Advancing Protein Identification for Imaging Mass Spectrometry for Pathology

推进病理学成像质谱的蛋白质鉴定

• 批准号: 8314708
• 负责人: MARSHALL Wayne BERN
• 金额: $ 34.86万
• 依托单位: PROTEIN METRICS, LLC
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-05 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8314708
• 关键词: Adoption; Algorithms; Area; Bioinformatics; Brain; Clinical; Clinical Pathology; Cohort Studies; Complex; Computer software; Controlled Study; Cut protein; Data; Development; Diagnostic; Digestion; Disease; Feasibility Studies; Heart Valves; Histocompatibility Testing; Human; Image; In Situ; Ions; Kidney; Liquid Chromatography; Liver; Mass Spectrum Analysis; Measurable; Measures; Medical Imaging; Methods; Metric; Modification; Molecular Profiling; Mus; Pathology; Peptides; Phase; Phosphorylation; Post-Translational Protein Processing; Preparation; Proteins; Proteomics; Protocols documentation; Public Health; Research; Research Personnel; Resolution; Sampling; Shotguns; Site; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Sum; Tissue Sample; Tissues; Translations; Work; data acquisition; disease diagnosis; glycosylation; improved; in vivo; instrument; link protein; mass analyzer; next generation; prognostic; software development; success; tandem mass spectrometry; tool

DESCRIPTION (provided by applicant): Molecular signatures collected from intact tissue sections by MALDI imaging mass spectrometry (MALDI IMS) have shown high potential for use as a prognostic or diagnostic pathology tool in the clinical setting. A major obstacle to the widespread deployment of MALDI IMS, however, is the difficulty of identifying the proteins contributing to the signatures. Researchers have tried a number of approaches, including in situ digestion, MALDI TOF/TOF tandem mass spectrometry, and top-down proteomics on specific image regions. In preliminary work, we have obtained promising experimental results using top-down proteomics on intact proteins in the 2 - 20 kDa range. However, the lack of successful algorithms and software to identify the proteins in IMS mass signatures poses a major bottleneck. In particular, available top-down proteomics software relies heavily on high-accuracy mass spectrometry. The requirement for high accuracy precludes the use of some of the most sensitive mass analyzers such as linear ion traps, especially useful for these very small and complex samples. Protein Metrics Inc. is a new software company building on six years of algorithms and software research at Palo Alto Research Center. We plan to extend Byonic, our next- generation proteomics search engine, to intact proteins up to about 20 kDa. For proteins larger than 20 kDa, we will also build software for middle-down proteomics, specifically for assembling large peptides (2 - 20 kDa) produced by limited digestion to recover the identity of the intact proteins observed in IMS. The proposed Phase I feasibility study will allow us to perform controlled studies to determine the best experimental and bioinformatics approaches. Phase II will then build commercial-grade software. The proposed project will advance the state of the art in imaging mass spectrometry. Translation of imaging mass spectrometry to routine clinical pathology use will advance the state-of-the-art in disease diagnosis and treatment, and advance medical imaging and public health. PUBLIC HEALTH RELEVANCE: The project will develop commercial software that will improve our ability to identify the proteins and modifications represented in imaging mass spectrometry molecular signatures. Project success will make imaging mass spectrometry much more useful as a clinical pathology tool.

描述(由申请人提供)：MALDI成像质谱仪(MALDI IMS)从完整的组织切片中收集的分子签名已显示出在临床环境中用作预后或诊断病理学工具的高潜力。然而，MALDI IMS广泛应用的一个主要障碍是难以识别有助于签名的蛋白质。研究人员尝试了许多方法，包括原位消化、MALDI TOF/TOF串联质谱学和特定图像区域的自上而下的蛋白质组学。在前期工作中，我们对2-20 kDa范围内的完整蛋白质进行了自上而下的蛋白质组学研究，获得了有希望的实验结果。然而，缺乏成功的算法和软件来识别IMS大规模签名中的蛋白质构成了一个主要的瓶颈。特别是，现有的自上而下的蛋白质组学软件在很大程度上依赖于高精度的质谱仪。对高准确度的要求排除了一些最灵敏的质量分析器的使用，例如线性离子陷阱，对这些非常小和复杂的样品特别有用。Protein Metrics Inc.是一家新的软件公司，建立在帕洛阿尔托研究中心六年的算法和软件研究基础上。我们计划将我们的下一代蛋白质组学搜索引擎Byonic扩展到大约20 kDa的完整蛋白质。对于大于20 kDa的蛋白质，我们还将建立中下游蛋白质组学软件，专门用于组装有限消化产生的大肽(2-20 kDa)，以恢复在IMS中观察到的完整蛋白质的身份。拟议的第一阶段可行性研究将使我们能够进行对照研究，以确定最佳的实验和生物信息学方法。然后，第二阶段将构建商业级软件。拟议的项目将促进成像质谱学的最先进水平。将成像质谱学转化为常规的临床病理学应用，将促进疾病诊断和治疗的最新水平，并促进医学成像和公共健康。 与公共健康相关：该项目将开发商业软件，以提高我们识别成像质谱学分子特征中所代表的蛋白质和修饰的能力。项目的成功将使成像质谱学作为一种临床病理工具更加有用。