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

脑内神经元-小胶质细胞双向信号相互作用的障碍是最常见的 阿尔茨海默病(AD)的突出但鲜为人知的机制。基因和途径 调控神经元-小胶质细胞的相互作用几乎没有被识别出来。空间转录组学(ST)研究进展 为研究神经元-小胶质细胞的相互作用提供了一个无与伦比的机会，但分析工具 没有得到很好的发展。根据是否可以对单个像元进行空间映射和剖面图，ST数据 可分为批量分辨率和单元格分辨率。块状扫描电子显微镜研究细胞的主要挑战--细胞 相互作用是缺乏细胞分辨率，而使用单细胞ST的主要挑战是低功率 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来使能单元-单元 识别与大块ST的相互作用，并将这些相互作用与阿尔茨海默病联系起来。我们将整合 来自单核RNA测序(SnRNAseq)和大块ST的细胞轮廓以注释ST中的斑点， 识别神经元和小胶质细胞丰富的斑点对，联合模拟多个神经元-小胶质细胞相互作用 利用这些斑点从不同年龄和AD状态的小鼠身上建立网络，并识别AD- 神经元-小胶质细胞相互作用的相关表型(网络边缘)。我们将应用拟议的联合空间 最近一项大规模ST研究的网络模型，该研究描绘了每个半球约500个点的20个转录组 从阿尔茨海默病野生型和转基因小鼠的大脑半球，并验证已发现的细胞- 利用独立的单细胞ST研究和阿尔茨海默病的SnRNAseq研究中的细胞相互作用 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，以提高空间网络的性能 研究用单细胞ST数据识别细胞间相互作用的能力和准确性。我们将为 基因表达和细胞-细胞距离的整个分布之间的异质性关联，以及 进一步聚合转录组范围的信号以同时识别(1)是否存在细胞-细胞 相互作用，(2)两个细胞具有强大相互作用的物理距离，(3)其表达的基因 水平会因相互作用而改变。然后，我们将利用确定的距离和更改后的交互 基因来构建细胞与细胞相互作用的空间网络。我们将该模型应用于SeqFISH数据 包括2,963个细胞的10,000个基因图谱，覆盖大脑皮层约0.5平方毫米的面积 脑室下区和嗅球区域，并使用独立的单细胞ST段数据和块状ST段数据进行 验证。这项研究中开发的计算/统计工具使细胞-细胞的识别成为可能 与大量ST和单细胞ST数据的交互，也有助于更广泛的科学界 对任何组织的ST数据进行建模。这项研究中发现的基因是潜在的治疗靶点。 阿尔茨海默病的治疗策略。

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 .