Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial

We report an extensive 600 MHz NMR trial of a quantitative lipoprotein and small molecule measurements in human blood serum and plasma. Five centers with eleven 600 MHz NMR spectrometers were used to analyze 98 samples including: 20 QCs, 37 commercially sourced, paired serum and plasma samples and 2 National Institute of Science and Technology, NIST, reference material 1951c replicates. Samples were analyzed using rigorous protocols for sample preparation and experimental acquisition. A commercial lipoprotein subclass analysis was used to quantify 105 lipoprotein subclasses and 24 low molecular weight metabolites from the nuclear magnetic resonance, NMR, spectra. For all spectrometers, the instrument specific variance in measuring internal quality controls, QCs, was lower than the percentage described by the National Cholesterol Education Program, NCEP, criteria for lipid testing (triglycerides<2.7%, cholesterol<2.8%; LDL-cholesterol<2.8%; HDL-cholesterol<2.3%), showing exceptional reproducibility for direct quantitation of lipoproteins in both matrices. The average RSD for the 105 lipoprotein parameters in the 11 instruments was 4.6% and 3.9% for the two NIST samples while it was 38% and 40% for the 37 commercially sourced plasmas and sera, respectively, showing negligible analytical compared to biological variation. The coefficient of variance, CV, obtained for the quantification of the small molecules across the 11 spectrometers was below 15% for 20 out of the 24 metabolites analyzed. This study provides further evidence of the suitability of NMR for high-throughput lipoprotein subcomponent analysis and small molecule quantitation with the exceptional reproducibility required for clinical and other regulatory settings

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided

peakPantheR: Peak Picking and ANnoTation of High resolution Experiments in R

An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files.An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files.v1.2.

The effects of kisspeptin on β‐cell function, serum metabolites and appetite in humans

Aims To investigate the effect of kisspeptin on glucose‐stimulated insulin secretion and appetite in humans. Materials and methods In 15 healthy men (age: 25.2 ± 1.1 years; BMI: 22.3 ± 0.5 kg m−2), we compared the effects of 1 nmol kg−1 h−1 kisspeptin versus vehicle administration on glucose‐stimulated insulin secretion, metabolites, gut hormones, appetite and food intake. In addition, we assessed the effect of kisspeptin on glucose‐stimulated insulin secretion in vitro in human pancreatic islets and a human β‐cell line (EndoC‐βH1 cells). Results Kisspeptin administration to healthy men enhanced insulin secretion following an intravenous glucose load, and modulated serum metabolites. In keeping with this, kisspeptin increased glucose‐stimulated insulin secretion from human islets and a human pancreatic cell line in vitro. In addition, kisspeptin administration did not alter gut hormones, appetite or food intake in healthy men. Conclusions Collectively, these data demonstrate for the first time a beneficial role for kisspeptin in insulin secretion in humans in vivo. This has important implications for our understanding of the links between reproduction and metabolism in humans, as well as for the ongoing translational development of kisspeptin‐based therapies for reproductive and potentially metabolic conditions.</p

The effects of kisspeptin on β‐cell function, serum metabolites and appetite in humans

Aims To investigate the effect of kisspeptin on glucose‐stimulated insulin secretion and appetite in humans. Materials and methods In 15 healthy men (age: 25.2 ± 1.1 years; BMI: 22.3 ± 0.5 kg m−2), we compared the effects of 1 nmol kg−1 h−1 kisspeptin versus vehicle administration on glucose‐stimulated insulin secretion, metabolites, gut hormones, appetite and food intake. In addition, we assessed the effect of kisspeptin on glucose‐stimulated insulin secretion in vitro in human pancreatic islets and a human β‐cell line (EndoC‐βH1 cells). Results Kisspeptin administration to healthy men enhanced insulin secretion following an intravenous glucose load, and modulated serum metabolites. In keeping with this, kisspeptin increased glucose‐stimulated insulin secretion from human islets and a human pancreatic cell line in vitro. In addition, kisspeptin administration did not alter gut hormones, appetite or food intake in healthy men. Conclusions Collectively, these data demonstrate for the first time a beneficial role for kisspeptin in insulin secretion in humans in vivo. This has important implications for our understanding of the links between reproduction and metabolism in humans, as well as for the ongoing translational development of kisspeptin‐based therapies for reproductive and potentially metabolic conditions.</p

PhenoMeNal: processing and analysis of metabolomics data in the cloud

Background: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings: The PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm. Conclusions: PhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and 'omics research domains