Infrared laser spectroscopy of mass-separated metabolites

质量分离代谢物的红外激光光谱

基本信息

  • 批准号:
    8829876
  • 负责人:
  • 金额:
    $ 17.34万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-04-01 至 2018-01-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Challenge: Developments of enabling technologies underpin continuing advances in biomolecular research. For instance, mass spectrometry (MS)-based sequencing techniques have spurned proteomics research in the past decade. Currently, there is no "gold standard" technique in metabolomics that allows a routine characterization of the thousands of constituents contained in these samples. NMR is limited to the more abundant analytes due to sensitivity issues. On the other hand, MS is typically capable of detecting many more features, but is often not able to structurally characterize these molecules. Rationale: By coupling tunable infrared (IR) lasers to mass spectrometry instrumentation, the IR spectra of mass- separated ions can be recorded. IR laser spectroscopy of ions combines the high sensitivity and ability to analyze complex mixtures of MS with the enhanced structural information from vibrational spectroscopy. The technique hence allows a chemical elucidation of many unknowns based on diagnostic vibrations and IR spectral fingerprints. Aim 1: Development of cryogenic mass spectrometry and multiplexed IR spectroscopy. In order to make IR spectroscopy a useful bioanalytical tool for biomolecular ions, it is essential that the IR spectra of analytes are well- resolved, and thus distinguishable, and that multiple analytes in mixtures can be probed simultaneously in a multiplexed fashion. We propose to develop a custom-built, cryogenic linear ion trap, where the ions are tagged with weakly-bound molecules (e.g. N2), which are selectively detached upon resonant IR absorption. Aim 2: IR spectroscopy of mass-separated metabolites. Our application of IR spectroscopy of biomolecules focuses on metabolites, where we expect the technique to have most potential. Control experiments on standard metabolites will establish how many analytes can be successfully probed in a multiplexed approach. The methodology will then be applied to selected metabolite samples from colon cancer studies, which have previously been analyzed by high- throughput liquid chromatography and high-resolution mass spectrometry. Aim 3: Structural elucidation of unknown metabolites by comparison to computed IR spectra and bioinformatics approaches. The ultimate goal of this proposal is to chemically characterize unknown biomarkers that cannot be identified by current MS approaches. This requires a comparison of the experimental data for each analyte, namely its mass and its IR spectrum, to putative matches from metabolite databases. The IR spectra of known standards (from aim 2) will serve as a training set and as a benchmark for implementing this identification methodology. Innovation and Impact: The techniques developed here are expected to have the largest impact in global metabolomics, where current tandem mass spectrometry methodologies limit the number of constituents that can be identified in these mixtures. We expect the enhanced structural information from vibrational spectroscopy to yield many new insights in biomarker discovery.
描述(由申请人提供):挑战:使能技术的发展支撑着生物分子研究的持续进步。例如,在过去的十年里,基于质谱学(MS)的测序技术已经抛弃了蛋白质组学研究。目前,代谢组学中还没有“黄金标准”技术,可以对这些样本中包含的数千种成分进行常规表征。由于敏感性问题,核磁共振仅限于更丰富的分析物。另一方面,MS通常能够检测到更多的特征,但通常不能从结构上表征这些分子。原理:通过将可调谐红外(IR)激光耦合到质谱仪上,可以记录质量分离离子的红外光谱。红外激光离子光谱结合了MS的高灵敏度和分析复杂混合物的能力,以及来自振动光谱的增强结构信息。因此,这项技术可以根据诊断振动和红外光谱指纹对许多未知因素进行化学解释。目的1:低温质谱学和多重红外光谱学的发展。为了使红外光谱成为生物分子离子的有用生物分析工具,红外光谱是必不可少的 分析物的光谱分辨率很好,因此可以区分,混合物中的多个分析物可以以多路复用的方式同时探测。我们建议开发一种定制的低温线性离子陷阱,其中离子被标记上弱结合分子(例如氮气),这些分子在共振红外吸收时被选择性地分离。目的2:质量分离代谢物的红外光谱。我们在生物分子红外光谱方面的应用主要集中在代谢物上,我们预计这项技术在这方面最具潜力。对标准代谢物的对照实验将确定在多重方法中可以成功探测多少分析物。然后,该方法将应用于从结肠癌研究中选择的代谢物样本,这些样本之前已经通过高通量液相色谱和高分辨率质谱仪进行了分析。目的3:通过与计算红外光谱和生物信息学方法的比较,阐明未知代谢物的结构。这项提议的最终目标是对目前的MS方法无法识别的未知生物标志物进行化学表征。这需要将每个分析物的实验数据,即其质量和红外光谱,与代谢物数据库中的推定匹配进行比较。已知标准的红外光谱(来自目标2)将作为实施这一鉴定方法的训练集和基准。创新和影响:这里开发的技术预计将对全球代谢组学产生最大影响,目前的串联质谱学方法限制了这些混合物中可以识别的成分的数量。我们期望来自振动光谱学的增强的结构信息将在生物标志物发现中产生许多新的见解。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Nicolas C Polfer其他文献

Nicolas C Polfer的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Nicolas C Polfer', 18)}}的其他基金

Infrared laser spectroscopy of mass-separated metabolites
质量分离代谢物的红外激光光谱
  • 批准号:
    8672493
  • 财政年份:
    2014
  • 资助金额:
    $ 17.34万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 17.34万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了