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  A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

Shi, Z., Li, H., Zhang, W., Chen, Y., Zeng, C., Kang, X., et al. (2022). A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies. Metabolites, 12(12): 1168. doi:10.3390/metabo12121168.

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Genre: Journal Article
Alternative Title : Metabolites

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 Creators:
Shi, Zhan1, Author
Li, Haohui1, Author
Zhang, Wei1, Author
Chen, Youxiang1, Author
Zeng, Chunyan1, Author
Kang, Xiuhua1, Author
Xu, Xinping1, Author
Xia, Zhenkun1, Author
Qing, Bei1, Author
Yuan, Yunchang1, Author
Song, Guodong1, Author
Caldana, C.2, Author           
Hu, Junyuan1, Author
Willmitzer, L.3, Author           
Li, Yan1, Author
Affiliations:
1external, ou_persistent22              
2Metabolic Regulation of Plant Growth, Department Gutjahr, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_3396322              
3Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753340              

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Free keywords: metabolomics, clinical cohort, LC-MS, GC-MS, quality control, data normalization, data modeling
 Abstract: As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

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Language(s): eng - English
 Dates: 2022-11-242022-11-24
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3390/metabo12121168
 Degree: -

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Title: Metabolites
Source Genre: Journal
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Publ. Info: MDPI AG
Pages: - Volume / Issue: 12 (12) Sequence Number: 1168 Start / End Page: - Identifier: Other: 2218-1989
CoNE: https://pure.mpg.de/cone/journals/resource/2218-1989