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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 two regions (a1 and a2) of 600 MHz NMR spectra of extracts of fresh and freeze dried broccoli tissue. Each tissue type contains three superimposed spectra. Higher variability observed in data when fresh tissue was extracted. (b) PCA analysis of full NMR dataset, binned to 0.01 ppm, from fresh (F) and freeze-dried (F/D) broccoli tissue
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Fig2: Comparison of two regions (a1 and a2) of 600 MHz NMR spectra of extracts of fresh and freeze dried broccoli tissue. Each tissue type contains three superimposed spectra. Higher variability observed in data when fresh tissue was extracted. (b) PCA analysis of full NMR dataset, binned to 0.01 ppm, from fresh (F) and freeze-dried (F/D) broccoli tissue

Mentions: In order to carry out the inter-laboratory comparison using NMR it was important to establish the stability of the “test” samples. In the proposed comparison study, whereby the technique of [1H]-NMR fingerprinting was to be conducted at different European laboratories over a 7 day period it was imperative that no variance would be introduced by the deterioration of the test samples during this time period. The test samples consisted of extracts of broccoli florets and in order to ensure extract stability the chosen protocols were applied to generate NMR samples that were repeatedly analysed, at 600 MHz, on a single instrument. This initial study also included an assessment of the stability of extracts of both fresh and freeze-dried tissue. Figure 1 shows the typical NMR spectra obtained from a polar (80:20 D2O:CD3OD) solvent extraction on both fresh (Fig. 1a) and freeze-dried (Fig. 1b) broccoli tissue. As with many NMR spectra obtained from plants, the spectrum is dominated by primary metabolites such as carbohydrates, amino acids and organic acids. Although there are a few peaks present in the fresh tissue spectrum that are absent from the freeze-dried tissue spectrum, there is good comparability between the two tissue types for the majority of peaks with only small changes evident between samples (e.g. peak 13, Fig. 1b2 which corresponds to sucrose). Importantly there are no peaks in the spectra of freeze-dried tissues that may have arisen due to the freeze-drying process. The analytical reproducibility is demonstrated in Fig. 2 which shows data generated from three separate tissue aliquots. Two common regions of the spectrum have been selected and include the region corresponding to the anomeric proton of α-glucose (5.25–5.15 ppm; Fig. 2a1) and secondly the region containing characteristic valine, leucine and isoleucine peaks (0.9–1.1 ppm, Fig. 2a2). For both regions there is good reproducibility for the freeze-dried samples but it is evident that there was more variability in the peak intensity of samples derived from fresh material, particularly for the glucose region which is known to be problematic in plant derived samples due not only to its proximity to the water suppression region of the spectrum but also due to unwanted carbohydrate conversion if enzyme activity is not eliminated in the extraction procedure (Baker et al. 2006). What was clear, however, was that there was little drift in the recorded chemical shifts and that, as expected, the stability of the instrumentation was excellent across all samples studied. PCA was carried out on the full NMR datasets obtained from fresh and freeze-dried samples (Fig. 2b). Clearly sample types separated in the direction of PC1 which described 72% of the variance, and a tighter clustering was obtained with the freeze-dried samples. From these data it was concluded that for the inter-lab comparison, samples would be generated from aliquots of freeze-dried tissue. In order to assess the stability of the solvent extracts over time, the same NMR samples were re-analysed 6 days later under identical NMR instrument conditions. Data from this comparison is shown in Fig. 3 and demonstrates that there has been no deterioration in sample quality and no qualitative or quantitative differences in peaks were observed. The datasets overlay perfectly when visualised together demonstrating that samples could be kept and re-analysed without the risk of deterioration during extended studies. Most importantly, with confidence in sample stability, any variation introduced during the inter-laboratory comparison could be ascribed to differences in magnetic field strength, instrument set-up and configuration or physical location differences.Fig. 1


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 two regions (a1 and a2) of 600 MHz NMR spectra of extracts of fresh and freeze dried broccoli tissue. Each tissue type contains three superimposed spectra. Higher variability observed in data when fresh tissue was extracted. (b) PCA analysis of full NMR dataset, binned to 0.01 ppm, from fresh (F) and freeze-dried (F/D) broccoli tissue
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Related In: Results  -  Collection

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Fig2: Comparison of two regions (a1 and a2) of 600 MHz NMR spectra of extracts of fresh and freeze dried broccoli tissue. Each tissue type contains three superimposed spectra. Higher variability observed in data when fresh tissue was extracted. (b) PCA analysis of full NMR dataset, binned to 0.01 ppm, from fresh (F) and freeze-dried (F/D) broccoli tissue
Mentions: In order to carry out the inter-laboratory comparison using NMR it was important to establish the stability of the “test” samples. In the proposed comparison study, whereby the technique of [1H]-NMR fingerprinting was to be conducted at different European laboratories over a 7 day period it was imperative that no variance would be introduced by the deterioration of the test samples during this time period. The test samples consisted of extracts of broccoli florets and in order to ensure extract stability the chosen protocols were applied to generate NMR samples that were repeatedly analysed, at 600 MHz, on a single instrument. This initial study also included an assessment of the stability of extracts of both fresh and freeze-dried tissue. Figure 1 shows the typical NMR spectra obtained from a polar (80:20 D2O:CD3OD) solvent extraction on both fresh (Fig. 1a) and freeze-dried (Fig. 1b) broccoli tissue. As with many NMR spectra obtained from plants, the spectrum is dominated by primary metabolites such as carbohydrates, amino acids and organic acids. Although there are a few peaks present in the fresh tissue spectrum that are absent from the freeze-dried tissue spectrum, there is good comparability between the two tissue types for the majority of peaks with only small changes evident between samples (e.g. peak 13, Fig. 1b2 which corresponds to sucrose). Importantly there are no peaks in the spectra of freeze-dried tissues that may have arisen due to the freeze-drying process. The analytical reproducibility is demonstrated in Fig. 2 which shows data generated from three separate tissue aliquots. Two common regions of the spectrum have been selected and include the region corresponding to the anomeric proton of α-glucose (5.25–5.15 ppm; Fig. 2a1) and secondly the region containing characteristic valine, leucine and isoleucine peaks (0.9–1.1 ppm, Fig. 2a2). For both regions there is good reproducibility for the freeze-dried samples but it is evident that there was more variability in the peak intensity of samples derived from fresh material, particularly for the glucose region which is known to be problematic in plant derived samples due not only to its proximity to the water suppression region of the spectrum but also due to unwanted carbohydrate conversion if enzyme activity is not eliminated in the extraction procedure (Baker et al. 2006). What was clear, however, was that there was little drift in the recorded chemical shifts and that, as expected, the stability of the instrumentation was excellent across all samples studied. PCA was carried out on the full NMR datasets obtained from fresh and freeze-dried samples (Fig. 2b). Clearly sample types separated in the direction of PC1 which described 72% of the variance, and a tighter clustering was obtained with the freeze-dried samples. From these data it was concluded that for the inter-lab comparison, samples would be generated from aliquots of freeze-dried tissue. In order to assess the stability of the solvent extracts over time, the same NMR samples were re-analysed 6 days later under identical NMR instrument conditions. Data from this comparison is shown in Fig. 3 and demonstrates that there has been no deterioration in sample quality and no qualitative or quantitative differences in peaks were observed. The datasets overlay perfectly when visualised together demonstrating that samples could be kept and re-analysed without the risk of deterioration during extended studies. Most importantly, with confidence in sample stability, any variation introduced during the inter-laboratory comparison could be ascribed to differences in magnetic field strength, instrument set-up and configuration or physical location differences.Fig. 1

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.