Charles Nichols (Presenter)
Bio: I have worked extensively with home built mass spectrometry instrumentation. My undergraduate studies investigated ion-neutral reactions using a home-built ion mobility drift cell-mass spectrometer. I received my Ph.D. in physical chemistry from JILA at the University of Colorado where I used a Flowing Afterglow-Selected Ion Flow Tube (FA-SIFT) and a Negative Ion Photoelectron Spectrometer (NIPES). My thesis work investigated ion thermochemistry and reaction dynamics. I recently transitioned to applied clinical chemistry. My current work investigates the role and utility of ion mobility measurements in the identification process for global untargeted metabolite and lipid measurements using high performance liquid chromatography-ion mobility-mass spectrometry (HPLC-IM-MS). I believe that ion mobility can assist identification of metabolites in an experiment.
Authorship: Charles M. Nichols, Stacy D. Sherrod, Jody C. May, John A. McLean
Center for Innovative Technology, Department of Chemistry, Vanderbilt University, Nashville, TN
Traditional global metabolomics workflows characterize molecular features by retention time (RT) and m/z. Notably, ion mobility (IM) spectrometry provides an orthogonal separation to liquid chromatography (LC) and mass spectrometry (MS). With LC-IM-MS, we gain an additional descriptor, namely collision cross section (CCS). However, CCS is not an intrensic value, and many parameters must be considered before CCS libraries can be generated and utilized for metabolite identification. This study is investigates the validity of CCS measurements alongside RT and m/z. It also investigates workflows for building CCS libraries and how to apply them to a real metabolomics dataset.
Ion mobility is a well-established analytical technique. The first instruments were developed around 1960. Until about 10 years ago, all ion mobility (IM) spectrometers were home built, and for this reason, the technique was not available to the masses. This all changed about 10 years ago when IM first became commercially available. Moreover, the first high-precision uniform field (UF) IM only became commercially available two years ago. UFIM is the only technique that conforms to the assumptions of the Mason-Schamp equation—the only fundamental relationship from which collision cross section (CCS) values can be generated. If the experimental conditions are carefully considered, CCS values can be used to assist in molecular identification for global metabolomics.
Traditional metabolomics experiments rely on liquid chromatography (LC) and tandem mass spectrometry (MS/MS). The molecular features in these experiments are characterized by retention time (RT), m/z, and ion abundance. Additional structural information is obtained by fragmentation. Even with these descriptors, molecular identification remains challenging. CCS provides an orthogonal identifier to traditional metabolomics data, and we are still working to standardize its utility.
This presentation discusses the challenges associated with measuring accurate and precise CCS values. Because CCS is not an intrinsic value such as mass, several experimental conditions must be characterized and reported when generating CCS libraries. We will reveal our approach to validating CCS measurements, and how we will apply this result to global metabolomics studies.
All experiments were performed using high performance LC (HPLC, 1290 Infinity, Agilent Technologies) coupled to an IM-MS with tandem MS/MS capabilities (IM-QTOF; 6560; Agilent Technologies).
Two IM methods for CCS measurement are evaluated using a mixture of nine exogenous metabolites. The first, traditional approach measures drift times using seven drift fields; the 'stepped-field' method generates CCS values from first principles. This method requires 3.5 minutes of analyte signal to obtain a CCS value. The second method obtains CCS using only one drift field, and this approach is critical because it can report CCS values on the timescale of a LC analyte elution peak.
For the global metabolomics experiment, MV-4-11 cells were cultured in media. Half of the samples were treated with a drug, and the metabolic flux was measured over six time points. After treatment, cells were harvested and lysed. Global untargeted metabolomics experiment were performed using a reverse-phase LC method on a C18 column (Zorbax Eclipse Plus C18, 2.1x50mm 1.8-micron; Agilent Technologies) that was optimized for lipids. Positive and negative polarity data was collected.
CCS values have been generated for nine metabolites using both stepped-field and single-field methods in efforts to validate the single-field approach. For this pilot study, there does appear to be a small systematic bias in the single-field data. This study is currently being expanded to include over 600 small molecule metabolites in efforts to better characterize this phenomenon.
Furthermore, global lipidomics data for the MV-4-11 treatment experiment was collected using UPLC-IM-MS. This data has been analyzed using a 3D (RT, m/z, and abundance) data analysis workflow. Principle Components Analysis (PCA) was used to visualize clustering of data groups. Significance was assessed using p-values and fold changes calculated from combined feature abundance data, and ~30 significant molecular features were identified. The 4D (RT, CCS, m/z, and abundance) data workflow has also been explored. The strengths of the 3D and 4D workflows will be discussed, and we propose ways to improve handling of 4D data.
Conclusions & Discussion
We are still working to validate the single-field method. The single-field CCS are precise, but their accuracy is being assessed against the ‘true’ CCS values that are obtained with the stepped-field method. The goal is to increase confidence in the CCS obtained from LC-IM-MS experiments, but more validation studies are required. We are expanding this study to include over 600 small metabolites.
Ion mobility provides an orthogonal separation to work alongside LC and MS. We are developing workflows that will integrates CCS values into metabolite annotation, while simultaneously building a metabolite CCS database. We are working to standardize the utility of IMS in the metabolomics community.
References & Acknowledgements:
This work was supported using the resources of the Center for Innovative Technology at Vanderbilt University (Nashville, TN). CMN acknowledges financial support from Agilent Technologies in the form of a postdoctoral fellowship.
|Grants||yes||from Agilent Technologies.|
IP Royalty: no
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