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Quantifying spatial organization in point-localization superresolution images using pair correlation analysis

Abstract

The distinctive distributions of proteins within subcellular compartments both at steady state and during signaling events have essential roles in cell function. Here we describe a method for delineating the complex arrangement of proteins within subcellular structures visualized using point-localization superresolution (PL-SR) imaging. The approach, called pair correlation photoactivated localization microscopy (PC-PALM), uses a pair-correlation algorithm to precisely identify single molecules in PL-SR imaging data sets, and it is used to decipher quantitative features of protein organization within subcellular compartments, including the existence of protein clusters and the size, density and number of proteins in these clusters. We provide a step-by-step protocol for PC-PALM, illustrating its analysis capability for four plasma membrane proteins tagged with photoactivatable GFP (PAGFP). The experimental steps for PC-PALM can be carried out in 3 d and the analysis can be done in 6–8 h. Researchers need to have substantial experience in single-molecule imaging and statistical analysis to conduct the experiments and carry out this analysis.

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Figure 1: Workflow for PC-PALM analysis.
Figure 2: Spatial organization of plasma membrane proteins expressed in COS-7 cells evaluated by PC-PALM analysis.

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Acknowledgements

We thank G. Patterson and Y. Fu (National Institute of Biomedical Imaging and Bioengineering) for DNA constructs and purified proteins, and H. Hess and G. Stengel (Howard Hughes Medical Institute, Janelia Farm Research Campus) for analysis software and valuable discussion.

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Authors

Contributions

P.S. and T.J.-T. designed and performed the experiments, analyzed the data and wrote the paper; J.L.-S. designed the experiments and wrote the paper.

Corresponding author

Correspondence to Jennifer Lippincott-Schwartz.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Data

Compute_Paircorrelation. Matlab code for computation of autocorrelation and cross-correlation functions from binary images of single molecule spatial distribution. (PDF 205 kb)

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Sengupta, P., Jovanovic-Talisman, T. & Lippincott-Schwartz, J. Quantifying spatial organization in point-localization superresolution images using pair correlation analysis. Nat Protoc 8, 345–354 (2013). https://doi.org/10.1038/nprot.2013.005

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