Data files were deconvoluted, integrated and aligned using MassHunter Profinder B.08.00 (Agilent Technologies Inc.). Peaks with amplitudes of less than 1000 counts were ignored. Compounds must have been present in at least 60% of replicates from one treatment to be included in statistical analyses. Peak areas were normalized to the naphthalene-D8 internal standard. Statistical analysis was performed using GeneSpring MPP (Agilent Technologies, Inc.), where p≤0.05 was used throughout to test for statistical significance. T-tests and ANOVA included the Benjamin-Hochberg false discovery rate corrections. Tukey’s honest significance difference was applied to ANOVAs post hoc. Prior to principal component analysis (PCA), the data was mean centered and scaled to unit standard deviation within each compound, across all samples. Putative compound identification was performed by comparing mass spectra to the NIST 2014 Library and by a comparison of calculated Kovats Indices to reported values, when available.
Masshunter profinder b 08
Manufactured by Agilent Technologies
Sourced in United States
MassHunter Profinder B.08.00 is a software application developed by Agilent Technologies for the processing and analysis of mass spectrometry data. It provides tools for the detection, identification, and quantification of compounds in complex samples.
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13 protocols using masshunter profinder b 08
1
Untargeted Metabolomic Analysis Pipeline
2
UHPLC/Q-TOF-MS Data Preprocessing Protocol
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The raw data obtained by the UHPLC/Q-TOF-MS positive ESI mode were cleaned of background noise and unrelated ions by the Batch Recursive Feature Extraction (BRFE) tool included in the MassHunter Profinder B.08.00 software (Agilent). The BRFE algorithm creates a list of masses and retention times, associated with the abundance of the possible components representing the full TOF masses from the spectral data. Each component is the sum of co-eluting ions that are related by charge-state envelope, isotopic distribution, and/or the presence of different adducts and dimmers. In order to find co-eluting adducts of the same feature, the following adducts were selected: +H, +Na, and +NH4 for UHPLC/Q-TOF-MS data.
Data were filtered according to these criteria: 1) features found in blanks and 2) absent in more than 50% of QCs. Additionally, data with a coefficient of variation (CV) lower than 30% in the QCs were kept.
Data were filtered according to these criteria: 1) features found in blanks and 2) absent in more than 50% of QCs. Additionally, data with a coefficient of variation (CV) lower than 30% in the QCs were kept.
3
Quantifying Oxidative Stress through GC-MS
4
Metabolomic Data Analysis Pipeline
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MassHunter Profinder B.08.00 (Agilent Technologies Inc.) was used to deconvolute, integrate and align the data. Peaks with amplitudes of less than 1000 counts were ignored. Compounds must have been present in at least 60% of replicates from one treatment to be included in statistical analyses. Peak areas were normalized to the internal standard peak area of each data file. Furthermore, a VOC from a bacterial isolate or anal swab sample must have been, on average, three times greater than the respective controls (media blanks, etc.) to be included in this analysis. Tentative compound identification was based on the combined comparing mass spectra to the NIST 2014 Library and by a comparison of the calculated matching of standard alkane retention indices (LRI) values, when available.
5
Comprehensive MS-based Profiling Workflow
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The QC samples were injected in triplicate in the full-scan MS acquiring mode from 100 to 1050 m/z, in the POS and NEG ion modes, to create an inclusion list to be used in the auto MS/MS mode. The data obtained in the MS experiment from the QC samples were extracted using the batch recursive feature extraction algorithm in MassHunter Profinder B.08.00 software (Agilent), then the features were evaluated individually among the replicates to ensure reproducibility and exported as CEF (Cluster Exchange Format) files. Mass Profiler software (Agilent) was used for alignment of features using retention time (RT) tolerance of up to 0.3 min and mass tolerance of ±15 ppm. Features with 100% occurrence in the replicates were used to create a target MS/MS inclusion list.
6
Metabolite Annotation Using Databases
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Raw data were imported into MassHunter Profinder B.08.00 (Agilent Technologies, Santa Clara, CA, USA) for peak deconvolution and time alignment. For the data obtained in positive ionization mode, adducts with H+, Na+, K+, NH4+ were selected as well as neutral loss of water. For negative ionization mode, adducts with H−, HCOO−, Cl− and the loss of a water molecule were considered. All MS signals lower than 200 counts were considered background noise. For peak alignment between samples, a retention time shift of 0.15 min was allowed as well as a 15 ppm error for mass detection. Afterwards, the data were filtered to retain only the features present in all replicates for each extraction method. Moreover, features detected in blank samples were discarded. The metabolites annotation was carried out using online databases such as LIPID MAPS, METLIN, and Human Metabolome Database through the CEU Mass Mediator platform (http://ceumass.eps.uspceu.es/ (accessed on 20 May 2021)) [44 (link)].
7
Quantitative LC-QTOF Analysis of Metabolic Pathway
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The LC-QTOF analysis of l -histidinol phosphate (Hisol-P), l -histidinol (Hisol) l -histidine (His), imidazole-glycerol phosphate (IGP), adenylosuccinate (AdSucc), 1-(5′-phosphoribosyl)-5-amino-4-imidazolecarboxamide (AICAR), phosphoribosyl-N-formylglycineamide (FGAR), inosine monophosphate (IMP), phosphoribosyl-aminoimidazolesuccinocarboxamide (SAICAR), adenosine monophosphate (AMP), and the pentose 5-phosphate pool (R5P) was carried out as previously described [17 (link)]. Automated data analysis with the “Batch Isotopologue Extraction” feature and natural isotopologue background subtraction was carried out in the MassHunter ProFinder (B.08.00, Agilent Technologies, Santa Clara, CA, USA) software. The relative isotopologue abundances (RIA) of the m + 5 isotopologue for the twelve measured metabolites for every time point was calculated according to:
Throughout the analysis, the internal standardl -norvaline was monitored to investigate the possible intensity drifts on a global MS level.
Throughout the analysis, the internal standard
8
Metabolite Identification using Open-Source MS-DIAL
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The putative metabolite identification was performed using an open-source software, MS-DIAL (RIKEN PRIMe). Collected MS/MS data was converted as.abf-files using Analysis Base File Converter program (Reifycs Inc.) and converted files were imported to MS-DIAL (versions 2.66 to 3.12). Public databases, Metlin and MassBank of North America (MoNA), and in-house LC–MS/MSMS standard library were downloaded to MS-DIAL for utilization of retention time, accurate mass, isotope ratio and MS/MS spectrum information for peak and metabolite identification. The built-in MS-DIAL library was utilized for lipid identification. Each matched spectrum was manually inspected. The guidelines from Sumner et al. (2007 (link)) were used for ranking metabolite identifications as follows: Compounds in identification level 1 were verified by comparing exact mass, retention time and MS/MS fragmentation spectra with in-house LC–MS/MSMS standard library. Compounds in level 2 were matched with exact mass and MSMS spectra from public databases mentioned above. MassHunter Profinder B.08.00 software (Agilent Technologies) was applied for targeted feature extraction to minimize the appearance of false negative features implemented with the manual inspection and integration of the targeted feature.
9
Comprehensive Metabolomic Profiling Protocol
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The full-scan analysis was performed in triplicate using the QC samples acquired over the range of 100 to 1050 m/z in the NEG mode to create an inclusion list to further create the auto MS/MS acquisition mode. Data were extracted using batch recursive feature extraction algorithm in MassHunter Profinder B.08.00 software (Agilent) and after evaluation exported as CEF (Cluster Exchange Format) files. The features were aligned on Mass Profiler software (Agilent) using retention time (RT) tolerance of up to 0.3 min and mass tolerance of ± 15 ppm. Features with a 100% occurrence in the replicates were used to create a target MS/MS inclusion list.
10
Data Preprocessing for Metabolomics LC-MS Analysis
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The data collected after LC-MS analyses in both positive and negative ion modes were reprocessed with the Agilent MassHunter Profinder B.08.00 software. The datasets were extracted using the Batch Recursive Feature Extraction (RFE) workflow integrated in the software. This workflow comprises two steps: the Batch Molecular Feature Extraction (MFE) and the Batch Find by Ion feature extraction (FbI). The MFE algorithm consisted of removing unwanted information including the background noise, and then creating a list of possible components that represent the full range of time-offlight (TOF) mass spectral data features, which are the sum of co-eluting ions that are related by charge-state envelope, isotopologue pattern, and/or the presence of different adducts and dimers. Additionally, the MFE is intended to detect coeluting adducts of the same feature, selecting the following adducts:
LC-MS negative ion mode, with neutral loss of water also included. The algorithm then aligns the molecular features across the study samples using the mass and RT to build a single spectrum for each compound group. The next step involves FbI using the median values derived from the MFE process to perform a targeted extraction to improve the reliability of finding and reporting features from complex datasets used for differential analysis [23] .
LC-MS negative ion mode, with neutral loss of water also included. The algorithm then aligns the molecular features across the study samples using the mass and RT to build a single spectrum for each compound group. The next step involves FbI using the median values derived from the MFE process to perform a targeted extraction to improve the reliability of finding and reporting features from complex datasets used for differential analysis [23] .
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