Kner Lab shares QSTORM work at Photonics West

The QSTORM team members from the Kner Lab will be sharing their work at Photonics West this week. On Feb. 7, in a session on Superresolution Microscopy, they will present “Multi-color quantum dot stochastic optical reconstruction microscopy (QSTORM)”. On Feb. 8, in a session on Adaptive Optics, they will present “Wavefront correction using machine learning methods for single molecule localization microscopy”. Good luck!

Here are the abstracts:
Multi-color quantum dot stochastic optical reconstruction microscopy (qSTORM)
Paper 9331-11
Time: February 7, 2015 2:45 PM – 3:05 PM
Author(s): Kayvan F. Tehrani, Jianquan Xu, Peter A. Kner, The Univ. of Georgia

Although Single Molecule Localization (SML) techniques have pushed the resolution of fluorescence microscopy beyond the diffraction limit, the accuracy of SML has been limited by the brightness of the fluorophores. The introduction of Quantum Dots (QD) for SML promises to overcome this barrier, and the QD Blueing technique provides a novel approach to SML microscopy. QDs have higher quantum yield making them brighter and providing a higher accuracy of localization. However in biological imaging, multi-color staining is very important for showing the features of the samples under study. Here we introduce two color super-resolution microscopy using Quantum Dot Blueing on biological samples.

Wavefront correction using machine learning methods for single molecule localization microscopy
Paper 9335-20
Time: February 8, 2015 8:30 AM – 8:50 AM
Author(s): Kayvan F. Tehrani, Jianquan Xu, Peter A. Kner, The Univ. of Georgia

Optical Aberrations are a major challenge in imaging biological samples. In particular, in single molecule localization (SML) microscopy techniques (STORM, PALM, etc.) a high Strehl ratio point spread function (PSF) is necessary to achieve sub-diffraction resolution. Distortions in the PSF shape directly reduce the resolution of SML microscopy. A challenge for wavefront correction in SML microscopy is a robust optimization metric, since image intensity cannot be used due to the naturally high fluctuations in photon emission by single molecules. Here we evaluate different intensity-independent metrics and compare different machine learning methods for AO wavefront optimization.

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