Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

Key nuclear processes in eukaryotes, including DNA replication, repair, and gene regulation, require extensive chromatin remodeling catalyzed by energy-consuming enzymes. It remains unclear how the ATP demands of such processes are met in response to rapid stimuli. We analyzed this question in the context of the massive gene regulation changes induced by progestins in breast cancer cells and found that ATP is generated in the cell nucleus via the hydrolysis of poly(ADP-ribose) to ADP-ribose. In the presence of pyrophosphate, ADP-ribose is used by the pyrophosphatase NUDIX5 to generate nuclear ATP. The nuclear source of ATP is essential for hormone-induced chromatin remodeling, transcriptional regulation, and cell proliferation.