Visual Analytics for Exploration and Hypothesis Generation Using Highly MultiplexedSpatial Data of Tissues and Tumors

使用组织和肿瘤的高度多重空间数据进行探索和假设生成的可视化分析

• 批准号: 10743329
• 负责人: Hanspeter Pfister
• 金额: $ 41.54万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-12 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10743329
• 关键词: 3-Dimensional; Acceleration; Algorithms; Architecture; Atlases; Back; Biological; Biological Markers; Biology; Cancer Research Project; Cell Communication; Cells; Clinical; Collaborations; Communication; Communities; Complex; Computer Analysis; Computer software; Coupled; Cultured Cells; Data; Data Analyses; Data Collection; Data Set; Databases; Development; Disease Progression; Education; Educational process of instructing; Elements; Evaluation; FAIR principles; Generations; Genes; Hematoxylin and Eosin Staining Method; Human; Human BioMolecular Atlas Program; Image; Image Analysis; Immunologic Surveillance; Individual; Informatics; International; Intuition; Invaded; Knowledge; Label; Laboratories; Length; Machine Learning; Malignant Neoplasms; Manuscripts; Measurement; Messenger RNA; Metadata; Methods; Microscope; Modeling; Morphology; Multimodal Imaging; Mutation; Neighborhoods; Normal tissue morphology; Oncologist; Online Systems; Paper; Participant; Pathologist; Pattern; Phase; Process; Publications; Publishing; Research; Resolution; Slide; Software Tools; Solid; Source; Specimen; Structure; Techniques; Technology; Testing; The Cancer Genome Atlas; Three-Dimensional Image; Tissue atlas; Tissue imaging; Tissues; Training; Transcript; United States National Institutes of Health; User-Computer Interface; Visual; Visualization; Visualization software; Weight; Work; analytical method; analytical tool; cancer diagnosis; cloud based; complex data; computer infrastructure; cost; data communication; data dissemination; data exploration; data integration; data repository; data sharing; data visualization; design; digital; digital pathology; diverse data; experimental study; high dimensionality; human-in-the-loop; improved; innovation; interest; interoperability; light weight; mass spectrometric imaging; microscopic imaging; multimodal data; multimodality; next generation; open data; open source; open source tool; professional atmosphere; programs; protein biomarkers; skills; small molecule; tool; transcriptomics; tumor; tumor initiation; tumor microenvironment; usability; web-based tool; whole slide imaging

PROJECT SUMMARY The recent development of highly multiplexed subcellular resolution tissue imaging promises to accelerate research into tumor initiation, progression, and immune surveillance and ultimately aid in the discovery of new biomarkers usable in a clinical setting. In parallel, spatially resolved measurement of transcript and small molecule abundances is achieving near single-cell resolution. NCI programs such as the Human Tumor Atlas Network (HTAN) are capitalizing on these developments to create public data repositories (“Tissue Atlases”) similar in scope and ambition to The Cancer Genome Atlas (TCGA). The greatest barriers to making such data routinely accessible to basic and translational cancer biologists lie not in data collection but rather data visualization and analysis. Existing software tools designed for cultured cell experiments or hematoxylin and eosin (H&E) based digital pathology are inadequate for high-plex data applications, and emerging tools do not meet the needs of either low-cost and efficient data sharing or sophisticated multi-modal machine learning from diverse data. This Informatics Technology for Cancer Research (ITCR) project will therefore develop, harden, and test standards-compliant software tools that make it possible to visualize, annotate, and quantify features of the tumor microenvironment spanning a 105-fold range in length scale (from ~100 nm to 1 cm) by building on a suite of interoperable, cloud-based MINERVA tools that also work well with existing commercial and open-source software. Key user communities include cell biologists, microscopists, pathologists and oncologists with expertise in imaging and tissue biology and computational biologists and bioinformaticians who process and integrate image and ‘omic data into atlases. These users work closely together in our laboratories but have distinct needs. Our tools will therefore support three phases of research: (i) initial data exploration via intuitive and easy-to-deploy web-based tools; (ii) hypothesis generation and testing via sophisticated ML-enabled visual analytics; and (iii) data publication and integration with existing knowledge, databases, and atlases. Our innovations will include the latest advances in visual encoding, ML/AI, and human-computer interfaces that enable human-in-the-loop analysis and explanatory and exploratory data visualization. Aim 1 will establish light-weight methods for low-cost visualization and communication of multiplex IF, H&E, and spatial omics data collected by HTAN and similar international consortia. Aim 2 will develop new ways for deeply exploring and analyzing the spatial data for hypothesis generation and testing, with a focus on quantifying morphology and cell-cell interactions in 2D whole-slide and high-resolution 3D images. Aim 3 will expand our MINERVA platform to enable collaborative analysis and data sharing across different audiences and data types to better understand how tumor architecture changes with disease progression and treatment. Together, our software will promote efficient hypothesis generation and testing from complex, multi-modal datasets as well as their annotation and distribution in accordance with FAIR data principles.

项目摘要 高度多重的亚细胞分辨率组织成像的最新发展有望加速 研究肿瘤启动，进展和免疫监视，并最终有助于发现新的 生物标志物可在临床环境中使用。同时，对转录本的空间分辨测量和小 分子丰度达到接近单细胞的分辨率。 NCI计划，例如人类肿瘤地图集 网络（HTAN）正在利用这些发展，以创建公共数据存储库（“组织地图集”） 与癌症基因组图集（TCGA）相似的范围和野心。制作此类数据的最大障碍 基本和翻译的癌症生物学家通常可以使用数据收集，而是数据收集 可视化和分析。现有的软件工具专为培养细胞实验或苏木精和 基于曙红（H＆E）的数字病理学不足以适用于高音数据应用程序，而新兴工具没有 从低成本和高效的数据共享或复杂的多模式机器学习的需求 多样的数据。因此，这种用于癌症研究（ITCR）项目的信息学技术将发展，硬化， 以及符合标准的软件工具，使得可以可视化，注释和量化的功能 肿瘤微环境跨越105倍长度范围（从〜100 nm到1厘米），通过在 可互操作的基于云的密涅瓦工具套件，这些工具也可以与现有的商业和开源配合 软件。关键用户社区包括细胞生物学家，显微镜医生，病理学家和肿瘤学家 成像和组织生物学以及计算生物学家和生物信息学家的专业知识，他们正在加工和生物信息学家 将图像和'OMIC数据集成到Atlase中。这些用户在我们的实验室中紧密合作，但 独特的需求。因此，我们的工具将支持研究的三个阶段：（i）通过直觉探索初始数据探索 以及易于控制的基于网络的工具； （ii）通过复杂的ML视觉效果的假设生成和测试 分析； （iii）数据出版和与现有知识，数据库和地图集的集成。我们的 创新将包括视觉编码，ML/AI和人类计算机界面的最新进展 启用人类在环境分析以及探索性和探索性数据可视化。 AIM 1将建立轻量级的方法，用于低成本可视化和多路复用的通信，如果H＆E和 HTAN和类似国际财团收集的空间OMIC数据。 AIM 2将开发新的方法 探索和分析假设生成和检验的空间数据，重点是量化 2D全分辨率和高分辨率3D图像中的形态和细胞 - 细胞相互作用。 AIM 3将扩大我们的 密涅瓦平台可实现不同受众和数据类型的协作分析和数据共享 更好地了解肿瘤结构如何随疾病进展和治疗而变化。在一起，我们的 软件将促进复杂，多模式数据集的有效假设生成和测试 根据公平数据原则，它们的注释和分发。