High-performance thin-layer chromatography in combination with a yeast-based multi-effect bioassay to determine endocrine effects in environmental samples.

Effect-directed analysis (EDA) that combines effect-based methods (EBMs) with high-performance thin-layer chromatography (HPTLC) is a useful technique for spatial, temporal, and process-related effect evaluation and may provide a link between effect testing and responsible substance identification. In this study, a yeast multi endocrine-effect screen (YMEES) for the detection of endocrine effects is combined with HPTLC. Simultaneous detection of estrogenic, androgenic, and gestagenic effects on the HPTLC plate is achieved by mixing different genetically modified Arxula adeninivorans yeast strains, which contain either the human estrogen, androgen, or progesterone receptor. Depending on the yeast strain, different fluorescent proteins are formed when an appropriate substance binds to the specific hormone receptor. This allows to measure hormonal effects at different wavelengths. Two yeast cell application approaches, immersion and spraying, are compared. The sensitivity and reproducibility of the method are shown by dose-response investigations for reference compounds. The spraying approach indicated similar sensitivities and higher precisions for the tested hormones compared to immersion. The EC10s for estrone (E1), 17β-estradiol (E2), 17α-ethinylestradiol (EE2), 5α-dihydrotestosterone (DHT), and progesterone (P4) were 95, 1.4, 10, 7.4, and 15 pg/spot, respectively. Recovery rates of E1, E2, EE2, DHT, and P4 between 88 and 120% show the usability of the general method in combination with sample enrichment by solid phase extraction (SPE). The simultaneous detection of estrogenic, androgenic, and gestagenic effects in wastewater and surface water samples demonstrates the successful application of the YMEES in such matrices. This promising method allows us to identify more than one endocrine effect on the same HPTLC plate, which saves time and material. The method could be used for comparison, evaluation, and monitoring of different river sites and wastewater treatment steps and should be tested in further studies.