The activity of genes in health and disease are manifested through the proteins which they encode. Ultimately, proteins
drive functional processes in cells and tissues and so by measuring individual protein levels, studying modifications and
discovering their sites of action we will understand better their function. It is possible to visualize the location of
proteins of interest in tissue sections using labeled antibodies which bind to the target protein. This procedure, known as
immunohistochemistry (IHC), provides valuable information on the cellular and sub-cellular distribution of proteins in
tissue. The project, atlas of protein expression, aims to create a quality, information rich database of protein expression
profiles, which is accessible to the world-wide research community. For the long term archival value of the data, the
accompanying validated antibody and protein clones will potentially have great research, diagnostic and possibly
therapeutic potential. To achieve this we had introduced a number of novel technologies, e.g. express recombinant
proteins, select antibodies, stain proteins present in tissue section, and tissue microarray (TMA) image analysis. These
are currently being optimized, automated and integrated into a multi-disciplinary production process. We had also
created infrastructure for multi-terabyte scale image capture, established an image analysis capability for initial
screening and quantization.
Tissue microarray (TMA) technology allows rapid visualization of molecular targets in thousands of tissue specimens at a time and provides valuable information on expression of proteins within tissues at a cellular and sub-cellular level. TMA technology overcomes the bottleneck of traditional tissue analysis and allows it to catch up with the rapid advances in lead discovery. Studies using TMA on immunohistochemistry (IHC) can produce a large amount of images for interpretation within a very short time. Manual interpretation does not allow accurate quantitative analysis of staining to be undertaken. Automatic image capture and analysis has been shown to be superior to manual interpretation. The aims of this work is to develop a truly high-throughput and fully automated image capture and analysis system. We develop a robust colour segmentation algorithm using hue-saturation-intensity (HSI) colour space to provide quantification of signal intensity and partitioning of staining on high-throughput TMA. Initial segmentation results and quantification data have been achieved on 16,000 TMA colour images over 23 different tissue types.