Framework for Accurate Single-Molecule Spectroscopic Imaging Analyses Using Monte Carlo Simulation and Deep Learning

Area of Science:

• Spectroscopy and Microscopy
• Computational Imaging
• Biophysics

Background:

• Accurate single-molecule spectral imaging is crucial for high-throughput spectroscopy and super-resolution microscopy.
• A standardized framework for analyzing single-molecule spectral data is currently lacking.

Purpose of the Study:

• To develop a standardized framework for accurate single-molecule spectral imaging data analysis.
• To introduce a supervised learning-based denoising method (SpecUNet) for single-molecule spectral images.

Main Methods:

• Generated ground truth (GT) single-molecule spectral imaging data using Monte Carlo simulations.
• Developed SpecUNet, a supervised learning-based denoising method.
• Established eight evaluation metrics to compare SpecUNet with existing methods using synthetic GT data.
• Validated SpecUNet performance experimentally.

Main Results:

• SpecUNet demonstrated superior performance in denoising single-molecule spectral images compared to existing methods.
• The framework accurately characterized single-molecule fluorescence spectral heterogeneity of Janelia Fluors.
• SpecUNet successfully decoded spectral responses of Nile Red under varying nanoscale chemical polarities.

Conclusions:

• The developed framework provides a standardized approach for single-molecule spectral data analysis.
• SpecUNet significantly enhances the accuracy and capability of spectral imaging analysis.
• This work advances high-throughput single-molecule spectroscopy and super-resolution microscopy.