Agilent profinder

Manufactured by Agilent Technologies

Sourced in United States

Agilent Profinder is a software application designed for the analysis and identification of small molecules in complex samples. It provides automated peak detection, molecular feature extraction, and compound identification capabilities to support metabolomics and other small molecule research applications.

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Lab products found in correlation

Agilent 1290 uplc, by Agilent Technologies (1 mentions) Agilent 6545 qtof, by Agilent Technologies (1 mentions) Quantitative analysis software, by Agilent Technologies (1 mentions) Simca p, by Sartorius (1 mentions)

Close spelling variants of this product

Profinder (18 citations) Agilent Profinder software (9 citations) Agilent Profinder B.06.00 (6 citations) Agilent MassHunter Profinder software (4 citations) Agilent MassHunter Profinder (4 citations) Agilent Profinder B.10.00 (2 citations)

10 protocols using agilent profinder

Serum Metabolomic Profiling Protocol

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The metabolomic analysis was based on our previous study (17 (link)). Serum samples (100 μl) were placed in a 1.5-ml centrifuge tube, and 400 μl of methanol–acetonitrile solvent (1:1) was added. After ultrasonic treatment in a water bath at 4°C for 10 min, the supernatant was centrifuged at 12,000 rpm for 15 min after being kept at −20°C for 1 h. The supernatant was removed, and nitrogen was blown in ice bath. The extract was redissolved in 80% methanol and filtered using a 0.22-μm microporous filter (Jinteng Co. Ltd., Tianjin, China). It was then analyzed using an Agilent 6545 Q-TOF LC/MS system (Agilent Technologies Co., Ltd., China). Agilent Profinder was used to correct the retention time, identify, extract, integrate, and align the peak, and finally output in CEF format. Statistical processing was performed using Agilent Massive Parallel. The Human Metabolome Database (HMDB; http://www.hmdb.ca/) and MassBank (http://www.massbank.jp) were used to search for and infer the structures of possible biomarkers. Variables with significant differences between the two groups were assessed using a t-test. CAMERA was used to remove adduct, isotope, or fragment ions and identify metabolites with significant differences.

Ding S., Jiang H., Fang J, & Liu G. (2021). Regulatory Effect of Resveratrol on Inflammation Induced by Lipopolysaccharides via Reprograming Intestinal Microbes and Ameliorating Serum Metabolism Profiles. Frontiers in Immunology, 12, 777159.

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LC-MS Data Processing Pipeline

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Raw LC-MS data was collected using Agilent MassHunter (version B.08.00), and processed using Agilent Profinder (version 10.0). Batch recursive feature extraction was used to deconvolute and align the molecular features in all samples, using a feature extraction cut-off of peak height ≥ 1000 counts, charge state = 0, 1, or 2. For binning and alignment, a retention time tolerance of 0.1 min and a 5 ppm cutoff was set. Samples were normalized to wet weight, and post-processing filters applied to eliminate features with: height < 2000 counts, features in the process blank, features present in < 80% of the QC samples, and features with ≥ 30% relative standard deviation.

Bennouna D., Solano M., Orchard T.S., DeVries A.C., Lustberg M, & Kopec R.E. (2019). The Effects of Doxorubicin-based Chemotherapy and Omega-3 Supplementation on Mouse Brain Lipids. Metabolites, 9(10), 208.

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Quantitative Mass Spectrometry Analysis

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Quality control samples were made by pooling all the samples in the queue. These samples were run intermittently to control for machine drift and sample stability. For targeted quantitative analysis, peak areas were extracted with Agilent Mass Hunter Workstation Software Quantitative Analysis for QQQ version B.07.01. Peak areas were normalized to internal standard before quantification. For flux samples, data were processed through Agilent Profinder. Statistics were performed with GraphPad Prism 7. Data were analyzed using Student’s t test or one-way ANOVA with Tukey’s post hoc correction.

Baek J., Sas K., He C., Nair V., Giblin W., Inoki A., Zhang H., Yingbao Y., Hodgin J., Nelson R.G., Brosius FC I.I.I., Kretzler M., Stemmer P.M., Lombard D.B, & Pennathur S. (2023). The deacylase sirtuin 5 reduces malonylation in nonmitochondrial metabolic pathways in diabetic kidney disease. The Journal of Biological Chemistry, 299(3), 102960.

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Targeted Metabolomics Analysis Using UPLC-QTOF

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Fifty microliters of each sample, QC, and process blank solution were added to individual PTFE auto sampler vials and randomized prior to analysis. An Agilent 1290 UPLC coupled to an Agilent 6545 Q-ToF (Agilent Technologies, Inc) was used for chromatography and mass spectrometry. Chromatography was performed with a Waters BEH amide 2.1 × 100 mm column with BEH amide precolumn (Waters Corporation). An initial concentration of 99% buffer A (ACN with 5% 10 mM NH4OAc ddH2O) and 1% buffer B (10 mM NH4OAc in ddH2O) was held for 1.66 min at a flow rate of 0.4 ml/min. B was decreased to 70% over 4.75 min then to 40% over 1.5 min. Finally, B was decreased to 30% over 0.68 min and held for 3.75 min. The system was allowed to re-equilibrate for 5.16 min between runs. Samples were analyzed by an Agilent 6545 with a Dual Jetstream ESI source in positive and negative modes with the following source parameters: Gas Temp = 275 °C, Drying Gas = 12 l/min, Nebulizer = 35 psig, Sheath Gas Temp 325 °C, Sheath Gas Flow 12 l/min, VCap = 3500 V, Nozzle Voltage = 250 V, Fragmentor = 100 V, Skimmer = 65 V. Data analysis was performed using Agilent Profinder and Quantitative Analysis software (Agilent Technologies, Inc).

Miljkovic M., Seguin A., Jia X., Cox J.E., Catrow J.L., Bergonia H., Phillips J.D., Stephens W.Z, & Ward D.M. (2023). Loss of the mitochondrial protein Abcb10 results in altered arginine metabolism in MEL and K562 cells and nutrient stress signaling through ATF4. The Journal of Biological Chemistry, 299(7), 104877.

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Untargeted UHPLC-QTOF-MS Metabolomics Analysis

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Full scan UHPLC-QTOF-MS data was processed using the batch recursive feature extraction algorithm in Agilent Profinder (B.06.00). This process bins mass spectral features according to expected isotope patterns, adducts, and charge states, and then aligns them across all samples. Feature groups that appeared in at least two samples in either the nectar or BRB powder were retained and re-extracted across all samples. The recursive nature of this workflow ensures high quality data for statistical analysis. Further filtering of the data was performed in Agilent Mass Profiler Professional. First, features unique to the processing blank, which consisted of all nectar components except BRB powder, were removed from analysis. The analysis was then restricted to features with retention times between 1–12.5 min and a calculated neutral mass <1200 amu. Finally, features that were present in at least 66.6% of nectar or BRB samples, and those with a CV<25% in either of these groups were retained for statistical analyses. All data was log₂ transformed and median-centered prior to analysis. Differential analysis was performed using an unpaired t-test (P < 0.05) with the Benjamini-Hochberg false discovery rate multiple testing correction applied.

Teegarden M.D., Schwartz S.J, & Cooperstone J.L. (2018). Profiling the impact of thermal processing on black raspberry phytochemicals using untargeted metabolomics. Food chemistry, 274, 782-788.

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Metabolite 13C Labeling Analysis

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Under the experimental conditions described previously using [U-¹³C₃]-glycerol (99%) and [U-¹³C₆]-glucose (99%), the extent of ¹³C labeling of each metabolite was determined by dividing the summed peak height ion intensities of all ¹³C-labeled species by the ion intensity of both labeled and unlabeled species using Agilent Profinder version B.8.0.00 service pack 3.

Thomson M., Liu Y., Nunta K., Cheyne A., Fernandes N., Williams R., Garza-Garcia A, & Larrouy-Maumus G. (2022). Expression of a novel mycobacterial phosphodiesterase successfully lowers cAMP levels resulting in reduced tolerance to cell wall–targeting antimicrobials. The Journal of Biological Chemistry, 298(8), 102151.

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13C Isotopologue Analysis of Metabolites

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Under the experimental conditions described above using [U-¹³C₃] glycerol (99%) and [U-¹³C₆] glucose (99%), the extent of ¹³C labelling for each metabolite was determined by dividing the summed peak height ion intensities of all ¹³C-labelled species by the ion intensity of both labelled and unlabelled species using the software Agilent Profinder version B.8.0.00 service pack 3.

Liu Y., Rebollo-Ramirez S, & Larrouy-Maumus G. (2020). Metabolomics reveals that the cAMP receptor protein regulates nitrogen and peptidoglycan synthesis in Mycobacterium tuberculosis. RSC Advances, 10(44), 26212-26219.

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UHPLC-MS Metabolomic Data Processing

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Raw UHPLC‐MS data from the pooled QC samples and process blanks were deconvoluted, aligned, and grouped using Agilent Profinder (B.08 SP3, Agilent, Santa Clara, CA, USA) recursive batch feature extraction, with a retention time window from 1.5 to 13.5 min, noise cutoff of 3000 ion counts, retention time tolerance of 0.2 min, and mass tolerance of 20 ppm. The resulting metabolites were manually inspected to remove compounds that were present in process blanks, correct errors in integration, and eliminate incorrectly extracted non‐peaks. The resulting metabolite list was then used for a targeted feature extraction of the samples and re‐extraction of the pooled QCs. Further manual inspection of peak quality, following the same guidelines, yielded 491 and 415 total metabolites in positive and negative modes, respectively.

Chatelaine H.A., Ramazani C.A., Spencer K., Olivo‐Marston S., Bailey M.T., McElroy J., Hatzakis E., Mathé E.A, & Kopec R.E. (2022). Dietary Energy Intake and Presence of Aberrant Crypt Foci Are Associated with Phospholipid, Purine, and Taurine Metabolite Abundances in C57BL/6N Mouse Colon. Molecular Nutrition & Food Research, 66(20), 2200180.

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UHPLC-MS Metabolomics Data Processing

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Raw UHPLC-MS data from the pooled QC samples and process blanks were deconvoluted, aligned, and grouped using Agilent Profinder (B.08 SP3, Agilent, Santa Clara, CA) recursive batch feature extraction, with a retention time window from 1.5 to 13.5 min, noise cutoff of 3000 ion counts, retention time tolerance of 0.2 min, and mass tolerance of 20 ppm. The resulting metabolites were manually inspected to remove compounds that were present in process blanks, correct errors in integration, and eliminate incorrectly extracted non-peaks. The resulting metabolite list was then used for a targeted feature extraction of the samples and re-extraction of the pooled QCs. Further manual inspection of peak quality, following the same guidelines, yielded 491 and 415 total metabolites in positive and negative modes, respectively.

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GC-MS and LC-MS Metabolite Identification

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GC–MS data was processed with AMDIS (V2.71) and LC–MS data with Agilent Profinder (V10.0 SP1, Agilent Technologies, Inc.). Metabolites for both data sets were identified with an in-house library (Supplementary Table 4)and comparison of unknown peaks to previous publications (Bhattacharya et al. 2010 (link); Ncube et al. 2014 (link)) or database NIST11 (http://chemdata.nist.gov/mass-spc/ms-search/). The metabolite data was relatively quantified to the respective internal standard and sample weight. Statistical analysis was performed with Simca P (13.0.3.0, Umetrics), XLSTAT (2017, Addinsoft), and Metaboanalyst (Xia and Wishart 2016 ). Data was subjected to auto-scaling for analysis with Metaboanalyst. Non-parametric ANOVA was performed due to the small sample size and included false discovery rate correction for multiple comparisons.

Drapal M., Enfissi E.M, & Fraser P.D. (2021). Metabolic effects of agro-infiltration on N. benthamiana accessions. Transgenic Research, 30(3), 303-315.

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