Analytical pitfalls and challenges in clinical metabolomics.
Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.
Center for Proteomics & Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC2300 RC Leiden, The Netherlands.
Isabelle
I
Kohler
Center for Proteomics & Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC2300 RC Leiden, The Netherlands.
Aswin
A
Verhoeven
Center for Proteomics & Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC2300 RC Leiden, The Netherlands.
Rico Je
RJ
Derks
Center for Proteomics & Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC2300 RC Leiden, The Netherlands.
Martin
M
Giera
D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods; D019937:Diagnostic Techniques and Procedures; D019075:Electrophoresis, Capillary / Q000379:methods; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D013058:Mass Spectrometry / Q000379:methods; D055432:Metabolomics / Q000379:methods*; D057285:Precision Medicine / Q000379:methods
GC; LC; MS; NMR; capillary electrophoresis; clinical metabolomics; data analysis; lipidomics