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 .

脑中神经元-小胶质细胞双向信号相互作用的功能障碍是最重要的 阿尔茨海默病（AD）的突出但知之甚少的机制。基因和途径， 调节神经元-小胶质细胞相互作用的基因几乎没有被识别出来。空间转录组学（ST） 为研究神经元-小胶质细胞相互作用提供了无与伦比的机会，但分析工具 没有得到很好的发展。根据单个细胞是否可以进行空间映射和分析，ST数据 可以分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 相互作用的主要障碍是缺乏细胞分辨率，而使用单细胞ST的主要挑战是低功耗 和识别的准确性。在目标1中，我们提出了一种新的联合空间网络模型， 与散装ST的相互作用鉴定，并将相互作用与阿尔茨海默病相关联。我们将整合 来自单核RNA测序（snRNAseq）的细胞谱与批量ST以注释ST中的点， 识别神经元和小胶质细胞富集点对，联合建模多个神经元-小胶质细胞相互作用 网络使用来自不同年龄和AD状态的小鼠的这些斑点，并确定AD- 与神经元-小胶质细胞相互作用相关的表型（网络边缘）。我们将应用建议的联合空间 最近的批量ST研究的网络模型，该研究对每个半球约500个点的转录组学进行了20 大脑半球从野生型和转基因小鼠阿尔茨海默氏病，并验证发现的细胞- 通过利用独立的单细胞ST研究和阿尔茨海默病的snRNAseq研究， 疾病在目标2中，我们提出了一种基于分位数的距离校准空间网络方法，以改善 利用单细胞ST数据识别细胞间相互作用的研究能力和准确性。我们将模拟 基因表达与细胞间距离的整个分布之间的异质性关联，以及 进一步聚集转录组范围的信号，以同时鉴定（1）是否存在细胞-细胞 相互作用，（2）两个细胞具有稳健相互作用的物理距离，以及（3）其表达 水平是通过相互作用而改变的。然后，我们将杠杆的确定距离和相互作用的变化 基因来构建细胞间相互作用的空间网络。我们将该模型应用于seqFISH+数据， 包括覆盖皮层中约0.5 mm 2面积的2，963个细胞的10，000个基因图谱 室下区和嗅球区，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/统计工具能够识别细胞-细胞 与批量ST和单细胞ST数据的相互作用，也有助于更广泛的科学界， 任何组织的模型ST数据。从这项研究中鉴定的基因是治疗的潜在靶点。 老年痴呆症的治疗策略