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      Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

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          Abstract

          Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high-complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1t R and 2t R was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.

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          Navigating freely-available software tools for metabolomics analysis

          Introduction The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. Objectives To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. Methods The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for. Results A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary. Conclusion This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking. Electronic supplementary material The online version of this article (doi:10.1007/s11306-017-1242-7) contains supplementary material, which is available to authorized users.
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            Exhaled Volatile Organic Compounds Are Able to Discriminate between Neutrophilic and Eosinophilic Asthma

            Rationale: Analysis of exhaled breath for asthma phenotyping using endogenously generated volatile organic compounds (VOCs) offers the possibility of noninvasive diagnosis and therapeutic monitoring. Induced sputum is indeed not widely available and markers of neutrophilic asthma are still lacking.Objectives: To determine whether analysis of exhaled breath using endogenously generated VOCs can be a surrogate marker for recognition of sputum inflammatory phenotypes.Methods: We conducted a prospective study on 521 patients with asthma recruited from the University Asthma Clinic of Liege. Patients underwent VOC measurement, fraction of exhaled nitric oxide (FeNO) spirometry, sputum induction, and gave a blood sample. Subjects with asthma were classified in three inflammatory phenotypes according to their sputum granulocytic cell count.Measurements and Main Results: In the discovery study, seven potential biomarkers were highlighted by gas chromatography-mass spectrometry in a training cohort of 276 patients with asthma. In the replication study (n = 245), we confirmed four VOCs of interest to discriminate among asthma inflammatory phenotypes using comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry. Hexane and 2-hexanone were identified as compounds with the highest classification performance in eosinophilic asthma with accuracy comparable to that of blood eosinophils and FeNO. Moreover, the combination of FeNO, blood eosinophils, and VOCs gave a very good prediction of eosinophilic asthma (area under the receiver operating characteristic curve, 0.9). For neutrophilic asthma, the combination of nonanal, 1-propanol, and hexane had a classification performance similar to FeNO or blood eosinophils in eosinophilic asthma. Those compounds were found in higher levels in neutrophilic asthma.Conclusions: Our study is the first attempt to characterize VOCs according to sputum granulocytic profile in a large population of patients with asthma and provide surrogate markers for neutrophilic asthma.
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              Metabolome in progression to Alzheimer's disease

              Mild cognitive impairment (MCI) is considered as a transition phase between normal aging and Alzheimer's disease (AD). MCI confers an increased risk of developing AD, although the state is heterogeneous with several possible outcomes, including even improvement back to normal cognition. We sought to determine the serum metabolomic profiles associated with progression to and diagnosis of AD in a prospective study. At the baseline assessment, the subjects enrolled in the study were classified into three diagnostic groups: healthy controls (n=46), MCI (n=143) and AD (n=47). Among the MCI subjects, 52 progressed to AD in the follow-up. Comprehensive metabolomics approach was applied to analyze baseline serum samples and to associate the metabolite profiles with the diagnosis at baseline and in the follow-up. At baseline, AD patients were characterized by diminished ether phospholipids, phosphatidylcholines, sphingomyelins and sterols. A molecular signature comprising three metabolites was identified, which was predictive of progression to AD in the follow-up. The major contributor to the predictive model was 2,4-dihydroxybutanoic acid, which was upregulated in AD progressors (P=0.0048), indicating potential involvement of hypoxia in the early AD pathogenesis. This was supported by the pathway analysis of metabolomics data, which identified upregulation of pentose phosphate pathway in patients who later progressed to AD. Together, our findings primarily implicate hypoxia, oxidative stress, as well as membrane lipid remodeling in progression to AD. Establishment of pathogenic relevance of predictive biomarkers such as ours may not only facilitate early diagnosis, but may also help identify new therapeutic avenues.
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                Author and article information

                Journal
                Anal Chem
                Anal Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                28 September 2020
                20 October 2020
                : 92
                : 20
                : 13953-13960
                Affiliations
                []School of Chemistry, University of Leicester , University Road, Leicester LE1 7RH, U.K.
                []Department of Respiratory Sciences, University of Leicester , University Road, Leicester LE1 7RH, U.K.
                [§ ]Leicester NIHR Biomedical Research Centre, Glenfield Hospital , Groby Road, Leicester LE3 9QP, U.K.
                Author notes
                [* ]Email: mjw77@ 123456le.ac.uk . Phone: +44 (0)116 2525681.
                Article
                10.1021/acs.analchem.0c02844
                7644112
                32985172
                4090cb83-24f0-41b6-8b41-41d49e721f35
                © 2020 American Chemical Society

                This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.

                History
                : 03 July 2020
                : 28 September 2020
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                ac0c02844
                ac0c02844

                Analytical chemistry
                Analytical chemistry

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