Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study.

Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study.
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DOI:
10.1007/s11306-014-0740-0
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发表时间:
2015
期刊:
影响因子:
3.6
通讯作者:
Rutledge, Douglas N.
Rutledge, Douglas N.
中科院分区:
医学3区
文献类型:
--
作者:
Martin, Jean-Charles;Maillot, Matthieu;Mazerolles, Gerard;Verdu, Alexandre;Lyan, Bernard;Migne, Carole;Defoort, Catherine;Canlet, Cecile;Junot, Christophe;Guillou, Claude;Manach, Claudine;Jabob, Daniel;Bouveresse, Delphine Jouan-Rimbaud;Paris, Estelle;Pujos-Guillot, Estelle;Jourdan, Fabien;Giacomoni, Franck;Courant, Frederique;Fave, Gaelle;Le Gall, Gwenaelle;Chassaigne, Hubert;Tabet, Jean-Claude;Martin, Jean-Francois;Antignac, Jean-Philippe;Shintu, Laetitia;Defernez, Marianne;Philo, Mark;Alexandre-Gouaubau, Marie-Cecile;Amiot-Carlin, Marie-Josephe;Bossis, Mathilde;Triba, Mohamed N.;Stojilkovic, Natali;Banzet, Nathalie;Molinie, Roland;Bott, Romain;Goulitquer, Sophie;Caldarelli, Stefano;Rutledge, Douglas N.

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Metabo-Ring倡议汇集了5台核磁共振仪器(核磁共振)和11台不同的质谱仪,目的是评估非靶向代谢组学方法在获得可比代谢组学图谱方面的可靠性。这是通过测量从不同的LC MS和核磁共振平台提取的公共光谱信息的比例来估计的。从两个不同条件下获得的生物样本由合作伙伴使用自己的内部方案进行分析。测试1检查了成年志愿者的尿样,无论是否添加了32种代谢物标准。测试2涉及低生物对比度的情况,比较喂食维生素D或不添加维生素D的饮食的大鼠的血浆。来自每个仪器的光谱信息被组合成单独的统计区块。检查块(例如,仪器)之间的相关性(RV系数)以及公共光谱信息的结构(公共分量和特定权重分析)。此外,在测试1中,盲目引入了一个离群点个体,并评估了各种平台对其的识别。尽管后处理产生的光谱特征数目有很大差异,分析条件和数据处理也不同,但方法内(核磁共振和LC MS)和跨方法(核磁共振与LC MS)的光谱信息高度收敛(平均从到91%)。没有注意到LCMS器械(TOF、QTOF、LTQ-Orbitrap)的影响。LCMS仪器最好地发现和表征了离群值个体。总而言之,即使没有事先的标准化,非靶向代谢组学分析也会在各种技术的仪器内部和之间报告一致的信息。本文的在线版本(doi:10.1007/s11306-0140740-0)包含补充材料,授权用户可以使用。
The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion of common spectral information extracted from the different LCMS and NMR platforms. Biological samples obtained from 2 different conditions were analysed by the partners using their own in-house protocols. Test #1 examined urine samples from adult volunteers either spiked or not spiked with 32 metabolite standards. Test #2 involved a low biological contrast situation comparing the plasma of rats fed a diet either supplemented or not with vitamin D. The spectral information from each instrument was assembled into separate statistical blocks. Correlations between blocks (e.g., instruments) were examined (RV coefficients) along with the structure of the common spectral information (common components and specific weights analysis). In addition, in Test #1, an outlier individual was blindly introduced, and its identification by the various platforms was evaluated. Despite large differences in the number of spectral features produced after post-processing and the heterogeneity of the analytical conditions and the data treatment, the spectral information both within (NMR and LCMS) and across methods (NMR vs. LCMS) was highly convergent (from 64 to 91 % on average). No effect of the LCMS instrumentation (TOF, QTOF, LTQ-Orbitrap) was noted. The outlier individual was best detected and characterised by LCMS instruments. In conclusion, untargeted metabolomics analyses report consistent information within and across instruments of various technologies, even without prior standardisation. The online version of this article (doi:10.1007/s11306-014-0740-0) contains supplementary material, which is available to authorized users.
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