Literature DB >> 23190334

Determination of total concentration of chemically labeled metabolites as a means of metabolome sample normalization and sample loading optimization in mass spectrometry-based metabolomics.

Yiman Wu1, Liang Li.   

Abstract

For mass spectrometry (MS)-based metabolomics, it is important to use the same amount of starting materials from each sample to compare the metabolome changes in two or more comparative samples. Unfortunately, for biological samples, the total amount or concentration of metabolites is difficult to determine. In this work, we report a general approach of determining the total concentration of metabolites based on the use of chemical labeling to attach a UV absorbent to the metabolites to be analyzed, followed by rapid step-gradient liquid chromatography (LC) UV detection of the labeled metabolites. It is shown that quantification of the total labeled analytes in a biological sample facilitates the preparation of an appropriate amount of starting materials for MS analysis as well as the optimization of the sample loading amount to a mass spectrometer for achieving optimal detectability. As an example, dansylation chemistry was used to label the amine- and phenol-containing metabolites in human urine samples. LC-UV quantification of the labeled metabolites could be optimally performed at the detection wavelength of 338 nm. A calibration curve established from the analysis of a mixture of 17 labeled amino acid standards was found to have the same slope as that from the analysis of the labeled urinary metabolites, suggesting that the labeled amino acid standard calibration curve could be used to determine the total concentration of the labeled urinary metabolites. A workflow incorporating this LC-UV metabolite quantification strategy was then developed in which all individual urine samples were first labeled with (12)C-dansylation and the concentration of each sample was determined by LC-UV. The volumes of urine samples taken for producing the pooled urine standard were adjusted to ensure an equal amount of labeled urine metabolites from each sample was used for the pooling. The pooled urine standard was then labeled with (13)C-dansylation. Equal amounts of the (12)C-labeled individual sample and the (13)C-labeled pooled urine standard were mixed for LC-MS analysis. This way of concentration normalization among different samples with varying concentrations of total metabolites was found to be critical for generating reliable metabolome profiles for comparison.

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Year:  2012        PMID: 23190334     DOI: 10.1021/ac3025625

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  18 in total

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Journal:  Metabolomics       Date:  2018-08-29       Impact factor: 4.290

2.  Effects of a blend of Saccharomyces cerevisiae-based direct-fed microbial and fermentation products in the diet of newly weaned beef steers: growth performance, whole-blood immune gene expression, serum biochemistry, and plasma metabolome1.

Authors:  James A Adeyemi; David L Harmon; D M Paulus Compart; Ibukun M Ogunade
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3.  Distinctive metabolic profiles between Cystic Fibrosis mutational subclasses and lung function.

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Journal:  Metabolomics       Date:  2021-01-04       Impact factor: 4.290

4.  Comparative effects of two multispecies direct-fed microbial products on energy status, nutrient digestibility, and ruminal fermentation, bacterial community, and metabolome of beef steers.

Authors:  Ibukun M Ogunade; Megan McCoun; Modoluwamu D Idowu; Sunday O Peters
Journal:  J Anim Sci       Date:  2020-09-01       Impact factor: 3.159

5.  Profiling novel metabolic biomarkers for Parkinson's disease using in-depth metabolomic analysis.

Authors:  Wei Han; Shraddha Sapkota; Richard Camicioli; Roger A Dixon; Liang Li
Journal:  Mov Disord       Date:  2017-09-07       Impact factor: 10.338

6.  Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model.

Authors:  Deying Chen; Xiaoling Su; Nan Wang; Yunong Li; Hua Yin; Liang Li; Lanjuan Li
Journal:  Sci Rep       Date:  2017-01-16       Impact factor: 4.379

7.  Baseline urine metabolic phenotype in patients with severe alcoholic hepatitis and its association with outcome.

Authors:  Jaswinder Singh Maras; Sukanta Das; Shvetank Sharma; Saggere M Shasthry; Benoit Colsch; Christophe Junot; Richard Moreau; Shiv Kumar Sarin
Journal:  Hepatol Commun       Date:  2018-04-16

8.  Alteration of the Canine Metabolome After a 3-Week Supplementation of Cannabidiol (CBD) Containing Treats: An Exploratory Study of Healthy Animals.

Authors:  Elizabeth M Morris; Susanna E Kitts-Morgan; Dawn M Spangler; Ibukun M Ogunade; Kyle R McLeod; David L Harmon
Journal:  Front Vet Sci       Date:  2021-07-16

9.  A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome.

Authors:  Franck Boizard; Valérie Brunchault; Panagiotis Moulos; Benjamin Breuil; Julie Klein; Nadia Lounis; Cécile Caubet; Stéphanie Tellier; Jean-Loup Bascands; Stéphane Decramer; Joost P Schanstra; Bénédicte Buffin-Meyer
Journal:  Sci Rep       Date:  2016-10-03       Impact factor: 4.379

10.  Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer.

Authors:  Tao Huan; Dean A Troyer; Liang Li
Journal:  Sci Rep       Date:  2016-08-31       Impact factor: 4.379

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