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Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Authors :
Anne-Lise Paupiah
Xavier Marques
Zaha Merlaud
Marion Russeau
Sabine Levi
Marianne Renner
Source :
Biological Imaging, Vol 3 (2023)
Publication Year :
2023
Publisher :
Cambridge University Press, 2023.

Abstract

Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains (“nanodomains”) in clusters with non-homogeneous distribution of detections.

Details

Language :
English
ISSN :
2633903X
Volume :
3
Database :
Directory of Open Access Journals
Journal :
Biological Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.06ea6d92830e4b539dd31631a6b5908a
Document Type :
article
Full Text :
https://doi.org/10.1017/S2633903X23000156