Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data

• DOI: 10.1093/bioinformatics/btx026
• Publication Date: 2017-01-21T01:17:14Z
• Authors: Ruby Peters; Marta Benthem Muñiz; Juliette Griffié; David J Williamson; George W Ashdown; Christian D Lorenz; Dylan M Owen
• Keywords: 570; Jurkat Cells; T-Lymphocytes; 0206 medical engineering; Image Processing, Computer-Assisted; Cluster Analysis; Humans; 02 engineering and technology; Algorithms; Single Molecule Imaging

AbstractMotivationUnlike conventional microscopy which produces pixelated images, SMLM produces data in the form of a list of localization coordinates—a spatial point pattern (SPP). Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular clustering within, for example, the plasma membrane. While SMLM cluster analysis is now well developed, techniques for analyzing fibrous structures remain poorly explored.ResultsHere, we demonstrate a statistical methodology, based on Ripley’s K-function to quantitatively assess fibrous structures in 2D SMLM datasets. Using simulated data, we present the underlying theory to describe fiber spatial arrangements and show how these descriptions can be quantitatively derived from pointillist datasets. We also demonstrate the techniques on experimental data acquired using the image reconstruction by integrating exchangeable single-molecule localization (IRIS) approach to SMLM, in the context of the fibrous actin meshwork at the T cell immunological synapse, whose structure has been shown to be important for T cell activation.Availability and ImplementationFreely available on the web at https://github.com/RubyPeters/Angular-Ripleys-K. Implemented in MatLab.Supplementary informationSupplementary data are available at Bioinformatics online.