MSACL 2016 US Abstract

IR-MALDESI: An Innovative Approach to Molecular Microscopy

David Muddiman (Presenter)
North Carolina State University

Bio: David C. Muddiman is the Jacob and Betty Belin Distinguished Professor of Chemistry and Founder and Director of the W.M. Keck FTMS Laboratory for Human Health Research at North Carolina State University in Raleigh, NC. Prior to moving his research group to North Carolina State University, he was a Professor of Biochemistry and Molecular Biology and Founder and Director of the Mayo Proteomics Research Center at the Mayo Clinic College of Medicine in Rochester, MN. Dr. Muddiman is Editor of Analytical and Biological Chemistry and Associate Editor of the Encyclopedia of Analytical Chemistry as well as on the Editorial Advisory Board of Mass Spectrometry Reviews, Molecular and Cellular Proteomics, Rapid Communications in Mass Spectrometry, and the Journal of Chromatography B. He also serves on the advisory board of the NIH Funded Complex Carbohydrate Research Center, University of Georgia an

Authorship: David C. Muddiman
North Carolina State University

Short Abstract This presentation will detail our efforts at the fundamental development and application of a novel mass spectrometry imaging technique called IR-MALDESI. This will be demonstrated for both targeted, quantitative studies as well as untargeted analyses.

Long Abstract

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging (MSI) is a powerful analytical platform for the visualization of endogenous and exogenous analytes within tissue sections. Application of MSI methodologies to drug distribution studies over the last decade has provided invaluable insight to the distribution of pharmaceuticals in preclinical and clinical trials, but quantitative MSI data is often unable to be achieved. Through optimization and careful definition of analytical figures of merit, IR-MALDESI MSI provides absolute quantification of small molecule drugs in tissue. In this MSI ionization method, an IR laser ablates a voxel of material at each rastered position, providing a complete and reproducible volume of a tissue section to be sampled with subsequent ionization of analytes by ESI. MSI variability from tissue microenvironments is reduced with the uniform incorporation of an internal standard for normalization of analyte on a per-voxel basis. Absolute quantification MSI is accomplished through the inclusion of a spatial calibration curve in the MSI analysis allowing direct correlation of observed ion abundance in MSI to absolute analyte concentration. The absolute quantification procedure has been automated in the MSiQuantification software tool, available within the open source, vendor neutral MSI software MSiReader v1.0, which improves throughput of quantitative MSI experiments. IR-MALDESI in the context of lipidomics will also be presented. Lipid profiles provide invaluable information for understanding the biological basis of many disease, and alterations in lipid metabolism have been linked to several diseases. Due to their remarkable structural diversity, different lipid classes exhibit significant differences in their ionization efficiency. Therefore, analyzing tissue sections in positive and negative modes is essential for obtaining comprehensive lipid coverage. To this end, an IR-MALDESI polarity switching MSI method was developed where adjacent voxels were analyzed with opposing polarities, and the electrospray solvent composition was optimized in order to improve ion abundance in both polarities and achieve the most comprehensive coverage of lipids and metabolites in both polarities. Data from the domestic hen model for spontaneous development of ovarian cancer will be presented. Healthy and cancerous hen ovarian tissue sections were analyzed using this method. Differences in spatial distribution and relative abundance of more than 700 analytes between the two tissue sections were simultaneously monitored. An approximate 2-fold increase in the number of lipids identified in the cancerous tissue was observed. Lipid classes such as fatty acids, phospholipids, and sphingolipids accounted for most of the difference in the number of identified lipids between the two tissue sections. These observations are in good agreement with those reported in the literature obtained using conventional methods such as LC-MS/MS and NMR.

References & Acknowledgements:

The authors also gratefully acknowledge the financial assistance received from the National Institutes of Health (R01GM087964) and Thermo Fisher Scientific for partial support to attend MSACL 2016.

Financial Disclosure

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