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。 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 但是，细胞同时分析数据以了解细胞间通信 遵循挑战。 需要去卷积来推断细胞电池通信网络（CCCN）。 将空间信息与配体 - 受体相互作用和下游常规知识的事先了解相结合 CCCN推断的基因。 需要评估以识别CCCN扰动。 解释细胞类型反卷积中相邻细胞之间细胞分配的空间相关性或 为了整合CCCN推断中配体 - 受体对和下游常规基因的先验知识。 没有开发出来的方法来比较两个对象之间的CCCN。 是为了开发计算，鲁棒和功效的生物信息学和计算工具来解除空间 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 CCCN在两组受试者之间。 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要实现此目标，我们提议1）开发图形 使用来自同一组织类型的SCRNA-SEQ对基于空间条形码的转录数据进行解析 空间信息的集成 整合空间信息，配体 - 受体对的先验知识以及下游的Corresspond 常规基因和3）开发了一个图形生成模型，该模型在疾病和 控制样品以识别细胞间通信中的分解。 研究设计和方法：Drs。 与合作者纳夫塔利·卡明斯基（Naftali Kaminski） 生物信息学，统计，基因组学和遗传学，生物学，转化研究和精确医学。 将开发正规图学习模型。 主要来自Kaminski博士实验室，该实验室还将执行验证和下游功能 研究。 使用容器的重量，便携式和自我无情方式。