ArticleStatistical methods in medical research2022
Unbiased and robust analysis of co-localization in super-resolution images.
Article in Statistical methods in medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From Uptake to Therapeutic Function in Engineered Exosome Delivery Systems.Research (Washington, D.C.) · 2026Review
- Membrane receptors cluster phosphatidylserine to activate LC3-associated phagocytosis.Nature cell biology · 2025Article
- Characterization of the tumor immune microenvironment in pregnancy-associated breast cancer through multiplex immunohistochemistry and transcriptome analyses.Breast cancer research : BCR · 2025Article
- KCBC - a correlation-based method for co-localization analysis of super-resolution microscopy images using bivariate Ripley'sJournal of applied statistics · 2024Article
- CXCR6 orchestrates brain CD8Nature immunology · 2023Article
- Localization of a TORC1-eIF4F translation complex during CD8Molecular cell · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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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Registered trials
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