zldmЗаводская лаборатория. Диагностика материаловIndustrial laboratory. Diagnostics of materials1028-68612588-0187ООО «Издательство «ТЕСТ-ЗЛ»10.26896/1028-6861-2020-86-7-12-19zldm-1238Research ArticleАНАЛИЗ ВЕЩЕСТВАSUBSTANCES ANALYSISАлгоритм сочетания хромато-масс-спектрометрического ненаправленного профилирования и многомерного анализа для выявления веществ-маркеров в образцах сложного составаAlgorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex compositionПлющенкоИ. В.PlyushchenkoI. V.

Иван Викторович Плющенко

119991, Москва, ГСП-1, Ленинские горы, 1, стр. 3

Ivan V. Plyushchenko

1/3 Leninskiye Gory, Moscow, 119991

plyushchenko.ivan@gmail.comШахматовД. Г.ShakhmatovD. G.

Дмитрий Геннадьевич Шахматов

123423, Москва, ул. Саляма Адиля, 2

Dmitry G. Shakhmatov

2 ul. Salyama Adilya, Moscow, 123423

РодинИ. А.RodinI. A.

Игорь Александрович Родин

119991, Москва, ГСП-1, Ленинские горы, 1, стр. 3

Igor A. Rodin

1/3 Leninskiye Gory, Moscow, 119991

Московский государственный университет имени М. В. Ломоносова, Химический факультетРоссияM. V. Lomonosov Moscow State University, Chemistry DepartmentRussian FederationГосударственный научный центр колопроктологии им. А. Н. Рыжих Минздрава РоссииРоссияState Scientific Center of ColoproctologyRussian Federation2020180720208671219Copyright © Плющенко И.В., Шахматов Д.Г., Родин И.А., 20202020Плющенко И.В., Шахматов Д.Г., Родин И.А.Plyushchenko I.V., Shakhmatov D.G., Rodin I.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.zldm.ru/jour/article/view/1238

Лавинообразное развитие методов статистической обработки данных, вычислительных мощностей, техники хромато-масс-спектрометрического анализа и омиксных технологий в последние десятилетия так и не привело к созданию унифицированного протокола для ненаправленного профилирования. Влияние систематических ошибок снижает воспроизводимость и достоверность результатов исследования, одновременно затрудняя объединение и анализ данных масштабных многодневных хромато-масс-спектрометрических экспериментов. В работе предложен алгоритм проведения омиксного профилирования для выявления потенциальных веществ-маркеров в образцах сложного состава на примере анализа образцов мочи разных клинических групп пациентов. Профилирование проведено методом жидкостной хромато-масс-спектрометрии. Выбор маркеров проводили методами многомерного анализа, в том числе машинного обучения и отбора переменных. Тестирование подхода выполняли с использованием независимого набора данных алгоритмами кластеризации и проецирования на главные компоненты.

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

жидкостная хроматографиямасс-спектрометрияметаболомикамногомерный анализхемометрикамашинное обучениеliquid chromatographymass spectrometrymetabolomicsmultivariate analysischemometricsmachine learningРабота поддержана Российским фондом фундаментальных исследований (РФФИ) (№ гранта: Аспиранты 19-33-90071).ReferencesArivaradarajan P., Misra G. (Eds.). Omics approaches, technologies and applications: integrative approaches for understanding OMICS data. 1st edition. — Singapore: Springer Nature, 2018. P. 158. DOI: 10.1007/978-981-13-2925-8_4.Arivaradarajan P., Misra G. (Eds.). Omics approaches, technologies and applications: integrative approaches for understanding OMICS data. 1st edition. — Singapore: Springer Nature, 2018. P. 158. DOI: 10.1007/978-981-13-2925-8_4.Gorrochategui E., Jaumot J., Lacorte S., Tauler R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow / TrAC. 2016. Vol. 82. P. 425 – 442. DOI: 10.1016/j.trac.2016.07.004.Gorrochategui E., Jaumot J., Lacorte S., Tauler R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow / TrAC. 2016. Vol. 82. P. 425 – 442. DOI: 10.1016/j.trac.2016.07.004.Argueso C. T., Assmann S. M., Birnbaum K. D., et al. Directions for research and training in plant omics: Big Questions and Big Data / Plant direct. 2019. Vol. 3. N 4. P. e00133. DOI: 10.1002/pld3.133.Argueso C. T., Assmann S. M., Birnbaum K. D., et al. Directions for research and training in plant omics: Big Questions and Big Data / Plant direct. 2019. Vol. 3. N 4. P. e00133. DOI: 10.1002/pld3.133.Lozano D. C. P., Thomas M. J., Jones H. E., Barrow M. P. Petroleomics: Tools, Challenges, and Developments / Annu. Rev. Anal. Chem. 2020. Vol. 13. P. 20. 1 – 20. 26. DOI: 10.1146/ annurev_anchem-091619-091824.Lozano D. C. P., Thomas M. J., Jones H. E., Barrow M. P. Petroleomics: Tools, Challenges, and Developments / Annu. Rev. Anal. Chem. 2020. Vol. 13. P. 20. 1 – 20. 26. DOI: 10.1146/ annurev_anchem-091619-091824.Ferranti P. The future of analytical chemistry in foodomics / Curr. Opin. Food Sci. 2018. Vol. 22. P. 102 – 108. DOI: 10.1016/ j.cofs.2018.02.005.Ferranti P. The future of analytical chemistry in foodomics / Curr. Opin. Food Sci. 2018. Vol. 22. P. 102 – 108. DOI: 10.1016/ j.cofs.2018.02.005.Bolotnik T. A., Timchenko Y. V., Plyushchenko I. V. Use of Chemometric Methods of Data Analysis for the Identification and Typification of Petroleum and Petroleum Products / J. Anal. Chem. 2019. Vol. 74. N 13. P. 1336 – 1340. DOI: 10.1134/S1061934819130045.Bolotnik T. A., Timchenko Y. V., Plyushchenko I. V. Use of Chemometric Methods of Data Analysis for the Identification and Typification of Petroleum and Petroleum Products / J. Anal. Chem. 2019. Vol. 74. N 13. P. 1336 – 1340. DOI: 10.1134/S1061934819130045.Kharyuk P., Nazarenko D., Oseledets I., et al. Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task / Sci. Rep. 2018. Vol. 8. N 1. P. 17053. DOI: 10.1038/s41598-018-35399-z.Kharyuk P., Nazarenko D., Oseledets I., et al. Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task / Sci. Rep. 2018. Vol. 8. N 1. P. 17053. DOI: 10.1038/s41598-018-35399-z.Cui X., Tang J., Yang Q., et al. Assessing the effectiveness of direct data merging strategy in long-term and large-scale pharmacometabonomics / Front. Pharmacol. 2019. Vol. 10. P. 127. DOI: 10.3389/fphar.2019.00127.Cui X., Tang J., Yang Q., et al. Assessing the effectiveness of direct data merging strategy in long-term and large-scale pharmacometabonomics / Front. Pharmacol. 2019. Vol. 10. P. 127. DOI: 10.3389/fphar.2019.00127.Yang Q., Hong J., Li Y., et al. A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies / Brief. Bioinform. 2019. DOI: 10.1093/bib/bbz137.Yang Q., Hong J., Li Y., et al. A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies / Brief. Bioinform. 2019. DOI: 10.1093/bib/bbz137.Holman J. D., Tabb D. L., Mallick P. Employing ProteoWizard to convert raw mass spectrometry data / Curr. Protoc. Bioinformatics. 2014. Vol. 46. N 1. P. 13.24.1 – 13.24.9. DOI: 10.1002/0471250953.bi1324s46.Holman J. D., Tabb D. L., Mallick P. Employing ProteoWizard to convert raw mass spectrometry data / Curr. Protoc. Bioinformatics. 2014. Vol. 46. N 1. P. 13.24.1 – 13.24.9. DOI: 10.1002/0471250953.bi1324s46.Chang H. Y., Chen C. T., Lih T. M., et al. iMet-Q: a user-friendly tool for label-free metabolomics quantitation using dynamic peak-width determination / PLOS one. 2016. Vol. 11. N 1. P. e0146112. DOI: 10.1371/journal.pone.0146112.Chang H. Y., Chen C. T., Lih T. M., et al. iMet-Q: a user-friendly tool for label-free metabolomics quantitation using dynamic peak-width determination / PLOS one. 2016. Vol. 11. N 1. P. e0146112. DOI: 10.1371/journal.pone.0146112.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019.Kuhn M., Johnson K. Applied predictive modeling. 1st edition. — New York: Springer, 2013. — 615 p. DOI: 10.1007/ 978-1-4614-6849-3.Kuhn M., Johnson K. Applied predictive modeling. 1st edition. — New York: Springer, 2013. — 615 p. DOI: 10.1007/ 978-1-4614-6849-3.Andrews J. L., McNicholas P. D. Variable selection for clustering and classification / J. Classif. 2014. Vol. 31. N 2. P. 136 – 153. DOI: 10.1007/s00357-013-9139-2.Andrews J. L., McNicholas P. D. Variable selection for clustering and classification / J. Classif. 2014. Vol. 31. N 2. P. 136 – 153. DOI: 10.1007/s00357-013-9139-2.Li B., Tang J., Yang Q., et al. NOREVA: normalization and evaluation of MS-based metabolomics data / Nucleic Acids Res. 2017. Vol. 45. N W1. P. W162 – W170. DOI: 10.1093/nar/gKx449.Li B., Tang J., Yang Q., et al. NOREVA: normalization and evaluation of MS-based metabolomics data / Nucleic Acids Res. 2017. Vol. 45. N W1. P. W162 – W170. DOI: 10.1093/nar/gKx449.Huber W., Von Heydebreck A., Sültmann H., et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression / Bioinformatics. 2002. Vol. 18. N 1. P. S96 – S104. DOI. 10. 1093/bioinformatics/18.suppl_1.S96.Huber W., Von Heydebreck A., Sültmann H., et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression / Bioinformatics. 2002. Vol. 18. N 1. P. S96 – S104. DOI. 10. 1093/bioinformatics/18.suppl_1.S96.Karpievitch Y. V., Taverner T., Adkins J. N., et al. Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition / Bioinformatics. 2009. Vol. 25. N 19. P. 2573 – 2580. DOI: 10.1093/bioinformatics/btp426.Karpievitch Y. V., Taverner T., Adkins J. N., et al. Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition / Bioinformatics. 2009. Vol. 25. N 19. P. 2573 – 2580. DOI: 10.1093/bioinformatics/btp426.Gautier L., Cope L., Bolstad B. M., Irizarry R. A. Affy-analysis of Affymetrix GeneChip data at the probe level / Bioinformatics. 2004. Vol. 20. N 3. P. 307 – 315. DOI: 10.1093/bioinformatics/btg405.Gautier L., Cope L., Bolstad B. M., Irizarry R. A. Affy-analysis of Affymetrix GeneChip data at the probe level / Bioinformatics. 2004. Vol. 20. N 3. P. 307 – 315. DOI: 10.1093/bioinformatics/btg405.

The authors declare that there are no conflicts of interest present.