Multiquant software

Manufactured by Thermo Fisher Scientific

Sourced in United States

MultiQuant software is a data processing and analysis tool developed by Thermo Fisher Scientific. The software is designed to streamline the quantitative analysis of mass spectrometry data, providing users with a comprehensive suite of tools for processing, reviewing, and reporting analytical results.

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Lab products found in correlation

Analyst 1, by Thermo Fisher Scientific (2 mentions) Colorimetric assay kit, by Cayman Chemical (1 mentions) Sciex 4000 qtrap mass spectrometer, by AB Sciex (1 mentions) Agilent 1200 hplc, by Agilent Technologies (1 mentions) Api 4000 q trap system, by Thermo Fisher Scientific (1 mentions) Seppac c18, by Waters Corporation (1 mentions) Aquity uplc, by Waters Corporation (1 mentions) Beh c18, by Waters Corporation (1 mentions) Qtrap 6500, by AB Sciex (1 mentions) 4000 qtrap mass spectrometer, by Thermo Fisher Scientific (1 mentions)

5 protocols using multiquant software

Lipidomics of ASC Adipocytes

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Total TG levels were measured using a colorimetric assay kit (Cayman Chemicals). Protein concentration was used to normalize intracellular TG content. For lipidomics, after 5 days of transgene expression ASC adipocytes were washed with fresh medium. Ice-cold isopropanol was added, and cells were scraped and collected into cold tubes. Extracts were incubated for 1 hr at 4°C, than vortexed and centrifuged at 2,300 × g for 10 min. The supernatant was used for mass spectroscopic analysis. All data were acquired using a Sciex 4000 QTRAP mass spectrometer as previously described (Rhee et al., 2011 (link)). MultiQuant software (version 1.1; Applied Biosystems/Sciex) was used for automated peak integration, and peaks were manually reviewed for quality of integration. Internal standard peak areas were monitored for quality control and used to normalize analyte peak areas.
For the transduced ASCs we averaged six replicates for metabolite abundance of 44 TG species and computed an abundance ratio of the two (IRF1/rtTA). We built a regression model, predicting the abundance ratio using number of double bonds and total number of carbon atoms as variables as well as an interaction term to assess whether the relationship with number of double bonds varies with carbon number. Statistical significance of regression coefficients was assessed using the F test.

Friesen M., Camahort R., Lee Y.K., Xia F., Gerszten R.E., Rhee E.P., Deo R.C, & Cowan C.A. (2017). Activation of IRF1 in Human Adipocytes Leads to Phenotypes Associated with Metabolic Disease. Stem Cell Reports, 8(5), 1164-1173.

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HPLC-MS/MS Analysis of AAI and ALI

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Agilent 1200 HPLC (Agilent Technologies, Santa Clara, CA, USA) was used. Kinetex-C 18 110A column (3 × 30 mm, 2.6 μm, Phenomenex) was operated at 30 °C and the flow rate was set at 0.8 mL/min for separation of AAΙ and ALΙ. The mobile phase consisted of (A) water containing of 10 mmol/L ammonium acetate and (B) acetonitrile. Gradient conditions were as follows: 0–0.5 min, 10% B; 0.5–0.8 min, 10–95% B; 0.8–2.5 min, 95% B. A 10 μL aliquot of each sample was injected. All samples were kept at 4 °C throughout the analysis.
MS was performed on an API 4000 Qtrap system (Applied Biosystems, Foster City, CA, USA). Electrospray ionization (ESI) was performed in the positive ion mode. Curtain gas (CUR), nebulizer gas (GS1), and turbo-gas (GS2) were set at 15 psi, 60 psi, and 60 psi, respectively. The ionspray voltage was 5.0 kV, and the temperature was 550 °C. Nitrogen was employed as the collision gas. AAΙ and ALΙ were analyzed using the scheduled MRM. Data acquisitions were performed using the Analyst 1.5.2 software (Applied Biosystems). Multiquant software (Applied Biosystems) was used to quantify AAΙ and ALΙ.

Zhou J., Yang Y., Wang H., Bian B., Yang J., Wei X., Zhou Y., Si N, & Zhao H. (2019). The Disturbance of Hepatic and Serous Lipids in Aristolochic Acid Ι Induced Rats for Hepatotoxicity Using Lipidomics Approach. Molecules, 24(20), 3745.

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Lipid Extraction and Quantification

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Lipids were extracted and isolated as described previously (Zhang et al. 2018a (link)). Briefly, samples were purified through an SPE column (500 mg × 6 mL Sep-Pac C18; Waters, Milford, MA, USA). The targeting components were next dried under N₂ and residues were reconstituted utilizing a methanol and water mixture (v/v, 1:1). A final 10 μL aliquot of each sample was introduced into an AQUITY UPLC (Waters) (BEH-C18 2.1 × 100 mm, 2.1 × 50 mm, 1.7 μm, Waters)-AB SCIEX QTRAP 6500 system (ESI mode). Moreover, the column, gradient program, and multiple reaction monitoring (MRM) were optimized during the monitoring processes (Zhang et al. 2018a (link)). Analyst® 1.6.2 and MultiQuant™ software (Applied Biosystems/Thermo Fisher Scientific, Foster City, CA, USA) were used to acquire and quantify all lipids.

Zhang Q., Yan G., Lei J., Chen Y., Wang T., Gong J., Zhou Y., Zhao H., Chen H., Zhou Y., Wu L., Zhang J., Zhang X., Wang J, & Li Y. (2020). The SP1-12LOX axis promotes chemoresistance and metastasis of ovarian cancer. Molecular Medicine, 26, 39.

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Rumen Metabolome Analysis by UPLC-MS/MS

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Fifty milligrams of rumen contents were thawed on ice, and 500 µL of 70% methanol internal standard extract was added at 4 °C. After shaking for 3 min, the mixture was left to stand at −20 °C for 30 min, followed by centrifugation at 12,000 r/min for 10 min at 4 °C. Next, 250 µL of the supernatant is centrifuged at 12,000 r/min for 5 min at 4 °C. Next, 150 µL of this supernatant is taken in the liner of the corresponding injection bottle for data acquisition and further analysis. The metabolome of rumen contents was analyzed by ultra-performance liquid chromatography (UPLC) and Tandem mass spectrometry (MS/MS) (QTRAP® 6500+, SCIEX, Framingham, MA, USA) [33 (link)–35 (link)]. After obtaining the LC/MS data of different samples, the extracted ion chromatographic peaks of all metabolites were integrated using MultiQuant software (Applied Biosystems, Foster, MA, USA) and the MetWare database (MWDB) database, respectively. The chromatographic peaks of the metabolites in different samples were corrected by integration [36 –38 (link)]. The relative concentrations of rumen metabolites were screened by FC (FC ≥ 2 and FC ≤ 0.5) and VIP (VIP ≥ 1) to identify the different metabolites. The identified metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) compound database, and the annotated metabolites were then mapped to the KEGG Pathway database [39 (link)].

Shi J., Lei Y., Wu J., Li Z., Zhang X., Jia L., Wang Y., Ma Y., Zhang K., Cheng Q., Zhang Z., Ma Y, & Lei Z. (2023). Antimicrobial peptides act on the rumen microbiome and metabolome affecting the performance of castrated bulls. Journal of Animal Science and Biotechnology, 14, 31.

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Metabolite Profiling by LC-MS

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Metabolites were extracted in ice-cold methanol. Endogenous metabolite profiles were obtained using two liquid chromatography-tandem mass spectrometry (LC-MS) methods as described [36 (link)]. Data were acquired using a 4000 QTRAP mass spectrometer (Applied Biosystems/MDS Sciex, Nutley, NJ, USA). Multi-Quant software (Applied Biosystems/MDS Sciex, Nutley, NJ, USA) was used for analysis. Metabolite levels were normalized to protein content [37 (link)].

Pang B., Zheng X.R., Tian J.X., Gao T.H., Gu G.Y., Zhang R., Fu Y.B., Pang Q., Li X.G, & Liu Q. (2016). EZH2 promotes metabolic reprogramming in glioblastomas through epigenetic repression of EAF2-HIF1α signaling. Oncotarget, 7(29), 45134-45143.

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