Multiquant

Manufactured by AB Sciex

Sourced in United States, Canada, Germany

MultiQuant is a software application designed for the quantitative analysis of mass spectrometry data. It provides a user-friendly interface for processing and analyzing complex data sets, enabling researchers to obtain accurate and reliable results.

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Lab products found in correlation

Qtrap 5500, by AB Sciex (11 mentions) Analyst 1, by AB Sciex (6 mentions) Peakview, by AB Sciex (6 mentions) Qtrap 5500 mass spectrometer, by AB Sciex (3 mentions) Tripletof 5600, by AB Sciex (3 mentions) Qtrap 6500, by AB Sciex (3 mentions) Acquity uplc system, by Waters Corporation (3 mentions) Analysttm1, by AB Sciex (3 mentions) Analyst tf, by AB Sciex (3 mentions) Analyst software, by AB Sciex (3 mentions) Peakview workstation, by AB Sciex (2 mentions) Zorbax eclipse plus c18 column, by Agilent Technologies (2 mentions) Compound discoverer, by Thermo Fisher Scientific (2 mentions) Zic hilic column, by Merck Group (1 mentions) Bond elut plexa, by Agilent Technologies (1 mentions) Qtrap5500 mass analyzer, by AB Sciex (1 mentions) Luna c18 column, by Phenomenex (1 mentions) Quantitative analysis software, by Agilent Technologies (1 mentions) Spss statistics 22, by IBM (1 mentions) Luna c8 column, by Phenomenex (1 mentions)

Spelling variants of this product

MultiQuant 2.1 software (77 citations) MultiQuant version 3.0.2 (16 citations) MultiQuant v3.0 (15 citations) MultiQuant® Version 2.1.1 (11 citations) MultiQuant software version 3.0.2 (9 citations) MultiQuant program (3 citations) MultiQuant® software (Version 3.0.2) (2 citations) MultiQuant v1.1 software (2 citations) MultiQuant Software V 3.0 (2 citations) Multiquant software (v3.0.2) (2 citations)

84 protocols using multiquant

Metabolome Profiling and Quantification

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Based on the self-built database MWDB V2.0 (Metware Biotechnology Co., Ltd. Wuhan, China) and public databases, such as MassBank (http://www.massbank.jp), KNApSAcK (http://kanaya.naist.jp/KNApSAcK), HMDB (Human Metabolome Database, http://www.hmdb.ca), and METLIN (Metabolite Link, http://metlin.scripps.edu/index.php), metabolite information of samples was matched with subjected existing mass spectrometry databases to qualitative analysis. The matching parameters, including Q1 precise molecular mass, secondary fragmentation, retention time and isotope peak were used in the intelligent matching method explored by Metware. In addition, MS1 tolerance and MS2 tolerance were set to 20 ppm and 20ppm to ensure that the metabolites could be identified accurately.
Metabolite quantification was performed by MRM of triple quadrupole mass spectrometry. In the MRM mode, the quadrupole filtered the precursor ions of the target substance and excluded the ions corresponding to other molecular weights to eliminate interference. After obtaining the metabolite mass spectrometry data, peak area integration was performed using Multi Quant (version 3.0.2, AB SCIEX, Concord, ON, Canada). Chromatographic peak area was used to determine the relative metabolite contents. The original abundance of metabolites was log-transformed to normalize the data and for homogeneity of variance.

Liu X., Chen Y., Zhang J., He Y., Ya H., Gao K., Yang H., Xie W, & Li L. (2022). Widely targeted metabolomics reveals stamen petaloid tissue of Paeonia lactiflora Pall. being a potential pharmacological resource. PLoS ONE, 17(9), e0274013.

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Quantifying Serum Apolipoproteins by LC-MS/MS

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The LC‐MS/MS peak areas were measured using MultiQuant (v3.0.2, AB Sciex). A separate (area ratio) versus (mole ratio) calibration curve with 1/x weighing was generated for each peptide MRM.

\begin{matrix} [Area ratio] & = & \frac{[Native peptide peak area]}{[IS peptide peak area]} \\ = & Slope \times \frac{[Mole of native peptide in standard]}{[Mole of IS peptide in standard]} \\ + Intercept \end{matrix}

The response ratios of the 100‐fold diluted unknowns for ApoA‐I were between the second and fourth standards, and for ApoB‐100 between the fourth and seventh standards (first being the highest standard).
Using the calibration curve slope and intercept for each MRM, protein concentrations in the unknown samples were determined by:

\begin{matrix} [Protein concentration] \\ = \frac{[Area ratio for unknown] - Intercept}{Slope} \\ \times [Mole of IS peptide in unknown] \\ \times \frac{1}{[Digested serum volume]} \end{matrix}

All calculated concentrations were exported into JMP (v11, SAS, Cary, NC). The reported ApoA‐I and ApoB‐100 concentrations were calculated as the average of the concentrations derived from corresponding MRMs (five MRMs for ApoA‐I and six MRMs for ApoB‐100).

Parks B.A., Schieltz D.M., Andrews M.L., Gardner M.S., Rees J.C., Toth C.A., Jones J.I., McWilliams L.G., Kuklenyik Z., Pirkle J.L, & Barr J.R. (2017). High throughput quantification of apolipoproteins A‐I and B‐100 by isotope dilution MS targeting fast trypsin releasable peptides without reduction and alkylation. Proteomics. Clinical Applications, 11(7-8), 1600128.

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Quantification of Metabolite Profiles

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Chromatographic separations of metabolites in supernatants and cell extracts were conducted on a ZIC-HILIC column (150 x 4.6 mm, 5 μm) (Merck, Darmstadt, Germany). A QTRAP 5500 (AB Sciex, Framingham, MA, USA) triple-quadrupole tandem mass spectrometer with Turbo V spray electron spray interface was used in positive ion mode for detection. Analyst and MultiQuant software (AB Sciex, Framingham, MA, USA) were used for data acquisition and data processing. Limit of quantification was defined as signal/noise ratio > 9. Acylcarnitine measurements were normalised to the protein amount per well, data of all single measurements are given in S1A–S1D Table.

Matejka K., Stückler F., Salomon M., Ensenauer R., Reischl E., Hoerburger L., Grallert H., Kastenmüller G., Peters A., Daniel H., Krumsiek J., Theis F.J., Hauner H, & Laumen H. (2019). Dynamic modelling of an ACADS genotype in fatty acid oxidation – Application of cellular models for the analysis of common genetic variants. PLoS ONE, 14(5), e0216110.

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Metabolic Profiling of IAV-Challenged PBMCs

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PBMCs (5 × 10⁶) from male C57BL/6 mice (8 weeks of age) left untreated or challenged with IAV for 12 hours were harvested. The metabolite extraction was performed as previously described (16 (link), 17 (link)). Briefly, the media were aspirated, and the cells were washed twice before lysing the cells. The metabolites were extracted using cold 80% methanol/water mixture and resuspended in 50% methanol/water mixture for further analysis using LC-MS/MS. A selected reaction monitoring LC-MS/MS method with positive and negative ion polarity switching on a Xevo TQ-S mass spectrometer was used for analysis. Peak areas integrated using MassLynx 4.1 were normalized to the respective protein concentrations. The data acquisition was carried out using Analyst 1.6 software, and peaks were integrated with MultiQuant (AB SCIEX, Framingham, MA, USA). MetaboAnalyst 3.0 software was used for heat map, principal components analysis, and pathway impact analysis.

Wang Q., Fang P., He R., Li M., Yu H., Zhou L., Yi Y., Wang F., Rong Y., Zhang Y., Chen A., Peng N., Lin Y., Lu M., Zhu Y., Peng G., Rao L, & Liu S. (2020). O-GlcNAc transferase promotes influenza A virus–induced cytokine storm by targeting interferon regulatory factor–5. Science Advances, 6(16), eaaz7086.

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Metabolomics Analysis of Methamphetamine Effects

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All data analyses were carried out in R version 3.4.0. Peak areas from positive ion mode, SPE Fraction 2 samples were obtained from MultiQuant (AB SCIEX, Framingham, MA, USA) software. Data for all 5 technical replicates for each condition (control and Meth) of each donor (six total) were combined to give a matrix with columns corresponding to samples and rows corresponding to metabolites. All data were log2 transformed and then normalized by subtracting the area of the internal standard abacavir in each sample. This data was visualized in a heat map presented in Figure 4 using the heatmap.2 function available in the gplots R package ¹⁷. Hierarchical clustering was performed on the matrix rows (metabolites). The peak area values were converted to z-scores to show relative downregulation (blue) and upregulation (red) of metabolites.
For differential expression analyses, we calculated the mean of each metabolite across the technical replicates separated by condition to obtain a matrix of mean peak abundance values. The moderated paired t test within the limma R package was used to determine the differences between control and Meth groups and to identify significantly differentially expressed metabolites ^{18 (link)–19}. The p-values were adjusted using the Benjamini & Hochberg method, and an adjusted p-value of no more than 0.1 was considered to be significant.

Pawlak K., Lech K., Vei A., Burch S., Zieschang S., Jaquet S., Yu F., Harwood E., Morsey B., Fox H.S, & Ciborowski P. (2019). Secreted metabolome of human macrophages exposed to methamphetamine. Analytical chemistry, 91(14), 9190-9197.

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Metabolite Extraction and Quantification Protocol

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Metabolite extraction was performed as previously described [7 (link), 24 (link)]. After confirming 80% confluence of the cells, culture media was replaced with fresh media for 2 hours prior to metabolite extraction. Media was aspirated, and the cells were washed twice with water to remove media remnants before lysing the cells. The polar metabolites were then extracted with a cryogenically cold, 80% methanol/water mixture. Metabolite extracts were analyzed using LC-MS/MS and a single reaction monitoring (SRM) method by utilizing AB SCIEX 5500 QTRAP® (Framingham, MA), as described previously [24 (link)]. Data acquisition was carried out using Analyst™1.6 software (AB SCIEX, Framingham, MA), and peaks were integrated with Multiquant™ (AB SCIEX, Framingham, MA). Peak areas were normalized to the respective protein concentrations, and the resultant peak areas were subjected to relative quantification analyses with MetaboAnalyst 3.0 [25 (link)].

Goode G., Gunda V., Chaika N.V., Purohit V., Yu F, & Singh P.K. (2017). MUC1 facilitates metabolomic reprogramming in triple-negative breast cancer. PLoS ONE, 12(5), e0176820.

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Oxylipins Quantification by LC-MS/MS

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Samples were lyophilized and resuspended in 1 ml 10% methanol containing deuterated internal standards (Standards are listed in Suppl. Table 1) followed by an extraction using solid reverse phase extraction columns (Bond Elut Plexa, Agilent). Fatty acid derivatives were eluted into 1.0 ml of methanol, lyophilized and resuspended in 100 µl of water/acetonitrile/formic acid (70:30:0.02, v/v/v; solvent A) and analysed by LC-MS/MS on an Agilent 1290 separation system. Samples were separated on a Synergi Hydro reverse-phase C18 column (2.1 × 250 mm; Phenomenex) using a gradient as follows: flow rate 0.3 µl/min, 1 min (0% solvent B: acetonitrile/isopropyl alcohol, 50:50, v/v;), 3 min (25% solvent B), 11 min (45% solvent B), 13 min (60% solvent B), 18 min (75% solvent B), 18.5 min (90% solvent B), 20 min (90% solvent B), 21 min (0% solvent B). The separation system was coupled to an electrospray interface of a QTrap 5500 mass spectrometer (AB Sciex). Compounds were detected in scheduled multiple reaction monitoring mode. For quantification a 12-point calibration curve for each analyte was used. Data analysis was performed using Analyst (v1.6.1) and MultiQuant (v2.1.1) (AB Sciex, Switzerland). Oxylipins measured are detailed in Suppl. Tables 2–3.

Banhos Danneskiold-Samsøe N., Sonne S.B., Larsen J.M., Hansen A.N., Fjære E., Isidor M.S., Petersen S., Henningsen J., Severi I., Sartini L., Schober Y., Wolf J., Nockher W.A., Wolfrum C., Cinti S., Sina C., Hansen J.B., Madsen L., Brix S, & Kristiansen K. (2019). Overexpression of cyclooxygenase-2 in adipocytes reduces fat accumulation in inguinal white adipose tissue and hepatic steatosis in high-fat fed mice. Scientific Reports, 9, 8979.

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Targeted Metabolomics of Endometrial Samples

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Metabolites extracted from endometrial tissue samples and Ishikawa cells were reconstituted with 100 µl of acetonitrile:water (v:v 50:50), and 5-µl sample solutions were injected into the UHPLC-MS. The metabolites were acquired using a targeted metabolomics method that was modified from a published protocol (28 (link)). The UHPLC was equipped with an HILIC column (XBridge Amide 3.5 µm, 4.6 × 100 mm) and samples were eluted with a gradient. Two buffers, buffer A (95% water and 5% acetonitrile with 20 mM ammonium hydroxide and 20 mM ammonium acetate, pH 9.0) and buffer B (acetonitrile), were used in the gradient. The total flow of the gradient was 0.25 ml/min, which started from 0 to 0.1 min, 85% B; 3.5 min, 32% B; 12 min, 2% B; 16.5 min, 2% B; and 16–17 min, 85% B. Metabolites in the samples were acquired by a QTRAP 5500+ (AB Sciex) mass spectrometer using targeted MRM methods containing 297 transitions. All transition peaks were integrated on a MultiQuant (AB Sciex) to obtain a metabolomics peak list.

Zheng Y., Zhu Y., Zhuge T., Li B, & Gu C. (2021). Metabolomics Analysis Discovers Estrogen Altering Cell Proliferation via the Pentose Phosphate Pathway in Infertility Patient Endometria. Frontiers in Endocrinology, 12, 791174.

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Lipid Metabolite Profiling by LCMS

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The R programming language (version 4.1.1) was applied for statistical analyses and the generation of graphs. The characteristic ions of each lipid metabolite were processed by multiple reaction monitoring based on the Metware database, and then MultiQuant (version 3.0.3, AB Sciex) software was used to analyse the chromatogram review and peak area integration of the off-board mass spectrometry file of the sample. Each chromate graphic peak represents a lipid metabolite, and the area under the peak corresponds to the relative content. The qualitative and quantitative analysis results of all lipid metabolites were calculated based on the linear equation and calculation formula. The variable importance in projection (VIP) was calculated based on orthogonal partial least squares discriminant analysis (OPLS-DA), and the P value and fold change (FC) of the nonparametric test were used in combination to screen the differential lipid metabolites. Then, the cut offs of VIP ≥ 1, FC > 1.5 or FC < 0.66 and P < 0.05 were used as standards to screen differential lipid metabolites.

Wu F., Wu F., Zhou Q., Liu X., Fei J., Zhang D., Wang W., Tao Y., Lin Y., Lin Q., Pan X., Sun K., Xie F, & Bai L. (2023). A CCL2+DPP4+ subset of mesenchymal stem cells expedites aberrant formation of creeping fat in humans. Nature Communications, 14, 5830.

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Metabolite Extraction and LC-MS/MS Analysis

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The metabolite extraction was performed as previously described (3). Briefly, the media were aspirated, and the cells were washed twice before lysing the cells. The metabolites were extracted using cold 80% methanol/water mixture and resuspended in 50% methanol/water mixture for further analysis using LC-MS/MS. A selected reaction monitoring LC-MS/MS method with positive and negative ion polarity switching on a Xevo TQ-S mass spectrometer was used for analysis. Peak areas integrated using MassLynx 4.1 were normalized to the respective protein concentrations. The data acquisition was carried out using Analyst 1.6 software, and peaks were integrated with MultiQuant (AB SCIEX, Framingham, MA, USA).

He Q.Q., Huang Y., Nie L., Ren S., Xu G., Deng F., Cheng Z., Zuo Q., Zhang L., Cai H., Wang Q., Wang F., Ren H., Yan H., Xu K., Zhou L., Lu M., Lu Z., Zhu Y, & Liu S. (2023). MAVS integrates glucose metabolism and RIG-I-like receptor signaling. Nature Communications, 14, 5343.

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