Format

Send to

Choose Destination
J Chromatogr A. 2016 Apr 22;1443:83-92. doi: 10.1016/j.chroma.2016.02.080. Epub 2016 Mar 3.

A method for comparative metabolomics in urine using high resolution mass spectrometry.

Author information

1
Metabolomics Facility, Centre for Cellular and Molecular Platforms, National Centre for Biological Sciences, GKVK, Bellary Road, Bangalore 560065, India.
2
Metabolomics Facility, Centre for Cellular and Molecular Platforms, National Centre for Biological Sciences, GKVK, Bellary Road, Bangalore 560065, India. Electronic address: kannanr@ccamp.res.in.

Abstract

Developing a workflow for metabolite profiling from biological fluids using mass spectrometry is imperative to extract accurate information. In this study, urine samples from smokers (n=10) and nonsmokers (n=10) were analyzed using an ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) system. For the analysis, two different chromatographic methods [Reversed phase chromatography (RPC) and Hydrophilic interaction liquid chromatography (HILIC)], in two ionization modes (positive and negative) were used. Spiked reserpine (positive ion mode) or taurocholate (negative ion mode) were used for data extraction and normalization. Quality controls (QCs), prepared by pooling urine samples from both smokers and non-smokers (each n=10), were used to assess the reproducibility of the method. The final data output from SIEVE 2.2 after applying a cut-off for QC coefficient of variation (CV) <20% and p-value <0.05 showed 165, 83, 177 and 100 unique components in RP positive/negative, HILIC positive/negative modes, respectively. Statistical analysis showed clustering of the two groups and the QCs, while the variable importance in projection (VIP) scores for the top fifteen metabolites in each of the four modes indicated the metabolites most responsible for the differences. Application of the developed workflow for comparative metabolomic analysis of urine in different diseased models will be of great use in the field of clinical metabolomics.

KEYWORDS:

Data analysis; Data normalization; HRMS; Mass spectrometry; Metabolite profiling; Urine

PMID:
27012786
DOI:
10.1016/j.chroma.2016.02.080
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center