Adaptive Deep Learning for High-Fidelity Quantitative Single-Molecule Imaging.

Area of Science:

• Molecular biology
• Bioimaging
• Deep learning

Background:

• Accurate quantification of molecular dynamics in live cells is crucial for understanding receptor signaling and developing therapies.
• Current deep learning methods for fluorescence imaging often suffer from intensity distortion and lack robustness, limiting their quantitative applications.

Purpose of the Study:

• To develop an adaptive deep learning framework for robust and quantitative molecular dynamics analysis in live-cell fluorescence imaging.
• To preserve absolute molecular intensities during imaging to enhance quantitative utility.

Main Methods:

• An adaptive deep learning framework was developed, constructing in situ training datasets from live imaging sequences.
• A self-feedback algorithm was integrated to maintain absolute molecular intensities.
• The framework was tested across various fluorophores including organic dyes, quantum dots, and fluorescent proteins.

Main Results:

• The framework achieved significant improvements in signal-to-noise ratio (≥ 3.9-fold) and localization precision (1.6-fold).
• It enhanced super-resolution reconstruction, stepwise photobleaching analysis, and live-cell single-molecule tracking.
• Application to programmed death-ligand 1 (PD-L1) revealed density-dependent clustering and drug-induced changes in membrane organization.

Conclusions:

• The adaptive, intensity-preserving deep learning framework offers a broadly applicable platform for high-precision quantitative bioimaging.
• This method overcomes limitations of current deep learning approaches in fluorescence imaging.
• It enables deeper insights into molecular dynamics and cellular processes, with implications for drug discovery and therapeutic strategies.