The HUPO Human Brain Proteome Project (HBPP) pilot studies have generated over 200 2-D gels from 8 participating laboratories. This data includes 67 single-channel and 60 DIGE gels comparing 30 whole frozen C57/BL6 female mouse brains, 10 each at embryonic day 16, postnatal day 7 (juvenile) and postnatal day 54-56 (adult); and 10 single-channel and 3 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 mass spectrometry (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 Advanced 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 (SEA) 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 HBPP data show significant intra-laboratory improvements in matching accuracy over a previous state-of-the-art technique, MIR, and the commercial Progenesis PG240 package (Nonlinear Dynamics, Newcastle, UK).
pubs.doc.ic.ac.uk: built & maintained by Ashok Argent-Katwala.