The first type of measure is pixel intensity-based coefficients where overlapping fluorescent pixel intensities of two channels are used to calculate Pearson, Manders (M1 or M2), Li or Costes coefficients 3, 4, 5. Two types of quantitative colocalisation measures are currently used. The overlap of signal between the different channels can be analysed in the resultant images, and this serves as a measure for colocalisation of the biological entities labelled by the fluorophores. Fluorescence microscopy can be used to visualise protein, DNA, and cell structures labelled with different fluorophores. For instance, the colocalisation of telomeric DNA and promyelocytic leukaemia (PML) protein is a marker of cells that utilise the alternative lengthening of telomeres (ALT) mechanism to maintain telomere length 1, while the E3 ubiquitin ligase Mdm2 binds and negatively regulates the tumour suppressor p53 2. Protein-DNA and protein-protein interactions are known to be markers and regulators of cellular and biological processes. MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R 2 = 0.81, P < 0.0001). We validated MatCol in a biological setting. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student’s t-test. Command-line execution allows batch processing of multiple images. MatCol can be used to visualise protein and/or DNA colocalisations 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. We have developed MatCol to address these needs. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. However, these methods cannot be used to study object-based colocalisations in biological systems. Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes.
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