Data processing was performed using the Smart Metabolites Database (Shimadzu, Kyoto, Japan), MS-DIAL version 3.08 [50 (link)], and the MRMPROBS program version 2.42 [51 (link)]. Peaks were recorded for the 45−600 m/z mass range, and were automatically detected via MS-DIAL using the peak detection option of a minimum peak height of 2000. A data quality check was conducted using the thresholds of −10 < RI < 10, dot production > 0.8, and presence > 0.6, and the remaining data was then manually checked. Ultimately, 172 metabolites were identified in the plasma samples. The relative quantities of the metabolites were calculated using the peak areas of each metabolite relative to that of the internal standard (2-isopropylmalic acid), and expressed as a percentage of an arbitrary control set to 100%.
Smart metabolites database
The Smart Metabolites Database is a comprehensive database that provides detailed information on a wide range of metabolites. The database includes chemical structures, physical properties, and analytical data for thousands of metabolites, making it a valuable tool for researchers and scientists working in the field of metabolomics.
Lab products found in correlation
10 protocols using smart metabolites database
GC-MS/MS Metabolomic Profiling Protocol
Data processing was performed using the Smart Metabolites Database (Shimadzu, Kyoto, Japan), MS-DIAL version 3.08 [50 (link)], and the MRMPROBS program version 2.42 [51 (link)]. Peaks were recorded for the 45−600 m/z mass range, and were automatically detected via MS-DIAL using the peak detection option of a minimum peak height of 2000. A data quality check was conducted using the thresholds of −10 < RI < 10, dot production > 0.8, and presence > 0.6, and the remaining data was then manually checked. Ultimately, 172 metabolites were identified in the plasma samples. The relative quantities of the metabolites were calculated using the peak areas of each metabolite relative to that of the internal standard (2-isopropylmalic acid), and expressed as a percentage of an arbitrary control set to 100%.
Serum Metabolomics Analysis by GC/MS
µl of serum was mixed with 5 µl of 1 mg/ml 2-isopropylmalic acid (Sigma-Aldrich, Tokyo, Japan) in distilled water as an internal
standard, and 250 µl of methanol–chloroform–water (2.5:1:1) mixture. Then samples were lyophilized, and added with 40 µl of 20 mg/mlmethoxyamine hydrochloride (Sigma-Aldrich), dissolved in pyridine for oximation. After mixing, the samples were shaken for 90 min at 30°C. Next 20 µl of N-methyl
N-trimethylsilyl-trifluoroacetamide (GL Science, Tokyo, Japan) was added for trimethylsilylation, and the mixture was incubated at 37°C for 45 min. The sample was subjected to GC/MS (GCMS
QP2010-Ultra; Shimadzu, Kyoto, Japan). The Shimadzu Smart Metabolites Database (Shimadzu) was used to identify metabolites. Samples were normalized by a pooled sample from control group. A
metabolic pathway analysis was performed using MetaboAnalyst [25 (link)]. Metabolites that significantly diffed between two groups were subjected to an
enrichment analysis (
Serum Metabolomics by GC/MS Analysis
GC-MS Metabolite Profiling Protocol
Serum Metabolomics Analysis by GC/MS
GC-MS Metabolite Identification Pipeline
GC-MS Analysis of Metabolites
Urinary Metabolite Identification by GC-MS
Area ratio = area value of compound Q.ION/area value of internal standard Q.ION, where Q.ION represents the peak area of a specific ion.
The relationships of detected metabolites were described on the metabolic pathway maps generated using the VANTED software (
Quantitative GC-MS/MS Metabolite Analysis
GC/MS Liver Metabolomics Protocol
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