Examination of 2-DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique.
The Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) pilot studies have generated over 200 2-D gels from eight participating laboratories. This data includes 67 single-channel and 60 DIGE gels comparing 30 whole frozen C57/BL6 female mouse brains, ten each at embryonic day 16, postnatal day 7 (juvenile) and postnatal day 54-56 (adult); and ten single-channel and three DIGE gels comparing human epilepsy surgery of the temporal front lobe with a corresponding post-mortem specimen. The samples were generated centrally and distributed to the participating laboratories, but otherwise no restrictions were placed on sample preparation, running and staining protocols, nor on the 2-D gel analysis packages used. Spots were characterised by MS and the annotated gel images published on a ProteinScape web server. In order to examine the resultant differential expression and protein identifications, we have reprocessed a large subset of the gels using the newly developed RAIN (Robust Automated Image Normalisation) 2-D gel matching algorithm. Traditional approaches use symbolic representation of spots at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in spot modelling and matching. With RAIN, image intensity distributions, rather than selected features, are used, where smooth geometric deformation and expression bias are modelled using multi-resolution image registration and bias-field correction. The method includes a new approach of volume-invariant warping which ensures the volume of protein expression under transformation is preserved. An image-based statistical expression analysis phase is then proposed, where small insignificant expression changes over one gel pair can be revealed when reinforced by the same consistent changes in others. Results of the proposed method as applied to the HUPO BPP data show significant intra-laboratory improvements in matching accuracy over a previous state-of-the-art technique, Multi-resolution Image Registration (MIR), and the commercial Progenesis PG240 package.
Dowsey
Andrew W
AW
Royal Society / Wolfson Foundation Medical Image Computing Laboratory, Department of Computing, Imperial College London, UK.
English
Jane
J
Pennington
Kyla
K
Cotter
David
D
Stuehler
Kai
K
Marcus
Katrin
K
Meyer
Helmut E
HE
Dunn
Michael J
MJ
Yang
Guang-Zhong
GZ
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D015180:Electrophoresis, Gel, Two-Dimensional; D004833:Epilepsy, Temporal Lobe / Q000378:metabolism / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D051379:Mice; D008810:Mice, Inbred C57BL; D010865:Pilot Projects; D020543:Proteome / Q000378:metabolism*; D013702:Temporal Lobe / Q000378:metabolism / Q000473:pathology
D020543:Proteome