Development of a Robust and Repeatable UPLC−MS Method for the Long-Term Metabolomic Study of Human Serum

Zelena, Eva; Dunn, Warwick B.; Broadhurst, David; Francis-McIntyre, Sue; Carroll, Kathleen M.; Begley, Paul; O’Hagan, Steve; Knowles, Joshua; Halsall, Antony; Wilson, Ian D.; Kell, Douglas B.

doi:10.1021/ac8019366

Cited by 544 publications

(399 citation statements)

References 46 publications

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“…GC-MS and UPLC-MS both showed a median repeated injection (RI) and quality control sample (QC) variability of 5 % and 10 %, respectively. For GC-MS, we could not find any published data on distributions of CV for repeated injection (instrument) variation to compare to, but for LC-MS data our median CV is lower than reported values 45,46 . This might be due to fewer replicates or our minimum quantification criterion (5 out of 5 detected features).…”

Section: Resultscontrasting

confidence: 78%

Cross-Platform Comparison of Caenorhabditis elegans Tissue Extraction Strategies for Comprehensive Metabolome Coverage

et al. 2011

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The nematode Caenorhabditis elegans is widely used as a model organism in many areas of the life sciences. Metabolite profiling (metabolomics/metabonomics) is a powerful means of assigning phenotypes to experimentally perturbed C. elegans samples (e.g. mutants, RNAi, or chemical treatments). Tissue extraction is a key step, and high-quality and reproducible extractions are essential to the success of metabolomics studies. We have performed an extensive comparison of different tissue extraction techniques with C. elegans, comparing two different solvent systems (chloroform/methanol and aqueous methanol) and six different tissue disruption techniques (including manual grinding in a cooled mortar, homogenization, and various grinding media in both reciprocating and orbital tissue mills). All twelve combinations were then compared by GC-MS, 1 H NMR spectroscopy, and UPLC-MS, and the results evaluated by both overall multivariate clustering approaches as well as distributions over individual metabolites/metabolite features of coefficient of variation and yield. The choice of solvent had more influence than the disruption method used, although the homogenizer results were clearly outliers. Overall, we concluded that bead-beating with 80% methanol solution was a good tradeoff -although it is important to note that the definition of the apparent 'best' method depended on which analytical platform was used to evaluate the results.3

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Section: Resultscontrasting

confidence: 78%

Cross-Platform Comparison of Caenorhabditis elegans Tissue Extraction Strategies for Comprehensive Metabolome Coverage

et al. 2011

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“…We prepared serum samples (140-L aliquots) by deproteinization and drying in a randomized order and according to a protocol described in Supplemental Methods, which accompanies the online version of this article at http:// www.clinchem.org/content/vol61/issue3, and elsewhere (23,28 ). We pooled sera from each participant (60 L) to create a single pooled QC sample, from which 140-L aliquots were deproteinized and dried as described above.…”

Section: Metabolite Profilingmentioning

confidence: 99%

Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2 Diabetes Mellitus in a Prospective, Nested Case Control Study

Drogan¹,

et al. 2015

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“…The study was limited to 120 samples to guarantee optimal measurement reproducibility from the UPLC-MS systems. 16 In the validation-phase of our investigation we performed a nested case-control study within the initial 596 recruits in Adelaide, Australia, of whom pregnancy outcome was known in 595 (99.8%). Forty-six women (7.7%) developed PE, and 267 (44.9%) had uncomplicated pregnancies.…”

Section: Participants and Specimensmentioning

confidence: 99%

Robust Early Pregnancy Prediction of Later Preeclampsia Using Metabolomic Biomarkers

et al. 2010

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Abstract-Preeclampsia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preeclampsia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using samples obtained at 15Ϯ1 weeks' gestation from 60 women who subsequently developed preeclampsia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma samples were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preeclampsia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15Ϯ1 weeks from 39 women who subsequently developed preeclampsia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preeclampsia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test. Key Words: preeclampsia Ⅲ metabolomics Ⅲ biomarkers Ⅲ screening Ⅲ hypertension P reeclampsia (PE) affects 5% of nulliparous pregnancies and globally afflicts Ϸ4 million women annually. It remains a leading cause of maternal death throughout the world and is responsible for significant baby morbidity and mortality. 1 Furthermore, PE has healthcare implications for the women later in life with an increased risk of hypertension, coronary artery disease, stroke, and type 2 diabetes mellitus. 2 Although the precise etiology of the disease is unclear, accumulating evidence suggests that the disease results from complex interaction between a poorly perfused placenta, because of defective remodeling of the uteroplacental arteries in early pregnancy, and a maternal response to placentalderived triggers, which results in widespread vascular endothelial cell dysfunction. 1,3,4 Widespread plasma alterations precede the clinical onset of PE, and there is intense interest in the identification of predictive biomarkers. 5 Numerous candidate biomarkers have been proposed for prediction of disease, including placental hormones, angiogenic factors, and lipids. 3,6 -8 To date, none (nor any combination) has emerged with the requisite specificity and sensitivity to be of clinical use. 5 Consequently, clinicians are unable to offer either targeted surveil...

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Development of a Robust and Repeatable UPLC−MS Method for the Long-Term Metabolomic Study of Human Serum

Cited by 544 publications

References 46 publications

Cross-Platform Comparison of Caenorhabditis elegans Tissue Extraction Strategies for Comprehensive Metabolome Coverage

Cross-Platform Comparison of Caenorhabditis elegans Tissue Extraction Strategies for Comprehensive Metabolome Coverage

Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2 Diabetes Mellitus in a Prospective, Nested Case Control Study

Robust Early Pregnancy Prediction of Later Preeclampsia Using Metabolomic Biomarkers

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