LC-MSE, Multiplex MS/MS, Ion Mobility, and Label-Free Quantitation in Clinical Proteomics
Proteomic tools can only be implemented in clinical settings if high-throughput, automated, sensitive, and accurate methods are developed. This has driven researchers to the edge of mass spectrometry (MS)-based proteomics capacity. Here we provide an over
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Introduction Proteomics has emerged as a promising field for biomarker identification in the post-genomic era. By the end of the 1990s, the combination of liquid chromatography and electrospray ionization mass spectrometry (ESI-MS) became the main tool for proteomic characterization. Technological advances in this area and the number of potential proteomic biomarkers have grown substantially in recent years, revealing a broader understanding of diseases. Considering the heterogeneity among proteomic datasets originating from these studies, scientists must now focus their efforts on validating this information, and establish the relevance of these proteins through applications in clinical practice [1, 2]. In order to achieve this, proteomic-based studies must first demonstrate improvements in design and standardized protocols to produce uniform and reliable results. Researchers should pay attention to pivotal details when defining patient cohorts, such as clinical stage, inclusion/exclusion criteria, sample quality, quality of controls,
Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications, Methods in Molecular Biology, vol. 1546, DOI 10.1007/978-1-4939-6730-8_4, © Springer Science+Business Media LLC 2017
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sample preservation/storage methods, protocol complexity, reproducibility, and adequacy of statistical analyses. This is a daunting task considering as most clinical studies are complex and can sometimes require large numbers of samples/participants as well as extended data processing periods. Although proteomics is in an early stage of exponential growth, the field currently offers extensive capabilities for elucidating functional protein interactions and for discovering novel biomarkers for therapeutics. In addition, proteomics can be used to determine the functional status of a disease and subsequently help to select treatment options. This approach has been termed pharmacoproteomics [3]. Despite sophisticated proteomic study designs using bottom-up shotgun proteomics, subsequent research has challenged the reliability or reproducibility of the results. These problems impact all of the “omics” fields and not just one technology [4]. This chapter describes some of the latest developments in the field of label-free MS approaches which may help to improve the discovery and validation of multiplex biomarkers for use in laboratory, preclinical and clinical studies.
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Systems Biology The use of quantitative high-throughput MS-based proteomics and comprehensive systems biology approaches has generated complex datasets which have the possibility to assist researchers in discovering the meaning of disease-related molecular changes, especially when combined with information generated by other omic approaches. In this context, clinical proteomics has the potential to enable early disease discovery through the development of multiplex assays that can aid clinical decisions. However, the term “biomarker” can be applied only to those that
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