Principal component analysis (PCA) and t-test analyses were performed by the software of ProfileAnalysis 2.0 (Bruker Daltonics, Billerica, MA, USA). The MS data from 0.5 to 17 min were used for analysis. The advanced bucket for time and mass was set to 0.3 s and 5 mDa, respectively. The S/N threshold was set to 5 under the “calculate compound” mode. The raw data of LC-MS was imported into the ProfileAnalysis, and PCA analysis was performed first. Additionally, the “count number” filter was applied. The “count number” means the encounters of a feature detected in the sample batch. It is used for reducing the low confident features. In this study, about half number of one group sample was used. If the sample numbers were different for different sample groups, the smallest sample number was selected to set the criteria. Then, the features were filtered by the “count number”, and the t-test calculation was performed. All tests were two-sided, and a p-value less than 0.05 was considered statistically significant.
Profile analysis 2
Manufactured by Bruker
Sourced in Germany
Profile Analysis 2.0 is a lab equipment product from Bruker. It is designed for profile analysis, providing core functionality for the intended application. The description is kept concise and factual, without extrapolation or interpretation.
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6 protocols using profile analysis 2
1
Statistical Analysis of Metabolomics Data
2
Lipid Diversity Analysis via PCA
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The Simpson diversity index (D), typically used to compare species diversity, was applied to the data to estimate lipid diversity (cf., Meador et al., 2014 (link)). The D values range from 0 to 1, such that a value approaching 1 represents high lipid diversity and values close to 0 represent no diversity.
Principal component analysis (PCA) was performed on the raw MS chromatograms with Bruker Profile Analysis 2.0 in a m/z range from 600 to 2000 and between 10 and 25 min., thus including all lipids. Rectangular bucketing was established with dimensions of 1 min and 1 m/z units and data were normalized to the sum of buckets in each analysis. Samples were grouped according to treatments. In order to maximize comparability among samples for PCA, one replicate sample from hydrogen-limited experiments that was measured 2 weeks later than the others was excluded. Since main differences are expected to occur during the stationary phase, we also chose to exclude all L-Exp samples and include end-harvest samples of control and hydrogen-limited treatments. Because the latter are comparable to the L-Stat samples, they were not shown in IPL characterization.
Principal component analysis (PCA) was performed on the raw MS chromatograms with Bruker Profile Analysis 2.0 in a m/z range from 600 to 2000 and between 10 and 25 min., thus including all lipids. Rectangular bucketing was established with dimensions of 1 min and 1 m/z units and data were normalized to the sum of buckets in each analysis. Samples were grouped according to treatments. In order to maximize comparability among samples for PCA, one replicate sample from hydrogen-limited experiments that was measured 2 weeks later than the others was excluded. Since main differences are expected to occur during the stationary phase, we also chose to exclude all L-Exp samples and include end-harvest samples of control and hydrogen-limited treatments. Because the latter are comparable to the L-Stat samples, they were not shown in IPL characterization.
3
Unsupervised LC/MS Data Analysis
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Extraction and bucketing of the raw LC/MS data and unsupervised PCA model was performed using Profile Analysis 2.0 (BrukerDaltonics), obtaining a [31x9300] matrix data of [analyses x (tR; m/z)], which was converted to .csv data for further analysis. Supervised PCA, clustering and dendrogram were applied to simplified data matrix of most relevant data using MATLAB v.7.6 (R2008a, MathWorks Inc., Natick, MA).
4
Metabolomic Profiling of Plant Organs
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The pretreatment of LC/MS data and the creation of a descriptor table (“bucket table”) were carried out with the software ProfileAnalysis 2.1 (Bruker Daltonik GmbH, Bremen, Germany) in the same way as in our previous study [14 (link)]. A value count of group (organ: leaves vs. twigs) attributes within bucket ≥ 10% was selected. The intensity of the resulting buckets was normalized to the internal standard. The bucket table thus obtained was used to calculate the PCA models.
5
Label-free Quantitative Proteomics Protocol
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Label-free quantitative proteomics was performed using the DataAnalysis 4.1, ProfileAnalysis 2.1, and ProteinScape 3.0 software packages from Bruker Daltonics (Bremen, Germany). Briefly, 10 μg of protein from each of the four pooled samples was digested and analyzed by nano-LC-MS/MS with replicated runs (n = 4) for the quantification of peptide ions and by nano-LC-MS/MS to acquire MS/MS spectra for protein identification. Peak intensities of peptide ions from each nano-LC-MS run were processed by the “Find Molecular Features” (FMF) algorithm in DataAnalysis 4.1, with the following parameters: a minimum retention time of 20 min, a maximum retention time of 90 min, a minimum compound length of eight spectra, and a smoothing width of 4. The intensity and elution time of each peptide ion were recorded as a quantitative “molecular feature” and used to construct a feature map. Feature abundances on different maps were compared using ProfileAnalysis 2.1 with a t-test to reveal the relative changes between peptide ions. The results of the t-test comparison and the quantification of all peptide ions were transferred to ProteinScape to obtain quantitative and qualitative data on the protein composition in each group.
6
Identification of Secondary Metabolites in MASE using LC-QTOF-MS
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Initially, 20% (w/v) of MASE diluted in distilled water flowed through in ACQUITY UPLC-BEH C18 column (Waters, USA) at a 0.5 ml min À1 flow rate. The mobile phase was prepared using a binary solvent manager; solvent A (0.1% formic acid) and solvent B (acetonitrile) (Sigma Aldrich, Germany). The gradients elution was 99% of solvent A and 1% of solvent B (0 min and 0 to 5 min), 65% of solvent A and 35% of solvent B (5 to 16 min), 0% of solvent A and 100% of solvent B (16 to 18 min), and 99% of solvent A and 1% of solvent B (18 to 20 min), at a 0.6 mL min À1 flow rate. The seal wash time and the highestpressure limit were set at 5 min and 1800 psi, respectively. Positive and negative ionization modes were performed at 2.50 kV capillary voltage and 1.2 bar nebulizer pressure. The drying gas was set at 8 L min À1 at 200 C, and the mass range was 50 to 1000 m/z. The sample was assessed using LC-QTOF-MS (Vion Ion Mobility QTOF MS, Waters, USA), and the data were analyzed using Profile Analysis 2.1 (Bruker, Germany). The presence of secondary metabolites in MASE was assigned using LC-QTOF-MS analysis. A comparison of mass spectrometry (MS) fragmentation patterns with Waters VR UNIFY library was conducted to identify the compounds based on the spectral match.
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