ADVANCED DATA PROCESSING FOR CAPILLARY LC/MS DATA

毛细管 LC/MS 数据的高级数据处理

• 批准号: 8357131
• 负责人: Ji Zhang
• 金额: $ 11.54万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8357131
• 关键词: Acceleration; Algorithms; Behavioral; Biological; Biological Assay; Biological Markers; Biomedical Research; Blood capillaries; Comprehension; Computational algorithm; Data; Data Analyses; Data Set; Detection; Disease; Disorder by Site; Enzyme-Linked Immunosorbent Assay; Experimental Autoimmune Encephalomyelitis; Funding; Gene Expression; Glucocorticoids; Goals; Grant; Health; Knowledge; Label; Liquid substance; Mass Spectrum Analysis; Methods; Modeling; National Center for Research Resources; Noise; Pathway Analysis; Peptides; Population; Principal Investigator; Process; Proteins; Proteomics; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resistance; Resources; Sampling; Sensitivity and Specificity; Series; Serum; Signal Transduction; Source; Specimen; Testing; Time; Tissues; Translating; United States National Institutes of Health; base; capillary; capillary liquid chromatography; computerized data processing; cost; cytokine; disease diagnosis; health disparity; high risk; improved; information processing; interest; liquid chromatography mass spectrometry; protein expression; racial and ethnic; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Mass spectrometry, especially, capillary liquid chromatography-mass spectrometry (LC/MS) is the most important tool for the acceleration of knowledge acquirement of the protein machinery underpinning biomedical research. However, it is has never been successfully applied to health disparities research, which aims at determining changes in protein expression under adverse societal, behavioral or environmental conditions to identify proteins that may be involved in diseases of high-risk racial and ethnic populations. Research in health disparities requires the ability to identify and quantify proteins with a wide dynamic range in abundance, particularly for serum specimens. Information processing, comprehension and interpretation are critical. A major bottleneck of protein biomarker discovery in health disparity research by label- free, LC/MS arises from the limitations of computer algorithms that are currently available to process this data. These algorithms quantify and determine the sensitivity and specificity of putative protein with strong positive or negative correlations to a disease state. However, low abundance protein biomarkers, often the most specific, are easily missed by computer algorithms. As a result, differentially expressed candidate biomarkers are hard to identify except in biological fluids proximal to the site of disease which have been shown to be significantly enriched in proteins that derive from diseased tissue. Our preliminary study show that popular signal processing methods are inadequate for low abundance protein detection. We observed that the deficiency of current algorithms arise from 1. a lack of complete and accurate modeling of LC/MS data, and 2. suboptimal processing methods. Consequently, the dramatic new improvements in capillary LC/MS cannot yet be fully translated into optimal discovery of protein biomarkers with greater sensitivity and selectivity which are the most important for disease diagnosis and treatment. Our long term goal is to develop a suit of advanced algorithms for biomedical research including protein biomarker identification, protein quantification, function and pathway analysis using a variety of highthoughput methods, and the combination of multiple information sources such as, proteomic and gene expression data. The application of these tools will significantly strengthen the capability of UTSA in health disparity research. The objective of this proposal is to develop advanced LC/MS peak detection, alignment, feature selection, time-series data analysis and accurate protein quantification tools that are sensitive to low abundance proteins and apply the tools. Our hypothesis is that improved signal processing for LC/MS can improve the sensitivity and specificity of protein biomarker identification, quantification and functional analysis. This hypothesis will be tested by applying the tools we develop to the research project led by Dr. Forsthuber entitled, "Biomarker Discovery in Glucocorticoid Resistance in EAE". To test this hypothesis and accomplish the objectives, we will carry out the following three specific aims: Aim 1. To improve the specificity and sensitivity of protein biomarker identification using capillary LC/MS data. 1. Accurately model peptide and noise signal in capillary LC/MS datasets 2. Develop a near optimal statistical peak, picking algorithms that provide soft information based on accurate modeling. 3. Develop a peak alignment method that will resolve weak peak identities in capillary LC-MS datasets; and 4. Develop a context based feature selection algorithm for biomarker identification based on soft information provided by the peak detection and alignment algorithms. Aim 2. To develop protein biomarker quantification and analysis tool based on time- series data. 1. Develop tools for the quantification of discovered protein markers over a time course. 2. Develop tools for analyzing time-series data for biomarker verification and analysis. Aim 3. Verify and apply the developed algorithms Establish the quantification accuracy and detection limit of known proteins of interest such as cytokines by applying the proposed algorithm. 1. Quantify level changes of known proteins of interest in GC resistance over a time course and establish their correlation with treatments in CNS tissue, CSF and serum. Verify quantification results in step 2 with Luminex and ELISA assay. 2. Perform differential analysis in GC-resistance study to discover new potential protein biomarkers.. 3. Quantify and characterize the discovered protein marker. Beyond the scope of this current project, the developed algorithms will be applied to other researcher's sample.

该副本是利用资源的众多研究子项目之一 由NIH/NCRR资助的中心赠款提供。对该子弹的主要支持 而且，副投影的主要研究员可能是其他来源提供的 包括其他NIH来源。 列出的总费用可能 代表subproject使用的中心基础架构的估计量， NCRR赠款不直接向子弹或副本人员提供的直接资金。 质谱法，尤其是毛细血管液相色谱 - 质量光谱法 （LC/MS）是加速知识获取的最重要工具 蛋白质机械基于生物医学研究。 但是，它从来没有 成功地应用于健康差异研究，旨在确定 在不利的社会，行为或环境中蛋白质表达的变化 鉴定可能涉及高危种族疾病的蛋白质的疾病 民族人口。健康差异的研究需要识别和 量化具有丰度的动态范围广泛的蛋白质，特别是对于血清 标本。信息处理，理解和解释至关重要。 一个 蛋白质生物标志物在健康差异研究中发现的主要瓶颈 - 免费，LC/MS来自当前的计算机算法的局限性 可用于处理此数据。这些算法量化并确定 具有强阳性或阴性的推定蛋白质的敏感性和特异性 与疾病状态的相关性。但是，低丰度蛋白生物标志物，通常 最具体的计算机算法很容易错过。结果，差异 表达的候选生物标志物除生物流体外都难以识别 靠近疾病部位，已被证明已显着富集 源自患病组织的蛋白质。 我们的初步研究表明，流行的信号处理方法不足 对于低丰度蛋白检测。我们观察到电流的不足 算法源于1。缺乏LC/MS数据的完整，准确的建模，并且 2。次优处理方法。因此，在 毛细管LC/MS尚无法完全转化为蛋白质的最佳发现 具有更高敏感性和选择性的生物标志物，最重要 疾病诊断和治疗。我们的长期目标是制定高级诉讼 生物医学研究算法，包括蛋白质生物标志物鉴定，蛋白质 使用各种高指标的量化，功能和途径分析 方法，以及多个信息源的组合，例如蛋白质组学和 基因表达数据。这些工具的应用将大大加强 UTSA在健康差异研究中的能力。 该提议的目的是 开发高级LC/MS峰检测，对齐，特征选择，时间序列 对低敏感的数据分析和准确的蛋白质定量工具 丰富的蛋白质并应用工具。我们的假设是改进的信号 LC/MS的处理可以提高蛋白质生物标志物的敏感性和特异性 识别，定量和功能分析。该假设将通过 将我们开发的工具应用于Forsthuber博士主持的研究项目，标题为“ “ EAE中的糖皮质激素耐药性中的生物标志物发现”。检验这一假设和 实现目标，我们将执行以下三个具体目标： 目标1。提高蛋白质生物标志物鉴定的特异性和灵敏度 使用毛细管LC/MS数据。 1。在毛细管LC/MS数据集中准确模拟肽和噪声信号 2。发​​展一个接近最佳的统计峰，采摘提供软的算法 基于准确建模的信息。 3。开发一种峰对准方法，该方法将解决弱峰身份 毛细管LC-MS数据集；和 4。开发基于上下文的特征选择算法，用于生物标记识别 基于峰检测和比对算法提供的软信息。 目标2。基于时间开发蛋白质生物标志物定量和分析工具 系列数据。 1。开发一段时间定量发现的蛋白质标记的工具 课程。 2。开发用于分析时间序列数据的工具，以进行生物标志物验证和 分析。 目标3。验证并应用开发的算法建立量化 已知感兴趣蛋白的准确性和检测极限，例如细胞因子 应用提出的算法。 1。量化A上已知的GC抗性蛋白的水平变化 时间过程并建立与中枢神经系统组织，CSF和 血清。用Luminex和ELISA测定验证第2步中的定量结果。 2。在GC抗性研究中进行差异分析以发现新的潜力 蛋白质生物标志物.. 3。量化并表征发现的蛋白质标记物。 除了当前项目的范围之外，开发的算法还将应用于 其他研究人员的样本。