LC-MS-based metabolomics - PubMed (original) (raw)

Review

LC-MS-based metabolomics

Bin Zhou et al. Mol Biosyst. 2012 Feb.

Abstract

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

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Figures

Fig. 1

Fig. 1

A typical workflow of a metabolomic study.

Fig. 2

Fig. 2

Ranges of applicability of APPI, APCI and ESI.

Fig. 3

Fig. 3

Preprocessing of a LC-MS dataset with 10 samples. The LC-MS raw data are first corrected for baseline effect, and then peak detection is performed on each EIC to detect the peak(s). For multiple samples peak alignment is used to correct for retention time drift. The peak list can be acquired after peak alignment for the dataset. Ion annotation is used to recognize the peaks originating from the same metabolite. The data are acquired using a UPLC-QTOF Premier instrument.

Fig.4

Fig.4

Metabolite ID verification by comparison of the MS2 spectrum of S-1-P (top) with experimental sample (bottom). The MS2 spectra are acquired with a precursor ion mass at 378 Da under negative ionization mode on a QSTAR Elite instrument.

Fig. 5

Fig. 5

Illustration of absolute quantitation of metabolites by QqQ-based SRM using isotope dilution technique.(a) SRM detection of analyte and its isotope-labelled IS; (b) absolute quantitation of four analytes by SRM: correlating signal ratio of analyte and IS to its respective standard curve.

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