Table of Contents
International Journal of Proteomics
Volume 2013, Article ID 760208, 7 pages
Research Article

The Human Urinary Proteome Fingerprint Database UPdb

1BHF Glasgow Cardiovascular Research Centre, University of Glasgow, 126 University Place, Joseph Black Building, Room B2-21, Glasgow G12 8TA, UK
2Tissue Injury and Repair Group, School of Clinical Sciences and Community Health, University of Edinburgh, 1st Floor Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK
3The Roslin Institute & R(D)VS, University of Edinburgh, Edinburgh EH25 9RG, UK
4School of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK

Received 3 June 2013; Accepted 29 August 2013

Academic Editor: Andrew J. Link

Copyright © 2013 Holger Husi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The use of human urine as a diagnostic tool has many advantages, such as ease of sample acquisition and noninvasiveness. However, the discovery of novel biomarkers, as well as biomarker patterns, in urine is hindered mainly by a lack of comparable datasets. To fill this gap, we assembled a new urinary fingerprint database. Here, we report the establishment of a human urinary proteomic fingerprint database using urine from 200 individuals analysed by SELDI-TOF (surface enhanced laser desorption ionisation-time of flight) mass spectrometry (MS) on several chip surfaces (SEND, HP50, NP20, Q10, CM10, and IMAC30). The database currently lists 2490 unique peaks/ion species from 1172 nonredundant SELDI analyses in the mass range of 1500 to 150000. All unprocessed mass spectrometric scans are available as “.xml” data files. Additionally, 1384 peaks were included from external studies using CE (capillary electrophoresis)-MS, MALDI (matrix assisted laser desorption/ionisation), and CE-MALDI hybrids. We propose to use this platform as a global resource to share and exchange primary data derived from MS analyses in urinary research.