Limits...
An inter-laboratory comparison demonstrates that [H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection.

Ward JL, Baker JM, Miller SJ, Deborde C, Maucourt M, Biais B, Rolin D, Moing A, Moco S, Vervoort J, Lommen A, Schäfer H, Humpfer E, Beale MH - Metabolomics (2010)

Bottom Line: With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment.Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics.Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [(1)H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.

View Article: PubMed Central - PubMed

ABSTRACT
In any metabolomics experiment, robustness and reproducibility of data collection is of vital importance. These become more important in collaborative studies where data is to be collected on multiple instruments. With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment. In this paper we report on an inter laboratory comparison of plant derived samples by [(1)H]-NMR spectroscopy across five different sites and within those sites utilising instruments with different probes and magnetic field strengths of 9.4 T (400 MHz), 11.7 T (500 MHz) and 14.1 T (600 MHz). Whilst the focus of the study is on consistent data collection across laboratories, aspects of sample stability and the requirement for sample rotation within the NMR magnet are also discussed. Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics. Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [(1)H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.

No MeSH data available.


Comparison of a broccoli extract run with and without rotation during spectra acquisition. a 600 MHz spectra region from 0.9 to 1.1 ppm. b 400 MHz spectra region from 0.9 to 1.1 ppm. Spectra have been scaled to the signal height of TSP. As a consequence, signals with linewidth broader that of the d4-TSP peak show higher amplitude in the non-spinning case than in the spinning case. Since quantification evaluates signal area rather than signal height, determination of concentration values is not affected
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2874487&req=5

Fig5: Comparison of a broccoli extract run with and without rotation during spectra acquisition. a 600 MHz spectra region from 0.9 to 1.1 ppm. b 400 MHz spectra region from 0.9 to 1.1 ppm. Spectra have been scaled to the signal height of TSP. As a consequence, signals with linewidth broader that of the d4-TSP peak show higher amplitude in the non-spinning case than in the spinning case. Since quantification evaluates signal area rather than signal height, determination of concentration values is not affected

Mentions: Sample spectral data (from the characteristic aliphatic valine, isoleucine and leucine region (0.9–1.1 ppm)) obtained in spinning and non spinning mode is shown in Fig. 5 and includes that from 600 MHz (Fig. 5a) and 400 MHz (Fig. 5b) instruments. Resolution was found to be slightly decreased in the non-spinning samples at both 400 and 600 MHz (e.g. mean line width of TSP peak at half height in 600 MHz spinning samples was 0.90 Hz compared to 1.19 Hz for non-spinning samples). This is accompanied with an apparent increase in signal intensity in the non-spinning samples over those which had been rotated (due to similar resolution differences in TSP signal and subsequent scaling thereof). This is offset by a lower resolution of the individual peaks within a particular signal and thus represents a broadening of the resonance due to errors in low order X/Y shims. At 600 MHz, the broadening of the peaks due to not rotating the sample is evident but in the case of most metabolomics studies where NMR data is employed, a bucketing or binning routine is employed to segment the dataset into equal size buckets and thus any small changes in peak width or slightly poorer resolution would be dealt with in post processing of the data.Fig. 5


An inter-laboratory comparison demonstrates that [H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection.

Ward JL, Baker JM, Miller SJ, Deborde C, Maucourt M, Biais B, Rolin D, Moing A, Moco S, Vervoort J, Lommen A, Schäfer H, Humpfer E, Beale MH - Metabolomics (2010)

Comparison of a broccoli extract run with and without rotation during spectra acquisition. a 600 MHz spectra region from 0.9 to 1.1 ppm. b 400 MHz spectra region from 0.9 to 1.1 ppm. Spectra have been scaled to the signal height of TSP. As a consequence, signals with linewidth broader that of the d4-TSP peak show higher amplitude in the non-spinning case than in the spinning case. Since quantification evaluates signal area rather than signal height, determination of concentration values is not affected
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2874487&req=5

Fig5: Comparison of a broccoli extract run with and without rotation during spectra acquisition. a 600 MHz spectra region from 0.9 to 1.1 ppm. b 400 MHz spectra region from 0.9 to 1.1 ppm. Spectra have been scaled to the signal height of TSP. As a consequence, signals with linewidth broader that of the d4-TSP peak show higher amplitude in the non-spinning case than in the spinning case. Since quantification evaluates signal area rather than signal height, determination of concentration values is not affected
Mentions: Sample spectral data (from the characteristic aliphatic valine, isoleucine and leucine region (0.9–1.1 ppm)) obtained in spinning and non spinning mode is shown in Fig. 5 and includes that from 600 MHz (Fig. 5a) and 400 MHz (Fig. 5b) instruments. Resolution was found to be slightly decreased in the non-spinning samples at both 400 and 600 MHz (e.g. mean line width of TSP peak at half height in 600 MHz spinning samples was 0.90 Hz compared to 1.19 Hz for non-spinning samples). This is accompanied with an apparent increase in signal intensity in the non-spinning samples over those which had been rotated (due to similar resolution differences in TSP signal and subsequent scaling thereof). This is offset by a lower resolution of the individual peaks within a particular signal and thus represents a broadening of the resonance due to errors in low order X/Y shims. At 600 MHz, the broadening of the peaks due to not rotating the sample is evident but in the case of most metabolomics studies where NMR data is employed, a bucketing or binning routine is employed to segment the dataset into equal size buckets and thus any small changes in peak width or slightly poorer resolution would be dealt with in post processing of the data.Fig. 5

Bottom Line: With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment.Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics.Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [(1)H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.

View Article: PubMed Central - PubMed

ABSTRACT
In any metabolomics experiment, robustness and reproducibility of data collection is of vital importance. These become more important in collaborative studies where data is to be collected on multiple instruments. With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment. In this paper we report on an inter laboratory comparison of plant derived samples by [(1)H]-NMR spectroscopy across five different sites and within those sites utilising instruments with different probes and magnetic field strengths of 9.4 T (400 MHz), 11.7 T (500 MHz) and 14.1 T (600 MHz). Whilst the focus of the study is on consistent data collection across laboratories, aspects of sample stability and the requirement for sample rotation within the NMR magnet are also discussed. Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics. Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [(1)H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.

No MeSH data available.