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Non-targeted mercapturic acid screening in urine using LC-MS/MS with matrix effect compensation by postcolumn infusion of internal standard (PCI-IS)

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY
卷 411, 期 29, 页码 7771-7781

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SPRINGER HEIDELBERG
DOI: 10.1007/s00216-019-02166-6

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LC-MS/MS; Human urine; Non-target screening; Postcolumn infusion

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While the targeted analysis of mercapturic acid (MA) metabolites in human urine is used to assess exposure to selected chemicals, this compound class has only rarely been addressed in non-target screening utilizing diagnostic neutral loss liquid chromatography tandem mass spectrometry (LC-MS/MS). Additionally, this type of analysis is severely affected by matrix effects (MEs) causing poor comparability of samples and distortion of signal intensities. However, MEs have been neglected in urinary MA non-target screening so far. Therefore, we developed a non-target screening method relying on neutral loss scanning for MAs using post column infusion of an isotope-labelled standard. For signal correction, we synthesized a structural analogue to MAs, N-acetyl-S-methyl-homocysteine-D-3, lacking the characteristic neutral loss of the MAs. For method development, 16 structurally different model MA compounds and 20 spiked urine samples were used. Twelve out of the 16 model compounds could be analysed by the developed method. We found severe matrix effects (largely signal suppression) for the spiked model compounds, with only 34% of all peaks' intensities changing by less than a factor of two. This could be compensated by the post column internal standard infusion with now 68% of all peaks' intensities changing by less than a factor of two. For three compounds, an over-compensation was observed resulting in an increase of signal of up to a factor of 16. In the 20 urine samples, altogether 558 native MAs (between 74 and 175 per sample) could be detected after ME compensation. These results indicate that a large number of so far uncharacterized MAs are present in urine, which yield a potential for biomarker discovery and pattern characterisation.

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