Chemical Analysis of Cells and Tissues with Time-of-Flight Secondary Ion Mass Spectrometry

In this work the chemical analysis of biological cells and tissues with time-of-flight secondary ion mass spectrometry (ToF-SIMS) was explored. ToF-SIMS has the ability to obtain a mass spectrum with submicron spatial resolution for imaging and is extremely surface sensitive. ToF-SIMS for biological sample analysis is still an emerging field, so the development and characterization of novel sample preparation and analysis methods is key to acquiring useable information. In this work, three different methods to prepare NIH/3T3 fibroblasts were investigated: chemically fixed, freeze-dried and frozen-hydrated. Chemical fixation followed by rinsing removed a majority of intracellular Cl-, improving the secondary ion yields of all organic positively charged secondary ions an average of 2.6x. Damage cross sections were reduced during frozen-hydrated analysis, improving the secondary ion yields of higher mass organic fragments. In a separate experiment, accurate 3D reconstructions of NIH/3T3 fibroblasts were produced. A simple z-correction was applied to the data cube, and the biggest assumption for that correction was validated. An intracellular lipid-rich region surrounding the nucleus was visualized.;ToF-SIMS applied to two different breast cancer systems. In the first, eight human breast cancer cell lines were distinguished form one another using mass spectra and principal component analysis (PCA). Not only was PCA to distinguish the cell lines form one another, it also highlighted the largest sources of variance between the cells. Phosphocholine, fatty acids, cholesterol and diacylglycerols (DAGs) were identified as key peaks. The identification of these species indicate that differences in lipid metabolism play an important role in separating the cell types from one another. Breast cancer tumor tissues were also investigated. Data from four tumors was collected. PCA applied to the spectra distinguished the four tissues from one another. Imaging PCA determined the largest sources of variance within an analysis area. Structures were identified by PCA that matched structures observed in serial-sectioned, conventionally-stained slices, and other domains that were not visible in the conventionally-stained slices. As with the breast cancer cell lines, phosphocholine, fatty acids, DAGs, cholesterol and vitamin were found to be large sources of variance, indicating lipid metabolism plays in important role in tumor differentiation.