Analytical and bioanalytical chemistry · ·Added 12 Nov 2026

LC-MS determination of dopaminergic catecholamines in tissues and biofluids using streamlined sample preparation and heterocyclic NHS-ester derivatization.

Tereza Hofmanova, Filip Divis, Martin Miko, Miroslav Lisa, Eva Cifkova, Rudolf Andrys

DOI ↗

Abstract

The accurate quantification of endogenous catecholamines within the dopaminergic pathway plays a crucial role in neurochemical and clinical diagnostics. However, this process is severely hindered by rapid analyte oxidation and complex matrix interferences. This study introduces a comprehensive bioanalytical strategy that connects structural chemical modification with streamlined sample processing. First, the influence of various heterocyclic scaffolds on derivatization agent synthesis, chemical stability, chemoselectivity, and mass spectrometric (MS) ionization enhancement was systematically evaluated. This evaluation identified β-CCarb-NHS as the most promising candidate. To complement this chemical platform, a simple sample preparation workflow was developed. Catecholamines were stabilized directly during sample homogenization using 0.02% formic acid (FA), followed by a rapid liquid-liquid extraction (LLE) utilizing water, ACN, and chloroform. This approach achieved effective deproteination and lipid removal while eliminating the need for solid-phase extraction (SPE) or time-consuming solvent evaporation. Utilizing deuterium-labeled internal standards throughout, the integrated method was fully validated for 11 target catecholamines across mammalian brain tissue, human serum, and urine. The workflow demonstrated excellent accuracy (86-116%), precision ( 99%), yielding competitive limits of detection (3-217 nM) from a minimal 1 µL injection volume. Finally, the practical utility of the protocol was demonstrated by successfully profiling endogenous baseline levels in real human serum, urine, and river nutria brain tissue. The simple, robust, and cost-effective nature of this workflow makes it ideal for the analysis of large sample sets.

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