Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry

Sangster, Tim; Wingate, Julie; Burton, Lyle; Teichert, Friederike; Wilson, Ian D.

doi:10.1002/rcm.3164

Cited by 55 publications

(50 citation statements)

References 18 publications

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“…Fourteen biologically identical serum samples (designated as QC samples) prepared from a pooled serum sample (available commercially from Sigma-Aldrich, Gillingham, UK) were analysed at intervals during a 40 h period. Data for each QC sample was acquired as analysis number 1,2,3,4,5,18,29,41,52,65,76,88,99,111 and were interdispersed with 97 other serum samples. Analysis of the samples was performed as described below.…”

Section: Reproducibility Study Using Biologically Identical Quality Cmentioning

confidence: 99%

“…It has been observed for GC-MS and LC-MS data that processed data can be highly variable in respect to the number of peaks reported and reproducibility of peak areas, accurate mass and retention time [75]. Small changes in a single software parameter can greatly influence the validity and information content of results [76] and validation of software operation is required [77].…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

Dunn

Broadhurst

Brown

et al. 2008

Journal of Chromatography B

166

138

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Section: Reproducibility Study Using Biologically Identical Quality Cmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

Dunn

Broadhurst

Brown

et al. 2008

Journal of Chromatography B

166

138

View full text Add to dashboard Cite

“…In addition, it can incorrectly remove ions with abundances that are intermittently reduced by adverse effects during ESI. This potential loss of data has been discussed recently by Wilson et al [31], who develop an algorithm using liquid chromatography mass spectrometry (LC-MS) data and propose first applying a peak intensity threshold across all mass spectra to generate one set of "real" peaks. This matrix of peak intensities will contain multiple missing values (i.e., zero intensities) since peaks present in some spectra will not be detected in others, as they are below the detection threshold.…”

mentioning

confidence: 98%

A signal filtering method for improved quantification and noise discrimination in fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data

Payne

Southam

Arvanitis

et al. 2009

J. Am. Soc. Mass Spectrom.

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Direct-infusion electrospray-ionization Fourier transform ion cyclotron resonance mass spectrometry (DI ESI FT-ICR MS) is increasingly being utilized in metabolomics, including the high sensitivity selected ion monitoring (SIM)-stitching approach. Accurate signal quantification and the discrimination of real signals from noise remain major challenges for this approach, with both adversely affected by factors including ion suppression during electrospray, ion-ion interactions in the detector cell, and thermally-induced white noise. This is particularly problematic for complex mixture analysis where hundreds of metabolites are present near the noise level. Here we address relative signal quantification and noise discrimination issues in SIM-stitched DI ESI FT-ICR MS-based metabolomics. Using liver tissue, we first optimized the number of scans (n) acquired per SIM window to address the balance between quantification accuracy versus acquisition time (and thus sample throughput); a minimum of n = 5 is recommended. Secondly, we characterized and computationally-corrected an effect whereby an ion's intensity is dependent upon its location within a SIM window, exhibiting a 3-fold higher intensity at the high m/z end. This resulted in significantly improved quantification accuracy. Finally, we thoroughly characterized a three-stage filter to discriminate noise from real signals, which comprised a signal-to-noise-ratio (SNR) hard threshold, then a "replicate" filter (retaining only peaks in r-out-of-3 replicate analyses), and then a "sample" filter (retaining only peaks in >s% of biological samples). We document the benefits of three-stage filtering versus one- and two-stage filters, and show the importance of selecting filter parameters that balance the confidence that a signal is real versus the total number of peaks detected.

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“…It is partly attributed to the MZmine software failing to utilise the entire data matrix and relying on peak detection procedures. Actually, the peak detection result strongly depends on software package and its associated parameters (unpublished data) or see reference (Sangster et al 2007). …”

Section: Analysis Of Data Processed By Mzminementioning

confidence: 99%

“…There are a range of software packages available either commercially (LECO, Waters, Shimadzu, Agilent, Applied Biosystems and SpectralWorks) or available as freeware from Open Source (including AMDIS, MZmine and XCMS) which provide deconvolution functions in their software packages. Previous research has shown that the deconvoluted results from different software packages produce distinctly different results and a range of software parameters influence chromatographic deconvolution results even for the same software package (Lu et al 2008) or see reference (Sangster et al 2007). Additionally, the deconvolution process is time-consuming and in a number of examples requires human input.…”

Section: Introductionmentioning

confidence: 97%

Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution

et al. 2010

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Metabolic footprinting has been applied as a non-invasive approach to study the behaviour and responses of cultured cells to a range of genetic and environmental perturbations. Gas chromatography interfaced with time-offlight mass spectrometry (GC-ToF-MS) has become a powerful tool for the analysis of metabolome-derived samples. Generally, two data analysis strategies are used to interrogate and understand the biological patterns within the multi-dimensional data. The first strategy, a commoner one, uses multivariate analysis after chromatographic and mass spectral deconvolution, and the second strategy directly employs multivariate analysis of non-deconvoluted data. Here, two strategies have been assessed for the separation and classification of metabolic footprints (exometabolomes) of two strains of Candida albicans grown on three different carbon sources (glycerol, glucose and galactose). We describe a semi-automated approach that simultaneously processes all samples using the chromatographic dimension data with principal components analysis (PCA), which can include data pre-processing before PCA analysis. The preprocessed and non-deconvoluted total ion chromatogram (TIC) data showed good separation of classes defined by growth on different carbon sources and when comparing the two strains grown on the same carbon source separation was achieved for strains grown on glucose and glycerol after preprocessing. The discrimination observed is greater for preprocessed and non-deconvoluted TIC data than for that of preprocessed and non-deconvoluted single ion chromatogram data. The results from the proposed approach with those produced by MZmine were compared. The results from MZmine data depicted separations in PCA space according to carbon source, but no separation was seen when studying strains grown on the same carbon source. Our research showed that the non-deconvoluted strategy is suitable for fast comparison of large sets of GC-MS data although it will not directly provide biological information. The non-deconvoluted strategy can avoid problems of analyzing complex samples using deconvolution software.

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Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry

Cited by 55 publications

References 18 publications

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

A signal filtering method for improved quantification and noise discrimination in fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data

Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution

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