Advancing Protein Identification for Imaging Mass Spectrometry for Pathology

描述（由申请方提供）：通过MALDI成像质谱法（MALDI IMS）从完整组织切片中收集的分子特征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对特征有贡献的蛋白质。研究人员已经尝试了许多方法，包括原位消化，MALDI TOF/TOF串联质谱，以及特定图像区域的自上而下蛋白质组学。在初步工作中，我们已经获得了有希望的实验结果，使用自上而下的蛋白质组学在2 - 20 kDa的范围内的完整蛋白质。然而，缺乏成功的算法和软件来识别IMS质量签名中的蛋白质构成了主要瓶颈。特别是，可用的自上而下的蛋白质组学软件严重依赖于高精度质谱。对高精度的要求排除了使用一些最灵敏的质量分析仪，如线性离子阱，特别是对这些非常小和复杂的样品有用。 Protein 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扩展到约20 kDa的完整蛋白质。对于大于20 kDa的蛋白质，我们还将构建用于中间蛋白质组学的软件，特别是用于组装通过有限消化产生的大肽（2 - 20 kDa），以恢复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。第二阶段将构建商业级软件。 拟议的项目将推进成像质谱法的最新技术水平。将成像质谱法转化为常规临床病理学应用将推动疾病诊断和治疗的最新发展，并促进医学成像和公共卫生。 公共卫生相关性：该项目将开发商业软件，以提高我们识别成像质谱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作为临床病理学工具更加有用。

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.