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

Ruby Peters, Marta Benthem Muñiz, Juliette Griffié, David J Williamson, George W Ashdown, Christian D Lorenz, Dylan M Owen

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
199 Downloads (Pure)


MOTIVATION: Unlike 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.

RESULTS: Here, we demonstrate statistical methodology, based on Ripley's K-function to quantitatively assess fibrous structures in 2D SMLM data sets. Using simulated data, we present the underlying theory to describe fiber spatial arrangements and show how these descriptions can be quantitatively derived from pointillist data sets. 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: Freely available on the web at https://github.com/RubyPeters/Angular-Ripleys-K Implemented in MatLab.

CONTACT: dylan.owen@kcl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)1703-1711
Issue number11
Early online date20 Jan 2017
Publication statusPublished - Jun 2017


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