Mass profiler professional

Manufactured by Agilent Technologies

Sourced in United States, Sweden

Mass Profiler Professional is a software application developed by Agilent Technologies for the analysis of mass spectrometry data. It provides tools for processing, visualizing, and interpreting data from various mass spectrometry platforms.

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72 protocols using mass profiler professional

Metabolomics Data Preprocessing Protocol

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Data were pre-processed using MassHunter Qualitative Analysis (MH Qual B.06.00, Agilent Technologies) and Mass Profiler Professional (MPP B.02.00, Agilent Technologies) software as previously reported.^{14 (link), 15 (link)} The resulting data matrix was then filtered through the Mass Profiler Professional software by retaining the features present in 100% of QC samples with a coefficient of variation (relative s.d., RSD) below 30%, and the features present in 100% of the samples of at least one of the groups under study. Finally, the CE data were normalized respect with to the IS (methionine sulphone). See Supplementary Experimental Procedure for further details.

Mastrangelo A., Martos-Moreno G.Á., García A., Barrios V., Rupérez F.J., Chowen J.A., Barbas C, & Argente J. (2016). Insulin resistance in prepubertal obese children correlates with sex-dependent early onset metabolomic alterations. International Journal of Obesity (2005), 40(10), 1494-1502.

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Mass Spectrometric Data Analysis Workflow

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Agilent Mass Hunter Qualitative Analysis software (Agilent Technologies, CA, USA) was used to process the data collected by Agilent Mass Hunter Workstation Data Acquisition software. Mass Hunter workstation software made use of the molecular feature extractor algorithm for data mining which was set at the mass of 15 ppm and retention time alignment of 0.2 minutes. The absolute height parameter of abundance was set at 200 to remove noise. Data was then processed using ‘Find by Formula’ algorithm parameters with charge states limited to 1 and absolute height >3000 counts. For positive ionization mode, the adducts used were H⁺ and Na⁺, while H- was used for negative ionization mode. The data was then converted from (.d) files) to (.cef) file using DA Reprocessor (Agilent Technologies, CA) software and transferred into Mass Profiler Professional (MPP) (Agilent Technologies, CA) software for analysis. The data were subjected to normalization, filtration, and recursion analysis.

Amir Hashim N.A., Ab-Rahim S., Wan Ngah W.Z., Nathan S., Ab Mutalib N.S., Sagap I., A. Jamal A.R, & Mazlan M. (2020). Global metabolomics profiling of colorectal cancer in Malaysian patients. BioImpacts : BI, 11(1), 33-43.

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Preprocessed Lipidomics Data Analysis

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Data preprocessing included filter entities, normalization of abundance lipid matrix, and exploratory analyses. Mass Hunter Qualitative results (.cef file) were imported into Mass Profiler Professional (MPP) (Agilent Technologies) for statistical analysis, where separate experiments were created for positive and negative-ion modes. Entities were filtered based on frequency, selecting those consistently present in all replicates of at least one treatment. A percentile shift normalization algorithm (75%) was used, and datasets were baselined to the median of all samples. The median of their abundance values was calculated when duplicated lipids with different retention times were present. Data normalization was followed by exploratory analysis using cluster analysis, principal component analysis (PCA), and box and whisker plots by samples and lipids to detect abundance patterns between samples and lipids and batch effects anomalous behavior in the data. At this point, samples behaving in an anomalous manner and outliers (values that lie over 1.5 × interquartile range (IQR) below the first quartile (Q1) or above the third quartile (Q3) in the data set) were excluded for presenting a robust batch effect with a critical impact on differential abundance analysis.

Perpiñá-Clérigues C., Mellado S., Català-Senent J.F., Ibáñez F., Costa P., Marcos M., Guerri C., García-García F, & Pascual M. (2023). Lipidomic landscape of circulating extracellular vesicles isolated from adolescents exposed to ethanol intoxication: a sex difference study. Biology of Sex Differences, 14, 22.

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Metabolomic Analysis of Osteoarthritis

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Peak identification and peak area normalization were performed on all data using MS-DIAL and Mass Profiler Professional (MPP, Agilent Technologies, Santa Ckara, CA, USA) software. The data were chromatographed for peak identification and matching as well as peak area normalization. The data were stored in files and imported into the SIMCA-P11.0 software package (Umetrics, Umea, Sweden) and multidimensional statistical analysis was performed using unsupervised PCA. In order to strengthen the differences between the OA group and the control group, a supervised pattern recognition method was further used for multidimensional statistical analysis, and OPLS-DA was established for the analysis.

Zhao J., Liu M., Shi T., Gao M., Lv Y., Zhao Y., Li J., Zhang M., Zhang H., Guan F., He K, & Chen L. (2021). Analysis of Serum Metabolomics in Rats with Osteoarthritis by Mass Spectrometry. Molecules, 26(23), 7181.

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Untargeted and Targeted Metabolite Profiling

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Data was processed according to the guideline provided by the Class Prediction with Agilent Mass Profiler Professional (Manual part number: 5991-1911EN). The steps included MassHunter Qualitative Analysis and Mass Hunter Profinder to identify untargeted and targeted (recursive) features in the sample data files. This analysis included extraction of molecular features, subtraction of background noise, filtering of data, statistical analysis, database search, and alignment. A compound exchange format file (.CEF) was created for each sample, and downstream analysis was performed using Mass Profiler Professional (MPP) (Agilent Technologies, Santa Clara, CA USA). METLIN Personal Metabolitedatabase^{28 (link)} was then used to identify endogenous and exogenous metabolites.

Yusof H.M., Ab-Rahim S., Wan Ngah W.Z., Nathan S., A Jamal A.R, & Mazlan M. (2020). Metabolomic characterization of colorectal cancer cell lines highlighting stage-specific alterations during cancer progression. BioImpacts : BI, 11(2), 147-156.

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Comprehensive GC-MS Data Processing

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Data pre-processing procedures were conducted according to Arcena, et al. [56 (link)]. Briefly, after obtaining the total ion chromatogram for each sample treatment (Figures S1 and S2), the GC-MS data file was pre-processed with the automated mass spectral deconvolution and identification system (AMDIS; version 2.72, build 140.24, Agilent Technologies, Santa Clara, CA, USA) to deconvolute overlapping peaks and filter interferences. After that, the deconvoluted spectrum was further processed with the Mass Profiler Professional (MPP; version 14.9.1, build 1316, Agilent Technologies, Santa Clara, CA, USA) to filter non-reproducible peaks and align them. This was followed by a manual checking and tentative identification of each volatile compound using the following three criteria to improve the confidence of identification: (i) match and reverse match with the NIST spectra library of no less than 90%; (ii) comparison of experimental retention index with RI, according to the literature; and (iii) matching retention time and spectra with authentic standards from different chemical groups of detected volatiles.

Khrisanapant P., Kebede B., Leong S.Y, & Oey I. (2022). Effects of Hydrothermal Processing on Volatile and Fatty Acids Profile of Cowpeas (Vigna unguiculata), Chickpeas (Cicer arietinum) and Kidney Beans (Phaseolus vulgaris). Molecules, 27(23), 8204.

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Metabolomics Data Analysis Pipeline

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The raw data from the GC analysis were deconvoluted and processed by Mass Profiler Professional (MPP; Version 12.1, Agilent, Technologies, Santa Clara, CA, USA). Compounds with a minimum abundance of 70% in all samples of one treatment were subjected to statistical analysis, which included one-way ANOVA followed by Tukey’s honestly significant difference (HSD) post hoc test (p < 0.01; fold change ≥ 2). The UPLC-ToF/MS data were subjected to the recursive workflow by Mass Hunter qualitative analysis and MPP, including peak picking, alignment of detected features, integration, and peak area calculation.
Each sample was normalized to the median of the baseline and log2 transformed. Compounds with a minimum abundance of 70% in all samples of one treatment were subjected to statistical analysis. One-way ANOVA (p < 0.01; fold change ≥ 2) followed by Tukey’s honestly significant difference (HSD) post hoc test (p < 0.01; fold change ≥ 2) was performed to identify the significantly different features. Differential compounds were tentatively identified using Mass Hunter Metlin PCD (version 4.0, 24768 compounds). Multivariate data analyses were carried out by MPP using the dataset identifying significant differentially abundant compounds.

Shi J., Wang J., Lv H., Peng Q., Schreiner M., Baldermann S, & Lin Z. (2021). Integrated proteomic and metabolomic analyses reveal the importance of aroma precursor accumulation and storage in methyl jasmonate-primed tea leaves. Horticulture Research, 8, 95.

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Lipidomics and Metabolomics Data Analysis

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Regarding to the lipidomics data analysis, data were processed by the LipidMatch suite [26], which uses MZmine 2 for feature extraction and an R script for lipid identification. Lipids were identified based on fragmentation spectra and accurate mass in silico libraries, which are part of the LipidMatch suite. Fragmentation spectra of the significant compounds from metabolomics were also compared to those present in METLIN and LIPIDMAPS databases, and their identities were confirmed. In case of metabolomics data, the data were processed by Profinder software (Agilent Technologies, Santa Clara, CA, USA) and MZmine 2.
The data matrices obtained from lipidomics and metabolomics data processing were imported to SIMCA for multivariate analysis and Mass Profiler Professional (MPP, Agilent technologies for univariate analysis and metabolite profiling. Logarithmic transformation and pareto scaling were used to pre-process the data. Two-way ANOVA (corrected p-value cut-off: 0.05; p-value computation: Asymptotic; Multiple Testing Correction: Benjamini-Hochberg) analysis was applied to the data matrix to filter significant entities affected by diet group and sampling point factors.

Cipryan L., Kosek V., García C.J., Dostal T., Bechynska K., Hajslova J, & Hofmann P. (2023). A lipidomic and metabolomic signature of a very low-carbohydrate high-fat diet and high-intensity interval training: an additional analysis of a randomized controlled clinical trial. Metabolomics, 20(1), 10.

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Metabolic Profiling Analysis Pipeline

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The acquired chromatographic and spectral data were processed by the Mass Profinder (MP) and Mass Profiler Professional (MPP) software (Agilent, Santa Clara, CA, USA), which generated a data matrix (containing peak areas, unique RT and m/z). Then all data were introduced to the SIMCA-P+13.0 software (Umetrics, Kinnelon, NJ) for PCA and OPLS-DA (p [CV-ANOVA] < 0.05). Different altered metabolites were displayed in the form of the score plot and loading plot. All lipids were matched by two databases, the Human Matabolome Database (HMDB, http://www.hmdb.ca/) and the Lipid Maps (http://www.lipidmaps.org/) with ppm <10.0. Significance was determined as VIP ≥ 1, p(corr)[1] < −0.8 or p(corr)[1] > 0.8, P < 0.05.
All data were expressed as the mean ± SEM. Differences among multiple groups were tested using one-way ANOVA followed by Dunnett’s post hoc comparisons. Comparisons between 3T3-L1-C and 3T3-L1-Cel as well as Normal-C and Normal-Cel groups in two mice lines were tested by Student’s t-test using SPSS 17.0 (IBM, Beijing, China). P-value of less than 0.05 was considered to be significant.

Zhang T., Zhao Q., Xiao X., Yang R., Hu D., Zhu X., Gonzalez F.J, & Li F. (2019). Modulation of Lipid Metabolism by Celastrol. Journal of proteome research, 18(3), 1133-1144.

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Metabolomics-based Phylogenetic Analysis of Citrus Germplasms

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The PCA diagram was drawn using the Mass Profiler Professional (MPP, B.02.01, Agilent), and the analysis parameters were the same as previously described^{32 (link)}. HCA was performed using R software to study the accumulation patterns and metabolic diversities of metabolites.
The metabolite data containing the 117 relative intensities of metabolites from 62 Citrus germplasms. The phylogenetic tree was built from the perspective of metabolomics using pairwise population distance by PHYLIP (version 3.69), and its visualization was performed using TreeView and MEGA5.

Wang S., Yang C., Tu H., Zhou J., Liu X., Cheng Y., Luo J., Deng X., Zhang H, & Xu J. (2017). Characterization and Metabolic Diversity of Flavonoids in Citrus Species. Scientific Reports, 7, 10549.

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