The topmost leaves of five plants were sampled for both genotypes at tillering, booting, flowering and grain filling stages, respectively; three replicates were performed for each sample. All samples were flash-frozen in liquid nitrogen, stored at −70°C until metabolite extraction. Metabolite extraction was carried out as described by Bowne et al. [35 (link)] and Zhao [36 (link)]. The extracted samples were then derivatized and analyzed by gas chromatography-mass spectrometry (GC-MS). Chromatograms and mass spectra were processed using the find algorithm implemented in GC-MS Postrun Analysis software (Shimadzu). Specific mass spectral fragments were detected in defined retention time windows using the mass spectral libraries of NIST08, NIST08S and Wiley 9 and the public domain mass spectra library of the Max Planck Institute in Germany (http://csbdb.mpimp-golm.mpg.de/csbdb/ ). A mixture of the leaves of both genotypes at four stages were extracted in bulk and used as a reference. The reference samples were run once every ten samples.
Gcms postrun analysis software
Manufactured by Shimadzu
Sourced in Japan
The GCMS Postrun Analysis software from Shimadzu is a data analysis tool designed for use with gas chromatography-mass spectrometry (GC-MS) systems. The software provides essential functions for reviewing and processing GC-MS data after a sample run.
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Lab products found in correlation
37 component fame standard mix, by Merck Group (2 mentions)
C8 c22 fame standard mix, by Merck Group (2 mentions)
Simca p 17, by Sartorius (1 mentions)
Tq8050, by Shimadzu (1 mentions)
Gc 2010, by Shimadzu (1 mentions)
Statistica 13.3 pl, by StatSoft (1 mentions)
Mstfa, by Merck Group (1 mentions)
Qp2010 plus gc ms, by Shimadzu (1 mentions)
17 protocols using gcms postrun analysis software
1
Metabolic Profiling of Plant Leaves
2
GC/MS Data Processing Workflow
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Raw GC/MS data were converted into CDF format (NetCDF) files using Shimadzu GCMS Postrun Analysis software and subsequently processed using the XCMS toolbox (http://metlin.scripps.edu/download/ ). The XCMS parameters consisted of the default settings with the following exceptions: xcmsSet (fwhm = 8, snthresh = 6, max = 200); retcor (method = “linear,” family = “gaussian,” plottype = “mdevden”); and a bandwidth of eight for first grouping command and four for the second grouping command39 (link)40 (link). The data set of the aligned mass ions was exported from XCMS and could be further processed using Microsoft Excel to normalize the data prior to multivariate analyses.
3
NIST-based Metabolite Identification and Relative Quantification
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Based on a total ion chromatogram (TIC), the NIST (National Institute of Standards and Technology) MS database was used for identifying the structures of the common peaks. The full scan mass spectra of these metabolites were searched and analyzed using biochemical databases including the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
The GC-MS Postrun analysis software (Shimadzu) was directly used for the quantitative analysis. The internal standards 2-isopropylmalic acid and heptadecanoic acid were used for normalization in the relative quantitative analysis for the nontargeted global metabolites' study and subsequent targeted fatty acid analysis, respectively.
The GC-MS Postrun analysis software (Shimadzu) was directly used for the quantitative analysis. The internal standards 2-isopropylmalic acid and heptadecanoic acid were used for normalization in the relative quantitative analysis for the nontargeted global metabolites' study and subsequent targeted fatty acid analysis, respectively.
4
GC/MS Data Processing Workflow
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Raw GC/MS data were converted into CDF format (NetCDF) files using Shimadzu GCMS Postrun Analysis software and subsequently processed using the XCMS toolbox. The XCMS parameters consisted of the default settings with the following exceptions: xcmsSet (fwhm = 8, snthresh = 6, max = 200); retcor (method = “linear,” family = “gaussian,” plottype = “mdevden”); and a bandwidth of eight for first grouping command and four for the second grouping command. The data set of the aligned mass ions was exported from XCMS and could be further processed using Microsoft Excel to normalize the data prior to multivariate analyses.
5
GC-MS Metabolomics Data Preprocessing
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The raw data generated was converted to mzXML using the GC-MS Postrun Analysis software (Shimadzu, Japan). The mzXML files were further processed for peak picking (signal to noise threshold = 5) and retention time correction using the XCMS package in R. The pre-processed metabolomics data were normalized separately in R which resulted in the best clustering of QC samples in principal component analysis (PCA) score plot. All semi-quantification was done using raw peak areas of selected features normalized to that of internal standard. Metabolite identification was done by comparing the GC-MS fragmentation mass spectra to those found in the National Institute of Standards and Technology (NIST) database. Identification of metabolites was confirmed by comparing the retention time and mass spectrum to pure standards if the similarity index was less than 90%.
6
GC-MS Data Analysis for Metabolite Identification
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GC-MS data were converted from Shimadzu GC-MS Postrun Analysis software (Shimadzu, Kyoto, Japan) to the netCDF format file and processed using MetAlign software for peak detection and alignment.62 (link) The resulting data (CSV-format file) were imported into AIoutput software for peak identification and prediction.63 (link) Data normalization was performed using the internal standard and total ion current values. OPLS-DA of GC-MS data was performed to visualize the variance of metabolites using SIMCA-P 17.0 (Umetrics, Umea, Sweden). Cross-validation was performed using a permutation test repeated 200 times. Metabolites with VIP > 1.0 and p < 0.05 were considered metabolites that could discriminate groups. Metabolites were identified by comparing their mass spectra with those in AIoutput software, NIST 20.0 library, standard reagents (Supplementary Figure S8), and the human metabolome database (HMDB, http://www.hmdb.ca ).
7
Sensitive GC-MS/MS Analysis of Analytes
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The GC-MS/MS analyses were conducted with a Shimadzu GC 2010 gas chromatograph coupled with a Shimadzu TQ-8050 tandem mass spectrometer (Shimadzu, Kyoto, Japan). The system was equipped with a PTV (Programmed Temperature Vaporization) injection inlet and an AOC-5000 autosampler. GC-MS Real Time Analysis and GC-MS Postrun Analysis software (Shimadzu, Kyoto, Japan) were used for instrument control and data analysis, respectively. The GC analysis was performed on a J&W DB5-MS+DG column (length 30 m, id 0.25 mm, film thickness 0.25 μm + 10 m Guard Column), the chromatographic conditions were the following: carrier gas helium (minimum purity 99.9995%) in constant linear velocity mode at 30 cm/sec, PTV Injector 60–200 °C at 240 °C/min 1 min then 330 °C for 19 min, septum purge 6 mL/min, Split 1:1; GC oven temperature program of 40 °C for 1 min, ramp 20 °C/min to 130 °C, hold 7.0 min then 40 °C/min until 280 °C (held for 3 min), injection volume 1 μL.
The mass spectrometer was operated in MRM mode (MRM transitions of all five analytes and the internal standard (IS) inTable 1 ) with the following conditions: electron impact ionization at 70 eV, MS transfer line temperature 280 °C, MS source temperature 200 °C, solvent delay 9.6 min, dwell time 300 ms, collision gas argon (minimum purity 99.9999%) with a collision cell pressure of 200 kPa and detector gain fixed at 1.4 kV.
The mass spectrometer was operated in MRM mode (MRM transitions of all five analytes and the internal standard (IS) in
8
Metabolomic Profiling of A549 Cells
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Detected compounds were identified first by matching the acquired spectrum with the NIST 2017 (Gaithersburg, MD, USA) Mass Spectra Library and additionally confirmed by the retention time of a reference substance. All substances used for identification of detected metabolites were purchased from Alchem (Alchem, Toruń, Poland). Chromatographic peaks were initially integrated using a customized method in GCMS Postrun Analysis software (Shimadzu, Kyoto, Japan) and, if necessary, manually corrected by an experienced analyst. A received compound table was produced in Metaboanalyst 5.0 online software [27 ], where peak areas were log-transformed. The statistical significance between VOC levels in A549 cells and reference the RPMI1640 medium was calculated in Statistica 13.3 PL software (StatSoft, Inc., Tulsa, OK, USA) using the U Mann-Whitney test, which is a nonparametric test to compare samples from two groups of independent observations, where p-values < 0.05 were considered to be significant. This test was chosen due to its stability to outliers and no requirement for the groups to be normally distributed. To determine LOD/LOQ and summarize the data, results of calibrations and optimization measurements were plotted using Microsoft Excel, while the results of in vitro experiments with A549 cells were plotted using Metaboanalyst 5.0 online software [27 ].
9
GC/MS Data Processing Protocol
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Raw GC/MS data were converted into CDF format (NetCDF) files using Shimadzu GCMS Postrun Analysis software and were subsequently processed using the XCMS toolbox. The XCMS parameters consisted of the default settings with the following exceptions: xcmsSet (fwhm = 8, snthresh = 6, max = 200); retcor (method = “linear”, family = “gaussian”, plottype = “mdevden”); a bandwidth of 8 was used for the first grouping command, and 4 was used for the second grouping command. The dataset of the aligned mass ions was exported from XCMS and could be further processed using Microsoft Excel to normalize the data prior to multivariate analyses.
10
Sterol Analysis of Marine Invertebrates
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Samples were extracted with a modified Bligh-Dyer method: briefly, either 300 µl aqueous Aiptasia or Nematostella homogenate was added to 750 µl HPLC-grade methanol, or 300 µl ultra-pure water was added to the Acropora sample already in 750 µl methanol or ethanol. After shaking at 70°C for 45 min, the mixture was extracted with 375 µl HPLC-grade chloroform and 300 µl ultra-pure water and centrifugation. The dried organic phase was then saponified with 500 µl of 5% KOH in a 9:1 methanol:water solution and incubating at 68°C for 1 hr. The mixture was then extracted with water and chloroform followed by centrifugation. Lipids in the dried organic phase were derivatized to trimethylsilyl ethers with 25–40 µl MSTFA (#69479, Sigma Aldrich) at 60°C for 0.5–1 hr and immediately analysed. 1 µl of each mixture was injected into a QP2010-Plus GC/MS (Shimadzu) and with a protocol (adapted from Schouten et al., 1998 (link)) as follows: oven temperature 60°C, increase to 130°C at 20 °C/min, then increase to 300°C at 4 °C/min and hold for 10 min. Spectra were collected between m/z 40 and 850 and were analysed in GCMS PostRun Analysis Software (Shimadzu) by comparison to the National Institute of Standards and Technology 2011 database. Relative sterol composition as percent of total sterols were calculated from integrated peak intensity on the total ion chromatograph for each sample.
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