Towards In Vivo Imaging of Tissue Metabolomics

组织代谢组学的体内成像

• 批准号: 10276342
• 负责人: Fan Lam
• 金额: $ 35.6万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-18 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10276342
• 关键词: Animals; Autopsy; Biochemical; Biological; Biological Markers; Biological Process; Biomedical Engineering; Cell Nucleus; Complex; Data; Development; Disease; Dreams; Generations; Goals; Heterogeneity; Human; Image; Imaging Techniques; Imaging technology; Machine Learning; Magnetic Resonance Imaging; Maps; Mass Spectrum Analysis; Measures; Metabolic; Metabolism; Molecular; NMR Spectroscopy; Physiological; Procedures; Prognosis; Research; Resolution; Sampling; Technology; Time; Tissue Sample; Tissue imaging; Tissues; base; biomedical scientist; complex biological systems; data acquisition; high dimensionality; imaging modality; imaging study; in vivo; in vivo imaging; instrumentation; magnetic resonance spectroscopic imaging; metabolic abnormality assessment; metabolomics; multimodality; non-invasive imaging; novel; programs; spectroscopic imaging; success; tool; translation to humans

PROJECT ABSTRACT: The ability to measure and quantify the composition and abundance of various metabolites in biological samples, also referred to as metabolomics, provides a unique window into the complex biological processes at different scales. So far, the field of metabolomics has mainly been driven by technologies based on mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. These technologies, although powerful, only measure metabolite profiles in homogenized biological extracts, e.g., biofluids or dissected tissues, thus losing the spatial information of the underlying metabolic processes. As spatial heterogeneity is a hallmark of metabolism, especially in complex biological systems such as animals and humans, obtaining spatially resolved metabolomics has been a dream of many biomedical scientists and engineers. In recent years, MS imaging (MSI) has emerged as a tool of choice for imaging metabolomics, which allows for the generation of spatially localized metabolite profiles from tissue sections. One major limitation of MSI is that it requires post-mortem or invasive tissue sampling, thus unable to probe metabolism at the most physiologically relevant states. This has limited its translation to human studies. MR spectroscopic imaging (MRSI) is another alternative for imaging metabolomics. It combines the powers of MRI and NMR spectroscopy to produce spatially resolved tissue metabolite profiles, noninvasively. However, MRSI is highly limited in its poor spatial resolutions. Furthermore, most MRSI studies only target a single nucleus (e.g., 1H), thus limited in the number of molecular species measured. The overall goal of the proposed research is to develop a research program that will pave a path towards in vivo imaging of tissue metabolomics. Specifically, we aim to develop an unprecedented high-resolution multinuclear MRSI technology that can simultaneously map a large number of metabolites in vivo, synergizing advancements in ultrahigh- field MRI instrumentation, fast data acquisition, and machine learning driven computational imaging techniques. We also propose a novel multimodal MRSI and MSI imaging framework for validating our multinuclear MRSI technology and integrating two complementary biochemical imaging modalities for tissue metabolic profiling. Novel computational approaches will be developed to analyze the high- dimensional metabolomic data. Success of the proposed research will establish a new paradigm for generating and analyzing imaging metabolomics data. This paradigm will transform metabolomics into a powerful noninvasive and tissue specific technology (from an invasive and nonspatial-specific one) for studying metabolism in living animals and humans. These advances will enable new means to unravel the metabolic basis of normal physiological functions and different diseases, inspiring developments of new biomarkers, novel treatments, disease prognosis and management strategies.

项目摘要： 测量和量化生物中各种代谢物的组成和丰度的能力 样品，也称为代谢组学，提供了了解复杂生物的独特窗口 不同规模的流程。迄今为止，代谢组学领域主要由技术驱动 基于质谱（MS）和核磁共振（NMR）光谱。这些 技术虽然强大，但只能测量均质生物提取物中的代谢物概况， 例如，生物流体或解剖组织，从而丢失潜在代谢的空间信息 流程。由于空间异质性是新陈代谢的一个标志，特别是在复杂的生物中 对于动物和人类等系统，获得空间解析的代谢组学一直是人们的梦想 许多生物医学科学家和工程师。近年来，MS 成像 (MSI) 已成为一种工具 代谢组学成像的选择，可以生成空间局部代谢物 组织切片的轮廓。 MSI 的一个主要限制是它需要尸检或侵入性检查 组织采样，因此无法探测最生理相关状态的新陈代谢。这有 限制了其对人类研究的翻译。 MR 光谱成像 (MRSI) 是另一种替代方案 成像代谢组学。它结合了 MRI 和 NMR 波谱的力量来产生空间 非侵入性地解析组织代谢谱。然而，MRSI 因其空间较差而受到很大限制。 决议。此外，大多数 MRSI 研究仅针对单个核（例如 1H），因此仅限于 测量的分子种类数。拟议研究的总体目标是开发一种 研究计划将为组织代谢组学的体内成像铺平道路。 具体来说，我们的目标是开发一种前所未有的高分辨率多核 MRSI 技术， 可以同时绘制体内大量代谢物的图谱，协同推进超高 现场 MRI 仪器、快速数据采集和机器学习驱动的计算成像 技术。我们还提出了一种新颖的多模态 MRSI 和 MSI 成像框架来验证我们的 多核 MRSI 技术并集成两种互补的生化成像模式 组织代谢谱。将开发新的计算方法来分析高 维度代谢组数据。拟议研究的成功将为以下领域建立一个新的范式： 生成和分析成像代谢组学数据。这种范式将把代谢组学转变为 一种强大的非侵入性和组织特异性技术（来自侵入性和非空间特异性技术） 用于研究活体动物和人类的新陈代谢。这些进步将使新的手段成为可能 揭示正常生理功能和不同疾病的代谢基础，启发 新生物标志物、新疗法、疾病预后和管理策略的发展。