Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies
Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies
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DOI：
10.1021/acs.analchem.0c01980
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发表时间：
2020-08-18
影响因子：
7.4
通讯作者：
Arita, Masanori
中科院分区：
文献类型：
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作者：
Tada, Ipputa;Chaleckis, Romanas;Arita, Masanori
数据独立采集质谱（DIA-MS）对于非靶向代谢组学中信息丰富的光谱注释至关重要。然而，所获得的MS 2光谱是高度复杂的，构成了重大的注释挑战。我们开发了一种基于相关性的反卷积（CorrDec）方法，该方法使用DIA-MS作为我们的MS-DIAL软件的更新，在多样品研究中使用离子丰度相关性。CorrDec基于前体和碎片离子的峰强度在样品之间相关的假设，并利用该定量信息来解卷积复杂的DIA光谱。CorrDec在化学标准品的稀释系列和224样本尿代谢组学研究中明显改善了原始MS-DIAL去卷积方法（MS 2Dec）的去卷积。CorrDec相对于MS 2Dec的主要优势是能够区分共洗脱的低丰度化合物。CorrDec需要测量多个样品才能成功地对DIA光谱进行去卷积;然而，我们的随机评估表明，CorrDec可以对只有10个独特样品的研究做出贡献。所提出的方法提高了化合物的注释和识别多样本研究，将是有用的应用在大型队列研究。
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.