Tracefinder software

Manufactured by Thermo Fisher Scientific

Sourced in United States, United Kingdom, Germany

TraceFinder software is a data acquisition, processing, and reporting solution for high-performance liquid chromatography (HPLC) and gas chromatography (GC) systems. It provides a comprehensive platform for qualitative and quantitative analysis of samples.

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TraceFinder (72 citations) TraceFinder software version 4.1 (6 citations) TraceFinderTM software version 3.3 sp1 (4 citations) Tracefinder Software 4.1 (3 citations) Thermo TraceFinder software (3 citations) TraceFinder 4.1 EFS Software (2 citations) TraceFinder software version 5.1 (2 citations)

71 protocols using tracefinder software

Intracellular Amino Acid and Metabolite Analysis

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Analysis of intracellular amino acids was performed by liquid chromatography (HPLC U3000, Dionex, Sunnyvale, CA, USA) coupled with a LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a heated ESI probe. MS analyses were performed in the positive FTMS mode at a resolution of 60,000 (at m/z 400). Analysis of intracellular central metabolites was performed by high performance anion exchange chromatography (Dionex ICS 5000+ system, Sunnyvale, USA) coupled with a LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a heated ESI probe. Samples were analyzed in the negative FTMS mode at a resolution of 60,000 (at m/z 400). Isotopic cluster of each amino acids and central metabolites was determined by extracting and integrating the exact mass of all 13C-isotopologues with Tracefinder software (Thermo Fisher Scientific). Isotopic cluster of each amino acids and central metabolites was determined by extracting and integrating the exact mass of all 13C-isotopologues with Tracefinder software (Thermo Fisher Scientific). The correction was performed with IsoCor adapted for higt resolution mass spectrometry. Carbon isotopolog distributions were expressed relative to the sum of all analyzed isotopologs.

Gentric G., Kieffer Y., Mieulet V., Goundiam O., Bonneau C., Nemati F., Hurbain I., Raposo G., Popova T., Stern M.H., Lallemand-Breitenbach V., Müller S., Cañeque T., Rodriguez R., Vincent-Salomon A., de Thé H., Rossignol R, & Mechta-Grigoriou F. (2019). PML-Regulated Mitochondrial Metabolism Enhances Chemosensitivity in Human Ovarian Cancers. Cell Metabolism, 29(1), 156-173.e10.

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Metabolomics Profiling using Mass Spectrometry

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According to the recommendation of the Metabolomics Standardization Initiative (MSI) (Sumner et al., 2007 (link)), first-level annotation required chromatographic retention time, primary mass spectrometry and secondary mass spectrometry information, which was consistent with the standards. At the second level, the polar metabolites were structurally annotated by searching against local databases, mzCloud library (Thermo Scientific, United States), Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Human Metabolome Database (HMDB). On the other hand, untargeted lipid data were processed with LipidSearch (Thermo Scientific, United States) software, including peak picking and lipid identification. For metabolite identification or structural annotation, accuracy of the mass of a precursor within ±10 ppm was a prerequisite. The AUC values were extracted as relative quantification information of polar metabolites and lipids with TraceFinder software (Thermo Scientific, United States). Regarding targeted bile acid detection, internal calibration was conducted with Analyst software and OS-MQ software (AB SCIEX, Singapore) for quantitative analysis of bile acids.

Deng D., Pan C., Wu Z., Sun Y., Liu C., Xiang H., Yin P, & Shang D. (2021). An Integrated Metabolomic Study of Osteoporosis: Discovery and Quantification of Hyocholic Acids as Candidate Markers. Frontiers in Pharmacology, 12, 725341.

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Polar Metabolite Identification and Quantification

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There are secondary annotations that need to be paid attention to according to the recommendations of the Metabolomics Standardization Initiative (MSI) (10 (link)). First, the chromatographic retention time and primary and secondary mass spectrometry information should be consistent with the standard. The second is to annotate the structure of polar metabolites by searching against a local library created using authentic standards as well as mzCloud library (Thermo Fisher Scientific, San Jose, CA). In addition, m/z of MS1 spectra was searched against a local HMDB metabolite chemical database (11 (link)). Mass accuracy of precursor within ±5 ppm was a prerequisite for metabolite identification or structural annotation. The area under curve values as extracted as quantitative information of polar metabolites with TraceFinder software (Thermo Fisher Scientific).

Liu J., Zhou Y., Liu H., Ma M., Wang F., Liu C., Yuan Q., Wang H., Hou X, & Yin P. (2022). Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner. Frontiers in Oncology, 12, 939606.

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Metabolomic Profiling of Cell Lines

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Metabolites were extracted and analysed as described previously^{57 (link)}. Briefly, cells were washed twice with ice-cold PBS before LC-MS extraction solution (5:3:2 methanol:acetonitrile:H₂O) was added at 1 ml per well of a six-well plate. Plates were incubated for 5 min at 4 °C. Next, extraction solution was transferred to microcentrifuge tubes and centrifuged at 16,000g for 10 min at 4 °C. Supernatants were transferred to glass HPLC vials and stored at −80 °C until analysis LC-MS analysis. For HPLC-MS analysis a ZIC-pHILIC column (SeQuant, VWR) was used and Exactive and Q-Exactive mass spectrometers (Thermo Fisher Scientific) were operated with electrospray (ESI) ionisation and polarity switching mode at scan range (m/z) 75–1000 at a resolution of 25,000 at 200 m/z. Data were acquired using Xcalibur software (Thermo Fisher Scientific) and peak areas of metabolites were determined using TraceFinder software (Thermo Fisher Scientific) by identifying metabolites using mass and known retention time following in-house analysis of commercial standards on our systems. Metabolites were normalised to cellular protein content by Lowry assay performed on extracted cell monolayers.

van der Reest J., Lilla S., Zheng L., Zanivan S, & Gottlieb E. (2018). Proteome-wide analysis of cysteine oxidation reveals metabolic sensitivity to redox stress. Nature Communications, 9, 1581.

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Metabolic Profiling of Plasma Samples

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Fasting plasma samples were collected at baseline before the implementation of dietary interventions and stored at −80 °C. Samples were shipped on dry ice to the Broad Institute (Boston, Massachusetts, USA) for metabolomics analyses. Liquid chromatography-tandem mass spectrometry was used to semi-quantitatively profile succinate, malate, citrate, aconitate, isocitrate and D/L-2-hydroxyglutarate (the method does not distinguish the enantiomers, that is, the D- and L-isomers co-elute) [17 (link)] on a system composed of a Shimadzu Nexera X2 U-HPLC (Shimadzu Corp.; Marlborough, MA) coupled to a Q Exactive hybrid quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific; Waltham, MA). Metabolite identities were confirmed using authentic reference standards. Raw data were processed via TraceFinder software (Thermo Fisher Scientific; Waltham, MA). Internal standard peak areas were monitored for quality control and to ensure system performance throughout analyses. Pooled plasma reference samples were also inserted every twenty samples as an additional quality control. Information about the mass to charge ratio and retention time is shown in Supplementary Table S1.

Bulló M., Papandreou C., García-Gavilán J., Ruiz-Canela M., Li J., Guasch-Ferré M., Toledo E., Clish C., Corella D., Estruch R., Ros E., Fitó M., Lee C.H., Pierce K., Razquin C., Arós F., Serra-Majem L., Liang L., Martínez-González M.A., Hu F.B, & Salas-Salvadó J. (2021). Tricarboxylic acid cycle related-metabolites and risk of atrial fibrillation and heart failure. Metabolism: clinical and experimental, 125, 154915.

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Comprehensive Ginsenoside Database Construction

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More than 100 ginsenosides have been identified since their first description in the 1960s by Shibata's group [23 (link)]. We used ginsenoside as a keyword to search in the PubChem Compound database (https://www.ncbi.nlm.nih.gov/pccompound/) and found 161 records containing chemical structure information. Then, we established a new database containing 161 ginsenosides according to structure information originating from reference compounds and PubChem records using TraceFinder software (Thermo Fisher Scientific Inc. version 2.0).

Li Y., Yang B., Guo W., Zhang P., Zhang J., Zhao J., Wang Q., Zhang W., Zhang X, & Kong D. (2022). Classification of three types of ginseng samples based on ginsenoside profiles: appropriate data normalization improves the efficiency of multivariate analysis. Heliyon, 8(12), e12044.

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HILIC-LCMS Metabolite Profiling

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Samples were diluted 1:2 with CH₃CN and 5 μL of each sample was applied to a HILIC column (Acclaim Mixed-Mode HILIC-1, 3 μm, 2.1 × 150 mm). Metabolites were separated at 30 °C by LC using a DIONEX Ultimate 3000 UPLC system and the following solvents: Solvent A consisting of 5 mM NH₄OAc in CH₃CN/H₂O (5/95, v/v) and solvent B consisting of 5 mM NH₄OAc in CH₃CN/H₂O (95/5, v/v). The LC gradient program was: 100% solvent B for 1 min, followed by a linear decrease to 40% solvent B within 5 min, then maintaining 40% B for 13 min, then returning to 100% B in 1 min and 5 min 100% solvent B for column equilibration before each injection. The flow rate was maintained at 350 μL/min. The eluent was directed to the hESI source of the Q Exactive mass spectrometer (QE-MS) from 1.85 min to 18.0 min after sample injection (Thermo Fisher Scientific Waltham, MA, USA). The scan range was set to 69.0 to 1000 m/z with a resolution of 70,000 and polarity switching (negative and positive ionisation). Peaks corresponding to the calculated metabolites masses taken from an in-house metabolite library (MIM +/− H⁺ ± 2 mmU) were integrated using TraceFinder software (Thermo Fisher Scientific, Waltham, MA, USA).

Peixoto J., Janaki-Raman S., Schlicker L., Schmitz W., Walz S., Winkelkotte A.M., Herold-Mende C., Soares P., Schulze A, & Lima J. (2021). Integrated Metabolomics and Transcriptomics Analysis of Monolayer and Neurospheres from Established Glioblastoma Cell Lines. Cancers, 13(6), 1327.

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Quantitative Metabolomics in Diverse Tissues

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For the quantitative demo data, we extracted intracellular metabolites from 5 different tissues (brain, liver, kidney, spleen and pancreas) from a ¹³C-tracing experiment with ¹³C-methanol in four different mice [8 (link)]. Metabolite extraction and LC-MS measurement were performed as described in [9 (link)]. Only the unlabelled metabolites are used here, to show the differences between the tissues. Peak areas were extracted using TraceFinder-software (Thermo Fisher), by comparing the mass to charge ratio (m/z) and the retention time against a custom library prepared with authentic standards. The dataset contains 99 metabolites measured for 3 time-points in 4 biological replicates.

Pietzke M, & Vazquez A. (2020). Metabolite AutoPlotter - an application to process and visualise metabolite data in the web browser. Cancer & Metabolism, 8, 15.

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Untargeted Lipidomics Analysis Workflow

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LipidSearch software (v4.1) was conducted to process untargeted lipidomics data including peak picking and lipid identification. The detected MS2 spectrum was searched against in silico predicted spectra of a diverse phospholipid, neutral glycerolipid, sphingolipid, glycosphingolipids, steroids, etc. The mass accuracy for precursor and MS/MS product ions searching were set at 5 ppm and 5 mDa, respectively. TraceFinder software (v4.1; Thermo Scientific, Waltham, MA, USA) was used to extract the area under curve (AUC) of all lipid molecules for lipid quantitative, and then the AUC data were transferred to Excel software (Microsoft, Redmond, WA, USA) for data normalization using a linear regression algorithm.

Du L., Chang T., An B., Liang M., Deng T., Li K., Cao S., Du Y., Gao X., Xu L., Zhang L., Li J, & Gao H. (2022). Transcriptomics and Lipid Metabolomics Analysis of Subcutaneous, Visceral, and Abdominal Adipose Tissues of Beef Cattle. Genes, 14(1), 37.

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Metabolite Profiling by LC-MS

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All extracted samples were then subjected to LC/MS analysis as previously described (Xiao et al., 2020 (link)) using a Luna-amino column (Phenomenex) using an UltiMate-3000 TPLRS LC using a Q-ExactiveTM HF-X mass spectrometer (Thermo). Buffer composition was: mobile phase A (20 mM ammonium acetate and 20 mM ammonium hydroxide in 5:95 v/v acetonitrile/water) and mobile phase B (10 mM ammonium hydroxide in 75:25 v/v acetonitrile/methanol). A 10 min gradient from 10% to 99% mobile phase A was used to separate metabolites. Additional settings can be found as previously described (Xiao et al., 2020 (link)). Peak integrated was performed using TraceFinder software (Thermo Fisher Scientific).

Mills E.L., Harmon C., Jedrychowski M.P., Xiao H., Gruszczyk A.V., Bradshaw G.A., Tran N., Garrity R., Laznik-Bogoslavski D., Szpyt J., Prendeville H., Lynch L., Murphy M.P., Gygi S.P., Spiegelman B.M, & Chouchani E.T. (2021). Cysteine 253 of UCP1 regulates energy expenditure and sex-dependent adipose tissue inflammation. Cell metabolism, 34(1), 140-157.e8.

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