GlyQ-IQ: glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D visualization.
GlyQ-IQ: glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D visualization.
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DOI：
10.1021/ac501492f
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发表时间：
2014-07-01
影响因子：
7.4
通讯作者：
Smith, Richard D.
中科院分区：
文献类型：
--
作者：
Kronewitter, Scott R.;Slysz, Gordon W.;Marginean, Ioan;Hagler, Clay D.;LaMarche, Brian L.;Zhao, Rui;Harris, Myanna Y.;Monroe, Matthew E.;Polyukh, Christina A.;Crowell, Kevin L.;Fillmore, Thomas L.;Carlson, Timothy S.;Camp, David G., II;Moore, Ronald J.;Payne, Samuel H.;Anderson, Gordon A.;Smith, Richard D.
糖组学五变量信息定量（GlyQ-IQ）是一种生物学指导的糖组学分析工具，用于识别液相色谱-质谱（LC-MS）数据中的N-聚糖。Glycomics LC-MS数据集具有卷积提取的离子色谱图，这对于使用现有软件工具进行解卷积具有挑战性。LC去卷积成组分片段在糖组学数据集中至关重要，因为色谱峰对应于不同的完整聚糖结构异构体。生物靶向分析方法为传统LC-MS数据处理提供了几个关键优势。关于个体靶的元素组成的先验聚糖信息允许通过利用精确的同位素分布信息来将色谱图生成和LC峰拟合集中在具有最高强度的同位素物质上来提高灵敏度。除了高特异性特征LC-MS检测之外，聚糖靶标注释还利用聚糖家族关系和源片段化，以提高分析的特异性。在这项工作中开发了GlyQ-IQ软件，并在分析人血清LC-MS数据集的N-聚糖组成的背景下进行了评价。一个案例研究，以证明如何GlyQ-IQ识别和删除混淆的色谱峰从高甘露糖聚糖异构体从人血清。此外，使用GlyQ-IQ从高分辨率纳米电喷雾-液相色谱-串联质谱（nESI-LC-MS/MS）数据集生成广泛的人血清N-聚糖谱。从单个样品中共检测到156种聚糖组合物和640种聚糖异构体。超过99%的GlyQ-IQ聚糖特征分配通过了手动验证，并得到了高分辨率质谱的支持。
Glycomics quintavariate-informed quantification (GlyQ-IQ) is a biologically guided glycomics analysis tool for identifying N-glycans in liquid chromatography–mass spectrometry (LC–MS) data. Glycomics LC–MS data sets have convoluted extracted ion chromatograms that are challenging to deconvolve with existing software tools. LC deconvolution into constituent pieces is critical in glycomics data sets because chromatographic peaks correspond to different intact glycan structural isomers. The biological targeted analysis approach offers several key advantages to traditional LC–MS data processing. A priori glycan information about the individual target’s elemental composition allows for improved sensitivity by utilizing the exact isotope profile information to focus chromatogram generation and LC peak fitting on the isotopic species having the highest intensity. Glycan target annotation utilizes glycan family relationships and in source fragmentation in addition to high specificity feature LC–MS detection to improve the specificity of the analysis. The GlyQ-IQ software was developed in this work and evaluated in the context of profiling the N-glycan compositions from human serum LC–MS data sets. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid chromatography–tandem mass spectrometry (nESI-LC–MS/MS) data set. A total of 156 glycan compositions and 640 glycan isomers were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high-resolution mass spectra.
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