Developing high-throughput IMS-MS and IMS-IMS-MS techniques for glycomics analysi

描述(由申请人提供)：食管腺癌(EAC)的发病率在过去20年里在美国增加，这是一个重大的健康挑战。考虑到接受过EAC治疗的患者的高死亡率，这一点尤其明显。目前科学研究的一个目标是确定与EAC相关的分子标记。考虑到碳水化合物在细胞过程中的多元作用，一个受到特别关注的领域是糖组分。比较糖组分图谱的一个限制因素是生物样品中存在着无数的糖链结构，这给分析化学家在成分解析和鉴定方面提出了挑战。这对于基于质谱学(MS)的分析平台来说尤其成问题，因为仅靠MS是无法实现同分异构体解析的。在这里，我们建议使用离子迁移率光谱(IMS)技术结合MS来快速表征血浆中的葡聚糖消化。具体地说，将开发多维IMS(IMS-IMS)方法，以提供最高效率的血浆样品表征。IMS-IMS与MS的结合允许快速拆分糖链异构体。这项使能技术允许对生物标志物验证所需的数百个血浆样本进行高通量比较。作为这里提出的研究的一部分，新开发的技术将应用于使用1000个血浆样本的总体研究来验证候选多糖的生物标记物。这里提出的工作可能会对疾病诊断以及跟踪与疾病进展(或治疗导致的回归)相关的生理变化的能力产生巨大的影响。此外，信息丰富的数据集还有可能为疾病的分子病因机制提供线索。

DESCRIPTION (provided by applicant): The increased incidence of Esophageal Adenocarcinoma (EAC) in the United States over the past two decades represents a significant health challenge. This is especially evident considering the high mortality rate of patients who have undergone treatment for EAC. A current goal of scientific research is to identify molecular markers associated with EAC. Considering the multivariate roles of carbohydrates in cellular processes, one field receiving particular attention is glycomics. A limiting factor for comparative glycomics profiling is the myriad glycan structures postulated to exist in biological samples which present challenges for analytical chemists in the form of component resolution and identification. This is especially problematic for mass spectrometry (MS)-based analytical platforms because isomer resolution cannot be achieved with MS alone. Here we propose the use of ion mobility spectrometry (IMS) techniques combined with MS for the rapid characterization of plasma glycan digests. Specifically, multidimensional IMS (IMS-IMS) methods will be developed to provide the highest efficiency characterization of plasma samples. The combination of IMS-IMS with MS allows for rapid resolution of glycan isomers. This enabling technology allows for high-throughput comparison of hundreds of plasma samples necessary for biomarker validation. As part of the research proposed here, the newly developed technology will be applied to biomarker validation of glycan candidates using a population study of 1000 plasma samples. The work proposed here could have tremendous implications for disease diagnostics as well as the ability to track physiological changes associated with disease progression (or regression resulting from therapy). In addition it is possible that the information-rich datasets will also provide clues into molecular causal mechanisms of disease.