{"title":"Low-resolution GC-MS in metabolic profiling of biological samples with the mass spectrometry. Updating of the method","authors":"A. Ukolov","doi":"10.47470/0869-7922-2022-30-3-139-148","DOIUrl":null,"url":null,"abstract":"Introduction. The introduction of metabolomic approaches into the practice of toxicological studies, as well as the expansion of the methodological capabilities of the laboratory for the determination of low-molecular, metabolic biomarkers of the effect, makes it possible to more effectively detect and identify new biomarkers. Material and methods. For metabolic profiling of blood plasma and urine samples, Shimadzu QP2010plus or Agilent 5975C gas chromatomass spectrometers were used. The results were processed using optimized databases of analytical characteristics of endogenous compounds and the AMDIS system; NIST/EPA/NIH 2017 was used to identify the detected compounds. Statistical processing was performed using Statistica. Results. A two-stage procedure for preparing blood plasma and urine samples for analysis by GC-MS was developed, a mixture of internal standards was selected, a list of compounds - endogenous metabolites was determined, and the metrological characteristics of their determination were evaluated. Limitations. The list of analytes suitable for determination by GC-MS is limited to volatile and conditionally volatile compounds. Conclusion. Using an optimized database of sample metabolites prepared for analysis according to a standardized procedure allows filtering out analytes with low reproducibility. Small (up to 100) chromatospectral databases make it possible to increase the reliability of identification, eliminate the effect of retention time drift, and, as a result, increase the statistical power of the entire experiment without increasing the number of laboratory animals.","PeriodicalId":23128,"journal":{"name":"Toxicological Review","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Toxicological Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47470/0869-7922-2022-30-3-139-148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Introduction. The introduction of metabolomic approaches into the practice of toxicological studies, as well as the expansion of the methodological capabilities of the laboratory for the determination of low-molecular, metabolic biomarkers of the effect, makes it possible to more effectively detect and identify new biomarkers. Material and methods. For metabolic profiling of blood plasma and urine samples, Shimadzu QP2010plus or Agilent 5975C gas chromatomass spectrometers were used. The results were processed using optimized databases of analytical characteristics of endogenous compounds and the AMDIS system; NIST/EPA/NIH 2017 was used to identify the detected compounds. Statistical processing was performed using Statistica. Results. A two-stage procedure for preparing blood plasma and urine samples for analysis by GC-MS was developed, a mixture of internal standards was selected, a list of compounds - endogenous metabolites was determined, and the metrological characteristics of their determination were evaluated. Limitations. The list of analytes suitable for determination by GC-MS is limited to volatile and conditionally volatile compounds. Conclusion. Using an optimized database of sample metabolites prepared for analysis according to a standardized procedure allows filtering out analytes with low reproducibility. Small (up to 100) chromatospectral databases make it possible to increase the reliability of identification, eliminate the effect of retention time drift, and, as a result, increase the statistical power of the entire experiment without increasing the number of laboratory animals.