novel bi- compartmental models to relate changes in CNS and peripheral immune system cells, with the goal of identifying putative interactions between these two sets of cells in synucleinopathies. Successful completion of the proposed investigations will establish an atlas of immune cell phenotype heterogeneity in synucleinopathies and shed light into the cross talk between immune cells localized inside and outside of the CNS in response to α-syn pathology. These findings will have an important positive impact on this area of research in that they will provide opportunities for biomarker identification and for therapeutic interventions and will advance our mechanistic understanding of PDD and DLB as well as possibly AD.