Robin Kemperman (Presenter)
University of Florida
Bio: Robin Kemperman received his BASc in chemistry from the HAN University of Applied Sciences in The Netherlands, where after he fulfilled his MSc in analytical chemistry at the University of Florida. He is currently a Ph.D. Candidate in analytical chemistry at the University of Florida and works under the direction of Dr. Richard Yost. His work covers a variety of aspects in mass spectrometry over the past years, including targeted analysis using triple quadrupole MS, high-field asymmetric waveform ion mobility spectrometry (FAIMS) and drift tube ion mobility spectrometry (DTIMS), both in combination with MS. He currently works on new approaches for metabolite profiling using DTIMS in paired with LC-MS.
Authorship: Robin H.J. Kemperman(1), Chris W.W. Beecher(2), Timothy J. Garrett(3,4), Richard A. Yost(1,3)
(1)Department of Chemistry, University of Florida, Gainesville, FL, USA (2)IROA Technologies, Bolton, MA, USA (3)Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, USA (4)Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL, USA
Isotopic ratio outlier analysis (IROA) has been used for metabolite profiling studies by LC-MS. This powerful methodology can identify biochemical compounds using its characteristic isotope pattern created by a 5% and 95% 13C-labeled media. Although significant differences between chemical noise and biological signals are obtained, complex mass spectra cause difficulties in interpretation and mass overlap. This study will show how ion mobility spectrometry (IM) can assist deconvolution of IROA patterns and add a significantly higher confidence level to compound identification using IM drift times and collision cross sections.
Metabolite profiling and biomarker discovery are significant areas in the field of metabolomics. LC-MS is one of the fundamental methodologies for metabolomics studies, one which yields hundreds of identified and unidentified features. Feature identification, a critical step in biomarker validation, is challenging and often not successful because there is not enough specific chemical information in LC-MS/MS analysis alone.
Isotopic ratio outlier analysis (IROA) is an isotopic labeling technique that uses enriched 13C isotopic signatures to provide definitive chemical information for any IROA detected pattern in MS-based analytical methods. Therefore, it is useful for compound identification in targeted and untargeted metabolomics studies. Furthermore, since the IROA signal can only be produced biologically, it serves to distinguish biological compounds from artifacts. IROA helps identify known and unknown compounds of biological origin, provides carbon number and molecular formula information, and distinguishes biochemical compounds from chemical noise. However, IROA generates complex mass spectra and is unable to resolve isomers without chromatography. This study shows how ion mobility spectrometry (IM) can provide an additional separation dimension for improved peak deconvolution and significantly improved metabolite identification using IM drift times and collision cross section (CCS).
Yeast (Saccharomyces cerevisiae) was grown on 5% and 95% 13C labeled media containing randomized labeled glucose as a single carbon source to enrich the natural abundance of 13C of all biologically formed metabolites. An extract from 95% 13C yeast was added to a pooled blood plasma sample as an internal standard for the phenotypic IROA injections. Extracts from both 5% and 95% 13C yeasts were pooled for the combined IROA injections. A standard mixture of known metabolites, including amino acids, was used as control. All samples were analyzed with an Agilent UHPLC 1290 Infinity II coupled to an Agilent 6560 IM-QTOF using electrospray ionization.
Initial LC-IM-MS experiments showed that all the isotopomers in an IROA isotope cluster retain the same drift time through the IM drift tube despite mass differences. The orthogonal CCS information derived from the IM drift times were used to further identify the IROA isotopologues and differentiate overlapping peaks and masses. For example, the IROA pattern with a m/z range of 182.0810-191.1115 for tyrosine (C9H11NO3) was detected at a drift time of 17.3 ms and had a calculated CCS of 145.6 Å2. Both, the phenotypical- and combined-IROA experiments matched the control. Moreover, the tyrosine IROA pattern has a difference of 9 mass units between the first and last measured isotopic mass, which corresponds to a 9-carbon compound.
Additionally, at a retention time of 6.3 min, two IM baseline-resolved IROA patterns were detected at different drift times (15.1 and 19.9 ms) for m/z 136.0620-141.0785. The characteristic IROA pattern indicated that both compounds contained 5 carbons and originated biochemically from the yeast cells. These two compounds could potentially be fragmentation products, different conformers, or differently protonated ions of adenine (C5H5N5).
As mentioned, IROA patterns create complex mass spectra that can result in overlapping mass spectra. Our initial experiments have shown that IM is able to deconvolute such mass spectra. More specifically, a doubly charged 10-carbon compound with IROA isotopologue (m/z 184.5070-189.5235) overlapped with tyrosine’s IROA pattern; however, IM resolved both IROA spectra observed at 15.5 ms (IROA interferent) and at 17.3 ms (tyrosine).
Conclusions & Discussion
Previous experiments using LC-MS have shown that IROA-labeled compounds and their fragments can be identified with a higher confidence level versus traditional metabolomics due to their characteristic IROA patterning. These benefits are implemented in a new IM-assisted IROA methodology, which adds an additional level of confidence using CCS values and IM drift times to deconvolute overlapping IROA spectra.
References & Acknowledgements:
The authors would like to thank Agilent Technologies for their support.
IP Royalty: no
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