Journal Article

DKFZ-2024-01537

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Automated Liquid Handling Extraction and Rapid Quantification of Underivatized Amino Acids and Tryptophan Metabolites from Human Serum and Plasma Using Dual-Column U(H)PLC-MRM-MS and Its Application to Prostate Cancer Study.

Kipura, T. ; Hotze, M. ; Hofer, A. ; Egger, A.-S. ; Timpen, L. E. ; Opitz, C.DKFZ* ; Townsend, P. A. ; Gethings, L. A. ; Thedieck, K. ; Kwiatkowski, M.

2024
MDPI Basel

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Please use a persistent id in citations: doi:10.3390/metabo14070370

Abstract: Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance.

Keyword(s): LC-MS ; amino acids ; automation ; mixed-mode chromatography ; prostate cancer ; tryptophan metabolites analysis

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Contributing Institute(s):

1. Metabolischer Crosstalk bei Krebserkrankungen (B350)
2. DKTK HD zentral (HD01)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2024

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