Liu X, Guy CS, Boada-Romero E, Green DR, Flanagan ME, Cheng C, Zhang H. Unbiased and robust analysis of co-localization in
super-resolution images.
Stat Methods Med Res 2022;
31:1484-1499. [PMID:
35450486 PMCID:
PMC9648350 DOI:
10.1177/09622802221094133]
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Abstract
Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy, provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false-positive errors and are restricted to only 2-dimensional images. In this paper, we develop a novel statistical method to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of co-localization of protein light chain 3 and lysosomal-associated membrane protein 1 in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science, and ecology.
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