DMS/NIGMS 1: Topological Study on Histological Images and Spatial Transcriptomics

DMS/NIGMS 1：组织学图像和空间转录组学的拓扑研究

• 批准号: 10592457
• 负责人: Chao Chen
• 金额: $ 21.51万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592457
• 关键词: Awareness; Cell Differentiation process; Cells; Cellular Structures; Collection; Complement; Data; Data Analyses; Development; Epithelial Cells; Foundations; Gene Activation; Gene Expression; Histology; Image; Image Analysis; Immune; Informatics; Knowledge; Maps; Methodology; Molecular; National Institute of General Medical Sciences; Neuroglia; Neurons; Neurosciences; Normal tissue morphology; Outcome; Play; Prognosis; Research Personnel; Resolution; Role; Signal Transduction; Techniques; Tissues; Work; anticancer research; bioimaging; disease diagnosis; histological image; image visualization; interest; learning algorithm; multiple omics; relating to nervous system; single-cell RNA sequencing; tissue mapping; transcriptomics; tumor; whole slide imaging

With the rapid advance of high-resolution transcriptomic profiling techniques, recent years have witnessed an increased interest in the study of the tissue microenvironment (TM) arising in cancer research and neuroscience, i.e., the collection of cells and structures in a tissue, such as neuron and glia in neural tissue or immune, stroma, and epithelial cells in tumors. The spatial configuration of these cells and structures plays pivotal roles in tissue function. Researchers have obtained high resolution transcriptomic and imaging data for TM study. The two types of information complement each other. High resolution transcriptomic profiling, such as single-cell RNA-seq (scRNA), provides cellular level molecular information, but does not carry local contextual information of cell, while histology image analysis provides detailed context, but does not provide corresponding cellular gene expression profiles. To combine them is challenging due to a lack of one-to-one correspondence between cells in transcriptomics and cells in histology images. This project intends to unify transcriptomics and bioimage informatics for a comprehensive study of TM, applying the advanced topological data analysis (TDA) methodology on the newly emerged spatial transcriptomic (ST) data. ST data provides localized spatial transcriptomics. TDA provides the foundation for studying rich contextual information in multi-omics. This project will produce a spatial-context-aware high-resolution mapping of TM transcriptomics. The outcome will be highly impactful. It will not only promote normal tissue level functionality characterization and mechanism study, but will also boost various types of diseases’ diagnosis, prognosis as well as their mechanistic studies. The PI/Co-PIs will create new topological approaches to extract rich contextual information from cells of multiple types in histology images. They will also propose new learning algorithms to integrate such topological information into localized ST scRNA data analysis for better differentiation of cells of different types and states, to build connection between spatial context and cell signaling gene activation, and to map transcriptomics information into whole slide image for visualization.

随着高分辨率转录组分析技术的快速发展，近年来人们已经忘记了 对癌症研究中组织微环境 (TM) 研究的兴趣日益浓厚 神经科学，即组织中细胞和结构的集合，例如神经组织中的神经元和神经胶质细胞 或肿瘤中的免疫细胞、基质细胞和上皮细胞这些细胞和结构的空间配置。 研究人员已经获得了高分辨率的转录组和成像数据。 TM 研究的两种类型的信息相互补充。 分析，例如单细胞 RNA 测序 (scRNA)，提供细胞水平的分子信息，但不提供 携带细胞的局部上下文信息，而组织学图像分析提供了详细的上下文，但不 由于缺乏相应的细胞基因表达谱，将它们结合起来具有挑战性。 转录组学中的细胞与组织学图像中的细胞之间的一一对应关系。 该项目旨在统一转录组学和生物图像信息学，以全面研究 TM，将先进的拓扑数据分析（TDA）方法应用于新出现的空间 转录组 (ST) 数据为局部空间转录组学提供了基础。 用于研究多组学中丰富的上下文信息该项目将产生一个空间上下文感知的模型。 TM 转录组学的高分辨率图谱不仅会产生巨大影响。 促进正常组织水平的功能表征和机制研究，同时也将促进各种 疾病类型的诊断、预后及其机制研究。 PI/Co-PI 将创建新的拓扑方法，从细胞中提取丰富的上下文信息 他们还将提出新的学习算法来整合此类图像。 将拓扑信息转化为局部 ST scRNA 数据分析，以便更好地分化不同细胞 类型和状态，建立空间背景和细胞信号基因激活之间的联系，并绘制地图 转录组学信息转化为整个幻灯片图像以进行可视化。