Accounting for limited detection efficiency and localization precision in cluster analysis in single molecule localization microscopy

Arun Shivanandan; Jayakrishnan Unnikrishnan; Aleksandra Radenovic

doi:10.1371/journal.pone.0118767

Accounting for limited detection efficiency and localization precision in cluster analysis in single molecule localization microscopy

PLoS One. 2015 Mar 20;10(3):e0118767. doi: 10.1371/journal.pone.0118767. eCollection 2015.

Authors

Arun Shivanandan¹, Jayakrishnan Unnikrishnan², Aleksandra Radenovic¹

Affiliations

¹ Laboratory of Nanoscale Biology, Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland.
² Audiovisual Communications Laboratory, School of Computer and Communication Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland.

Abstract

Single Molecule Localization Microscopy techniques like PhotoActivated Localization Microscopy, with their sub-diffraction limit spatial resolution, have been popularly used to characterize the spatial organization of membrane proteins, by means of quantitative cluster analysis. However, such quantitative studies remain challenged by the techniques' inherent sources of errors such as a limited detection efficiency of less than 60%, due to incomplete photo-conversion, and a limited localization precision in the range of 10-30 nm, varying across the detected molecules, mainly depending on the number of photons collected from each. We provide analytical methods to estimate the effect of these errors in cluster analysis and to correct for them. These methods, based on the Ripley's L(r) - r or Pair Correlation Function popularly used by the community, can facilitate potentially breakthrough results in quantitative biology by providing a more accurate and precise quantification of protein spatial organization.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cluster Analysis
Computer Simulation
Image Processing, Computer-Assisted
Limit of Detection
Microscopy / methods*

Grants and funding

AS was funded by a Swiss National Competence Center for Biomedical Imaging PhD fellowship. JU was supported by the ERC Advanced Grant—SPARSAM No. 247006. AR acknowledges financial support by FNS grants No. 200021-125319 and No. 200021-132206. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.