Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values

BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. RESULTS: LMN uses a ranking system (1-3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine. CONCLUSIONS: LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.BACKGROUND: 脂组学（Lipidomics）是对生物系统或底物内脂质进行全面定量检测的新兴学科，在改善临床诊断以及增进我们对健康与疾病的认知方面具备巨大潜力。尽管脂质多样的生物学功能赋予了其临床应用价值，但脂质结构与浓度的多样性却使得脂组学分析面临重重挑战。若缺乏与每种脂质种类匹配的内标（internal standards），研究者通常会为一类广泛的相关脂质单独使用内标。为助力该相对定量流程的标准化与自动化，我们开发了LipidMatch归一化工具（LipidMatch Normalizer, LMN），其网址为http://secim.ufl.edu/secim-tools/，可适配绝大多数开源脂组学分析流程。 RESULTS: LMN采用1至3级的分级体系，为目标分析物分配脂质内标。等级1代表内标与目标分析物的脂质类别及加合物（adduct）均匹配，等级3则代表二者的加合物与类别均不匹配。若某一脂质类别对应多个内标，则会选择保留时间与目标分析物最为接近的内标。用户还可指定某一内标所对应的脂质类别，例如可说明醚键磷脂酰胆碱（ether-linked phosphatidylcholine）可通过磷脂酰胆碱（phosphatidylcholine）进行半定量。LMN可适配任意脂质鉴定软件与特征峰识别软件，本研究中其被用于对经LipidMatch与MZmine注释的美国国家标准与技术研究院（National Institute of Standards and Technology, NIST）标准参考物质SRM 1950人血浆样本中的脂质进行定量。 CONCLUSIONS: LMN可集成至开源分析流程中，该流程可完成液相色谱-串联质谱（LC-MS/MS）研究所需的全部数据处理步骤，包括特征峰识别、注释与定量。借助LMN，我们发现在部分场景下，峰高与峰面积的选择、特定加合物的使用，以及正、负极性数据的选用，均会对最终得到的脂质浓度产生显著影响。