Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125, Torino, Italy.
Division of General Medicine, IRCCS Istituto Auxologico Italiano Ospedale S. Giuseppe, 28824, Piancavallo, Italy.
Anal Bioanal Chem. 2021 Jan;413(2):403-418. doi: 10.1007/s00216-020-03008-6. Epub 2020 Nov 3.
This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO.Graphical abstract.
本研究考察了全面二维气相色谱与飞行时间质谱联用（GC×GC-TOF MS）和可变离化能（即串联离化™）的信息潜力，以研究一小部分肥胖个体唾液代谢特征的变化。该研究为有效利用串联离化数据的互补性质提供了一个概念验证。样品取自两组严重肥胖（BMI > 40 kg/m）患者，分别命名为代谢健康肥胖（MHO）和代谢不健康肥胖（MUO）。基于模板匹配的模式识别，对单数据流和通过组合两个离化能（12 和 70 eV）的原始信号获得的融合数据进行了无目标指纹分析。结果表明，在较低能量（即 12 eV）下，与 70 eV 的参考信号相比，总信号强度低一个数量级，但 2D 峰响应的变化范围更大，扩展了动态范围。融合数据结合了 70 eV 和 12 eV 的优势，使样本指纹的覆盖范围更加全面。多变量统计分析、主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）显示出相当好的患者聚类，前两个主成分（PCs）解释的总方差从 70 eV 的 54%增加到 12 eV 的 59%，对于融合数据则增加到 71%。通过 PLS-DA，通过比较保留数据和 70 eV 光谱特征，可以突出和推定鉴别成分。在所分析的最具信息性的分析物中，MHO 患者唾液中乳糖的相对含量较高，而 MUO 患者唾液中 N-乙酰-D-葡萄糖胺、尿素、葡萄糖醛酸γ-内酯、2-脱氧核糖、N-乙酰神经氨酸甲酯和 5-氨基戊酸的含量较高。通过将来自不同类别的个体的原始数据组合在一起获得的复合每个类别的图像的可视化特征指纹与模式识别算法相结合，突出了代谢物的变化，即 MUO 与 MHO。
