Introduction: The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values and data heterogeneity from technical variability. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS.
Methods: We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We have used analysis of variance (ANOVA) model to study the effects due to various sources of variability.
Results and conclusion: We found that the FFPE TSM is better than the FR TSM. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design.
Introduction: Glioblastoma (GBM) is the most common primary malignant brain tumor in humans and, even with aggressive treatment that includes surgical resection, radiation (IR), and chemotherapy administration, prognosis is poor due to tumor recurrence. There is evidence that within GBMs a small number of glioma stem-like cells (GSLCs) exist, which are thought to be therapy resistant and are thus capable of repopulating a tumor after treatment. Like most cancers, GBMs largely employ aerobic glycolysis to create ATP, a phenomenon known as the Warburg Effect. There is no consensus on the metabolic characteristics of cancer stem cells. GSLCs have been shown to rely more heavily on oxidative phosphorylation, but there is also evidence that cancer stem cells can adapt their metabolism by fluctuating between energy pathways or acquiring intermediate metabolic phenotypes. We hypothesized that the metabolism of GSLCs differs from that of differentiated GBM tumor cell lines, and that the steady state metabolism would be differentially altered following radiation treatment.
Materials and methods: We evaluated the oxygen consumption rate, extracellular acidification rate, and metabolic enzyme levels of GBM cell lines and GSLCs before and after irradiation using extracellular flux assays. We also measured absolute metabolite levels in these cells via mass spectroscopy with and without radiation treatment.
Results: GSLCs were found to be significantly more quiescent in comparison to adherent GBM cell lines, highlighted by lower glycolytic and maximal respiratory capacities as well as lower oxygen consumption and extracellular acidification rates. Analysis of individual metabolite concentrations revealed lower total metabolite concentrations overall but also elevated levels of metabolites in different energy pathways for GSLCs compared to GBM cell lines. Additionally, the metabolism of both GSLCs and GBM cell lines were found to be altered by IR.
Conclusions: While there is not one metabolic alteration that distinguishes irradiated GSLC metabolism from that of GBM cell lines, therapies targeting more metabolically quiescent tumor cells and thus the resistant GSLC population may increase a cancer's sensitivity to radiotherapy.
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