A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas
1 Department of Immunology, Genetics, Pathology, Science for Life Laboratory, Uppsala University, Dag Hammarskjölds väg 20, Uppsala, 751 85 Sweden
2 Science for Life Laboratory, Royal Institute of Technology, Tomtebodavägen 23A, Solna, 171 65, Sweden
3 Lab Surgpath, 204 Bombay Market, Tardeo Main Road, Mumbai, 400 034, India
Citation and License
BMC Medicine 2012, 10:103 doi:10.1186/1741-7015-10-103Published: 12 September 2012
The complexity of tissue and the alterations that distinguish normal from cancer remain a challenge for translating results from tumor biological studies into clinical medicine. This has generated an unmet need to exploit the findings from studies based on cell lines and model organisms to develop, validate and clinically apply novel diagnostic, prognostic and treatment predictive markers. As one step to meet this challenge, the Human Protein Atlas project has been set up to produce antibodies towards human protein targets corresponding to all human protein coding genes and to map protein expression in normal human tissues, cancer and cells. Here, we present a dictionary based on microscopy images created as an amendment to the Human Protein Atlas. The aim of the dictionary is to facilitate the interpretation and use of the image-based data available in the Human Protein Atlas, but also to serve as a tool for training and understanding tissue histology, pathology and cell biology. The dictionary contains three main parts, normal tissues, cancer tissues and cells, and is based on high-resolution images at different magnifications of full tissue sections stained with H & E. The cell atlas is centered on immunofluorescence and confocal microscopy images, using different color channels to highlight the organelle structure of a cell. Here, we explain how this dictionary can be used as a tool to aid clinicians and scientists in understanding the use of tissue histology and cancer pathology in diagnostics and biomarker studies.