Improved Metabolite ID by Cross-Platform HPLC Retention-Augmented MS Analysis

描述（申请人提供）：高效液相色谱（HPLC）是所有生物科学中的重要工具。在代谢组学中，HPLC已经发展成为与质谱（MS）相结合的核心技术。代谢组学已经有助于更好地了解糖尿病、胰岛素抵抗和心血管疾病，并导致发现早期检测癌症的生物标志物。然而，该领域的进一步发展在很大程度上依赖于分析仪器的进步，这将提高对大量代谢物的识别。高效液相色谱保留是一种化学特异性指标，与质谱数据非常正交。不幸的是，在代谢组学中，由于LC保留机制复杂，更重要的是，梯度洗脱保留时间非常依赖于HPLC仪器之间的许多差异，因此它在很大程度上没有被使用。初步研究表明，LC保留数据与MS精确质量相结合可显著提高分析物的鉴别概率；使用低端lc -单四极杆质谱，明确的鉴定从7307个未知数中的88个增加到3232个，提高了LC-FTICR质谱在同一组未知数上的鉴定能力。在这一建议中，概述了一种强大的HPLC保留数据库的新方法以及一种新的保留“投影”系统。该系统使用一组30个精心选择的标准溶质加入样品的保留时间来反向计算有效梯度和流速曲线必须是多少才能得到这些保留时间。然后，这些谱图可以用来计算其他化合物的保留率，准确度达到前所未有的水平。它们解释了HPLC仪器、梯度、流速和柱尺寸之间的差异。此外，我们将扩展系统，以考虑柱老化的保留漂移。这种易于使用的保留投影系统精确到0.23%的梯度时间（例如，在20分钟的梯度中2.8秒）。有三个具体目标：1)选择一组最佳的标准固定相和校准溶质，并确定HPLC保留投影系统的最终准确性；2)制定保留数据库的建立和使用标准协议，并确定投影系统在多个实验室/操作人员之间的可重复性；3)生成1000个代谢物保留数据库，并确定其对LC-MS代谢物鉴定的价值。高效液相色谱保留投影系统具有准确性高、适用性广、易于使用等特点，将极大地促进依赖LC-MS鉴定未知物的代谢组学及相关领域的研究。这些领域即将在人类疾病的病因、诊断、预防和治疗方面取得非凡的发现。该系统将提供比低端LC-MS仪器更强的识别能力，大大拓宽了能够进行这些尖端实验的实验室基础。

DESCRIPTION (provided by applicant): High Performance Liquid Chromatography (HPLC) is a vital tool in all biological sciences. In metabolomics, HPLC has evolved to become, in conjunction with mass spectrometry (MS), the central technique. Metabolomics has already contributed to a greater understanding of diabetes, insulin resistance, and cardiovascular disease, and has lead to the discovery of biomarkers for early detection of cancer. However, further advances in this field are heavily dependent on advances in analytical instrumentation that will improve identification of large numbers of metabolites. HPLC retention is a chemically-specific metric that is very orthogonal to MS data. Unfortunately, in metabolomics it has largely gone unused because the mechanism of LC retention is complex and more importantly, gradient elution retention times are very dependent on many differences between HPLC instruments. Preliminary work shows that use of LC retention data in combination with MS exact mass dramatically improves analyte identification probability; using a low-end LC-single quadrupole MS, unambiguous identifications rose from 88 (of 7,307 unknowns) to 3,232-bettering the identification power of LC-FTICR MS on the same set of unknowns. In this proposal, a novel approach to a robust HPLC retention database is outlined along with a novel retention "projection" system. This system uses the retention times of a set of 30 judiciously chosen standard solutes spiked into the sample to back-calculate what the effective gradient and flow rate profiles must have been to give those retention times. Those profiles can then be used to calculate the retention of other compounds with unprecedented accuracy. They account for differences between HPLC instruments, gradients, flow rates, and column dimensions. Additionally, we will extend the system to account for retention drift with column aging. This easy-to-use retention projection system is accurate to ¿0.23% of the gradient time (e.g. ¿2.8 seconds in a 20 min gradient). There are three specific aims: 1) Select an optimal set of standard stationary phases and calibration solutes and determine the ultimate accuracy of the HPLC retention projection system, 2) Develop standard protocol for building and using the retention database and determine the reproducibility of the projection system among multiple labs/operators, and 3) Generate a 1,000 metabolite retention database and determine its value for metabolite identification by LC-MS. Due to its high accuracy, wide applicability, and ease of use, the HPLC retention projection system will considerably accelerate research in metabolomics and related fields that rely on the identification of unknowns by LC-MS. Such fields are on the brink of extraordinary discoveries in the causes, diagnosis, prevention, and cure of human diseases. This system will offer far more identification power out of even low-end LC-MS instruments, significantly broadening the base of labs capable of performing these cutting-edge experiments. PUBLIC HEALTH RELEVANCE: The work describes a methodology which will drastically improve the ability of existing LC-MS instrumentation, a vital tool in clinical analysis and biological research, to identify large numbers of small molecules in blood, urine, tissue, and other sources of human origin without any additional cost. It will not only improve high-end instrumentation, but will also allow the large number of researchers with only basic LC-MS instruments to perform cutting-edge analyses. This will broaden the base of laboratories capable of performing research in the growing field of metabolomics that promises extraordinary discoveries in the causes, diagnosis, prevention, and cure of human diseases such as cancer, diabetes, and cardiovascular disease.