Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Imaging Mass Spectrometry of Biological Tissues

Mass spectrometry is a powerful analytical tool for the identification and quantification of chemical molecules from a sample. It operates by separating and measuring the molecular masses of these chemicals. To perform matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of biological tissues, the sampling of chemicals is focused to a very small area of a tissue section (< 0.1 mm) using a laser beam. In a spot-by-spot (or pixel-by-pixel) fashion, the abundances and masses of the chemicals on the tissue are recorded. The data set is then displayed as heat maps to reveal the 2-dimensional distribution of any detected molecules. When combined with strategically designed sample treatment, researchers are able to correlate the changes in molecular distribution to biological functions. Ideally, statistical software should be able to identify changes in a non-targeted and unbiased manner. In practice, the presence of chemical noise, signals that do not directly originate from the analytes, can drown out small changes in analyte signals, making them undetectable by statistical tools. This is particularly problematic for the IMS of low molecular weight (MW) compounds, because the chemical matrix required to promote the laser ionization of analytes can generate a substantial level of background noise in the low-MW region. This region consists of many important metabolites and signaling molecules, such as biogenic amines and neurotransmitters. Hence, further research is needed to improve IMS for the non-targeted profiling of these small analytes.

This research grant will support the exploration of three approaches to improve the quality of tissue IMS in the low-MW region. Firstly, novel materials are proposed as the laser absorbing matrices which will reduce chemical noise. Studies will focus on the use of metal oxide nanoparticles, and their uniform application on the tissue surfaces, to facilitate the analysis of low-MW metabolites and neurotransmitters in rat brain tissue. The second approach is to derivatize the analytes with on-tissue chemical reactions, to enhance the ionization efficiency, and to label them for differentiation from background. Finally, post-analysis data processing will remove the matrix-related background noise. Identification of background noise signals will be assisted with the use of matrix compounds labeled with special isotopes. Together, the three proposed approaches will significantly improve the quality of IMS in the non-targeted profiling low-MW chemicals in tissue.