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      Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

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          Abstract

          Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.

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          Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.

          Arun Sreekumar, Laila Poisson, Thekkelnaycke M Rajendiran … (2009)
          Multiple, complex molecular events characterize cancer development and progression. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we profiled more than 1,126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
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            MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data

            Kenneth Haug, Reza Salek, Pablo Conesa Zamora … (2012)
            MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
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              High-throughput tissue extraction protocol for NMR- and MS-based metabolomics.

              Huifeng Wu, Andrew Southam, Adam Hines … (2008)
              In metabolomics, tissues typically are extracted by grinding in liquid nitrogen followed by the stepwise addition of solvents. This is time-consuming and difficult to automate, and the multiple steps can introduce variability. Here we optimize tissue extraction methods compatible with high-throughput, reproducible nuclear magnetic resonance (NMR) spectroscopy- and mass spectrometry (MS)-based metabolomics. Previously, we concluded that methanol/chloroform/water extraction is preferable for metabolomics, and we further optimized this here using fish liver and an automated Precellys 24 bead-based homogenizer, allowing rapid extraction of multiple samples without carryover. We compared three solvent addition strategies: stepwise, two-step, and all solvents simultaneously. Then we evaluated strategies for improved partitioning of metabolites between solvent phases, including the addition of extra water and different partition times. Polar extracts were analyzed by NMR and principal components analysis, and the two-step approach was preferable based on lipid partitioning, reproducibility, yield, and throughput. Longer partitioning or extra water increased yield and decreased lipids in the polar phase but caused metabolic decay in these extracts. Overall, we conclude that the two-step method with extra water provides good quality data but that the two-step method with 10 min partitioning provides a more accurate snapshot of the metabolome. Finally, when validating the two-step strategy using NMR and MS metabolomics, we showed that technical variability was considerably smaller than biological variability.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Data
                Journal ID (iso-abbrev): Sci Data
                Title: Scientific Data
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2052-4463
                Publication date (Electronic): 10 June 2014
                Publication date Collection: 2014
                Volume: 1
                Electronic Location Identifier: 140012
                Affiliations
                [1 ] School of Biosciences, University of Birmingham , Edgbaston, Birmingham, B15 2TT, UK
                [2 ] Department of Medicine, University of Alberta , Edmonton, AB, Canada T6G 2EI
                [3 ] NERC Biomolecular Analysis Facility – Metabolomics Node (NBAF-B), University of Birmingham , Edgbaston, Birmingham, B15 2TT, UK
                Author notes
                [a ] M.R.V. (email: m.viant@ 123456bham.ac.uk )
                []

                J.K co-designed the study and undertook the experimental work to collect the DIMS metabolomics dataset.R.W organised, structured and deposited the datasets into MetaboLights.D.B designed and wrote the batch correction and spectral cleaning algorithms.M.V co-designed the study and was the academic lead.All authors contributed to the writing of the final paper.

                Article
                Publisher Item ID: sdata201412
                DOI: 10.1038/sdata.2014.12
                PMC ID: 4381748
                PubMed ID: 25977770
                SO-VID: 93268891-5ef0-498a-9f4d-dd16d70fed20
                Copyright © Copyright © 2014, Macmillan Publishers Limited
                License:

                This work is licensed under a Creative Commons Attribution 4.0 international License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.

                History
                Date received : 04 April 2014
                Date accepted : 09 May 2014
                Categories
                Subject: Data Descriptor

                Data availability:

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