MINER2.0 Combines ImageJ and R for Fast Nearest Neighbour Colocalization of 2D Multi‐Channel Fluorescence Images

Colocalization analysis has become a common tool of microscopic subcellular localization studies. There are many tools and packages available for colocalization analyses. MINER2.0 differs from many other packages by providing user‐friendly, highly customizable and scalable Nearest Neighbour analyses, drawing on principles of centroid localization used in single molecule imaging. Methods and Results To circumvent issues related to colocalization indices that rely on (often) arbitrarily thresholding color channels, our analysis pipeline compares predefined sets of regions of interest (ROIs) or can perform a de novo search for the K‐Nearest Neighbours (KNN) around the predefined ROIs. The algorithm calculates and reports: puncta area, mean intensities, full‐width‐half‐max sizes and intensity, and distances between the centroid or perimeter of reference ROIs and the centers of ROIs in up to two comparator ROI sets. Users can define ROI centroids as center of mass, geometric center or the biaxial Gaussian fit of the puncta. This latter method can statistically detect differences in distances between centroids in separate image channels of 1.4 pixels. At higher magnifications this allows detection of distances separating centroids in separate color channels at <90 nm, below the diffraction limit of optical microscopy. The pipeline is written as a user‐friendly macro for ImageJ/Fiji to ease of access. New in version 2.0, we moved from a brute force KNN calculation in ImageJ with an O(n2) time complexity to a Kd‐tree calculation in R with O(nLog(n)) complexity. This reduces the KNN calculation for large ROI sets (i.e. >1000 ROIs) from hours in ImageJ to typically <10 s in R. This enables batched analysis of large image sets on standard desktop computers (e.g. 36,000 ROIs in six 2048 × 2048 pixel images analyzed in <1.5 hours). We discuss three use cases: (1) the analysis of anti‐colocalization of the vesicular nucleotide transporter with diverse vesicle and lysosomal markers in Neuro2A cells, (2) analysis of the decrease of mitochondria containing mitochondrial DNA in A7r5 cells treated with diverse stressors including angiotensin II, 5,6‐dideoxycytidine, rotenone and hydrogen peroxide, and (3) the prevalence of micronuclei as markers of genotoxic stress in A7r5 cells expressing loss‐of‐function variants of polymerase gamma that is responsible for replicating mitochondrial DNA. Conclusion MINER2.0 provides a user‐friendly interface for fast batch processing of spatial colocalization analyses with minimal subjective bias. It provides users with extensive output parameters to allow detailed understanding of the spatial relationships of fluorescently labelled structures within cells. Support or Funding Information Natural Sciences and Engineering Research Council of Canada