Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 31, Issue 8, Pages 1484-1499Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/09622802221094133
Keywords
Co-localization; point process; Pearson's correlation; spatial statistics; super-resolution images; stochastic optical reconstruction microscopy
Categories
Funding
- NCI [P50CA221747-04, P30CA060553]
- NIA [P30AG072977]
- NIH [P50GM115279, P30CA021765]
- EMBO Long-Term Fellowship [ALTF1526-2016]
- American Lebanese and Syrian Associated Charities(ALSAC)
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This paper presents a novel statistical method for accurate analysis of protein co-localization in high-resolution images. It addresses the limitations of traditional methods in producing false-positive errors and being restricted to 2D images. The method shows excellent performance in simulation studies and real cell analysis, and can be applied to co-localization analysis in other disciplines.
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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