Compound discoverer software 3

Manufactured by Thermo Fisher Scientific

Sourced in United States

Compound Discoverer Software 3.1 is a data processing software designed for the identification and characterization of unknown compounds. The software provides tools for chromatographic data analysis, mass spectral deconvolution, and compound identification.

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Lab products found in correlation

Xcalibur 4.0 qual browser, by Thermo Fisher Scientific (3 mentions) Tracefinder software 4, by Thermo Fisher Scientific (3 mentions) Q exactive plus orbitrap high resolution mass spectrometer, by Thermo Fisher Scientific (1 mentions) Acquity uplc hss t3 column, by Waters Corporation (1 mentions)

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Compound Discoverer software Compound Discoverer 3.1 Compound Discoverer

7 protocols using compound discoverer software 3

GXF Metabolite Profiling by UPLC-HRMS

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GXF formula was analyzed using U3000 UPLC system equipped with Q Exactive Plus Orbitrap high-resolution mass spectrometer (Thermo Fisher Scientific). Chromatographic separation was performed on a ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 1.8 μm, Waters). The column temperature was set at 35 ℃. The flow rate was 0.3 mL/min. The mobile phase consisted of deionized water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B). The mass spectrometer analysis was conducted in both positive and negative ion modes. The scan mass ratio was within the mass range of m/z 50–1500.
The analysis data were analyzed by Compound Discoverer software 3.2 (Thermo Fisher Scientific). The mass deviations of characteristic peak element matching, molecular formula prediction and isotope distribution matching were all set to within 5 ppm. Chemical identification was based on chromatographic elution behavior, mass spectrometry fragment patterns and mass spectrometry databases (MZ cloud, MZ vault).

Liu Z., Xiang H., Xiang D., Xiao S., Xiang H., Xiao J., Ren H., Hu P., Liu H, & Peng M. (2022). Revealing potential anti-fibrotic mechanism of Ganxianfang formula based on RNA sequence. Chinese Medicine, 17, 23.

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Metabolomic Analysis of Biomarkers

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All data were presented as the mean ± SEM. The comparisons between two groups were performed with an unpaired two-tailed Student’s t-test using SPSS 23.0. The comparisons between several groups were performed with a one-way analysis of variance (ANOVA) using SPSS 23.0. Value of p < 0.05 was considered significant differences. The mass spectrometry-generated metabolomics data is in RAW file format. The quantitative analysis of the compound was conducted using Compound Discoverer Software 3.2 (Thermo Fisher Scientific, USA). The data was subject to principle components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using SIMCA-P version 14.1 software. The metabolites with VIP >1 and p < 0.05 were considered as potential biomarkers, and the online databases HMDB (http://www.hmdb.ca) were used to identify the compounds by matching the mass spectrum. Pathway enrichment was acquired according to the MetaboAnalyst 5.0 (http://www.metaboanalyst.ca/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) database.

Zhang S., Li C., Feng T., Cao S., Zhou H., Li L., Hu Q., Mao X, & Ji S. (2021). A Metabolic Profiling Study of Realgar-Induced Acute Kidney Injury in Mice. Frontiers in Pharmacology, 12, 706249.

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Untargeted and Targeted Metabolomic Analysis

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For untargeted analysis, principal component analysis (PCA) was performed using Compound Discoverer software 3.2 (Thermo Fisher Scientific, Waltham, MA, USA). For targeted analysis, PCA was performed using Lab Profiling Solutions software version 5.6 (Shimadzu, Kyoto, Japan) and R-Studio (RStudio, PBC, MA, USA). PCs were calculated using prcomp function and PCA scores plots were generated using the following packages in R: ggplot2, ggfortify, grid, and gridExtra. Differential analysis using volcano plots allowed significant differences between groups to be determined. Univariate statistical analyses were performed using unpaired t-test and one-way ANOVA, with the level of significance set at p < 0.05. Central network analysis was performed in R-studio (RStudio, PBC, MA, USA). using the igraph package.

Kelly P.E., Ng H.J., Farrell G., McKirdy S., Russell R.K., Hansen R., Rattray Z., Gerasimidis K, & Rattray N.J. (2022). An Optimised Monophasic Faecal Extraction Method for LC-MS Analysis and Its Application in Gastrointestinal Disease. Metabolites, 12(11), 1110.

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Untargeted Sweat Metabolomics by LC-MS

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Raw files generated by the Q Exactive HF instrument were analysed using the Compound Discoverer Software 3.1 (Thermo Fisher Scientific). Identified compounds were manually reviewed using Xcalibur 4.0 Qual browser and Freestyle (version 1.3.115.19) (both Thermo Fisher Scientific) and the obtained MS2 spectra were compared to reference spectra, which were retrieved from mzcloud (Copyright © 2013–2020 HighChem LLC, Slovakia). The match factor cut-off from mzcould was 80, while the mass tolerances were 5 and 10 ppm on MS1 and MS2 levels, respectively. Moreover, the identity of compounds suggested by Compound Discoverer was verified by analysing purchased standards using the same LC-MS method. The Tracefinder Software 4.1 (Thermo Fisher Scientific) was used for peak integration and calculation of peak areas. The generated batch table was exported and further processed with Microsoft Excel (version 1808), GraphPad Prism (version 6.07) and the Perseus software (version 1.6.12.0)^{70 (link)}, the letter being used for the principal component analysis. Untargeted metabolic profiling by mass spectrometry delivered more than 50000 reproducible sweat-specific features per analysis. Microsoft PowerPoint (version 1808) was used for creating figures.

Brunmair J., Gotsmy M., Niederstaetter L., Neuditschko B., Bileck A., Slany A., Feuerstein M.L., Langbauer C., Janker L., Zanghellini J., Meier-Menches S.M, & Gerner C. (2021). Finger sweat analysis enables short interval metabolic biomonitoring in humans. Nature Communications, 12, 5993.

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Untargeted Metabolomics Data Analysis

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Raw files generated by the Q Exactive HF were searched in the Compound Discoverer Software 3.1 (Thermo Fisher Scientific) applying a user-defined workflow. All identified compounds with a match factor ≥ 80 were manually reviewed using Xcalibur 4.0 Qual browser (Thermo Fisher Scientific) and the obtained MS2 spectra were compared to reference spectra taken from mzcloud (Copyright © 2013–2020 HighChem LLC, Slovakia). A maximum retention time shift of 0.1 min was allowed and the mass tolerance was restricted to 10 ppm for MS1 and MS2. Identified compounds were also verified using purchased analytical standards applying the same LC–MS method. The Tracefinder Software 4.1 (Thermo Fisher Scientific) was used for peak integration and calculation of peak areas. Generated batch tables were exported and further process with Microsoft Excel, GraphPad Prism (for two-tailed, paired t-tests) and the Perseus [45 (link)] (for principal component analysis) software. ACD/Labs’ ChemSketch (Freeware) 2020.1.1 was used to draw structures.

Brunmair J., Bileck A., Stimpfl T., Raible F., Del Favero G., Meier-Menches S.M, & Gerner C. (2021). Metabo-tip: a metabolomics platform for lifestyle monitoring supporting the development of novel strategies in predictive, preventive and personalised medicine. The EPMA Journal, 12(2), 141-153.

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Polyphenol Identification and Quantification

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The standards and extracts were analyzed as described earlier [39 (link)]. In short, UHPLC (Thermo Vanquish UHPLC C18 System, Waltham, MA, USA) coupled to an Advion expressionL^® compact mass spectrometer (CMS; Advion Inc., Ithaca, NY, USA). Compound Discoverer™ software 3.1 (Thermo scientific, Waltham, MA, USA) was used to control the LC and CMS instrumentation and data acquisition. The m/z ratio and LC retention time was used to identify and confirm individual polyphenols against authentic standards. Polyphenol standard curves for flavonoids were derived from integrated areas under UV absorption peaks with five replicates.

Agarwal N., Kolba N., Jung Y., Cheng J, & Tako E. (2022). Saffron (Crocus sativus L.) Flower Water Extract Disrupts the Cecal Microbiome, Brush Border Membrane Functionality, and Morphology In Vivo (Gallus gallus). Nutrients, 14(1), 220.

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Compound Identification and Quantification

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Raw files generated by the Q Exactive HF instrument were analysed using the Compound Discoverer Software 3.1 (Thermo Fisher Scientific). Identified compounds were manually reviewed using Xcalibur 4.0 Qual browser (Thermo Fisher Scientific) and the obtained MS2 spectra were compared to reference spectra, which were retrieved from mzcloud (Copyright © 2013-2020 HighChem LLC, Slovakia). The match factor cut-off from mzcould was 80, while the mass tolerances were 5 and 10 ppm on MS1 and MS2 levels, respectively. Moreover, the identity of compounds suggested by Compound Discoverer was verified by analysing purchased standards using the same LC-MS method. The Tracefinder Software 4.1 (Thermo Fisher Scientific) was used for peak integration and calculation of peak areas. The generated batch table was exported and further processed with Microsoft Excel, GraphPad Prism and the Perseus software, 50 the letter being used for the principle component analysis.

Brunmair J., Niederstaetter L., Neuditschko B., Bileck A., Slany A., Janker L., Feuerstein M., Langbauer C., Gotsmy M., Zanghellini J., Meier S.M., & Gerner C. (2020). Finger Sweat Analysis Enables Short Interval Metabolic Biomonitoring in Humans.

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