scholarly journals Hyperspectral super-resolution imaging with far-red emitting fluorophores using a thin-film tunable filter

2020 ◽  
Vol 91 (12) ◽  
pp. 123703
Author(s):  
Adriano Vissa ◽  
Maximiliano Giuliani ◽  
Peter K. Kim ◽  
Christopher M. Yip
2019 ◽  
Author(s):  
Adriano Vissa ◽  
Maximiliano Giuliani ◽  
Peter K. Kim ◽  
Christopher M. Yip

New innovations in single-molecule localization microscopy (SMLM) have revolutionized optical imaging, enabling the characterization of biological structures and interactions with unprecedented detail and resolution. However, multi-colour or hyperspectral SMLM can be particularly challenging due to non-linear image registration issues, which affect image quality and data interpretation. Many of these arise as a consequence of differences in illumination optics (beam profile, power density, polarization, point spread function) for the different light sources. This is particularly acute in evanescent-wave based approaches (TIRF) where beam shape, decay depth, and power density are important. A potential useful approach would be to use a single excitation wavelength to perform hyperspectral localization imaging.. We report herein on the use of a variable angle tunable thin-film filter to spectrally isolate far-red emitting fluorophores. This solution was integrated into a commercial microscope platform using an open-source hardware design, enabling the rapid acquisition of SMLM images with ~ 15-20 nm spectral resolution.. By characterizing intensity distributions, average intensities, and localization frequency through a range of spectral windows, we identified an optimal fluorophore pair for two-colour SMLM. Fluorophore crosstalk between the different spectral windows was assessed by examining the effect of varying the photon output thresholds on the localization frequency and fraction of data recovered. The utility of this approach was demonstrated by hyper-spectral super-resolution imaging of the interaction between the mitochondrial protein, TOM20 and the peroxisomal protein, PMP70.


2019 ◽  
Vol 8 (10) ◽  
pp. 1378-1382 ◽  
Author(s):  
Joshua A. Hinckley ◽  
Dana V. Chapman ◽  
Konrad R. Hedderick ◽  
Katharine W. Oleske ◽  
Lara A. Estroff ◽  
...  

2015 ◽  
Vol 44 (4) ◽  
pp. 426004
Author(s):  
庞辉 PANG Hui ◽  
杜春雷 DU Chun-lei ◽  
邱琪 QIU Qi ◽  
邓启凌 DENG Qi-ling ◽  
张满 ZHANG Man ◽  
...  

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.


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