A Computational IMage Analysis Platform (CIMAP) for HuBMAP

HuBMAP 的计算图像分析平台 (CIMAP)

• 批准号: 10532531
• 负责人: Sanjay Jain
• 金额: $ 74.99万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-03 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10532531
• 关键词: 3-Dimensional; Anatomy; Atlases; Basic Science; Biological Models; Biology; Cell Count; Cells; Clinical Research; Clinical assessments; Computers; Data; Data Set; Discipline; Engineering; Environment; Genes; Goals; Histology; Human; Image; Image Analysis; Internet; Knowledge; Link; Liver; Lung; Machine Learning; Mentorship; Microscopy; Modeling; Molecular; Morphology; Multiomic Data; Ontology; Organ; Play; Proteins; Public Health; RNA; Renaissance; Research Project Grants; Resolution; Scientist; Skin; Technology; Tissue imaging; Tissues; Transcript; Translational Research; Validation; base; cell type; clinical decision-making; clinical diagnosis; computational pipelines; drug discovery; high dimensionality; imaging modality; innovation; lymph nodes; machine learning pipeline; metabolome; molecular imaging; next generation; novel; preclinical study; prognostication; recruit; submicron; success; synergism; tool; transcriptomics; underrepresented minority student

Abstract: Advancement in high-resolution microscopy has opened unprecedented opportunities to investigate cells and tissues spatially at sub-micron level, via molecular imaging of gene transcripts, proteins or metabolomes. Parallel advances in computer-based hardware technologies and AI/ machine learning (ML) also offer a vehicle to study such multi-omics data in high dimensionality. An outstanding challenge involves a fusion of such data and thorough understanding of the fused data in all possible domains, including in basic science, clinical or pre-clinical studies using model systems, clinical diagnosis, prognostication, and drug discovery. Human Bio-Molecular Atlas Project (HuBMAP) consortium is an avenue for generating high-resolution multi-omics data at single cell resolution using a multitude of spatial molecular omics technologies. Common imaging modality that connects all these data types is brightfield histology microscopy, which is inexpensive and integrates the above-mentioned multi-omics data with clinical decision making. This HIVE Tools proposal aims to develop and implement novel machine learning pipelines to predict cell types and/or states from brightfield histology images using spatial protein- and/or RNA-based technology data with concurrent brightfield histology. This will enable using these spatial omics data as a bridge to link histology with high content single cell data sets and thus create a single exploration space from histology to biomolecules in distinct cell types. As a first step, we will employ select data collected under HuBMAP or generated via this HIVE team using CODEX as well as spatial transcriptomics (ST), and develop the proposed computational pipeline. We will demonstrate mapping of cell types and cell states to brightfield histology images on the same section from which the molecular data are generated, as well as on the independent adjacent section via registration, and finally on an independent validation tissue section. We will subsequently explore application of this approach to other HuBMAP organs including lymph node, skin, liver and lung. We will also develop 3D scalable graphics of cell types being detected using our pipeline, with a goal to develop ontological framework integrating atoms to anatomy for an objective understanding of variability in reference human atlas. We will create synergies with other HIVE teams to integrate the developed pipelines, tools with HuBMAP web-cloud portal as an easy-to-use, plug-and-play end-user plugin that is openly accessible to quantify cell counts, types, features, as well as states via uploading brightfield histology tissue images to the portal. Our innovative translational science teams’ model will recruit underrepresented minority students in STEM from biology as well as from engineering disciplines to provide them a mentorship environment and scientific opportunities within our team and that of collaborators. This strategy will develop a next generation renaissance scientist, who will be able to continue investigating along the proposed direction combining knowledge from biology, imaging, and engineering in a single research project.

摘要：高分辨率显微镜的发展为在亚微米水平上研究细胞和组织提供了前所未有的机会，通过基因转录物、蛋白质或代谢组的分子成像。基于计算机的硬件技术和人工智能/机器学习（ML）的并行进步也为研究这种高维多组学数据提供了一种工具。一个突出的挑战涉及到这些数据的融合和对所有可能领域的融合数据的透彻理解，包括基础科学、使用模型系统的临床或临床前研究、临床诊断、诊断和药物发现。人类生物分子图谱计划（HuBMAP）联盟是一种使用多种空间分子组学技术以单细胞分辨率生成高分辨率多组学数据的途径。连接所有这些数据类型的常见成像模式是明场组织学显微镜，其价格低廉，并将上述多组学数据与临床决策相结合。该HIVE Tools提案旨在开发和实施新的机器学习管道，以使用基于蛋白质和/或RNA的空间技术数据和并发明场组织学，从明场组织学图像中预测细胞类型和/或状态。 这将使使用这些空间组学数据作为桥梁，将组织学与高含量的单细胞数据集联系起来，从而创建从组织学到不同细胞类型中的生物分子的单一探索空间。 作为第一步，我们将使用在HuBMAP下收集的或通过HIVE团队使用CODEX以及空间转录组学（ST）生成的数据，并开发拟议的计算管道。 我们将在生成分子数据的同一切片上，以及通过配准在独立的相邻切片上，最后在独立的验证组织切片上，演示细胞类型和细胞状态到明场组织学图像的映射。我们随后将探索这种方法在其他HuBMAP器官（包括淋巴结、皮肤、肝脏和肺）中的应用。 我们还将开发使用我们的管道检测到的细胞类型的3D可扩展图形，目标是开发将原子与解剖结构相结合的本体框架，以便客观地了解参考人体图谱中的变异性。我们将与其他HIVE团队建立协同效应，将开发的管道，工具与HuBMAP网络云门户整合为一个易于使用的，即插即用的最终用户插件，可以通过将明场组织学组织图像上传到门户来公开访问以量化细胞计数，类型，特征以及状态。我们创新的转化科学团队的模式将招募来自生物学和工程学科的STEM中代表性不足的少数民族学生，为他们提供我们团队和合作者中的导师环境和科学机会。这一战略将培养下一代的文艺复兴科学家，他们将能够继续沿着沿着提出的方向进行研究，将生物学，成像和工程学的知识结合在一个研究项目中。