Automated Plasmonic Resonance Scattering Imaging Analysis via Deep Learning.

Area of Science:

• Nanotechnology
• Biophotonics
• Computational Biology

Background:

• Plasmonic nanoparticles exhibit unique optical and chemical properties, making them valuable in various scientific fields.
• Single-particle light scattering dark-field microscopy (DFM) is a promising technique for plasmonic nanoparticle scatterometry.
• Extracting true scattering signals from noisy intracellular environments remains a significant challenge for DFM.

Purpose of the Study:

• To develop an automated, high-throughput local surface plasmon resonance (LSPR) scatterometry technique.
• To overcome the limitations of noise interference in DFM for intracellular analysis.
• To enable robust and accurate scatterometry in dynamic cellular environments.

Main Methods:

• Implementation of a U-Net convolutional deep learning neural network for image analysis.
• Training the deep neural network to distinguish nanoparticle scattering from background noise.
• Construction of a DFM image semantic analytical model using U-Net architecture.

Main Results:

• The U-Net based method demonstrated superior accuracy, generalization ability, and robustness compared to traditional approaches.
• The technique successfully identified and analyzed plasmonic nanoparticle scattering in complex intracellular settings.
• Demonstrated proof of concept by monitoring intracellular cytochrome c changes during UV-induced apoptosis in MCF-7 cells.

Conclusions:

• The proposed deep learning approach offers a powerful new strategy for high-throughput LSPR scatterometry in biological and chemical applications.
• This method enhances the capability of DFM for intracellular analysis by effectively managing noise.
• The technique provides a novel platform for scatterometry study and imaging analysis in chemistry.