Motivation: New bioimaging techniques have recently been proposed to visualise the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyse the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens in order to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level.

Results: The proposed approach analyses cell phenotypes in normal and cancerous colon tissue imaged using the robotically controlled Toponome Imaging System (TIS) microscope. It involves segmenting the DAPI-labelled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. These were analysed using two new measures, Difference in Sums of Weighted cO-dependence/Anti-co-dependence profiles (DiSWOP and DiSWAP) for overall co-expression and anti-co-expression, respectively. These novel quantities were extracted using 11 TIS image stacks from either cancerous and normal human colorectal specimens. This approach enables one to easily identify protein pairs which have significantly higher/lower co-expression levels in cancerous tissue samples when compared to normal colon tissue

http://bioinformatics.oxfordjournals.org/content/early/2013/11/21/bioinformatics.btt676.abstract