- Open Access
High quality metabolomic data for Chlamydomonas reinhardtii
© Lee and Fiehn; licensee BioMed Central Ltd. 2008
- Received: 09 August 2007
- Accepted: 28 April 2008
- Published: 28 April 2008
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°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.
- Partial Less Square
- Steel Ball
- Metabolite Profile
- Partial Less Square Model
- Partial Less Square Analysis
Chlamydomonas reinhardtii is a model system for photosynthetic organisms  including studies on metabolism [2–4]. It has been studied since long as a particularly sturdy organism that can be genetically modified in multiple ways and for which community resources are available including mutant stock centers and a fully sequenced genome. Chlamydomonas may also be used for studying the response to availability of macronutrients e.g. phosphate, sulfur, carbon, and nitrogen  which was extended to broad profiling of responses of gene expression or metabolite levels [6, 7]. The focus of such studies is to understand the complexity of regulatory circuits and reorganization of cellular modules in response to suboptimal conditions which may then lead to insights that could potentially be extended to vascular plants.
Metabolites can be regarded as the ultimate output of the cellular machinery. Therefore, comprehensive metabolic phenotyping may help to unravel subtle stages of cellular reorganization if highly accurate quantifications can be achieved. Analytical methods have to be constantly improved in order to achieve this aim. One of the main concerns for developing analytical methods for quantifying microbial metabolites is to prevent undesirable changes of internal metabolites during the period of harvesting. The aim is to stop any metabolic activity as fast as possible without altering the internal metabolic signature. Yeast may be regarded as good proxy for Chlamydomonas with respect to sample preparation as both are eukaryotic organisms exerting comparatively sturdy cell walls, unlike bacterial models which are known to be more easily disrupted by physicochemical methods. Yeast metabolism has been preferably quenched by cold methanol treatments . Nevertheless, even mild quenching methods unavoidably may lead to some degree of metabolite leakage by weakening cell walls. Consequently minimal concentrations of methanol and/or centrifugation times were tested, as well as alternative methods such as rapid filtration, for bacteria [9, 10]. Other studies have focused on the optimization of extraction methods to obtain a comprehensive overview of metabolism despite the known diverse physicochemical nature of metabolite structures. Different cellular disruption methods were investigated for mycobacteria  and extraction efficacies were optimized for metabolite profiling of a variety of matrices such as Arabidopsis tissues , E. coli [13, 14]), yeast cells  or blood plasma ; each yielding quite different protocols. These efforts document that sample preparation methods have to be carefully worked out and cannot be transferred from one field of application to the other without in-depth validation.
We have previously reported an initial protocol for metabolite profiling of Chlamydomonas strains and demonstrated that losses of internal metabolites were minimal during the quenching and centrifugations steps . However, the final method proved to be very labor intensive and unpractical for higher number of samples because it involved grinding samples with mortar and pestle and high volumes of quenching solutions. We here report to advance this protocol by focusing on high reproducibility of quantitative metabolite profiling results by standardizing algae growth, by miniaturizing the sample volumes and by altering the cell disruption method, and the quenching and extraction procedure. In addition, we here report improved data acquisition and data processing steps that were not used before for Chlamydomonas profiling.
Experimental details are given according to the minimal reporting standards laid out for microbial biology context information, chemical analysis and data processing, as published by the Metabolomics Standards Initiative.
The Chlamydomonas reinhardtii strain CC125 was used for all studies. The strain was cultivated in TAP medium  at 23°C under constant illumination with cool-white fluorescent bulbs at a fluence rate of 70 μmol m-2 s-1 and with continuous shaking. Cryopreserved stocks  were used to inoculate a starter culture, which was harvested at late log-phase and used to inoculate a new culture at a starting density of 5 × 105 cells/mL. All cell numbers were counted with a Coulter automated cell counter. After 48 h, cells were harvested by centrifugation, washed twice with sterile 20 mM TRIS pH 7.0, supplied with 300 mM CaCl2, 400 mM MgCl2, and 7 mM KCl, and resuspended at a starting density of 2.5 × 106 cells/mL in TRIS-buffered media under standard growth conditions  using 20 mL total volume in 125 mL beakers. 1 mL of cell culture was harvested at 1 h, 4 h, 10 h, and 22 h.
Preparation of cell extracts
At the incubation site 1 mL cell suspensions were injected into 1 mL of -70°C cold quenching solution composed of 70 % methanol in water using a thermo block above dry ice. Centrifuge tubes containing the solution during harvest were cooled in a pre-chilled cooling box to keep sample temperature below -20°C. Cells were collected by centrifugation at 16,100 rcf for 2 min with the centrifuge and rotor cooled at -20°C. Supernatant was decanted and residual liquid carefully removed. The pellet was flash frozen in liquid nitrogen and lyophilized at -50°C in a 2 mL round bottom Eppendorf tube.
Lyophilized cells were disrupted using the ball mill MM 301 (Retsch GmbH & Co., Germany) in two variants for 3 minutes: (a) using 0.5 ml of glass beads (500 μm i.d., Sigma, St.Louis MO) concomitant with 0.5 ml extraction solvent and (b) using a single 5 mm i.d. steel ball, followed by the addition of 0.5 mL extraction solvent and vortexing. After 2 min centrifugation at 16,100 rcf, the supernatants were removed (200 μl for the glass bead method, 350 μl for the steel ball method) followed by a secondary extraction step using an additional 800 μl extraction solvent, centrifugation and adding the supernatant (700 μl for the glass bead method) to the first aliquot. Hence, for both methods the same fraction of extraction solvent was used (70% of total extraction volume).
Five different extraction solvent systems were tested: methanol:chloroform:water (5:2:2) as published for plant organs [12, 20], methanol:isopropanol:water (5:2:2), 100% methanol , acetonitrile:isopropanol:water (5:2:2) as published for blood plasma extractions  and methanol:chloroform:water (10:3:1) which was previously found to be most suitable for Chlamydomonas profiling . Solvent ratios are given as volumetric measures. All solvents were degassed by directing a gentle stream of nitrogen through the solvent for 5 min and were used pre-chilled to -20°C prior to extraction. HPLC grade methanol, isopropanol, LC grade acetonitrile and purified grade chloroform were supplied by Mallinckrodt Baker Inc. (Phillipsburg. NJ, USA). Pure water was supplied by a MilliQ gradient A10 unit (Millipore Corporation, Billerica, MA, USA) with a residual total organic carbon content of less than 5 ppb.
Extracts were concentrated to dryness in a vacuum concentrator. Dried extracts were kept at -80°C for up to 4 weeks before derivatization and analysis by GC-TOF mass spectrometry.
Data acquisition by GC-TOF mass spectrometry
A mixture of internal retention index markers was prepared using fatty acid methyl esters of C8, C9, C10, C12, C14, C16, C18, C20, C22, C24, C26, C28 and C30 linear chain length, dissolved in chloroform at a concentration of 0.8 mg/ml (C8-C16) and 0.4 mg/ml (C18-C30). 2 μl of this RI mixture were added to the dried extracts. 5 μl of a solution of 20 mg/ml of 98% pure methoxyamine hydrochloride (CAS No. 593-56-6, Sigma, St.Louis MO) in pyridine (silylation grade, Pierce, Rockford IL) was added and shaken at 30°C for 90 min to protect aldehyde and ketone groups. 45 μl of MSTFA.1%TMCS (1 ml bottles, Pierce, Rockford IL) was added for trimethylsilylation of acidic protons and shaken at 37°C for 30 min. Reaction mixtures were transferred to 2 ml clear glass autosampler vials with microinserts (Agilent, Santa Clara CA) and closed by 11 mm T/S/T crimp caps (MicroLiter, Suwanee GA).
A Gerstel automatic liner exchange system with multipurpose sample MPS2 dual rail and two derivatization stations was used in conjunction with a Gerstel CIS cold injection system (Gerstel, Muehlheim, Germany). For every 10 samples, a fresh multibaffled liner was inserted (Gerstel #011711-010-00) using the Maestro1 Gerstel software vs. 184.108.40.206. Before and after each injection, the 10 μl injection syringe was washed three times with 10 μl ethyl acetate. 1 μl sample was filled using 39 mm vial penetration at 1 μl/s fill speed, injecting 0.5 μl at 10 μl/s injection speed at initial 50°C which was ramped by 12°C/s to final 250°C and hold for 3 minutes. The injector was operated in splitless mode, opening the split vent after 25 s.
An Agilent 6890 gas chromatograph (Santa Clara CA) was controlled by the Leco ChromaTOF software vs. 2.32 (St. Joseph MI).
A 30 m long, 0.25 mm i.d. Rtx-5Sil MS column with 0.25 μm 95% dimethyl 5% diphenyl polysiloxane film and additional 10 m integrated guard column was used (Restek, Bellefonte PA).
99.9999% pure Helium with built-in purifier (Airgas, Radnor PA) was set at constant flow of 1 ml/min. The oven temperature was held constant at 50°C for 1 min and then ramped at 20°C/min to 330°C at which it was held constant for 5 min.
A Leco Pegasus IV time of flight mass spectrometer was controlled by the Leco ChromaTOF software vs. 2.32 (St. Joseph, MI) and operated by Do Yup Lee.
The transfer line temperature between gas chromatograph and mass spectrometer was set to 280°C.
Electron impact ionization at 70V was employed with an ion source temperature of 250°C.
After 290 s solvent delay, filament 1 was turned on and mass spectra were acquired at mass resolving power R = 600 from m/z 85–500 at 10 spectra s-1 and 1800 V detector voltage without turning on the mass defect option. Recording ended after 1200 s. The instrument performed autotuning for mass calibration using FC43 (Perfluorotributylamine) before starting analysis sequences.
Files were preprocessed directly after data acquisition and stored as ChromaTOF-specific *.peg files, as generic *.txt result files and additionally as generic ANDI MS *.cdf files.
ChromaTOF vs. 2.32 was used for data preprocessing without smoothing, 3 s peak width, baseline subtraction just above the noise level, and automatic mass spectral deconvolution and peak detection at signal/noise levels of 10:1 throughout the chromatogram. For each peak, the apex masses and the complete spectrum with absolute intensities were exported, along with retention time, peak purity, noise, signal/noise ratio, unique ion and unique ion signal/noise ratio. Further metadata were also exported but are not yet used in the BinBase algorithm . Result *.txt files were exported to a data server with absolute spectra intensities and further processed by the BinBase algorithm. This algorithm used the settings: validity of chromatogram (<10 peaks with intensity >10^7 counts s-1), unbiased retention index marker detection (MS similarity>800, validity of intensity range for high m/z marker ions), retention index calculation by 5th order polynomial regression. Spectra were cut to 5% base peak abundance and matched to database entries from most to least abundant spectra using the following matching filters: retention index window ± 2,000 units (equivalent to about ± 2 s retention time), validation of unique ions and apex masses (unique ion must be included in apexing masses and present at >3% of base peak abundance), mass spectrum similarity must fit criteria dependent on peak purity and signal/noise ratios and a final isomer filter (if two closely related isomer spectra were found, the spectra with the closer proximity of the database RI value was taken and the alternative isomer spectrum was re-assessed). Failed spectra were automatically entered as new database entries if s/n >25, purity <1.0 and presence in the biological study design class was >80%. All thresholds reflect settings for ChromaTOF vs. 2.32. BinBase automatically recognizes data processed by ChromaTOF vs. 3.25 but thresholds for spectra quality are not yet validated. Quantification was reported as peak height from the absolute ion intensity using the unique ion as default quantification mass, unless a different quantification ion was manually set in the BinBase administration software Bellerophon. A quantification report table was produced for all database entries that were positively detected in more than 80% of the samples of a study design class (as defined in the SetupX database ). This procedure results in 10–30% missing values which could be caused by true negatives (compounds that were below detection limit in a specific sample) or false negatives (compounds that were present in a specific sample but that did not match quality criteria in the BinBase algorithm. A subsequent post-processing module was employed to automatically replace missing values from the *.cdf files using the open access mzmine software  under the following parameters: for each positively detected spectrum, the average retention time was calculated and intensities of the quantification ions were subtracted by the lowest background intensity in a retention time region of ± 5 s. The resulting report table did not comprise any missing values, but replaced values were labeled as 'low confidence' by color coding.
Result files were transformed by calculating the sum intensities of all structurally identified compounds for each sample and subsequently dividing all data associated with a sample by the corresponding metabolite sum. The resulting data were multiplied by a constant factor for convenience of obtaining values without decimals. Intensities of identified metabolites with more than one peak (e.g. for the syn- and anti-forms of methoximated reducing sugars) were summed to only one value in the transformed data set. The original non-transformed data set was retained.
Statistical analyses were performed on all continuous variables using the Statistica software vs. 7.1 (StatSoft, Tulsa OK). Univariate statistics for multiple study design classes was performed by breakdown and one-way ANOVA. F-statistics and p-values were generated for all metabolites. Data distributions were displayed by box-whisker plots, giving the arithmetic mean value for each category, the standard error as box and whiskers for 1.96 times the category standard error to indicate the 95% confidence intervals, assuming normal distributions. Multivariate statistics was performed by unsupervised principal component analysis (PCA) to obtain a general overview of variance of metabolic phenotypes in the study, by entering metabolite values without study class assignments. In addition, supervised partial least square statistics were performed which requires information about the assigned study classes. Three plots were obtained for each PCA and PLS model: (i) the scree-plot for the Eigenvalues of the correlation or covariance matrix. This is considered as a simple quality check and should have a steep descent with increasing number of Eigenvalues. (ii) Secondly, 2D score scatter plots were generated for at least the first three dimensionless principal components. 3D plots are generated for better distinguishing metabolic phenotypes. (iii) Thirdly, loadings plots were generated for each vector in PCA or PLS showing the impact of variables on formation of vectors. Metabolites near the coordinate center had no separation power; conversely, variables far away from the coordinate center were important for building PCA and PLS models. Variables that are located close to each other are strongly correlated.
Daily quality controls were used. These comprised two method blanks (involving all the reagents and equipment used to control for laboratory contamination) and four calibration curve samples spanning one order of dynamic range and consisting of 31 pure reference compounds. 0.5 μl injection volumes and split ratio of 1/5 were used. Intervention limits were established and laid out in a Standard Operating Procedure to ensure the basic validation of the instrument for metabolite profiling.
For this study, only two of the four possible MSI-categories of metabolite identifications were used, 'identified compounds' and 'unknown compounds'. Both categories were unambiguously assigned by the BinBase identifier numbers, using retention index and mass spectrum as the two most important identification criteria. Additional confidence criteria were given by mass spectral metadata, using the combination of unique ions, apex ions, peak purity and signal/noise ratios as given in data preprocessing. Every database entry in BinBase is routinely matched against the Fiehn mass spectral library that had been generated using identical instrument parameters as given above. Currently, the Fiehn library hosts 713 unique metabolites with a total of 1,197 unique spectra. BinBase entries were named manually by matching mass spectra and retention index. For named BinBase compounds, PubChem numbers and KEGG identifiers were added. In addition, all reported compounds (identified and unknown metabolites) are reported by the quantification ion and the full mass spectrum which is encoded as string.
Chlamydomonas cultures can be grown in a highly reproducible manner
Chlamydomonas GC-MS profiles encompass free fatty acids if special injection systems are used
Method improvements for quenching Chlamydomonas cell cultures
We have utilized the previously published method , here called the benchmark protocol, for step wise improvements of the sample preparation process. Rapid stop of metabolism by inactivating enzymes as fast and as mild as possible are mandatory to retain a valid snapshot of metabolism at the point of harvest [15, 27]. Accordingly, cells were rapidly quenched to temperatures below -20°C at harvest, although, inevitably, certain enzymes with very high turnover rates may still remain active long enough to alter metabolite ratios, e.g. for ADP/ATP. GC-TOF mass spectrometry cannot determine ADP and ATP and hence, metabolite profiling techniques and optimizations used here aimed only at the detectable compounds.
Methanol concentrations in the quenching solution were carefully increased in order to facilitate lower chill temperatures of the quenching solutions and correspondingly, lower volumes that were required for quenching. At a methanol/water ratio of 70:30, the quenching solution could be cooled to -70°C without phase separation. When this solution was used in 1:1 ratio with room temperature cell cultures under rapid vortexing, a final temperature of -20°C was measured and a final methanol concentration of 35%.
This final 35% methanol concentration was higher than used in the benchmark protocol (26%) and therefore, a higher amount of leakage could potentially occur. We have here determined leakage by analyzing intracellular peak intensities in comparison to concentrations in the media because an alternative route by using radioactive labeling would not reveal differences between compound classes and might also be compromised by a fraction of radioactivity that would be stored in insoluble fractions of the cell.
This calculation is also somewhat problematic because intracellular levels get altered when no quenching is employed due to continuing activity of intracellular metabolism and responses to the stress conditions during centrifugation. In many cases, however, this leads rather to an overestimation of leakage. For example, proline was systematically found at higher intracellular levels without quenching giving higher estimates for the 'relative leakage' which in fact was untrue. In the opposite, citrate was found at lower intracellular levels without quenching, giving actually rise to 'negative leakage' data. For such cases, we also compared the absolute difference in peak intensities in the media, to make sure potential leakages were not obscured by the differences in intracellular metabolism. In total, this method can be regarded as a conservative estimate of the number of peaks that were leaking into the medium due to compounds like proline which resulted in false overestimations of leakage due to continuing intracellular metabolism at 'no quenching' harvest conditions.
Number of peaks that were found to be leaking at 26% and at 35% final methanol concentration in the quenching solution, calculated by the difference to peak intensities under non-quenching harvest conditions.
# of peaks leaking at 26% MeOH
# of peaks leaking at 35% MeOH
Homogenization of cells performs better with steel ball than with glass bead milling
Before homogenization, cells were centrifuged and lyophilized at -50°C. The lyophilization step was introduced in addition to the benchmark protocol in order to store and accumulate samples for larger studies. Lyophilization eliminates any remaining interstitial water and thus efficiently inhibits any enzymatic reaction during storage. Additionally, residuals of the quenching solution are removed which enables parallel analysis of transcript and metabolite levels from the same sample, as it had been worked out for Saccharomyces cerevisiae .
Under the benchmark protocol, Chlamydomonas cells have to be disrupted by hand grinding with mortar and pestle in liquid nitrogen prior to extraction. Although this procedure certainly provides a valid homogenate of individual cells, it is certainly too laborious and consumes large quantities of liquid nitrogen when a high number of samples are to be prepared. Instead, two alternative methods were tested for efficiency of cell disruption and quantitative precision of metabolite profiles ('study 1'). One method involved glass beads for grinding in a mixer mill, similarly to protocols used in microbial preparations or minute quantities of plant tissues . The other method employed a single 5 mm i.d. steel ball, similar to protocols that are successfully used for sample preparation of plant tissues . In both comparisons, approximately 6 × 106 Chlamydomonas cells were used, equivalent to around 0.75 mg dry weight after lyophilization of cells. This low amount of material demanded that the extraction step was carried out concomitant with the homogenization in order to prevent losses due to adsorption on surfaces. Therefore, two different extraction mixtures were employed to test different recoveries of metabolites. In addition, great care had to be taken not to remove cellular debris in conjunction with taking out the supernatant extraction solvent. This proved especially difficult for the grinding method employing glass beads due to the large volume of glass beads. In order to achieve high extraction efficiency, two subsequent extraction steps were employed for both methods and the supernatant aliquots were combined. Extracts were dried and prepared for metabolite profiling as given in the methods section.
Technical error rates for metabolite profiling of Chlamydomonas reinhardtii cells.
Different solvent systems have little impact on metabolite profiling in Chlamydomonas
Study 1 used showed a higher efficacy by using steel ball grinding compared to the classic glass bead disruption which can best be seen in table 2. A second study on method optimization was carried out utilizing five different systems for extraction solvents. Since it was intended to use even lower number of cells for biological studies, only 2.5 × 106 cells/ml were employed for comparing the impact of different solvent systems on the metabolite extraction efficiency. Therefore, studies 1 and 2 were not fully identical and results could not directly be plotted in a single multivariate graph whereas overall precisions can be compared using median data as given in table 2. Methanol/chloroform/water (MCW) was used at volume ratios 5/2/2  as used in study 1 and also successfully employed in extraction of plant tissues. In addition, a second MCW mixture was used at volume ratios 10/3/1 (MCW-2) as reported before as benchmark method for metabolite profiling of Chlamydomonas cells. Reportedly, the relative amount of water was reduced in the MCW-2 mixture because the Chlamydomonas cells themselves comprised a high amount of water; however, we here tested lyophilized cells which reasonably required adding more water back to a 5/2/2 solvent ratio. Pure methanol was employed as comparison due to a report on successful application of this method for yeast metabolite profiling . In order to remain as mild as possible, boiling extraction, acidic or alkaline conditions were not employed because these parameters tend to focus on specific chemical classes (e.g. acids or amines). Additionally, ternary mixtures of water, isopropanol and methanol (WiPM) and water, isopropanol and acetonitrile (WiPA) at solvent ratios 2/5/5 were tested because these solvent systems were demonstrated to be highly useful for human tissues and blood plasma . Isopropanol is an excellent solvent for lipophilic constituents but it does not dissolve very nonpolar compounds such as waxes. Therefore, WiPA methods can be applied as a method for metabolome extraction excluding certain fractions. For example, LC/MS analysis of plant tissues is enabled by WiPA methods despite the potentially high wax contents in plants. Furthermore we have observed that any solvent mixture containing chloroform immediately enforces rapid denaturation and coagulation of cellular proteins. Depending on the sample matrix, this process may be helpful by establishing a complete stop of all enzymatic activity but it also might be detrimental by increasing the risk of co-precipitation of lipophilic metabolites and compounds that are closely associated with protein complexes.
Chlamydomonas reinhardtii cell cultures comprise drastically different metabolic phenotypes depending on time points of harvest
Compared with proteins or nucleic acids, metabolite pools comprise more rapid molecular turnover and possess a wide variety of physicochemical properties. Therefore, methods need to be carefully developed and validated with respect to metabolome coverage and analytical precision. Taking all our evaluation criteria into account, we cautiously conclude that cell homogenization by steel ball milling concomitant with methanol:chloroform:water extraction (5:2:2) is best suited for Chlamydomonas metabolite profiling. The optimized method presented here for metabolite profiling of Chlamydomonas reinhardtii enables rapid analysis of a high number of replicate samples with considerably lower efforts than the previously published benchmark method. Technical errors were lower than reported for most other systems, including plant tissues. We could prove that Chlamydomonas cultures undergo drastic changes in overall metabolic levels depending on the duration of growth and the number of cell cycles. This work lays the ground to more in depth studies of biochemical networks in this model organism.
Do Yup Lee was partially funded by an Agilent Technologies Foundation grant which we gratefully acknowledge.
- Hell R, Leustek T: Sulfur metabolism in plants and algae – a case study for an integrative scientific approach. Photosynthesis Research. 2005, 86 (3): 297-298. 10.1007/s11120-005-9027-7.View ArticlePubMedGoogle Scholar
- Lohr M, Im CS, Grossman AR: Genome-based examination of chlorophyll and carotenoid biosynthesis in Chlamydomonas reinhardtii. Plant Physiology. 2005, 138 (1): 490-515. 10.1104/pp.104.056069.PubMed CentralView ArticlePubMedGoogle Scholar
- Jain M, Shrager J, Harris EH, Halbrook R, Grossman AR, Hauser C, Vallon O: EST assembly supported by a draft genome sequence: an analysis of the Chlamydomonas reinhardtii transcriptome. Nucleic Acids Research. 2007, 35 (6): 2074-2083. 10.1093/nar/gkm081.PubMed CentralView ArticlePubMedGoogle Scholar
- Gutman BL, Niyogi KK: Chlamydomonas and Arabidopsis. A dynamic duo. Plant Physiology. 2004, 135 (2): 607-610. 10.1104/pp.104.041491.PubMed CentralView ArticlePubMedGoogle Scholar
- Gonzalez-Ballester D, de Montaigu A, Higuera JJ, Galvan A, Fernandez E: Functional genomics of the regulation of the nitrate assimilation pathway in Chlamydomonas. Plant Physiology. 2005, 137 (2): 522-533. 10.1104/pp.104.050914.PubMed CentralView ArticlePubMedGoogle Scholar
- Lilly JW, Maul JE, Stern DB: The Chlamydomonas reinhardtii organellar genomes respond transcriptionally and post-transcriptionally to abiotic stimuli. Plant Cell. 2002, 14 (11): 2681-2706. 10.1105/tpc.005595.PubMed CentralView ArticlePubMedGoogle Scholar
- Bolling C, Fiehn O: Metabolite profiling of Chlamydomonas reinhardtii under nutrient deprivation. Plant Physiology. 2005, 139 (4): 1995-2005. 10.1104/pp.105.071589.PubMed CentralView ArticlePubMedGoogle Scholar
- de Koning W vDK: A method for the determination of changes of glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH. Analytical biochemistry. 1992, 204: 118-123. 10.1016/0003-2697(92)90149-2.View ArticlePubMedGoogle Scholar
- Wittmann C, Kromer JO, Kiefer P, Binz T, Heinzle E: Impact of the cold shock phenomenon on quantification of intracellular metabolites in bacteria. Analytical Biochemistry. 2004, 327 (1): 135-139. 10.1016/j.ab.2004.01.002.View ArticlePubMedGoogle Scholar
- Bolten CJ, Kiefer P, Letisse F, Portais JC, Wittmann C: Sampling for Metabolome Analysis of Microorganisms. Analytical Chemistry. 2007, 79 (10): 3843-3849. 10.1021/ac0623888.View ArticlePubMedGoogle Scholar
- Jaki BU, Franzblau SG, Cho SH, Pauli GF: Development of an extraction method for mycobacterial metabolome analysis. Journal of Pharmaceutical and Biomedical Analysis. 2006, 41 (1): 196-200. 10.1016/j.jpba.2005.10.022.PubMed CentralView ArticlePubMedGoogle Scholar
- Gullberg J, Jonsson P, Nordstrom A, Sjostrom M, Moritz T: Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Analytical Biochemistry. 2004, 331 (2): 283-295. 10.1016/j.ab.2004.04.037.View ArticlePubMedGoogle Scholar
- Schaub J, Schiesling C, Reuss M, Dauner M: Integrated Sampling Procedure for Metabolome Analysis. Biotechnology Progress. 2006, 22: 1434-1442. 10.1021/bp050381q.View ArticlePubMedGoogle Scholar
- Maharjan RP, Ferenci T: Global metabolite analysis: the influence of extraction methodology on metabolome profiles of Escherichia coli. Analytical Biochemistry. 2003, 313 (1): 145-154. 10.1016/S0003-2697(02)00536-5.View ArticlePubMedGoogle Scholar
- Villas-Boas SG, Hojer-Pedersen J, Akesson M, Smedsgaard J, Nielsen J: Global metabolite analysis of yeast: evaluation of sample preparation methods. Yeast. 2005, 22 (14): 1155-1169. 10.1002/yea.1308.View ArticlePubMedGoogle Scholar
- Jiye A, Trygg J, Gullberg J, Johansson AI, Jonsson P, Antti H, Marklund SL, Moritz T: Extraction and GC/MS analysis of the human blood plasma metabolome. Analytical Chemistry. 2005, 77 (24): 8086-8094. 10.1021/ac051211v.View ArticleGoogle Scholar
- Harris E: The Chlamydomonas Sourcebook:. A Comprehensive Guide to Biology and Laboratory Use. 1998Google Scholar
- Crutchfield A, Diller K, Brand J: Cryopreservation of Chlamydomonas reinhardtii (Chlorophyta). European Journal of Phycology. 1999, 34: 43-52. 10.1080/09670269910001736072.View ArticleGoogle Scholar
- Farr TJ, Huppe HC, Turpin DH: Coordination of Chloroplastic Metabolism in N-Limited Chlamydomonas-Reinhardtii by Redox Modulation .1. The Activation of Phosphoribulosekinase and Glucose-6-Phosphate-Dehydrogenase Is Relative to the Photosynthetic Supply of Electrons. Plant Physiology. 1994, 105 (4): 1037-1042.PubMed CentralPubMedGoogle Scholar
- Weckwerth W, Wenzel K, Fiehn O: Process for the integrated extraction identification, and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks. Proteomics. 2004, 4 (1): 78-83. 10.1002/pmic.200200500.View ArticlePubMedGoogle Scholar
- Kind T, Fiehn O: Metabolite profiling in blood plasma. Metabolomics: Methods and Protocols. Edited by: Weckwerth W, Totowa NJ. 2006, Humana Press, 3-18.Google Scholar
- Fiehn O, Wohlgemuth G, Scholz M: Setup and Annotation of Metabolomic Experiments by Integrating Biological and Mass Spectrometric Metadata. Proc Lect Notes Bioinformatics. 2005, 3615: 224-239.Google Scholar
- Scholz M, Fiehn O: SetupX – a public study design database for metabolomic projects. Pacific Symposium on Biocomputing. 2007, 12: 169-180.Google Scholar
- Katajamaa M, Miettinen J, Oresic M: MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics. 2006, 22 (5): 634-636. 10.1093/bioinformatics/btk039.View ArticlePubMedGoogle Scholar
- Denkert C, Budczies J, Kind T, Weichert W, Tablack P, Sehouli J, Niesporek S, Konsgen D, Dietel M, Fiehn O: Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Research. 2006, 66 (22): 10795-10804. 10.1158/0008-5472.CAN-06-0755.View ArticlePubMedGoogle Scholar
- Giroud C, Gerber A, Eichenberger W: Lipids of Chlamydomonas-Reinhardtii – Analysis of Molecular-Species and Intracellular Site(S) of Biosynthesis. Plant and Cell Physiology. 1988, 29 (4): 587-595.Google Scholar
- Koek MM, Muilwijk B, Werf van der MJ, Hankemeier T: Microbial metabolomics with gas chromatography/mass spectrometry. Analytical Chemistry. 2006, 78 (4): 1272-1281. 10.1021/ac051683+.View ArticlePubMedGoogle Scholar
- Theiss C, Bohley P, Voigt J: Regulation by Polyamines of Ornithine Decarboxylase Activity and Cell Division in the Unicellular Green Alga Chlamydomonas reinhardtii. Plant Physiology. 2002, 128: 1470-1479. 10.1104/pp.010896.PubMed CentralView ArticlePubMedGoogle Scholar
- Martins AM, Sha W, Evans C, Martino-Catt S, Mendes P, Shulaev V: Comparison of sampling techniques for parallel analysis of transcript and metabolite levels in Saccharomyces cerevisiae. Yeast. 2007, 24 (3): 181-188. 10.1002/yea.1442.View ArticlePubMedGoogle Scholar
- Portillo M, Fenoll C, Escobar C: Evaluation of different RNA extraction methods for small quantities of plant tissue: Combined effects of reagent type and homogenization procedure on RNA quality-integrity and yield. Physiologia Plantarum. 2006, 128 (1): 1-7. 10.1111/j.1399-3054.2006.00716.x.View ArticleGoogle Scholar
- Weckwerth W, Loureiro ME, Wenzel K, Fiehn O: Differential metabolic networks unravel the effects of silent plant phenotypes. Proceedings of the National Academy of Sciences of the United States of America. 2004, 101 (20): 7809-7814. 10.1073/pnas.0303415101.PubMed CentralView ArticlePubMedGoogle Scholar
- SetupX – Study Design Database. Public repository of metabolomics data at the UC Davis Genome Center. [http://fiehnlab.ucdavis.edu:8080/m1/main_public.jsp]
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