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nanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images
Cristina Izquierdo-Lozano1, Niels van Noort1, Stijn van Veen1
1Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, 5612AZ Eindhoven, The Netherlands. f.grisoni@tue.nl.
Nanoscale
|October 30, 2024
Summary
A new Matlab app, nanoFeatures, offers automatic, quantitative analysis of super-resolution microscopy images for nanoparticles. This tool extracts size, shape, and molecular data, advancing nanomaterial characterization for scientists.
Area of Science:
- Nanotechnology
- Materials Science
- Biophysics
Background:
- Super-resolution microscopy, including Single-Molecule Localization Microscopy (SMLM) techniques like STORM, PALM, and PAINT, is crucial for characterizing synthetic nanomaterials.
- Current SMLM image analysis often lacks robust quantitative methods, limiting studies to qualitative or semi-quantitative assessments.
- Accurate quantitative analysis is essential for understanding nanoparticle properties and their applications, such as in drug delivery.
Purpose of the Study:
- To develop a robust and accurate method for the automatic and quantitative analysis of SMLM images of nanoparticles.
- To introduce nanoFeatures, a cross-platform Matlab-based application designed for this purpose.
- To facilitate the extraction of single-particle and single-molecule level quantitative features from super-resolution data.
Main Methods:
- Development of nanoFeatures, a Matlab application utilizing clustering algorithms to process SMLM localization data.
- Identification of individual nanoparticles from raw localization lists.
- Extraction of quantitative features including size, shape, and molecular abundance per nanoparticle.
- Implementation of quality control measures to enhance data reliability and mitigate artifacts.
Main Results:
- nanoFeatures enables automatic and quantitative analysis of super-resolution microscopy images.
- The app successfully identifies nanoparticles and extracts detailed single-particle and single-molecule quantitative information.
- Integrated quality controls improve the reliability and accuracy of the analyzed data.
- The intuitive interface makes nanoFeatures accessible to non-experts in super-resolution microscopy.
Conclusions:
- nanoFeatures provides an essential tool for quantitative characterization of synthetic nanomaterials using SMLM.
- The application bridges the gap between advanced imaging capabilities and analytical limitations.
- By facilitating detailed feature extraction, nanoFeatures aids in understanding the structure-property-efficiency relationships of nanomaterials.
