Integrative protocol for quantifying cholesterol-related sterols in human serum samples and building decision support systems

• Published in: STAR protocols Vol. 5; no. 3; p. 103213
• Main Authors: Kočar, Eva, Katz, Sonja, Pušnik, Žiga, Skubic, Cene, Režen, Tadeja, Martins dos Santos, Vitor A.P., Mraz, Miha, Moškon, Miha, Rozman, Damjana
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20.09.2024; Elsevier
• Subjects: Computer sciences; Metabolism; Molecular Biology; Molecular Biology; Metabolism; Computer sciences
• ISSN: 2666-1667; 2666-1667
• DOI: 10.1016/j.xpro.2024.103213

The growing interest in clinical diagnostics has recently focused on metabolic biomarkers. Here, we present a protocol for sample preparation, extraction of cholesterol-related sterols, and quantification of 10 sterols in human blood serum samples using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). We also describe steps of machine learning techniques to develop novel decision-making systems that offer potential benefits in disease monitoring and surveillance by measuring metabolic pathways. For complete details on the use and execution of this protocol, please refer to Kočar et al.1 and Skubic et al.2 [Display omitted] •Instructions for the extraction of sterols from human serum samples•Steps for detecting and quantifying 10 non-polar sterols using LC-MS/MS•Guidelines for machine learning in developing novel decision support systems Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The growing interest in clinical diagnostics has recently focused on metabolic biomarkers. Here, we present a protocol for sample preparation, extraction of cholesterol-related sterols, and quantification of 10 sterols in human blood serum samples using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). We also describe steps of machine learning techniques to develop novel decision-making systems that offer potential benefits in disease monitoring and surveillance by measuring metabolic pathways.