Towards In Vivo Imaging of Tissue Metabolomics

项目摘要： 测量和量化生物体内各种代谢物的组成和丰度的能力 样品，也被称为代谢组学，提供了一个独特的窗口，复杂的生物 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。这些 技术虽然强大，但仅测量均质化生物提取物中的代谢物谱， 例如，在一个实施例中，生物流体或解剖的组织，从而丢失了潜在的代谢的空间信息 流程.由于空间异质性是代谢的标志，特别是在复杂的生物学中， 系统，如动物和人类，获得空间分辨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，MS成像（MSI）已经成为一种诊断的工具。 选择成像代谢组学，它允许产生空间定位的代谢物 组织切片的轮廓。MSI的一个主要局限性是它需要死后或侵入性检查， 组织取样，因此不能探测在最生理相关状态下的代谢。这 将其翻译限于人类研究。磁共振波谱成像（MRSI）是另一种选择， 成像代谢组学它结合了核磁共振成像和核磁共振光谱的力量， 非侵入性地解析组织代谢物谱。然而，MRSI在其较差的空间分辨率方面受到高度限制。 决议。此外，大多数MRSI研究仅针对单个核（例如，1H），因此， 测量的分子种类数。拟议研究的总体目标是制定一个 该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， 可以同时在体内绘制大量代谢物， 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的多模态MRSI和MSI成像框架，用于验证我们的 多核MRSI技术和整合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- 三维代谢组学数据。拟议研究的成功将为以下方面建立一个新的范例： 生成并分析成像代谢组学数据。这种模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（从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， 阐明正常生理功能和不同疾病的代谢基础， 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。

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.