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Metabolites. 2018 Aug 5;8(3). pii: E44. doi: 10.3390/metabo8030044.

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry.

Author information

1
Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. jatinnandania@gmail.com.
2
Roche Diagnostics GmbH, Nonnenwald 2, 82377 Penzberg, Germany. jatinnandania@gmail.com.
3
Computational Systems Medicine group, University of Helsinki, 00290 Helsinki, Finland. cugopal@gmail.com.
4
Technical Research Center of Finland, P.O. Box 1000, 02044 Espoo, Finland. cugopal@gmail.com.
5
Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. alberto.pessia@helsinki.fi.
6
Network Pharmacology for Precision Medicine Group, University of Helsinki, 00290 Helsinki, Finland. alberto.pessia@helsinki.fi.
7
Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. meri.kokkonen@gmail.com.
8
Finnish Customs Laboratory, Tekniikantie 13, 02150 Espoo, Finland. meri.kokkonen@gmail.com.
9
Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland. vidya.velagapudi@helsinki.fi.

Abstract

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

KEYWORDS:

LC-MS; automation; biomarkers; cross-platform comparability; high-throughput; metabolomics; multianalyte method; semiquantitation; targeted; validation

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