Classical MALDI-MS versus CE-based ESI-MS proteomic profiling in urine for clinical applications

Albalat, A., Husi, H., Stalmach, A. , Schanstra, J. P. and Mischak, H. (2014) Classical MALDI-MS versus CE-based ESI-MS proteomic profiling in urine for clinical applications. Bioanalysis, 6(2), pp. 247-66. (doi: 10.4155/bio.13.313)

Full text not currently available from Enlighten.

Abstract

Human urine is an attractive and informative biofluid for medical diagnosis, which has been shown to reflect the (patho)-physiology of not only the urogenital system, but also others such as the cardiovascular system. For this reason, many studies have concentrated on the study of the urine proteome, aiming to find relevant biomarkers that could be applied in a clinical setting. However, this goal can only be achieved after reliable quantitative and qualitative analysis of the urinary proteome. In the last two decades, MS-based platforms have evolved to become indispensable tools for biomarker research. In this review, we will present and compare two of the most clinically relevant analytical platforms that have been used for the study of the urinary proteome, namely CE-based ESI-MS and classical MALDI-MS. These platforms, although not directly comparable, have been extensively used in proteomic profiling and therefore their comparison is fundamentally relevant to this field.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stalmach, Dr Angelique and Albalat, Dr Amaya and Husi, Dr Holger and Mischak, Professor Harald
Authors: Albalat, A., Husi, H., Stalmach, A., Schanstra, J. P., and Mischak, H.
Subjects:Q Science > QH Natural history > QH345 Biochemistry
R Medicine > R Medicine (General)
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
Journal Name:Bioanalysis
ISSN:1757-6180
ISSN (Online):1757-6199
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
546181Development of an efficient, robust MS-based platform for early detection of acute kidney injuryHarald MischakMedical Research Council (MRC)G1000791RI CARDIOVASCULAR & MEDICAL SCIENCES