Julia Denes (Presenter)
University of Cambridge
Bio: Dr Julia Denes is a postdoctoral research associate at University of Cambridge, Department of Biochemistry. Her research area is lipidomics and ion mobility mass spectrometry. She has a PhD in analytical chemistry from the Hungarian Eotvos Lorand University and she held postdoctoral positions at the Justus Liebig University in Germany and at Imperial College London. Her experience includes mass spectrometry, ambient ionization techniques, metabolomics and newborn screening. She joined to the research group of Julian Griffin in August 2015.
Authorship: Julia Denes (1), Helene Mobbs (1), Liz Bentley (2), Tertius Hough (2), Roger D. Cox (2), Julian L. Griffin (1,3)
(1) University of Cambridge, Cambridge, UK (2) MRC Harwell, Harwell, UK (3) MRC Human Nutrition Research, Cambridge, UK
| Short Abstract The use of ion mobility coupled with high resolution LC-MS/MS has been applied to investigate the lipidomic changes in blood plasma for a mouse model of type 2 diabetes. The ability to separate ions across four dimensions (retention time, mass to charge ratio, fragmentation, ion mobility) has allowed us to improve detection of lipid species obtained from a complex matrix. In addition, multiplexing and all ion fragmentation were applied to increase sensitivity and selectivity of the analysis. Phospholipid distribution of plasma samples from diabetic mice model was compared with controls and significant differences were identified. Identification of biomarkers and differentiation of structural isomers was facilitated by collisional cross section data. Results clearly show the potential of the technique in the field of lipidomics. |
Long Abstract
Introduction
Currently, liquid chromatography-mass spectrometry (LC/MS) is the most comprehensive tool used in lipidomics for the analysis of complex mixtures, such as those obtained from blood plasma or tissue extracts. However, there are still major challenges in the field, with one of the biggest being the separation of both structural isomers and stereoisomers. In biological systems the isomer distribution has an important role in many processes including many pathologies which accompany diseases. Hence, the capability of separating isomers is essential to understand these processes. Ion mobility is a technique that can offer the differentiation of analytes which are isobaric in mass, but differ in structure, based on their mobility through a neutral gas (drift tube) and separation according to collisional cross-sectional (CCS) area. Ion mobility coupled with mass spectrometry provides complementary information and an additional dimension to the existing three dimensional system (retention time, mass to charge ratio, fragmentation) without additional analysis time and enables differentiation of several isomer pairs based on CCS data. Plasma samples from diabetic mouse model were collected to demonstrate the capability of ion mobility as performed using the Agilent 6560 Ion Mobility QToF LC/MS system.
Methods
Plasma samples were prepared using a simple methanol extraction, dried under nitrogen gas flow and reconstituted with isopropanol-methanol-water mixture. A nine-minute gradient was applied for reversed-phase chromatographic separation on a C18 column, resulting in three distinct groups of compounds in the chromatogram in positive ion mode. In addition, negative ion mode data were acquired using the same gradient. Acquisitions were made in different modes: multiplexing with 3-bit and 4-bit pulsing sequence, and all ion fragmentation, and data were compared regarding sensitivity and selectivity of the analysis. Demultiplexing was done by the software tool provided by Agilent and processing filters were optimized. Data analysis was carried out using Mass Profiler for feature extraction and feature identification. Simca P was used for the application of statistical methods including principal components analysis (PCA), partial least squares discriminate analysis (PLS-DA) and orthogonal partial least squares discriminate analysis (OPLS-DA).
Results
In this preliminary study 12 samples were analysed, 5 wild type and 7 RAB1A knockout mice, with the latter mouse identified as diabetic following a glucose tolerance test. The goal of the study was the investigation of phospholipid and neutral lipid distribution with special attention to low abundant species and isomeric compounds to identify changes associated with the development of diabetes. In positive ion mode several classes of phospholipids were detected, including lysophospholipids, phosphatidylcholines and phosphatidylinositols, as well as diglycerides and triglycerides. These classes are partially separated on the LC column, but in addition they are separated further in the drift tube by their collisional cross sections resulting in the so called “two dimensional ion mobilogram” (drift time vs m/z). Identification of compounds was based on the phospholipid database of METLIN (containing m/z), retention time and CCS from literature and measurements of non-labelled standards. More than 500 compounds were identified in positive ion mode and more than 300 in negative ion mode.
Triglycerides (TGs) are one of the most interesting compounds in terms of diabetic studies, because of their important role in energy metabolism and their contribution to ectopic lipid deposition. The abundance of these species can be quite low in plasma, especially in fasting samples. Therefore, the improvement of sensitivity afforded by the QToF is an important issue for such analyses.
Multiplexing is a tool that provides an improvement in signal to noise ratio, and hence increased sensitivity, by increasing the duty cycle using signal multiplexing by means of the application of Hadamard-type transforms. Our data suggest an average of a three-fold increase in sensitivity when applying multiplexing compared to measurements without using multiplexing techniques. We found no significant difference in our data when either 3-bit or 4-bit multiplexing pulsing sequence were applied.
Statistical analysis showed significant differences between the samples from wild type and heterozygote mice. PCA, as unsupervised statistical pattern recognition method showed only partial separation of the two groups. However, PLS-DA and OPLS-DA provided good separation. We were able to identify potential biomarkers among triglycerides (e.g. TG(50:2), TG(52:2)) as well as among other types of lipids (e.g. PC(22:4/22:5))
Conclusions
We demonstrate how ion mobility mass spectrometry is able to improve the separation and identification of biomarkers in metabolomics studies, especially for lipidomics. Further analyses of other types of samples (e.g. tissue extracts) in diabetic animal models and studies of other metabolic diseases are planned in order to extend the application of this powerful technique and instrumentation.
References & Acknowledgements:
| Description | Y/N | Source |
| Grants | yes | Agilent Technologies and MRC (MC_UP_A90_1006) |
| Salary | no | |
| Board Member | no | |
| Stock | no | |
| Expenses | no |
IP Royalty: no
| Planning to mention or discuss specific products or technology of the company(ies) listed above: | yes |