An ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry workflow that targets 56 selected steroids and 5 prostaglandin metabolites and enables broad-spectrum monitoring of endocrine dynamics for basic, translational, and clinical research.
Abstract
Steroid hormones and their metabolites orchestrate female reproductive physiology. Key analytes such as 17β-estradiol and progesterone are integral to standard clinical practice worldwide. For more advanced endocrine monitoring of female reproduction, current analytical approaches lack automated throughput, broad metabolite coverage, and sufficient sensitivity. Herein, we present an ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) workflow that targets 56 selected steroids (estrogens, progestogens, androgens, glucocorticoids, and steroid conjugates) and 5 prostaglandin metabolites for quantitative profiling using 250 μL of plasma within a single pipeline. The workflow integrates optimized, automated protein precipitation-enhanced solid-phase extraction and two complementary chromatographic separations with tailored eluent additives and ionization modes to accommodate the chemical diversity of the broad target panel. A full method validation demonstrated excellent performance, with low limits of quantification (5–200 pg/mL) determined in charcoal-stripped plasma as a surrogate matrix. The workflow was applied to authentic cattle (n = 5) and human (n = 18) plasma samples from males and, particularly, females across the female reproductive cycle and selected pregnancy stages. Both well-established endocrine hormones and their metabolites, as well as analytes whose roles in reproduction remain insufficiently characterized, were among the 42 and 48 targets detected in cattle and human samples, respectively. Compound-specific, valley-split, and composite quantification were reported separately. This demonstrates extensive target coverage and robust quantitative performance across biological matrices with distinct physiological contexts. The workflow thus offers a robust tool for steroidomic studies and enables broad-spectrum monitoring of endocrine dynamics for basic, translational, and clinical research.
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