Metabolomic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies

Gagnebin, Yoric; Tonoli, David; Lescuyer, Pierre; Ponte, Belén; Seigneux, Sophie de; Martin, Pierre‐Yves; Schappler, Julie; Bernard, Julien; Rudaz, Serge

doi:10.1016/j.aca.2016.12.029

Cited by 147 publications

(103 citation statements)

References 28 publications

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“…Metabolite concentrations in urine, for example, can vary greatly between samples. In such cases, a straight comparison of metabolic profiles without correction could result in biased or unclear results . Therefore it is often necessary to normalize between urine samples to improve comparability.…”

Section: Data Visualization Preprocessing and Analysismentioning

confidence: 99%

“…All the above‐mentioned methods are “data driven” in that normalization is performed based only on signals arising from the actual sample without using external information, such as intrinsic matrix properties. For example, creatinine or osmolality can be used for normalization of urine both pre‐ and post‐analysis . It should, however, be noted that normalizing by creatinine or osmolarity has been criticized since these levels are not necessarily constant for various diseases or other physiological conditions .…”

Section: Data Visualization Preprocessing and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Ulaszewska

Weinert

Trimigno

et al. 2018

Molecular Nutrition Food Res

195

156

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The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

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Section: Data Visualization Preprocessing and Analysismentioning

confidence: 99%

Section: Data Visualization Preprocessing and Analysismentioning

confidence: 99%

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Ulaszewska

Weinert

Trimigno

et al. 2018

Molecular Nutrition Food Res

195

156

View full text Add to dashboard Cite

show abstract

“…In the particular case of urine metabolomics applied to kidney failure, the creatinine/osmolality and analyte concentration relationship could be altered, and normalization of these measurements was critical. Therefore, one of the most popular normalization strategies, relative concentration to one reference compound (urinary creatinine) (Gagnebin et al, 2017), was applied. In this assay, the quantification results were all expressed as relative concentrations after urinary creatinine normalization.…”

Section: Clinical Applicationmentioning

confidence: 99%

Targeted metabolomic analysis of 33 amino acids and biogenic amines in human urine by ion‐pairing HPLC‐MS/MS: Biomarkers for tacrolimus nephrotoxicity after renal transplantation

Xia

Wang

et al. 2018

Biomedical Chromatography

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Calcineurin inhibitor nephrotoxicity, especially for the widely used tacrolimus, has become a major concern in post-transplant immunosuppression. Multiparametric amino acid metabolomics is useful for biomarker identification of tacrolimus nephrotoxicity, for which specific quantitative methods are highlighted as a premise. This article presents a targeted metabolomic assay to quantify 33 amino acids and biogenic amines in human urine by high-performance liquid chromatography coupled with tandem mass spectrometry. Chromatographic separation was carried out on an Agilent Zorbax SB-C column (3.0 × 150 mm, 5 μm) with addition of an ion-pairing agent in the mobile phase, and MS/MS detection was achieved in both the positive and negative multiple reaction monitoring modes. Good correlation coefficients (r > 0.98) were obtained for most analytes. Intra- and inter-day precision, stability, carryover and incurred sample reanalysis met with the acceptance criteria of the guidance of the US Food and Drug Administration. Analysis on urine from healthy volunteers and renal transplantation patients with tacrolimus nephrotoxicity confirmed symmetric dimethylarginine and serine as biomarkers for kidney injury, with AUC values of 0.95 and 0.81 in receiver operating characteristic analysis, respectively. Additionally, symmetric dimethylarginine exhibited a tight correlation with serum creatinine, and was therefore indicative of renal function. The targeted metabolomic assay was time and cost prohibitive for amino acid analysis in human urine, facilitating the biomarker identification of tacrolimus nephrotoxicity.

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“…To correct signal drifts and remove batch effects, quality control sample (QCS) over the entire time course of large-scale study has been applied to concatenate data of multiple analytical blocks into a single dataset (24,25) and been recognized as an essential measurement in pre-processing large-scale metabolomics data (26). Moreover, normalization is recommended to be further employed (27), the aim of which is to improve the differential profile by detecting and decreasing unwanted variations arising from errors in sample preparation (28) and other biological fluctuations (21,27). Normalization is now widely considered as an integral part of data processing (29) and ≥19 methods (Supplementary Table S1) are utilized for MS-based metabolomics data (18,28,30).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, some of the important tools and approaches are not provided by available online pipelines. These include the QCM-based removal of overall unwanted variation (18) and sequential strategy integrating QCS-based correction and normalization (27). Therefore, it is necessary to provide a publicly available service for comprehensively and comparatively evaluating the normalization performance of those methods used in MS-based metabolomics study.…”

Section: Introductionmentioning

confidence: 99%

NOREVA: normalization and evaluation of MS-based metabolomics data

Tang

Yang

et al. 2017

Nucleic Acids Research

327

186

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Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.

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Metabolomic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies

Cited by 147 publications

References 28 publications

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Targeted metabolomic analysis of 33 amino acids and biogenic amines in human urine by ion‐pairing HPLC‐MS/MS: Biomarkers for tacrolimus nephrotoxicity after renal transplantation

NOREVA: normalization and evaluation of MS-based metabolomics data

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