Mass Spectral Imaging for Chemical Analysis of Cancerous Tissues Using Time-of-Flight Secondary Ion Mass Spectrometry

Abstract

Mass spectrometry imaging is unique as it has becoming a versatile and interdisciplinary technique; it openly crosses the boundaries between chemistry and biology as it combines detailed chemical and spatial information within biological samples. In this work, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to analyze human breast cancer tissue and murine β cell neoplasia. ToF-SIMS for tissue sample analysis is an emerging field, so the development of characterization methods is critical to provide a framework for multiple tissue comparisons. The work presented here demonstrates a multivariate analysis method to isolate and analyze specific tissue regions of interest by utilizing principal components analysis (PCA) of ToF-SIMS images is presented, which allowed separation of cellularized areas from stromal areas. These PCA-generated regions of interest were then used as masks to reconstruct representative spectra from specifically stromal or cellular regions. The advantage of this unsupervised selection method is a reduction in scatter in the spectral PCA results when compared to analyzing all tissue areas or analyzing areas highlighted by a pathologist. Using this method, stromal and cellular regions of breast tissue biopsies taken pre- versus post-chemotherapy demonstrate chemical separation. Fatty acids (i.e. palmitic, oleic, and stearic), monoacylglycerols, diacylglycerols and vitamin E profiles were distinctively different between the pre- and post-therapy tissues. Utilizing this method, which provides a framework to compare a multiple tissue samples using imaging ToF-SIMS, 23 pre-treated breast cancer tissue biopsies were analyzed. Using the PCA generated masks, it was possible to compare regions with a focus on metabolic changes occurring within breast cancer tissue to reveal the chemical profile of chemoresistance. Comparing ToF-SIMS cellular and stromal region data from specific subtypes, e.g. triple negative, has shown promise in defining chemical differences between patients that respond to chemotherapy and those that do not. These differences were related primarily to fatty acids and sphingomyelin. In another experiment, ToF-SIMS was applied to generate a high resolution in situ molecular analysis of Myc-induced pancreatic β cell islet tumors to investigate the tumor microenvironment. Employing PCA, we show that it is possible to chemically distinguish cancerous islets from normal tissue, in addition to intratumor heterogeneity. These heterogeneities can then be imaged and investigated using another modality such as second harmonic generation (SHG) microscopy. Using these techniques with a specialized mouse model, we found significant metabolic changes occurring within β cell tumors and the surrounding tissues. Specific alterations within the lipid, amino acid, and nucleotide metabolism were observed, demonstrating that ToF-SIMS can be utilized to identify large-scale changes that occur in generated in the tumor microenvironment and could thereby increase our understanding of tumor progression and the tumor microenvironment.