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High quality metabolomic data for Chlamydomonas reinhardtii.

Lee do Y, Fiehn O - Plant Methods (2008)

Bottom Line: Glass beads were compared to metal balls for milling, and five different extraction solvents were tested.Additionally, all peaks were annotated in an automated way using the GC-TOF database BinBase instead of manual investigation of a single reference chromatogram.Median precision of analysis was used to decide for the eventual procedure which was applied to a proof-of-principle study of time dependent changes of metabolism under standard conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of California Davis, Genome Center, Davis, CA 95616, USA. ofiehn@ucdavis.edu.

ABSTRACT
The green eukaryote alga Chlamydomonas reinhardtii is a unicellular model to study control of metabolism in a photosynthetic organism. We here present method improvements for metabolite profiling based on GC-TOF mass spectrometry focusing on three parameters: quenching and cell disruption, extract solvent composition and metabolite annotation. These improvements facilitate using smaller cell numbers and hence, smaller culture volumes which enable faster and more precise sampling techniques that eventually lead to a higher number of samples that can be processed, e.g. for time course experiments. Quenching of metabolism was achieved by mixing 1 ml of culture to 1 ml of -70 degrees C cold 70% methanol. After centrifugation, cells were lyophilized and disrupted by milling using 2-6E6 lyophilized cells, around 500-fold less than previously reported. Glass beads were compared to metal balls for milling, and five different extraction solvents were tested. Additionally, all peaks were annotated in an automated way using the GC-TOF database BinBase instead of manual investigation of a single reference chromatogram. Median precision of analysis was used to decide for the eventual procedure which was applied to a proof-of-principle study of time dependent changes of metabolism under standard conditions.

No MeSH data available.


Related in: MedlinePlus

Frequency distribution of precision of C. reinhardtii metabolite profiling; N = 9; study 1. Blue: steel ball disruption + extraction by water/isopropanol/acetonitrile (WiPM). Orange: glass bead + methanol/chloroform/water (MCW). Both identified and unidentified metabolites were used as reported by the mass spectral database BinBase. CV = coefficient of variance.
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Figure 4: Frequency distribution of precision of C. reinhardtii metabolite profiling; N = 9; study 1. Blue: steel ball disruption + extraction by water/isopropanol/acetonitrile (WiPM). Orange: glass bead + methanol/chloroform/water (MCW). Both identified and unidentified metabolites were used as reported by the mass spectral database BinBase. CV = coefficient of variance.

Mentions: A typical GC-TOF chromatogram consisted of about 750 peaks that were detectable at s/n 10 using the ChromaTOF vs. 2.25 software [31]. However, consistent and reliable metabolite profiling needs to ensure that all reported peaks fulfill high quality criteria with respect to mass spectral matching and retention index, and that each peak in a chromatogram can only be associated to a single metabolite. Common vendor software does not ensure this scrutiny. We have therefore reduced the number of reported peaks by employing a multi-tiered mass spectral annotation approach as outlined in the method section. 334 metabolite signals were determined to be consistent and reproducible peaks using the BinBase algorithm, of which 80 could be identified as non-redundant metabolites. Data can be downloaded at [32], detailing all detected peaks, identified metabolites, retention indices, mass spectra and quantification ions. In addition, raw chromatogram files can be downloaded to enable researcher comparisons of different data processing methods. Extraction efficiency of metabolites was estimated by comparing the sum intensity of all identified peaks. Three of the four methods gained similar overall peak intensities, except for the method employing grinding by glass beads and extraction with methanol/chloroform/water (MCW). Overall metabolite extraction efficiency by the glass bead/MCW method was 30–40% lower than by the other three methods (p = 0.00014) and it further comprised a clear outlier sample with even lower extraction efficiency. In order to enable comparison of relative extraction efficiency of individual compounds and to compare the overall metabolite profiles, data were normalized to the sum intensity of identified peaks of each chromatogram. Multivariate statistics demonstrated for both unsupervised principal components analysis (PCA) and supervised partial least square (PLS) analysis that the dominant factor separating metabolic profiles was associated with the method of grinding and homogenization rather than the two solvents that were employed. Only supervised PLS analysis could separate the two solvent systems (fig. 3) for each grinding method, but not unsupervised PCA. Univariate analysis of variance showed that a number of metabolites were equally well extracted by the four methods (e.g. alanine, fig. 3) whereas other compounds were either found at higher levels under metal ball grinding (e.g. glutamate, fig. 3) or at lower levels (e.g. 1-monopalmitin). Very few metabolites were only detectable by using one solvent system but not the other, specifically arabitol. The differential extraction efficiency of metabolites using the two different grinding systems could not easily be attributed to specific chemical classes. However, two considerations allowed a clear decision in favor of using steel ball homogenization: (a) sample handling by using steel balls is easier and therefore more reliable for high throughput applications and (b) the reproducibility of metabolite quantifications was best using steel ball grinding and methanol/chloroform/water (MCW) extraction (fig. 4 and table 2). Figure 4 exemplifies the frequency at which metabolites were quantified in 10% CV precision intervals for the steel ball/MCW method and the glass bead/MCW method. A high number of compounds had very high technical error rates using the glass bead/MCW method that resulted to a poor median precision of 30% CV for the identified metabolites and 39% over all 334 metabolite signals (table 2). The steel ball/MCW method provided good precision with a median of 16% CV for the identified compounds and 22% technical error over all metabolites (figure 4 and table 2). In comparison, extractions with water/isopropanol/methanol had intermediate precision but again, the steel ball grinding method proved to perform better than homogenizations by glass beads (table 2). Therefore, homogenization by steel ball milling was carried forward to study 2, investigating the impact of different extraction solvents.


High quality metabolomic data for Chlamydomonas reinhardtii.

Lee do Y, Fiehn O - Plant Methods (2008)

Frequency distribution of precision of C. reinhardtii metabolite profiling; N = 9; study 1. Blue: steel ball disruption + extraction by water/isopropanol/acetonitrile (WiPM). Orange: glass bead + methanol/chloroform/water (MCW). Both identified and unidentified metabolites were used as reported by the mass spectral database BinBase. CV = coefficient of variance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2377246&req=5

Figure 4: Frequency distribution of precision of C. reinhardtii metabolite profiling; N = 9; study 1. Blue: steel ball disruption + extraction by water/isopropanol/acetonitrile (WiPM). Orange: glass bead + methanol/chloroform/water (MCW). Both identified and unidentified metabolites were used as reported by the mass spectral database BinBase. CV = coefficient of variance.
Mentions: A typical GC-TOF chromatogram consisted of about 750 peaks that were detectable at s/n 10 using the ChromaTOF vs. 2.25 software [31]. However, consistent and reliable metabolite profiling needs to ensure that all reported peaks fulfill high quality criteria with respect to mass spectral matching and retention index, and that each peak in a chromatogram can only be associated to a single metabolite. Common vendor software does not ensure this scrutiny. We have therefore reduced the number of reported peaks by employing a multi-tiered mass spectral annotation approach as outlined in the method section. 334 metabolite signals were determined to be consistent and reproducible peaks using the BinBase algorithm, of which 80 could be identified as non-redundant metabolites. Data can be downloaded at [32], detailing all detected peaks, identified metabolites, retention indices, mass spectra and quantification ions. In addition, raw chromatogram files can be downloaded to enable researcher comparisons of different data processing methods. Extraction efficiency of metabolites was estimated by comparing the sum intensity of all identified peaks. Three of the four methods gained similar overall peak intensities, except for the method employing grinding by glass beads and extraction with methanol/chloroform/water (MCW). Overall metabolite extraction efficiency by the glass bead/MCW method was 30–40% lower than by the other three methods (p = 0.00014) and it further comprised a clear outlier sample with even lower extraction efficiency. In order to enable comparison of relative extraction efficiency of individual compounds and to compare the overall metabolite profiles, data were normalized to the sum intensity of identified peaks of each chromatogram. Multivariate statistics demonstrated for both unsupervised principal components analysis (PCA) and supervised partial least square (PLS) analysis that the dominant factor separating metabolic profiles was associated with the method of grinding and homogenization rather than the two solvents that were employed. Only supervised PLS analysis could separate the two solvent systems (fig. 3) for each grinding method, but not unsupervised PCA. Univariate analysis of variance showed that a number of metabolites were equally well extracted by the four methods (e.g. alanine, fig. 3) whereas other compounds were either found at higher levels under metal ball grinding (e.g. glutamate, fig. 3) or at lower levels (e.g. 1-monopalmitin). Very few metabolites were only detectable by using one solvent system but not the other, specifically arabitol. The differential extraction efficiency of metabolites using the two different grinding systems could not easily be attributed to specific chemical classes. However, two considerations allowed a clear decision in favor of using steel ball homogenization: (a) sample handling by using steel balls is easier and therefore more reliable for high throughput applications and (b) the reproducibility of metabolite quantifications was best using steel ball grinding and methanol/chloroform/water (MCW) extraction (fig. 4 and table 2). Figure 4 exemplifies the frequency at which metabolites were quantified in 10% CV precision intervals for the steel ball/MCW method and the glass bead/MCW method. A high number of compounds had very high technical error rates using the glass bead/MCW method that resulted to a poor median precision of 30% CV for the identified metabolites and 39% over all 334 metabolite signals (table 2). The steel ball/MCW method provided good precision with a median of 16% CV for the identified compounds and 22% technical error over all metabolites (figure 4 and table 2). In comparison, extractions with water/isopropanol/methanol had intermediate precision but again, the steel ball grinding method proved to perform better than homogenizations by glass beads (table 2). Therefore, homogenization by steel ball milling was carried forward to study 2, investigating the impact of different extraction solvents.

Bottom Line: Glass beads were compared to metal balls for milling, and five different extraction solvents were tested.Additionally, all peaks were annotated in an automated way using the GC-TOF database BinBase instead of manual investigation of a single reference chromatogram.Median precision of analysis was used to decide for the eventual procedure which was applied to a proof-of-principle study of time dependent changes of metabolism under standard conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of California Davis, Genome Center, Davis, CA 95616, USA. ofiehn@ucdavis.edu.

ABSTRACT
The green eukaryote alga Chlamydomonas reinhardtii is a unicellular model to study control of metabolism in a photosynthetic organism. We here present method improvements for metabolite profiling based on GC-TOF mass spectrometry focusing on three parameters: quenching and cell disruption, extract solvent composition and metabolite annotation. These improvements facilitate using smaller cell numbers and hence, smaller culture volumes which enable faster and more precise sampling techniques that eventually lead to a higher number of samples that can be processed, e.g. for time course experiments. Quenching of metabolism was achieved by mixing 1 ml of culture to 1 ml of -70 degrees C cold 70% methanol. After centrifugation, cells were lyophilized and disrupted by milling using 2-6E6 lyophilized cells, around 500-fold less than previously reported. Glass beads were compared to metal balls for milling, and five different extraction solvents were tested. Additionally, all peaks were annotated in an automated way using the GC-TOF database BinBase instead of manual investigation of a single reference chromatogram. Median precision of analysis was used to decide for the eventual procedure which was applied to a proof-of-principle study of time dependent changes of metabolism under standard conditions.

No MeSH data available.


Related in: MedlinePlus