Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC–MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC–MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC–MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC–MS and GC–MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC–MS and GC × GC–MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC–MS processing compared to targeted GC–MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC–MS were somewhat higher than with GC–MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC–MS was demonstrated; many additional candidate biomarkers were found with GC × GC–MS compared to GC–MS

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies

Long-term survival after initial hospital admission for peripheral arterial disease in the lower extremities

ABSTRACT: Background As the population ages, peripheral arterial disease (PAD) in the lower extremities will become a larger public health problem. Awareness in patients as well clinicians of the high risk of morbidity and mortality is important but seems currently low. Insights in absolute mortality risks following admission for PAD in the lower extremities can be useful to improve awareness as they are easy to interpret. Methods A nationwide cohort of 4,158 patients with an initial admission for PAD in the lower extremities was identified through linkage of the national hospital and population register in 1997 and 2000. Results Over 60% of 4,158 patients were men. 28 days, 1 year and 5 year mortality risk were 2.4%, 10.3% and 31.0% for men and 3.5%, 10.4% and 27.4% for women. Coronary heart disease and stroke were frequent cause of death. Five years mortality risk was higher for men compared to women (HR 1.36, 95% CI 1.21-1.53). Conclusions Our findings demonstrate that, 5 year mortality risk is high, especially in men and comparable to that of patients admitted for acute myocardial infarction or ischemic stroke. Though, in general population the awareness of the severity of PAD in the lower extremities is significantly lower than that for any other cardiovascular disease and it seems that cardiovascular risk factor management for prevention in PAD patients is very modes

Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

Contains fulltext : 93904.pdf (publisher's version ) (Open Access

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results

Exploratory GC/MS-Based Metabolomics of Body Fluids

Part of the Methods in Molecular Biology book series (MIMB, volume 1730)GC/MS-based metabolomics is a powerful tool for metabolic phenotyping and biomarker discovery from body biofluids. In this chapter, we describe an untargeted metabolomic approach for plasma/serum and fecal water sample profiling. It describes a multistep procedure, from sample preparation, oximation/silylation derivatization, and data acquisition using GC/QToF to data processing consisting in data extraction and identification of metabolites

Yeast metabolomics : sample preparation for a GC/MS-based analysis

Metabolome sample preparation is one of the key factors in metabolomics analyses. The quality of the metabolome data will depend on the suitability of the experimental procedures to the cellular system (e.g., yeast cells) and the analytical performance. Here, we summarize a protocol for metabolome analysis of yeast cells using gas chromatography-mass spectrometry (GC-MS). First, the main phases of a metabolomics analysis are identified: sample preparation, metabolite extraction, and analysis. We also provide an overview on different methods used to quench samples and extract intracellular metabolites from yeast cells. This protocol provides a detailed description of a GC-MS-based analysis of yeast metabolome, in particular for metabolites containing amino and/or carboxyl groups, which represent most of the compounds participating in the central carbon metabolism