Unbiased and robust analysis of co-localization in super-resolution images

  • Xueyan Liu (Creator)
  • Clifford S. Guy (Creator)
  • Emilio Boada-Romero (Creator)
  • D. R. Green (Creator)
  • Margaret E. Flanagan (Northwestern University) (Creator)
  • Cheng Cheng (Creator)
  • Hui Zhang (Creator)

Dataset

Description

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.
Date made available2022
PublisherSAGE Journals

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