ArticleNature communications2022
A density-based enrichment measure for assessing colocalization in single-molecule localization microscopy data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- The Effect of Ligand Reactivity and Carrier Mechanics on Thiol-Mediated Cellular Uptake for miRNA Nano-Drugs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Diverse intracellular trafficking of insulin analogs by machine learning-based colocalization and diffusion analysis.iScience · 2026Article
- Advanced Imaging for Live-Cell Spatiotemporal Monitoring: Technologies and Applications.Research (Washington, D.C.) · 2026Review
- Identifying intermolecular interactions in single-molecule localization microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- RNA Helicase DDX5 in Association With IFI16 and the Polycomb Repressive Complex 2 Silences Transcription of the Hepatitis B Virus by Interferon.Journal of medical virology · 2024Article
- Optimizing Voronoi-based quantifications for reaching interactive analysis of 3D localizations in the million range.Frontiers in bioinformatics · 2023Article
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7 authors.
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Abstract
Dual-color single-molecule localization microscopy (SMLM) provides unprecedented possibilities for detailed studies of colocalization of different molecular species in a cell. However, the informational richness of the data is not fully exploited by current analysis tools that often reduce colocalization to a single value. Here, we describe a tool specifically designed for determination of co-localization in both 2D and 3D from SMLM data. The approach uses a function that describes the relative enrichment of one molecular species on the density distribution of a reference species. The function reframes the question of colocalization by providing a density-context relevant to multiple biological questions. Moreover, the function visualize enrichment (i.e. colocalization) directly in the images for easy interpretation. We demonstrate the approach's functionality on both simulated data and cultured neurons, and compare it to current alternative measures. The method is available in a Python function for easy and parameter-free implementation.
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