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Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Deep learning using a residual deconvolutional network enables real-time high-density single-molecule localization

Zhiwei Zhou¹, Junnan Wu², Zhengxia Wang³

¹Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China.

Biomedical Optics Express

|April 20, 2023

Summary

This summary is machine-generated.

We developed FID-STORM, a deep learning method for faster, real-time single molecule localization microscopy (SMLM) image processing. This approach significantly accelerates high-density localization without sacrificing accuracy, enabling live analysis of biological samples.

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Area of Science:

Biophysics
Microscopy
Computational Biology

Background:

Deep learning accelerates single molecule localization microscopy (SMLM).
Existing deep learning methods for high-density localization are too slow for real-time processing due to computational complexity.
U-shape network architectures contribute to processing bottlenecks.

Purpose of the Study:

To develop a high-density localization method for real-time SMLM image processing.
To overcome the speed limitations of current deep learning-based SMLM techniques.
To improve localization accuracy and processing speed in SMLM.

Main Methods:

Proposed FID-STORM, a novel method utilizing an improved residual deconvolutional network.
Extracted features directly from low-resolution raw images, bypassing image interpolation.
Implemented TensorRT model fusion and GPU acceleration for sum of localization images.
Utilized an Nvidia RTX 2080 Ti graphics card for processing.

Main Results:

Achieved a processing speed of 7.31 ms/frame (256x256 pixels), enabling real-time data processing.
FID-STORM demonstrated a ~26x speed gain compared to Deep-STORM without loss of reconstruction accuracy.
Validated performance using both simulated and experimental SMLM data.

Conclusions:

FID-STORM enables real-time, high-density SMLM data processing.
The method offers significant speed improvements over existing techniques while maintaining accuracy.
An ImageJ plugin is available for the FID-STORM method, facilitating broader adoption.