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Differential mobility analysis-mass spectrometry coupled to XCMS algorithm as a novel analytical platform for metabolic profiling

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Abstract

The development of additional analytical instruments is of great interest to expand metabolome coverage. Differential mobility analyzers (DMAs) are a type of ion mobility spectrometers that can be straightforwardly interfaced with commercial mass spectrometers. In this pilot study, we explored the capabilities of an ion mobility-mass spectrometry platform, based on interfacing a Differential Mobility Analyzer with a commercial quadrupole time of-flight mass spectrometer (DMA-QTOF), to phenotype the metabolic urinary fingerprint of a cohort of prostate cancer patients (n = 8) and a group of healthy counterparts (n = 20). The resolving power of the DMA and the QTOF was ∼55 and ∼6,500, respectively. The transmission efficiency of the DMA was 50%. We illustrate the benefits of incorporating the DMA through the separation of isobaric species according to their electrical mobility, which were not fully resolved by the high resolution QTOF. In addition, we show that the bidimensional electrical mobility-mass spectra obtained can be successfully processed with the XCMS routine, extending its potential to ion mobility-mass spectrometry-based platforms. Data mining with XCMS revealed seven features significantly down-regulated in cancer patients (P < 0.05). These peaks were the input of principal component analysis, showing a clear separation tendency from prostate cancer patients and healthy controls. NIST MS search algorithm was used to classify the samples according to their class, with a resulting 75% sensitivity and 80% specificity. We pursued further fragmentation experiments for structural elucidation of the most discriminant metabolites, thereby illustrating the full potential of this analytical platform for the task. In summary, DMA-MS/MS provides an additional level of separation as compared to traditional mass spectrometry-based methods, thereby increasing the array of multi-analytical platforms available to global metabolite profiling and metabolite identification.

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Acknowledgments

We thank Ms. M. Hernández, Mr. A. Casado, and rest of SEADM team for their assistance during the experiments. Ms. F. Dibari and Mr. L. Zingaro from ISB are acknowledged for their advice with XCMS data analysis. Dr. Duchoslav (AB SCIEX) is acknowledged for her support with the wiff to mzdata translator. The research leading to these results has received funding from SEADM, and PML was supported by a Marie Curie Intra European Fellowship (PIEF-GA-2008–220511) within the Seventh European Community Framework Programme FP7/2007–2013.

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Correspondence to Pablo Martínez-Lozano.

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Martínez-Lozano, P., Criado, E., Vidal, G. et al. Differential mobility analysis-mass spectrometry coupled to XCMS algorithm as a novel analytical platform for metabolic profiling. Metabolomics 9 (Suppl 1), 30–43 (2013). https://doi.org/10.1007/s11306-011-0319-y

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