Application of differential mobility-mass spectrometry for untargeted human plasma metabolomic analysis
Differential mobility spectrometry (DMS) has been gaining popularity in small molecule analysis over the last few years due to its selectivity towards a variety of isomeric compounds. While DMS has been utilized in targeted liquid chromatography-mass spectrometry (LC-MS), its use in untargeted discovery workflows has not been systematically explored. In this contribution, we propose a novel workflow for untargeted metabolomics based solely on DMS separation in a clinically relevant chronic kidney disease (CKD) patient population. We analyzed ten plasma samples from early- and late-stage CKD patients. Peak finding, alignment, and filtering steps performed on the DMS-MS data yielded a list of 881 metabolic features (unique mass-to-charge and migration time combinations). Differential analysis by CKD patient group revealed three main features of interest. One of them was putatively identified as bilirubin based on high-accuracy MS data and comparison of its optimum compensation voltage (COV) with that of an authentic standard. The DMS-MS analysis was four times faster than a typical HPLC-MS run, which suggests a potential for the utilization of this technique in screening studies. However, its lower separation efficiency and reduced signal intensity make it less suitable for low-abundant features. Fewer features were detected by the DMS-based platform compared with an HPLC-MS-based approach, but importantly, the two approaches resulted in different features. This indicates a high degree of orthogonality between HPLC- and DMS-based approaches and demonstrates the need for larger studies comparing the two techniques. The workflow described here can be adapted for other areas of metabolomics and has a value as a prescreening method to develop semi-targeted workflows and as a faster alternative to HPLC in large biomedical studies.
KeywordsDifferential mobility spectrometry Mass spectrometry Biomarker discovery Untargeted metabolomics Chronic kidney disease
Body mass index
Chronic kidney disease
Clinical Phenotyping Resource and Biobank Core at the University of Michigan
Counts per second
Differential mobility spectrometry
Estimated glomerular filtration rate
False discovery rate
Feature of interest
High-performance liquid chromatography
Principal component analysis
Partial least squares-discriminant analysis
(Relative) standard deviation
Total ion current
Urinary protein-to-creatinine ratio
Variable importance in projection (PLS-DA)
Extracted ion chromatogram
The authors thank Dr. Farsad Afshinnia (University of Michigan) for helping in statistical data analysis and Dr. J. Larry Campbell (Sciex) for providing access to MarkerView software. We also appreciate the assistance of the Michigan Kidney Translational Core Clinical Phenotyping Resource and Biobank Core Investigator Group. It includes Matthias Kretzler and Debbie Gipson (University of Michigan, Ann Arbor), Keith Bellovich (St. Clair Nephrology Research, Detroit), Zeenat Bhat (Wayne State University, Detroit), Crystal Gadegbeku (Temple University Health System, Philadelphia), Susan Massengill (Levin Children’s Hospital, Charlotte), and Kalyani Perumal (JH Stroger Hospital, Chicago).
This work was supported by the Postdoctoral Translational Science Program from the Michigan Institute for Clinical and Health Research UL1TR000433 (to S.W.) and National Institutes of Health grants P30DK089503, DK082841, P30DK081943, U2C ES026553, and DK097153 (to S.P.).
Compliance with ethical standards
All human studies were approved by the Institutional Review Board (IRB) for the University of Michigan and the CPROBE ancillary studies committee.
Conflict of interest
The authors declare that they have no conflicts of interest.
- 3.Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8(1):615.Google Scholar
- 4.Sas KM, Kayampilly P, Byun J, Nair V, Hinder LM, Hur J, et al. Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications. JCI Insight. 2016;1(15).Google Scholar
- 16.Chen Z, Coy SL, Pannkuk EL, Laiakis EC, Fornace AJ, Vouros P. Differential mobility spectrometry-mass spectrometry (DMS-MS) in radiation biodosimetry: rapid and high-throughput quantitation of multiple radiation biomarkers in nonhuman primate urine. J Am Soc Mass Spectr. 2018.Google Scholar
- 19.Wernisch S, Afshinnia F, Rajendiran TM, Pennathur S. Differential mobility – mass spectrometry metabolomics platform for biomarker discovery in chronic kidney disease. Annual Meeting of the American Society for Mass Spectrometry; 2017; Indianapolis, IN.Google Scholar
- 20.Stevens LA, Schmid CH, Greene T, Zhang YL, Beck GJ, Froissart M, et al. Comparative performance of the CKD Epidemiology Collaboration (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) Study equations for estimating GFR levels above 60 mL/min/1.73 m2. Am J Kidney Dis. 2010;56(3):486–95.CrossRefGoogle Scholar
- 23.Anwar A, Psutka J, Walker SWC, Dieckmann T, Janizewski JS, Larry Campbell J, et al. Separating and probing tautomers of protonated nucleobases using differential mobility spectrometry. Int J Mass Spectrom. 2017.Google Scholar