Department of Genetics, The Graduate University for Advanced Studies, SOKENDAI, 1111 Yata, Mishima, Shizuoka 411-8540, Japan.
Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan.
Anal Chem. 2020 Aug 18;92(16):11310-11317. doi: 10.1021/acs.analchem.0c01980. Epub 2020 Jul 30.
Data-independent acquisition mass spectrometry (DIA-MS) is essential for information-rich spectral annotations in untargeted metabolomics. However, the acquired MS2 spectra are highly complex, posing significant annotation challenges. We have developed a correlation-based deconvolution (CorrDec) method that uses ion abundance correlations in multisample studies using DIA-MS as an update of our MS-DIAL software. CorrDec is based on the assumption that peak intensities of precursor and fragment ions correlate across samples and exploits this quantitative information to deconvolute complex DIA spectra. CorrDec clearly improved deconvolution of the original MS-DIAL deconvolution method (MS2Dec) in a dilution series of chemical standards and a 224-sample urinary metabolomics study. The primary advantage of CorrDec over MS2Dec is the ability to discriminate coeluting low-abundance compounds. CorrDec requires the measurement of multiple samples to successfully deconvolute DIA spectra; however, our randomized assessment demonstrated that CorrDec can contribute to studies with as few as 10 unique samples. The presented methodology improves compound annotation and identification in multisample studies and will be useful for applications in large cohort studies.
数据非依赖性采集质谱（DIA-MS）对于非靶向代谢组学中的信息丰富的光谱注释至关重要。然而，所获得的 MS2 光谱非常复杂，这给注释带来了重大挑战。我们开发了一种基于相关的解卷积（CorrDec）方法，该方法利用 DIA-MS 在多样本研究中的离子丰度相关性，作为我们的 MS-DIAL 软件的更新。CorrDec 基于这样的假设，即前体离子和碎片离子的峰强度在样本之间相关，并利用这种定量信息来解卷积复杂的 DIA 光谱。CorrDec 明显改善了原始 MS-DIAL 解卷积方法（MS2Dec）在化学标准品稀释系列和 224 个样本尿液代谢组学研究中的解卷积效果。CorrDec 相对于 MS2Dec 的主要优势在于能够区分共洗脱的低丰度化合物。CorrDec 需要测量多个样本才能成功地解卷积 DIA 光谱；然而，我们的随机评估表明，CorrDec 可以为具有多达 10 个独特样本的研究做出贡献。所提出的方法改善了多样本研究中的化合物注释和鉴定，并将有助于在大型队列研究中的应用。
