Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum.

A method for performing untargeted metabolomic analysis of human serum has been developed based on protein precipitation followed by Ultra Performance Liquid Chromatography and Time-of-Flight mass spectrometry (UPLC-TOF-MS). This method was specifically designed to fulfill the requirements of a long-term metabolomic study, spanning more than 3 years, and it was subsequently thoroughly evaluated for robustness and repeatability. We describe here the observed drift in instrumental performance over time and its improvement with adjustment of the length of analytical block. The optimal setup for our purpose was further validated against a set of serum samples from 30 healthy individuals. We also assessed the reproducibility of chromatographic columns with the same chemistry of stationary phase from the same manufacturer but from different production batches. The results have allowed the authors to prepare SOPs for "fit for purpose" long-term UPLC-MS metabolomic studies, such as are being employed in the HUSERMET project. This method allows the acquisition of data and subsequent comparison of data collected across many months or years.

Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum.

A method for the preparation and GC-TOF-MS analysis of human serum samples has been developed and evaluated for application in long-term metabolomic studies. Serum samples were deproteinized using 3:1 methanol/serum, dried in a vacuum concentrator, and chemically derivatized in a two-stage process. Samples were analyzed by GC-TOF-MS with a 25 min analysis time. In addition, quality control (QC) samples were used to quantify process variability. Optimization of chemical derivatization was performed. Products were found to be stable for 30 h after derivatization. An assessment of within-day repeatability and within-week reproducibility demonstrates that excellent performance is observed with our developed method. Analyses were consistent over a 5 month period. Additional method testing, using spiked serum samples, showed the ability to define metabolite differences between samples from a population and samples spiked with metabolites standards. This methodology allows the continuous acquisition and application of data acquired over many months in long-term metabolomic studies, including the HUSERMET project (http://www.husermet.org/).

Molecular phenotyping of a UK population: defining the human serum metabolome.

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.

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 quality of DNA extracted from liquid or dried blood is not adversely affected by storage at 4 degrees C for up to 24 h.

BACKGROUND: A consistent and stable source of DNA is an essential requirement for many Biobanks. An important pre-analytical variable is the delay between sample collection and sample processing. METHODS: Fresh blood samples (n = 80) were collected and either processed immediately or after storage for 24 h. The samples were either stored as liquid blood at 4 degrees C or as dried blood spots at ambient temperature on three types of paper-based storage media. The quality of the DNA extracted from the samples was measured. RESULTS: No difference was observed between fresh and stored blood samples. CONCLUSIONS: The quality of DNA extracted from liquid or dried blood is not adversely affected by storage at 4 degrees C for up to 24 h.

Transcript profiling of functionally related groups of genes during conditional differentiation of a mammalian cochlear hair cell line.

We have used Affymetrix high-density gene arrays to generate a temporal profile of gene expression during differentiation of UB/OC-1, a conditionally immortal cell line derived from the mouse cochlea. Gene expression was assessed daily for 14 days under differentiating conditions. The experiment was replicated in two separate populations of cells. Profiles for selected genes were correlated with those obtained by RT-PCR, TaqMan analysis, immunoblotting, and immunofluorescence. The results suggest that UB/OC-1 is derived from a population of nonsensory epithelial cells in the greater epithelial ridge that have the potential to differentiate into a hair-cell-like phenotype, without the intervention of Math1. Elements of the Notch signaling cascade were identified, including the receptor Notch3, with a transient up-regulation that suggests a role in hair cell differentiation. Several genes showed a profile similar to Notch3, including the transcriptional co-repressor Groucho1. UB/OC-1 also expressed Me1, a putative partner of Math1 that may confer competence to differentiate into hair cells. Cluster analysis revealed expression profiles for neural guidance genes associated with Gata3. The temporal dimension of this analysis provides a powerful tool to study genetic mechanisms that underlie the conversion of nonsensory epithelial cells into hair cells.