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使用液相色谱-串联质谱法对大规模队列研究中的靶向血浆脂质进行质量控制

Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry.

作者信息

Hirayama Akiyoshi, Ishikawa Takamasa, Takahashi Haruka, Yamanaka Sanae, Ikeda Satsuki, Hirata Aya, Harada Sei, Sugimoto Masahiro, Soga Tomoyoshi, Tomita Masaru, Takebayashi Toru

机构信息

Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan.

Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0082, Kanagawa, Japan.

出版信息

Metabolites. 2023 Apr 13;13(4):558. doi: 10.3390/metabo13040558.

Abstract

High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography-mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics.

摘要

高通量代谢组学推动了大规模队列研究的开展。长期研究需要基于批次的多次测量,这就需要复杂的质量控制(QC)来消除意外偏差,以获得具有生物学意义的定量代谢组学图谱。采用液相色谱-质谱联用技术对279批次测量中的10833个样本进行分析。定量图谱包括147种脂质,如酰基肉碱、脂肪酸、葡萄糖神经酰胺、乳糖神经酰胺、溶血磷脂酸和孕酮。每一批次包含40个样本,每10个样本中有5个QC样本进行测量。来自QC样本的定量数据用于对样本数据的定量图谱进行归一化处理。147种脂质的批内和批间中位数变异系数(CV)分别为44.3%和20.8%。归一化后,CV值分别下降了42.0%和14.7%。还评估了这种归一化对后续分析的影响。所展示的分析将有助于获得大规模代谢组学的无偏定量数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dce/10146188/1d341b1133ec/metabolites-13-00558-g001.jpg

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