Nanoparticles (NPs) have been suggested as efficient matrixes for small molecule profiling and imaging by laser-desorption ionization mass spectrometry (LDI-MS), but so far there has been no systematic study comparing different NPs in the analysis of various classes of small molecules. Here, we present a large scale screening of 13 NPs for the analysis of two dozen small metabolite molecules. Many NPs showed much higher LDI efficiency than organic matrixes in positive mode and some NPs showed comparable efficiencies for selected analytes in negative mode. Our results suggest that a thermally driven desorption process is a key factor for metal oxide NPs, but chemical interactions are also very important, especially for other NPs. The screening results provide a useful guideline for the selection of NPs in the LDI-MS analysis of small molecules.

Mass spectrometry imaging (MSI) is a powerful tool that has advanced our understanding of complex biological processes by enabling unprecedented details of metabolic biology to be uncovered. Through the use of high-spatial resolution MSI, metabolite localizations can be obtained with high precision. Here we describe our recent progress to enhance the spatial resolution of matrix-assisted laser desorption/ionization (MALDI) MSI from ∼50 μm with the commercial configuration to ∼5 μm. Additionally, we describe our efforts to develop a 'multiplex MSI' data acquisition method to allow more chemical information to be obtained on a single tissue in a single instrument run, and the development of new matrices to improve the ionization efficiency for a variety of small molecule metabolites. In combination, these contributions, along with the efforts of others, will bring MSI experiments closer to achieving metabolomic scale.

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Multi-matrix, dual polarity, tandem mass spectrometry imaging strategy applied to a germinated maize seed: toward mass spectrometry imaging of an untargeted metabolome AbstractMass spectrometry imaging (MSI) provides high spatial resolution information that is unprecedented in traditional metabolomics analyses; however, the molecular coverage is often limited to a handful of compounds and is insufficient to understand overall metabolomic changes of a biological system. Here, we propose an MSI methodology to increase the diversity of chemical compounds that can be imaged and identified, in order to eventually perform untargeted metabolomic analysis using MSI. In this approach, we use the desorption/ionization bias of various matrixes for different metabolite classes along with dual polarities and a tandem MSI strategy. The use of multiple matrixes and dual polarities allows us to visualize various classes of compounds, while data-dependent MS/MS spectra acquired in the same MSI scans allow us to identify the compounds directly on the tissue. In a proof of concept application to a germinated corn seed, a total of 166 unique ions were determined to have high-quality MS/MS spectra, without counting structural isomers, of which 52 were identified as unique compounds. According to an estimation based on precursor MSI datasets, we expect over five hundred metabolites could be potentially identified and visualized once all experimental conditions are optimized and an MS/MS library is available. Lastly, metabolites involved in the glycolysis pathway and tricarboxylic acid cycle were imaged to demonstrate the potential of this technology to better understand metabolic biology. 2 Table of ContentsMass spectrometry imaging strategy to allow for visualization and identification of compounds on tissue to help understand plant metabolism AbstractMass spectrometry imaging (MSI) provides high spatial resolution information that is unprecedented in traditional metabolomics analyses; however, the molecular coverage is often limited to a handful of compounds and is insufficient to understand overall metabolomic changes of a biological system. Here, we propose an MSI methodology to increase the diversity of chemical compounds that can be imaged and identified, in order to eventually perform untargeted metabolomic analysis using MSI. In this approach, we use the desorption/ionization bias of various matrixes for different metabolite classes along with dual polarities and a tandem MSI strategy. The use of multiple matrixes and dual polarities allows us to visualize various classes of compounds, while data-dependent MS/MS spectra acquired in the same MSI scans allow us to identify the compounds directly on the tissue. In a proof of concept application to a germinated corn seed, a total of 166 unique ions were determined to have high-quality MS/MS spectra, without counting structural isomers, of which 52 were identified as unique compounds. According to an estimation based on precursor MSI datasets, we expect over five hundred metaboli...