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： 测量和量化各种代谢产物组成和丰度的能力 样品，也称为代谢组学，为复杂的生物学提供了一个独特的窗口 在不同尺度的过程。到目前为止，代谢组学领域主要是由技术驱动的 基于质谱（MS）和核磁共振（NMR）光谱法。这些 技术虽然强大，但仅测量同质生物提取物中的代谢物谱，但 例如，生物流体或解剖组织，因此失去了基本代谢的空间信息 过程。由于空间异质性是代谢的标志，尤其是在复杂的生物学中 诸如动​​物和人类等系统获得空间分辨的代谢组学一直是梦想 许多生物医学科学家和工程师。近年来，MS成像（MSI）已成为 成像代谢组学的选择，可以生成空间局部的代谢物 来自组织切片的轮廓。 MSI的主要限制是它需要验尸或侵入性 组织采样，因此无法在最相关的状态下探测代谢。这就是 将其翻译成人类研究。 MR光谱成像（MRSI）是另一种选择 成像代谢组学。它结合了MRI和NMR光谱的能力，以在空间上产生 分辨的组织代谢产物谱，无创优。但是，MRSI的空间差高度限制 决议。此外，大多数MRSI研究仅针对单个核（例如1H），因此在 测量的分子物种数量。拟议研究的总体目标是开发 研究计划将为组织代谢组学的体内成像铺平道路。 具体而言，我们旨在开发一种前所未有的高分辨率多核MRSI技术 可以同时在体内绘制大量代谢物，使超高的进步协同作用 现场MRI仪器，快速数据获取和机器学习驱动的计算成像 技术。我们还提出了一个新颖的多模式MRSI和MSI成像框架，以验证我们 多核MRSI技术并整合了两种互补的生化成像方式 组织代谢分析。将开发新的计算方法来分析高 维代谢组数据。拟议研究的成功将建立一个新的范式 生成和分析成像代谢组学数据。这种范式将使代谢组学变成 一种强大的非侵入性和组织特异性技术（来自一种侵入性和非专门的技术） 用于研究活的动物和人类的新陈代谢。这些进步将使新的手段 揭示正常生理功能和不同疾病的代谢基础，鼓舞人心 新生物标志物，新型治疗，疾病预后和管理策略的发展。