We show here the identity of Alamar Blue as resazurin. The`resazurin reduction test' has been used for about 50 years to monitor bacterial and yeast contamination of milk, and also for assessing semen quality. Resazurin (blue and nonfluorescent) is reduced to resorufin (pink and highly fluorescent) which is further reduced to hydroresorufin (uncoloured and nonfluorescent). It is still not known how this reduction occurs, intracellularly via enzyme activity or in the medium as a chemical reaction, although the reduced fluorescent form of Alamar Blue was found in the cytoplasm and of living cells nucleus of dead cells. Recently, the dye has gained popularity as a very simple and versatile way of measuring cell proliferation and cytotoxicity. This dye presents numerous advantages over other cytotoxicity or proliferation tests but we observed several drawbacks to the routine use of Alamar Blue. Tests with several toxicants in different cell lines and rat primary hepatocytes have shown accumulation of the fluorescent product of Alamar Blue in the medium which could lead to an overestimation of cell population. Also, the extensive reduction of Alamar Blue by metabolically active cells led to a final nonfluorescent product, and hence an underestimation of cellular activity.

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.

The mammalian gut microbiota interact extensively with the host through metabolic exchange and co-metabolism of substrates. Such metabolome-metabolome interactions are poorly understood, but might be implicated in the aetiology of many human diseases. In this paper, we assess the importance of the gut microbiota in influencing the disposition, fate and toxicity of drugs in the host, and conclude that appropriate consideration of individual human gut microbial activities will be a necessary part of future personalized health-care paradigms.

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BackgroundQuality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.Aim of reviewThis tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.Key scientific concepts of reviewSystem suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.

Evidence is growing that the gut microbiota modulates the host response to chemotherapeutic drugs, with three main clinical outcomes: facilitation of drug efficacy; abrogation & compromise of anti-cancer effects, and mediation of toxicity. The implication is that gut microbiota are critical to the development of personalized cancer treatment strategies and, therefore, a greater insight into prokaryotic co-metabolism of chemotherapeutic drugs is now required. This thinking is based on evidence from human, animal and in vitro studies and gut bacteria are intimately linked to the pharmacological effects of chemotherapies (5-fluorouracil, cyclophosphamide, irinotecan, oxaliplatin, gemcitabine, methotrexate) and novel targeted immunotherapies such as anti-PD-L1 and anti-CLTA-4. The gut microbiota modulate these agents through key mechanisms, structured as the 'TIMER' mechanistic framework: namely Translocation, Immunomodulation, Metabolism, Enzymatic degradation, andReduced diversity and ecological variation The gut microbiota can now, therefore, be targeted to improve efficacy and reduce the toxicity of current chemotherapy agents. In this Review, we outline the implications of pharmacomicrobiomics in cancer therapeutics and define how the microbiota might be modified in clinical practice to improve efficacy and reduce the toxic burden of these compounds.

Inflammatory bowel diseases (IBD) including Crohn's disease (CD) and ulcerative colitis (UC) have a major impact on the health of individuals and populations. Accurate diagnosis of inflammatory bowel disease (IBD) at an early stage, and correct differentiation between Crohn's disease (CD) and ulcerative colitis (UC), is important for optimum treatment and prognosis. We present here the first characterization of fecal extracts obtained from patients with CD and UC by employing a noninvasive metabonomics approach, which combines high resolution 1H NMR spectroscopy and multivariate pattern recognition techniques. The fecal extracts of both CD and UC patients were characterized by reduced levels of butyrate, acetate, methylamine, and trimethylamine in comparison with a control population, suggesting changes in the gut microbial community. Also, elevated quantities of amino acids were present in the feces from both disease groups, implying malabsorption caused by the inflammatory disease or an element of protein losing enteropathy. Metabolic differences in fecal profiles were more marked in the CD group in comparison with the control group, indicating that the inflammation caused by CD is more extensive in comparison with UC and involves the whole intestine. Furthermore, glycerol resonances were a dominant feature of fecal spectra from patients with CD but were present in much lower intensity in the control and UC groups. This work illustrates the potential of metabonomics to generate novel noninvasive diagnostics for gastrointestinal diseases and may further our understanding of disease mechanisms.