To assign structural identities to differentially expressed metabolites (p < 0.05) in CLN2 disease subjects vs. healthy control individuals (Welch’s t-test, unpaired, unequal variance), LC/MS data were searched against an in-house Personal Compound Database and Library (PCDL) comprised of 806 accurate mass metabolite standards with defined formulae and chromatographic retention times. Differentially-expressed molecular features that were not found in our in-house database were initially compared against the METLIN Database (Agilent Technologies) and the Human Metabolome Database (HMDB; http://www.hmdb.ca ). Potential structural matches were verified by comparison with pure metabolite reference standards that were analyzed on the same LC/MS platform to define chromatographic retention time and tandem mass spectrometry (MS/MS) fragmentation spectra at multiple collision energies (10, 20 and 40 eV).
Metlin database
Manufactured by Agilent Technologies
Sourced in China
The METLIN database is a comprehensive metabolite and chemical structure database developed by Agilent Technologies. It serves as a reference for the identification and analysis of metabolites and small molecules in various research and analytical applications.
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7 protocols using metlin database
1
Metabolite Identification in CLN2 Disease
2
Metabolite Identification Protocol
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The resulting metabolites were identified using the ID Browser tool in the MPP software package B.14.9, a widely used annotation module, matching an accurate mass with the mass tolerance, retention time, and isotope pattern in the following databases: the Agilent METLIN database, Human Metabolome Database, Kyoto Encyclopaedia of Genes and Genomes, and BioCyc. Moreover, if there were commercially available authentic standards, we compared the MS/MS patterns and chromatographic retention times of resultant metabolites.
3
Comprehensive Database of Measured Mass Spectra
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Only compounds with measured mass spectra were used. In silico predicted MS/MS spectra available in certain public databases [12 (link)] were not considered in our study. A merged list of InChIKeys was initially created from public and commercial datasets published by Vinaixa et al. 2016 [13 (link)]. This list was further updated with new entries and resources [16 (link),17 (link)] yielding: 9419 InChIKeys of compounds from the METLIN database [18 (link)] provided by Agilent Technologies; 399 InChIKeys from ReSpect [19 (link)]; 1171 InChIKeys from the Wiley MS for ID database provided by Herbert Oberacher; 3401 InChIKeys from the GNPS [20 (link)]; 11,009 InChIKeys from MassBank [21 (link)]; 3480 InChIKeys from mzCloud provided by Robert Mistrik (21 June 2016); 1034 InChIKeys from the HMDB [12 (link)] (downloaded on 21 June 2016); and 242,463 InChIKeys from NIST 14 provided by Stephen Stein and Dmitrii Tchekhovskoi. These InChIKey lists (which often contained duplicated entries) were merged for a total of 261,330 non-redundant InChIKey, containing 253,927 non-redundant InChIKey first-block. The InChIKey mapping was performed using the first block of the string, thus not taking into account charge or stereochemistry.
4
Plasma Metabolite Extraction and Identification
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Briefly, 100 µL plasma was combined with 300 µL methanol/water mixed solvent (0.1% formic acid, v/v). Next, the mixture was homogenized. After 15 min of ultrasonic in 20 ºC water baths, vortex for 2 min, and centrifugation at 12,000 × g for 20 min at 4 ºC, the supernatant was taken. Moreover, the machine (Quadrupole time-of-flight liquid-mass system, Agilent Technology Co., Ltd., Beijing, China) was used for detection. Agilent Profinder software was used to conduct retention time correction, peak recognition, peak extraction, peak integration, peak alignment and other work on the original MS data, and then CEF files were generated. Then the Agilent Massive Parallel Processor software was used for statistical processing, and the associated Metlin database was used for substance identification (Bioacme Coa, Wuhan, Hubei, China). Heatmaps of differential metabolites were drawn in R with heatmap package version 1.0.12. The values of variable-importance projection > 1 and P < 0.05 were considered significantly different.
5
Metabolomics Analysis of Complex Samples
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All of the MS data were stored as “.d” format and then converted into “.cef” format with the Profinder software (Agilent, Santa Clara, CA). Finally, the Mass Profiler Professional Software (MPP) was used to perform no-targeted metabolomics analysis. Only the compounds with a minimum absolute abundance of 2000 counts and more than 2 ions found were selected for the subsequent analysis. The peaks in the chromatograms of different samples were aligned using a retention time window of 0.3 min ± 0.2% and a mass window of 20 ppm ± 2.0 mD. Then, the peak intensity from different samples was normalized to the median of each group. The normalized data were processed by PCA using the MPP software to find all the significant compounds. These compounds were identified by searching in METLIN Database (Agilent) and comparing the accurate mass charge ratio with other databases, such as the HMDB and KEGG database.
6
Bacterial Metabolite Profiling by LC-MS
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LC-MS was performed on the extracted bacterial metabolite at the NCL Venture Centre, Pune, Maharashtra, India. Using the Agilent XDB-C18 column (3 × 150 mm, 3.5 µ), separation was performed at 40°C. Sample gradient elution was carried out using 0.1% (v/v) formic acid in water (A) and 0.1% (v/v) formic acid in acetonitrile (B) at a mobile flow rate of 0.3 mL per min. The mobile phase gradient used was 0 min at 95% A, 2 min at 95% A, 25 min at 5% A, 28.1 min at 95% A, and 30 min at 95% A. The analysis was carried out using the Agilent Q-ToF G6540B device connected to the Agilent 1,260 Infinity II HPLC platform. Detection was performed using a mass-ion-source dual AJS ESI for positive ionizations in the mass scan range of 100–1700. The compounds were identified using Agilent’s METLIN database; furthermore, a few compounds were identified by comparing the predicted molecular weights, molecular formulas, and mass spectra with databases such as ChemSpider and PubChem. The LC-MS data of the metabolite were submitted to MetaboLights platform as per the steps described by Yurekten et al. (2024) (link).
7
TCAF2 Profiling via HRLCMS
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The TCAF2 was subjected to HRLCMS analysis using an Agilent (6550 ifunnel Q-TOF's) system consisting of a hip sampler, a binary pump, a column component, and Q-TOF with an electron ionization spray. Chromatographic separation was performed on a 1290 infinity UHPLC system fitted with a Hypersil gold column (C18X 2.1 mm-3Micron). The solution consisted of 0.1% formic acid in water (A), or 90% acetonitrile, 10% water, and 0.1% formic acid (B) as a mobile phase. The flow rate was adjusted to 0.3 mL/min with a 5 µL injection volume. The solvent system used was as follows: 0-1 min of 95% (A) and 5% (B), 1-20 min of 100% solvent (B), 20-25 min of 100% solvent (B), 25-26 min of 95% (A) and 5% (B), and 26-30 min of 95% (A) and 5% (B).
For mass detection Q-TOF, a mass spectrometer (Agilent technologies, CA, USA) was operated with dual AJS ESI (Agilent technologies, CA, USA) as an ion source and a scan range of 150-1000 M/Z. The capillary tension was set at 3500 V, the gas flow was set at 13 L/min with a 250 • C temperature. The sheath gas flow rate was 11 L/min at 300 • C. The nebulizer gas was set at 35 psi gas flow pressure. Q-TOF data acquisition and evaluation of mass spectrometry were carried out using Agilent Metlin database.
For mass detection Q-TOF, a mass spectrometer (Agilent technologies, CA, USA) was operated with dual AJS ESI (Agilent technologies, CA, USA) as an ion source and a scan range of 150-1000 M/Z. The capillary tension was set at 3500 V, the gas flow was set at 13 L/min with a 250 • C temperature. The sheath gas flow rate was 11 L/min at 300 • C. The nebulizer gas was set at 35 psi gas flow pressure. Q-TOF data acquisition and evaluation of mass spectrometry were carried out using Agilent Metlin database.
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