Targeted and Untargeted OMICS for Disease Biomarkers Using LC-MS

Download or read book Targeted and Untargeted OMICS for Disease Biomarkers Using LC-MS written by Shashank Gorityala and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has seen enormous growth in the clinical space in the past decade. In the United States, majority of the clinical reference laboratories use LC-MS/MS for the steroid assays due to its ability to offer analytical specificity, sensitivity, and high-throughput which are limitations of immunoassays. This technique has also shown its potential in biomarker studies, deciphering the biochemical events, and its application to answer clinically challenging questions has helped to progress in the direction of precision medicine. In this work, we have exploited the LC-MS/MS characteristics of specificity and sensitivity to develop the first validated method for the quantitation of dihydrotestosterone (DHT) and its metabolites in various biomatrices such as mouse serum, mouse prostate tissue and supernatant of cell culture media. Chemical derivatization was used to improve the detection limits of the analytes. The calibration range for DHT and its metabolites was 0.0500 - 50.0 ng/mL. The principles of method development, and validation according to FDA-ICH guidelines are depicted in this quantitative bioassay. In the second half of this publication, we showed the synergistic capabilities of high-resolution mass spectrometry in combination with the new bioinformatic tools to perform a multiomic analysis of exosome cargo in which the proteins and lipids were profiled, semi-quantitated, statistically modeled, and differentially analyzed to predict the molecular mechanisms through the protein-protein interaction networks. We showed for the first time the sample preparation steps which enabled the simultaneous extraction of proteins and lipids from same exosome sample. We used two sets of cell lines of human T-cells i.e., control and infected with HIV-1 as a model for this study. A number of open source bioinformatic tools were used for the identification, semi quantitation, functional enrichment, pathway analysis, and protein interaction networks to predict the possible molecular mechanisms.