Imaging Mass Spectrometry for metabolome mapping

用于代谢组图谱的成像质谱法

• 批准号: 10175695
• 负责人: Zhandong Liu
• 金额: $ 20.96万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10175695
• 关键词: 2019-nCoV; Algorithms; Area; Award; Biological Markers; Brain; Brain Diseases; Clinical; Code; Complement; Data; Data Collection; Development; Diagnostic; Disease; Disease model; Equipment; Health; Homeostasis; Human; Hybrids; Image; Individual; Label; Lead; Machine Learning; Maps; Mass Spectrum Analysis; Metabolic; Methods; Modeling; Nerve Degeneration; Neurologist; Neurons; Organism; Organoids; Patients; Play; Positioning Attribute; Process; Prognostic Marker; Protocols documentation; Role; Sensitivity and Specificity; Source; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Testing; Thinness; Tissues; Translations; animal tissue; automated algorithm; biological systems; biomarker discovery; cell type; diagnostic biomarker; high-throughput drug screening; human disease; instrument; mass spectrometer; metabolic phenotype; metabolome; nerve stem cell; novel; quantitative imaging; response; screening; small molecule; targeted imaging; tissue regeneration; tool

SUMMARY In response to NOT-GM-20-013, we are requesting a supplement to our R01 5R01GM120033-04 for an MALDI imaging source unit to be attached to an existing Q ExactiveMass Spectrometer (Ultra-High Mass Range Hybrid Quadrupole-Orbitrap™) for spatial mapping of metabolites in thin tissue sections. Within our R01 award, to analyze NMR metabolome data we are developing two novel, powerful, and automated algorithms that capitalize on recent developments in machine learning. We have coded these algorithms and tested their sensitivity and specificity on both synthesized and real data. We then applied these methods to human disease models and identified putative biomarkers. To validate these biomarkers, we have developed methods to analyze animal tissues and human brain organoids using imaging mass spectrometry (IMS), which permits spatial localization of metabolites without labeling. This targeted IMS metabolic phenotyping approach complements our untargeted NMR methods: it allows us to determine whether the individual metabolites identified by NMR represent bona fide biomarkers and to develop metabolic hypotheses for their association with disease. We submit this request for imaging mass spectrometer hardware because a nearby IMS facility on which we have relied has closed and no other IMS facility exists in greater Houston area. Performing the IMS studies ourselves, with the help of collaborators, will accelerate our discovery about the role small molecules and metabolites play in health and disease. This instrument will help us better i) perform metabolome screens to identify the effects of SARS-CoV-2 on neural cell types in human brain organoid models; ii) perform high-throughput drug screening to stimulate neural stem cells to produce new neurons in the brain organoid models to regenerate damaged tissue; and iii) use our NMR algorithms to develop a protocol for quantitative imaging. None of these studies will be possible without the imaging mass spectrometer. Given our access to state-of-the-art equipment, data-collection expertise, and new analytical algorithms that are especially sensitive and specific to NMR spectral data, we are uniquely positioned to advance biomarker and diagnostics tools and screening methods for metabolites and synthetic small molecules. Using an imaging mass spectrometer to map metabolite distribution may help us discover diagnostic and prognostic biomarkers not only for SARS-CoV-2, but for a broad spectrum of brain disorders that lead to neurodegeneration. Such broad usage of our platform would be transformative for neuroscientists, neurologists, and their patients.

摘要 针对NOT-GM-20-013，我们要求对我们的R01 5R01GM120033-04进行补充 将MALDI成像源单元连接到现有的Q Exactive质谱仪(超高质量 Range混合四极轨道™)用于薄层组织切片中代谢物的空间映射。在我们的R01中 为了分析核磁共振代谢组数据，我们正在开发两种新的、强大的、自动化的算法 这利用了机器学习的最新发展。我们已经对这些算法进行了编码并测试了它们的 对合成数据和真实数据的敏感性和特异性。然后，我们将这些方法应用于人类疾病。 模型并确定了可能的生物标志物。为了验证这些生物标记物，我们已经开发了方法来 使用成像质谱仪(IMS)分析动物组织和人脑有机化合物，这允许 代谢物的空间定位而无需标记。这种有针对性的IMS代谢表型方法 补充了我们的非靶向核磁共振方法：它允许我们确定单个代谢物是否 通过核磁共振鉴定代表真正的生物标志物，并为它们之间的关联开发代谢假说 带着疾病。我们提交此成像质谱仪硬件申请是因为附近的IMS 我们所依赖的设施已经关闭，大休斯顿地区不存在其他IMS设施。 在合作者的帮助下，我们自己进行IMS研究，将加速我们对 小分子和代谢物在健康和疾病中的作用。 这台仪器将帮助我们更好地i)进行代谢组筛查，以确定SARS-CoV-2对 人脑器官模型中的神经细胞类型；ii)进行高通量药物筛选以刺激 神经干细胞在大脑器官模型中产生新的神经元，以再生受损组织；以及iii) 使用我们的核磁共振算法来开发一种定量成像协议。所有这些研究都不可能实现 没有成像质谱仪。鉴于我们可以使用最先进的设备，数据收集 专业知识和对核磁共振光谱数据特别敏感和特定的新分析算法，我们是 独一无二地定位于先进的生物标记物和代谢物的诊断工具和筛选方法 合成的小分子。使用成像质谱仪绘制代谢物分布图可能会帮助我们 不仅发现SARS-CoV-2的诊断和预后生物标志物，而且发现广泛的脑部生物标志物 导致神经退化的紊乱。如此广泛地使用我们的平台将对 神经学家、神经学家和他们的病人。