BEEP Learning: Multi-View Image Decomposition for Massively Multiplexed Biological Fluorescence Microscopy.

Area of Science:

• Biophysics
• Microscopy
• Machine Learning

Background:

• Fluorescence imaging enables specific mapping of biological structures.
• Distinguishing multiple fluorophores is challenging due to broad emission spectra and noise.
• Current methods struggle with high multiplexing in fluorescence microscopy.

Purpose of the Study:

• To develop a novel machine learning framework, Bleaching-Excitation-Emission Photodynamics (BEEP) learning.
• To enhance the number of distinguishable fluorophores in fluorescence imaging.
• To improve the robustness and accuracy of fluorescence unmixing.

Main Methods:

• Developed a multi-view fluorescence unmixing approach integrating emission spectra, excitation variability, and bleaching dynamics.
• Utilized a rank-one-tensor-based generalized linear model.
• Extracted excitation-specific spectral and bleaching signatures from reference images.

Main Results:

• BEEP learning significantly outperforms conventional and partially multi-view methods.
• Demonstrated improved robustness and accuracy in highly multiplexed fluorescence imaging.
• Validated on simulated and real images of microbial populations.

Conclusions:

• BEEP learning offers a powerful new approach for advanced fluorescence imaging.
• The framework effectively expands the capability to distinguish multiple fluorophores.
• This method provides enhanced accuracy for complex biological sample analysis.