Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

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The electronic nose is capable of rapidly and noninvasively discriminating prostate cancer and benign prostatic hyperplasia using urine headspace in patients undergoing surgery.

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Our results indicate that the tubular wall of the human testis is a reservoir for cells of the adult Leydig cell lineage and that the steroidogenic potential of these cells can be activated in culture.

This study describes a validated LC-MS/MS method for assaying 23 steroids within a single run from 150 µl of human plasma, serum or prostatic tissue homogenate. Isotope-labeled steroids were used as internal standards. Samples were extracted with toluene, and ketosteroids were derivatized with hydroxylamine prior to LC-MS/MS analysis. The steroids were separated on a C18 column and methanol was used as an organic solvent with the addition of 0.2 mM ammonium fluoride to improve underivatized estradiol (E2) ionization. Certified reference serums as well as plasma samples, and homogenates of prostate tissue were utilized in the method validation. The specificity of the method was inspected with a total of 27 steroids. The validation proved that the method was suitable for the quantitative analysis of a wide panel of androgens (testosterone, T (3.3 pM-13 nM); androstenedione, A4 (3.3 pM-13 nM); 5α-androstanedione, DHA4 (13 pM-13 nM); dehydroepiandrosterone, DHEA (67 pM-133 nM); dihydrotestosterone, DHT (33 pM-33 nM); 11ketodihydrotestosterone, 11KDHT (13 pM-13nM); 11-ketotestosterone, 11KT (33 pM-6.7 nM); 11βhydroxyandrostenedione, 11bOHA4 (33 pM-13 nM); 11β-hydroxytestosterone, 11OHT (13 pM-33 nM)), as well as estrogens (estrone, E1 (3.3 pM-13 nM)), progestagens (17α-hydroxypregnenolone, 17OHP5 (32 pM-127 nM); 17α-hydroxyprogesterone, 17OHP4 (67 pM-133 nM); progesterone, P4 (3.3 pM-13 nM); pregnenolone, P5 (6.6 pM-13 nM)), and glucocorticoids (cortisol, F (33 pM-134 nM); cortisone E (66 pM-131 nM); corticosterone, B (33 pM-67 nM); 11-deoxycortisol, S (33 pM-66 nM); 21-hydroxyprogesterone, 21OHP4 (32 pM-13 nM)). Furthermore, E2 (335 pM-134 nM) and 11α-hydroxyandrostenedione, 11aOHA4 (33 pM-33 nM) could be analyzed if the concentration in the sample was high enough. In addition, aldosterone, A (128 pM-64 nM) and 11-ketoandrostenedione, 11KA4 (33 pM-13 nM) could be analyzed semiquantitatively. The limits of quantification for all compounds ranged from 0.9 to 91 pg/ml, and from 0.009 to 0.9 pg/mg tissue. Compared to our previous method, this new method also permits the analysis of the more challenging steroids, like DHT, DHEA and P5, and a panel of 11-ketosteroids.