Graph Learning of Cell-cell Communications in Spatial Transcriptomics

空间转录组学中细胞间通信的图学习

• 批准号: 10504269
• 负责人: Zuoheng Wang
• 金额: $ 34.28万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-06 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504269
• 关键词: Address; Algorithms; Attention; Bar Codes; Bioinformatics; Biological Process; Biology; Caliber; Cell Communication; Cell surface; Cells; Computer software; Control Groups; Data; Data Analyses; Data Set; Disease; Face; Genes; Genetic; Genetic Transcription; Genomics; Goals; Graph; Homeostasis; Individual; Knowledge; Laplacian; Lead; Learning; Ligands; Light; Location; Measurement; Measures; Methods; Modeling; Pythons; Research; Research Design; Research Methodology; Research Project Grants; Research Project Summaries; Resolution; Sampling; Slide; Spottings; System; Technology; Tight Junctions; Tissues; Training; Translational Research; Validation; Variant; Vesicle; autoencoder; base; bioinformatics tool; cell type; computerized tools; design; disorder control; exosome; experience; graph learning; graph neural network; human tissue; intercellular communication; light weight; network models; open source; portability; precision medicine; receptor; single-cell RNA sequencing; spatial integration; statistics; study population; therapeutic development; transcriptome; transcriptomics

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.

项目摘要 研究项目：组织中细胞间通信的许多机制取决于物理接近性 在细胞之间。基于空间条形码的转录组数据提供了重要和重要信息 通过测量转录组数据和空间位置了解完整组织中的细胞间通信 细胞的简单。但是，分析这些数据以了解细胞间通信面对 以下挑战。首先，基于空间条形码的转录组数据缺乏单细胞分辨率和细胞类型 需要反卷积来推断细胞电池通信网络（CCCN）。其次，这是至关重要的，但受到挑战 将空间信息与配体 - 受体相互作用的事先了解和下游调节 CCCN推断的基因。最后，疾病和对照组之间CCCN的分布分布差异 需要评估以识别相关的CCCN扰动。当前方法无法 考虑细胞类型反卷积中相邻细胞之间细胞类型组成的空间相关性或 在CCCN推论中整合配体 - 受体对和下游调节基因的先验知识。那里 是否没有开发出用于比较两组受试者之间的CCCN的现有方法。该应用程序的目标 是为了开发准确，健壮，有效的生物信息学和计算工具来解析空间 基于条形码的转录组数据，使用空间转录组数据推断CCCN，并评估差异 两组受试者之间的CCCN。我们的长期目标是鉴定疾病相关的细胞间 从空间转录组数据中进行的沟通变化超出了通过研究发现的内容 单个细胞类型或细胞。为了实现这一目标，我们建议1）开发图形laplacian正规化模型 使用来自同一组织类型的SCRNA-SEQ数据对基于空间条形码的转录数据进行解析 空间信息的集成； 2）开发一个正规的图形注意网络模型，以推断CCCN 集成空间信息，配体 - 受体对的先验知识以及相应的下游 调节基因； 3）开发一个图形通用模型，该模型比较疾病与疾病之间的CCCN 控制样品以鉴定细胞间通信中相关的疾病扰动。 研究设计和方法：Drs。 Xiting Yan和Zuoheng Wang将共同领导拟议的研究 与合作者纳夫塔利·卡明斯基（Naftali Kaminski）博士一起，是一支经验的团队 生物信息学，统计，基因组学和遗传学，生物学，转化研究和精密医学。 将开发正规化的图形学习模型。我们主要研究人群的数据集将会 Kaminski博士实验室的初选，该实验室还将执行发现验证和下游功能 研究。 R和Python软件包将在一个灯光下开发并免费作为开源软件。 使用容器的重量，便携式和自给自足的方式。