Statistical methods for studying cell-cell interactions using spatial transcriptomics for Alzheimer's disease

使用空间转录组学研究阿尔茨海默病细胞间相互作用的统计方法

• 批准号: 10554331
• 负责人: Xiaoyu Song
• 金额: $ 16.9万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10554331
• 关键词: AD transgenic mice; Age; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Alzheimer' s disease therapeutic; Area; Atlases; Brain; Brain Diseases; Brain region; Calibration; Categories; Cell Communication; Cell Separation; Cell model; Cells; Central Nervous System; Cerebral hemisphere; Communication; Communities; Complement; Computing Methodologies; Data; Degenerative Disorder; Dementia; Development; Disease; Elderly; Enhancers; Exhibits; Gene Expression; Genes; Goals; Homeostasis; Human; Impaired cognition; Individual; Joints; Late Onset Alzheimer Disease; Ligands; Location; Maps; Methods; Microglia; Modeling; Mus; Neighborhoods; Neurobiology; Neuronal Dysfunction; Neurons; Pathway interactions; Phenotype; Publishing; Research; Research Personnel; Research Project Grants; Resolution; Risk; Sampling; Signal Transduction; Social Distance; Spottings; Statistical Methods; Systems Biology; Tissues; Transgenic Mice; Validation; Work; analytical tool; directional cell; disease phenotype; gene discovery; gene network; genetic variant; improved; insight; network models; neuropathology; neuroprotection; neurotransmission; next generation; novel; olfactory bulb; programs; random forest; receptor; single cell analysis; single nucleus RNA-sequencing; subventricular zone; targeted treatment; therapeutic target; tool; transcriptome; transcriptomics

The malfunction of neuron-microglia bidirectional signaling interactions in the brain is one of the most prominent but poorly understood mechanisms for Alzheimer's Disease (AD). Genes and pathways that regulate neuron-microglia interactions are barely identified. The development of spatial transcriptomics (ST) provides an unparalleled opportunity for studying neuron-microglia interactions, but the analytical tools have not been well developed. Depending on whether individual cells can be spatially mapped and profiled, ST data can be categorized into bulk and single-cell resolutions. The main challenge for using bulk ST to study cell-cell interactions is the lack of cellular resolution, and the major challenge for using single-cell ST is the low power and accuracy for identifications. In Aim 1, we propose a novel joint spatial network model to enable cell-cell interaction identification with bulk ST and associate the interactions with Alzheimer's Disease. We will integrate the cellular profiles from single-nucleus RNA sequencing (snRNAseq) with bulk ST to annotate the spots in ST, identify neuron and microglia enriched pairs of spots, jointly model multiple neuron-microglia interaction networks using these spots from mice with different age and AD status, and identify the association of AD- related phenotypes with neuron-microglia interactions (network edges). We will apply the proposed joint spatial network model to a recent bulk ST study that profiled the transcriptomics for ~500 spots per hemisphere for 20 cerebral hemispheres from wild-type and transgenic mice of Alzheimer's Disease, and validate discovered cell- cell interactions by leveraging independent single-cell ST studies and in snRNAseq studies of Alzheimer's Disease. In Aim 2, we propose a quantile-based distance-calibrated spatial network method to improve the study power and accuracy for identifying cell-cell interactions with single-cell ST data. We will model the heterogeneous association between gene expression and the entire distribution of cell-cell distance, and further aggregate transcriptome-wide signals to simultaneously identify (1) whether there exists a cell-cell interaction, (2) the physical distance for two cells to have robust interactions, and (3) genes whose expression levels are changed by interactions. Then, we will leveraged the identified distance and interaction changed genes to build spatial networks for cell-cell interactions. We will apply the model to seqFISH+ data that includes 10,000 gene profiles for 2,963 cells covering an area of approximately 0.5 mm2 in the cortex subventricular zone and olfactory bulb regions, and use independent single-cell ST data and bulk ST data for validation. The computational/statistical tools developed in this study enables the identification of cell-cell interactions with bulk ST and single-cell ST data, and is also helpful for a broader scientific community to model ST data for any tissues. The identified genes from this study are potential targets for therapeutic strategies of the Alzheimer's Disease .

双向信号传导相互作用在大脑中的功能不全是最多的信号传导相互作用之一 显着但知之甚少的阿尔茨海默氏病（AD）的机制。基因和途径 几乎找不到调节神经元 - 神经元相互作用。空间转录组学（ST）的发展 为研究神经元 - 神经相互作用提供了无与伦比的机会，但是分析工具具有 没有得到很好的发展。根据是否可以在空间映射和剖析单个单元格，ST数据 可以分类为批量和单细胞分辨率。使用散装ST研究细胞细胞的主要挑战 相互作用是缺乏细胞分辨率，使用单细胞ST的主要挑战是低功率 和识别的准确性。在AIM 1中，我们提出了一种新型的关节空间网络模型来启用细胞细胞 与散装ST的相互作用鉴定，并将相互作用与阿尔茨海默氏病相关联。我们将整合 来自单核RNA测序（SNRNASEQ）的细胞谱，带有大量ST，以注释ST中的斑点 识别富含斑点的神经元和小胶质细胞对，共同建模多个神经元 - 神经元相互作用 网络使用来自不同年龄和广告状态的小鼠的这些斑点，并确定AD的关联 具有神经元 - 神经相互作用的相关表型（网络边缘）。我们将应用建议的关节空间 网络模型的最近大量ST研究，该研究介绍了每个半球约500个景点的转录组学 来自阿尔茨海默氏病的野生型和转基因小鼠的脑半球，并验证发现细胞 通过利用独立的单细胞ST研究和阿尔茨海默氏症的SNRNASEQ研究来进行细胞相互作用 疾病。在AIM 2中，我们提出了一种基于分位数的距离校准的空间网络方法，以改善 研究能力和准确性，以识别与单细胞ST数据的细胞细胞相互作用。我们将建模 基因表达与细胞细胞距离的整个分布之间的异质关联， 进一步的整个转录组信号同时识别（1）是否存在细胞细胞 相互作用，（2）两个细胞具有可靠相互作用的物理距离，以及（3）其表达的基因 相互作用改变了级别。然后，我们将利用确定的距离和相互作用变化 基因建立用于细胞 - 细胞相互作用的空间网络。我们将将模型应用于seqfish+数据 包括10,000个基因轮廓，用于2,963个细胞，覆盖皮质中约0.5 mm2的面积 脑室下区域和嗅球区域，并使用独立的单细胞ST数据和批量ST数据 验证。本研究中开发的计算/统计工具可以识别细胞细胞 与散装ST和单细胞ST数据的互动，对更广泛的科学界也很有帮助 模拟任何组织的ST数据。这项研究的鉴定基因是治疗的潜在靶标 阿尔茨海默氏病的策略。