AUTHOR=Panconi Luca , Owen Dylan M. , Griffié Juliette TITLE=Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates JOURNAL=Frontiers in Bioinformatics VOLUME=3 YEAR=2023 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2023.1237551 DOI=10.3389/fbinf.2023.1237551 ISSN=2673-7647 ABSTRACT=

Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern—a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.