Method for co-cluster analysis in multichannel single-molecule localisation data
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We demonstrate a combined univariate and bivariate Getis and Franklin’s local point pattern analysis method to investigate the co-clustering of membrane proteins in two-dimensional single-molecule localisation data. This method assesses the degree of clustering of each molecule relative to its own species and relative to a second species. Using simulated data, we show that this approach can quantify the degree of cluster overlap in multichannel point patterns. The method is validated using photo-activated localisation microscopy and direct stochastic optical reconstruction microscopy data of the proteins Lck and CD45 at the T cell immunological synapse. Analysing co-clustering in this manner is generalizable to higher numbers of fluorescent species and to three-dimensional or live cell data sets.
KeywordsCluster analysis Super-resolution Co-localisation PALM STORM
D.M.O. is supported by a Marie Curie Career Integration Grant (CIG) Ref 334303. KG is supported by the National Health and Medical Research Council of Australia and the Australian Research Council.