Localisation microscopy with quantum dots using non-negative matrix factorisation.

Area of Science:

• Microscopy and Imaging
• Biophysics
• Quantum Dot Applications

Background:

• Super-resolution microscopy faces challenges with highly overlapping fluorophores and intermittent signals.
• Quantum dots offer bright and stable fluorescence but require advanced processing for localization microscopy.
• Existing methods struggle to accurately resolve individual sources in dense, noisy datasets.

Purpose of the Study:

• To develop a novel computational method for analyzing noisy, overlapping fluorescence data.
• To enable high-resolution imaging of individual quantum dot sources with unknown properties.
• To improve the accuracy of axial localization in super-resolution microscopy.

Main Methods:

• Non-negative matrix factorization with iterative restarts (iNMF) was employed.
• The model accommodates arbitrary point spread function (PSF) shapes and blinking behaviors.
• Performance was benchmarked against CSSTORM, 3B, and bSOFI algorithms.

Main Results:

• iNMF successfully recovered high-resolution images of individual sources from highly overlapping data.
• The method demonstrated superior performance compared to existing techniques in super-resolution imaging with quantum dots.
• Axial localization information was retrieved from the shape of the recovered PSF.

Conclusions:

• Iterative non-negative matrix factorization (iNMF) is a powerful tool for super-resolution imaging of quantum dots.
• iNMF overcomes limitations of overlapping signals and unknown source characteristics.
• The technique enhances localization microscopy by providing accurate source identification and axial positioning.