Towards In Vivo Imaging of Tissue Metabolomics

组织代谢组学的体内成像

• 批准号: 10490471
• 负责人: Fan Lam
• 金额: $ 37.34万
• 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/10490471
• 关键词: Animals; Autopsy; Biochemical; Biological; Biological Markers; Biological Process; Biomedical Engineering; Cell Nucleus; Complex; Data; Development; Disease; Disease Management; 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; Research; Resolution; Sampling; Technology; Time; Tissue Sample; Tissue imaging; Tissues; base; biomedical scientist; complex biological systems; data acquisition; disease prognosis; high dimensionality; imaging modality; imaging study; in vivo; in vivo imaging; instrumentation; magnetic resonance spectroscopic imaging; mass spectrometric 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 样本，也被称为代谢组学，为了解复杂的生物学提供了一个独特的窗口 不同规模的流程。到目前为止，代谢组学领域主要是由技术驱动的 基于质谱学(MS)和核磁共振(核磁共振)波谱。这些 技术虽然强大，但只测量均质生物提取物中的代谢物分布， 例如生物体液或解剖的组织，因此丢失了潜在代谢的空间信息 流程。因为空间异质性是新陈代谢的标志，特别是在复杂的生物学中 像动物和人类这样的系统，获得空间分辨的代谢组学一直是一个梦想 许多生物医学科学家和工程师。近年来，质谱学成像(MSI)作为一种 成像代谢组学的选择，它允许产生空间定位的代谢物 组织切片上的侧写。MSI的一个主要限制是它需要死后或侵入性 组织采样，因此无法探测生理上最相关状态的新陈代谢。这有 将其翻译限制在人类研究上。磁共振波谱成像(MRSI)是另一种替代方法 影像代谢组学。它结合了核磁共振和核磁共振光谱学的力量，可以在空间上产生 非侵入性地分解组织代谢物图谱。然而，MRSI在其较差的空间上受到高度限制 决心。此外，大多数MRSI研究只针对单个核(例如，1H)，因此局限于 所测量的分子物种的数量。拟议研究的总体目标是开发一种 将为组织代谢组学体内成像铺平道路的研究计划。 具体地说，我们的目标是开发一种前所未有的高分辨率多核磁共振成像技术， 可以同时映射体内的大量代谢物，协同超高... 现场MRI仪器、快速数据采集和机器学习驱动的计算成像 技巧。我们还提出了一种新的多模式MRSI和MSI成像框架来验证我们的 多核磁共振成像技术和两种互补生化成像模式的整合 组织新陈代谢图谱。将开发新的计算方法来分析高 次元代谢数据。拟议研究的成功将为 生成和分析成像代谢组学数据。这一范式将把代谢组学转变为 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 揭开正常生理功能和不同疾病的代谢基础，鼓舞人心 新的生物标志物、新的治疗方法、疾病预后和管理策略的发展。