Optimized labeling of membrane proteins for applications to super-resolution imaging in confined cellular environments using monomeric streptavidin

2017 ◽  
Vol 12 (4) ◽  
pp. 748-763 ◽  
Author(s):  
Ingrid Chamma ◽  
Olivier Rossier ◽  
Grégory Giannone ◽  
Olivier Thoumine ◽  
Matthieu Sainlos
Keyword(s):  
Membrane Proteins ◽  
Super Resolution ◽  
Resolution Imaging

scholarly journals Mapping the dynamics and nanoscale organization of synaptic adhesion proteins using monomeric streptavidin

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Ingrid Chamma ◽  
Mathieu Letellier ◽  
Corey Butler ◽  
Béatrice Tessier ◽  
Kok-Hong Lim ◽  
...  
Keyword(s):  
High Resolution ◽  
Membrane Proteins ◽  
Steric Hindrance ◽  
Transmembrane Protein ◽  
Super Resolution ◽  
Leucine Rich Repeat ◽  
Versatile Tool ◽  
Resolution Imaging ◽  
Dual Colour ◽  
Specific Labelling

Abstract The advent of super-resolution imaging (SRI) has created a need for optimized labelling strategies. We present a new method relying on fluorophore-conjugated monomeric streptavidin (mSA) to label membrane proteins carrying a short, enzymatically biotinylated tag, compatible with SRI techniques including uPAINT, STED and dSTORM. We demonstrate efficient and specific labelling of target proteins in confined intercellular and organotypic tissues, with reduced steric hindrance and no crosslinking compared with multivalent probes. We use mSA to decipher the dynamics and nanoscale organization of the synaptic adhesion molecules neurexin-1β, neuroligin-1 (Nlg1) and leucine-rich-repeat transmembrane protein 2 (LRRTM2) in a dual-colour configuration with GFP nanobody, and show that these proteins are diffusionally trapped at synapses where they form apposed trans-synaptic adhesive structures. Furthermore, Nlg1 is dynamic, disperse and sensitive to synaptic stimulation, whereas LRRTM2 is organized in compact and stable nanodomains. Thus, mSA is a versatile tool to image membrane proteins at high resolution in complex live environments, providing novel information about the nano-organization of biological structures.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

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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