DNA was isolated from feeder-free ESC (n = 3 of each) and analyzed by LC/MS at Einstein Mass Spectrometry core following published methods (31 (link)). Briefly, 2 μg of DNA was digested (37°C, >1 h) with a cocktail of nuclease enzymes (DNA Degradase Plus™, Zymo Research; 2.5 μl 10X DNA Degradase Reaction buffer, 1 μl DNA Degradase Plus) and upon addition of aqueous formic acid (25 μl, 0.1% v/v; final concentration 40 ng digested DNA/μl) was injected onto a reverse phase UPLC column (Eclipse C18 2.1 × 50 mm, 1.8 particle size, Agilent). Peak areas for dC, 5mdC and 5hmdC were measured using Agilent Mass Hunter Quantitative Analysis version B.06.00. 5hmC levels were quantified as % of all Cs.
Agilent masshunter quantitative analysis
Manufactured by Agilent Technologies
Sourced in United States
Agilent MassHunter Quantitative Analysis is a software solution designed for comprehensive quantitative analysis of mass spectrometry data. It provides a suite of tools for processing, analyzing, and reporting quantitative results from a variety of mass spectrometry techniques.
Automatically generated - may contain errors
Lab products found in correlation
Eclipse c18, by Agilent Technologies (1 mentions)
Dna degradase plus, by Zymo Research (1 mentions)
Agilent masshunter quantitative analysis software, by Agilent Technologies (1 mentions)
Masshunter profinder, by Agilent Technologies (1 mentions)
Agilent masshunter qualitative analysis, by Agilent Technologies (1 mentions)
Agilent masshunter unknowns analysis, by Agilent Technologies (1 mentions)
Agilent mass profiler professional, by Agilent Technologies (1 mentions)
8 protocols using agilent masshunter quantitative analysis
1
Quantification of DNA Modifications
2
Fatty Acid Quantification by GC-MS
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The 5977A mass spectrometer was operated with an electron energy of 70 eV, and the MS was operated in both scan and selective ion monitoring (SIM) modes. All scan mass spectra data were recorded at a rate of 5 Hz from m/z 40–400 after a 5-min solvent delay. The selective ions, m/z 69 for methyl oleate; m/z 74 for palmitate, palmitoleate, and stearate methyl esters; m/z 79 for α-linolenate and γ-linolenate methyl esters; and m/z 81 for linoleate methyl ester were identified as suitable ions in SIM mode with enhanced detection sensitivity and quantification purposes. Data were acquired utilizing Agilent MassHunter software (version B7.06.274), while quantification was achieved with Agilent MassHunter Quantitative Analysis (version 10.0.707.0). The NIST database (version 2.3) was utilized for tentative compound identification. The 6 quantifiable fatty acids were later confirmed with analytical reference standards.
3
Determining LOD and LOQ Accurately
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Limit of detection (LOD) and limit of quantitation (LOQ) were determined using the LINEST function of excel and data from the Agilent Mass Hunter Quantitative Analysis where a signal ratio of 3.3:1 from baseline was used for LOD and LOQ was determined using signal ratio of 10:1 from baseline. R2 values and equations were calculated using Agilent Mass Hunter Quantitative Analysis software, where the calibration curve fit origins were forced through zero.
4
Comprehensive LC-MS Data Analysis
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Acquired data were inspected to assess the data quality with Agilent MassHunter Qualitative Analysis Ver. B.08.00 (Agilent Technologies, Santa Clara, CA, United States). Spectral de-convolution was performed with the Agilent Unknown Analysis tool (Ver. B.08.00. Agilent Technologies, Santa Clara, CA, United States). Alignment of the retention time drift was performed with Mass Profiler Professional ver. B.12.1 (Agilent Technologies, Santa Clara, CA, United States) software. Assignment of the target ion and the qualifiers, entire batch pre-processing, and manual inspection of the data, including the peak area and RT integration, were performed using Agilent MassHunter Quantitative Analysis (Ver. B.08.00, Agilent Technologies, Santa Clara, CA, United States). Compound identification was performed with the NIST (National Institute of Standards and Technology, Gaithersburg, MD, United States) mass spectra library (Ver. 2017). Raw data have been cleared of unrelated features and contaminants.
5
Automated Spectral Data Analysis Pipeline
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LC-MS and CE-MS data were cleaned from background noises and unrelated ions by recursive analysis in Mass Hunter Profinder (B.08.00, Agilent Technologies, Santa Clara, CA, USA) software. In the first step, Molecular Feature Extraction (MFE), the algorithm performs chromatographic deconvolution to find all features in the analyzed samples and align across all the selected sample files using mass and retention/migration time. In the second step, MFE results are used to perform recursive feature extraction, where the Find by Ion (FbI) algorithm uses the median mass, median retention/migration time, and composite spectrum calculated from the aligned features to improve reliability. Spectral deconvolution with Agilent Unknown Analysis software (Ver. B.08.00. Agilent Technologies, Santa Clara, CA, USA) was used to extract the data acquired by GC-MS analysis. Alignment of drift (by retention time and mass) and data filtering were performed with the Mass Profiler Professional ver. B.12.1 (Agilent Technologies, Santa Clara, CA, USA) software. Assignment of the target ion and the qualifiers, entire batch pre-processing and manual inspection of the acquired data including peak area and RT integration was performed with Agilent MassHunter Quantitative Analysis (Ver. B.08.00, Agilent Technologies, Santa Clara, CA, USA).
6
Metabolite Quantification in Cardiac and Brain Tissues
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Cardiac and brain levels of glucose‐6‐phosphate (G6P), fructose‐6‐phosphate (F6P), fructose‐1,6‐bisphosphate (F1,6‐P2), glutamine and UDP‐GlcNAc were determined using a previously validated LC‐MS method.17 MS signals were extracted using the software Agilent MassHunter Quantitative Analysis (B.07.00 version; Agilent Technologies; Santa Clara, USA).
7
Metabolomic Analysis of Biological Samples
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Peak finding software AMDIS (https://chemdata.nist.gov), Agilent MassHunter Qualitative Analysis and Agilent MassHunter Quantitative Analysis (Agilent Technologies Inc., Santa Clara, CA, USA) were used for analysis of the raw data. The data were normalised to the internal standard (ISTD) response and sample weight. For primary metabolite identification, the NIST Standard Reference Database 78 (version 5.8, https://dx.doi.org/10.18434/T4W30F) and an in-house mass spectral database were used, while the MS-DIAL lipid database was used for lipid identification (Tsugawa et al., 2020) (link). The online tool MetaboAnalyst (Metaboanalyst.ca, 2020) was then utilised for statistical analysis of the normalised raw data and generation of PCA plots, t -Test and One-Way Analysis of Variance (ANOVA) was applied to the data. The ANOVA was used with a false discovery rate (FDR)-adjusted p value of 0.05 and using the Benjamin Hochberg method (1995) to determine the metabolites and lipids that changed significantly in comparison to the control. The comparisons between treatment and control for metabolites and lipids are presented as log 2 -transformed fold change values. The log 2 -fold changes of primary metabolites and lipids were calculated and plotted using stats R package and using the ggplot2 package.
8
Data Processing of Multiomics Analyses
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Liquid chromatography–mass spectrometry and CE–MS data processing were performed in MassHunter Profinder software version B.06.00 using Molecular Feature Extraction and Recursive Feature Extraction tools. For GC–MS data treatment, deconvolution and identification were carried out using Agilent MassHunter Unknowns Analysis version B.07.00. Data obtained in the Unknowns Analysis Tool were aligned in Agilent Mass Profiler Professional version B.12.1 and exported to Agilent MassHunter Quantitative Analysis version B.07.00 to obtain the abundance of the compounds. Data matrix obtained from Profinder (CE–MS and LC–MS data) and Quantitative analysis (GC–MS data) were exported to Excel Software (Microsoft Office 2013) for data filtration. As the QC surrogate was used, features were filtered by selecting the data present in at least 80% of the samples in 1 of the 2 groups: cachexia and controls (Appendix S1 ).
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