Mass spectrometry (MS) is a powerful tool for qualitative and quantitative biological sample analysis, with its high sensitivity, broad applicability, and strong robustness. While insights into ‘what’ and ‘how’ can be provided by mass spectrometry, MS technique coupled with traditional separation methods, such as liquid chromatography LC-MS, do not provide a satisfactory key to the question ‘where’ with high enough resolution. Accordingly, two different types of MS methods are being developed to analyze sample species with spatial resolution, one being single cell mass spectrometry (SCMS), and the other as mass spectrometry imaging (MSI).
In general, SCMS provides chemical insight of individual cells, whereas MSI can reveal the spatial distributions of chemical substances at a micrometer resolution. With the in-house microscale sampling device developed in the Yang group, the Single-probe, SCMS and MSI could be conducted. Both methods require specific protocols for sample treatment, experiment operation, data acquisition, and data analysis. Both SCMS and MSI studies are focused on analysis of small molecules (e.g., metabolites, lipids, and drug compounds). Different from SCMS studies, MSI measurements allow for acquiring spatial information on top of the chemical information provided by MS, and the spatial information has brought more complexity in the output data which require more advanced data analysis tools.
In this dissertation, the background of spatially resolved MS methods, i.e., MSI techniques, are first introduced, followed by a summary of previously published studies on quantitative SCMS metabolomics projects. In Chapter 3, MSI attempts on three different types of samples, including mice retina, patient breast, and co-cultured cancer cell spheroids, are introduced. In Chapter 4, the metabolomic profile changes of heart tissues upon Trypanosoma Cruzi infection have been investigated with the Single-probe MSI technique. The compatibility between MS and two commonly adopted strategies, fixation and staining, is studied, suggesting that X-gal staining has significantly altered the chemical profile. In Chapter 5, to handle SCMS data with better efficiency and higher mass accuracy, a Python-based MS data pretreatment platform with an easy-to-use graphical user interface (GUI) and an innovative peak alignment algorithm is developed to be compatible with improvised SCMS experiments. In Chapter 6, advanced statistical methods used for MS data processing are discussed in the context of MSI study of mice brain with Alzheimer's Disease as an example. The fusion of ion images from MSI and fluorescence images from immunohistology staining has improved the spatial resolution to a higher level, leading to more precise mapping of chemical substances and more findings involved in Alzheimer’s Disease development
