Children’s Medical Research Institute (CMRI) was Australia’s first dedicated paediatric medical research facility and has been helping to save the lives of children for over 57 years. At CMRI, fluorescence microscopy is routinely used to visualise protein, DNA, and cell structures labelled with different fluorophores. The degree of signal overlap between the different channels is analysed in the resultant images, and this serves as a measure for colocalisation of the biological entities labelled by the fluorophores. Researchers were performing this colocalisation work manually, which was time consuming, tedious, and prone to human error.

Using Image Processing Toolbox™ they have developed a novel method to automatically identify regions of fluorescent signal on two channels, identify the colocated parts of these regions, and calculate the statistical significance of the colocalisation. Using GUIDE, a user interface is developed to visualise signal colocalisation and fine-tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal-to-noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed signal colocalisations compared to overlap by random chance using the Student’s t-test. Our validation revealed a highly significant correlation between manual and automatic identification of colocalisations. Therefore, our automatic method has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.