Graph Learning of Cell-cell Communications in Spatial Transcriptomics

项目总结 研究项目：组织中细胞间通讯的许多机制依赖于物理上的接近 在细胞之间。基于空间条形码的转录数据为 通过测量转录数据和空间位置了解完整组织中的细胞间通讯 多个细胞同时存在。然而，分析这些数据以了解细胞间通信面临着 在挑战之后。首先，基于空间条形码的转录数据缺乏单细胞分辨率，也缺乏细胞类型 需要去卷积来推断小区间通信网络(CCCN)。其次，它是关键的，但具有挑战性 将空间信息与配体-受体相互作用和下游调控的先验知识相结合 CCCN推断的基因。最后，疾病组和对照组之间CCCNs分布的差异 需要进行评估，以确定与疾病相关的CCCN扰动。目前的方法不能 在细胞类型反卷积中解释相邻细胞之间的细胞类型组成的空间相关性 在CCCN推断中整合配基-受体对和下游调控基因的先验知识。那里 目前还没有开发出方法来比较两组受试者之间的CCCN。此应用程序的目标是 是开发准确、健壮和高效的生物信息学和计算工具来去卷积空间 基于条形码的转录数据，使用空间转录数据推断CCCN，并评估 两组受试者之间的CCCN。我们的长期目标是识别与疾病相关的细胞间 空间转录数据带来的交流变化超出了调查发现的范围 单个细胞类型或细胞。为了实现这一目标，我们建议1)建立一个图拉普拉斯正则化模型 使用来自相同组织类型的scRNA-seq数据对基于空间条形码的转录数据进行去卷积 2)提出了一种规则化的图注意网络模型来推理CCCNs 整合空间信息、配基-受体对和相应下游的先验知识 以及3)开发一个图形生成模型，将CCCN在疾病和 对照样本，以识别细胞间通信中与疾病相关的扰动。 研究设计和方法：严锡庭博士和王作恒博士将共同领导这项拟议的研究 与合作者Naftali Kaminski博士一起，一个经验丰富、致力于以下领域的专家团队 生物信息学、统计学、基因组学和遗传学、生物学、转化研究和精确医学。 将开发正则化图学习模型。我们主要研究人群的数据集将会出现 主要来自卡明斯基博士的实验室，该实验室还将执行发现验证和下游功能 学习。R和PYTHON包将作为开源软件开发并免费分发 重量轻，携带方便，使用容器自给自足。

PROJECT SUMMARY Research Project: Many mechanisms of intercellular communications in tissue depend on the physical proximity between cells. Spatial barcoding-based transcriptomic data provide important and essential information to understand intercellular communications in intact tissue by measuring transcriptomic data and spatial locations of cells simultaneously. However, analysis of these data to understand intercellular communications faces the following challenges. First, spatial barcoding-based transcriptomic data lacks single-cell resolution, and cell type deconvolution is needed to infer cell-cell communication networks (CCCNs). Second, it is critical but challenging to integrate spatial information with prior knowledge of ligand-receptor interactions and downstream regulated genes for CCCN inference. Finally, difference in the distribution of CCCNs between disease and control groups needs to be assessed to identify disease associated CCCN perturbations. Current methods are not able to account for spatial correlation of cell type compositions between neighboring cells in cell type deconvolution nor to integrate prior knowledge of ligand-receptor pairs and downstream regulated genes in CCCN inference. There is no existing method developed to compare CCCNs between two groups of subjects. The goal of this application is to develop accurate, robust, and efficient bioinformatic and computational tools to deconvolve spatial barcoding-based transcriptomic data, infer CCCNs using spatial transcriptomic data, and assess differences in CCCNs between two groups of subjects. Our long-term objective is to identify disease associated intercellular communication changes from spatial transcriptomic data beyond what has been discovered by investigating individual cell types or cells. To achieve this goal, we propose to 1) develop a graph Laplacian regularized model to deconvolve spatial barcoding-based transcriptomic data using scRNA-seq data from same tissue type with integration of spatial information; 2) develop a regularized graph attention network model to infer CCCNs by integrating spatial information, prior knowledge of ligand-receptor pairs and corresponding downstream regulated genes; and 3) develop a graphical generative model that compares CCCNs between disease and control samples to identify disease associated perturbations in intercellular communications. Research design and methods: Drs. Xiting Yan and Zuoheng Wang will jointly lead the proposed research together with collaborator Dr. Naftali Kaminski, a team of experienced, committed experts in the fields of bioinformatics, statistics, genomics and genetics, biology, translational research and precision medicine. Regularized graph learning models will be developed. The datasets for our main study populations will come primarily from Dr. Kaminski’s lab, which will also execute discovery validations and downstream functional studies. R and python packages will be developed and freely distributed as open-source software in a light weight, portable and self-sufficient way using containers.