DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks.

Area of Science:

• Biophysics
• Materials Science
• Cell Biology

Background:

• Fluorescence recovery after photobleaching (FRAP) is a key microscopy technique for measuring diffusion.
• Conventional FRAP analysis relies on time-consuming non-linear least squares fitting of models to recovery data.
• Batch analysis of large FRAP datasets is particularly slow and may require multiple initial parameter guesses for convergence.

Purpose of the Study:

• To develop a machine learning-based approach, DeepFRAP, for rapid and accurate parameter estimation in FRAP experiments.
• To significantly reduce the computational time required for FRAP data analysis.
• To provide a robust alternative to traditional least squares fitting for diffusion coefficient estimation.

Main Methods:

• Generation of a large dataset of simulated FRAP recovery curves with realistic noise using a numerical FRAP model.
• Training deep neural network regression models on simulated data for predicting FRAP parameters, including the diffusion coefficient.
• Comparison of DeepFRAP's performance against conventional least squares estimation on simulated and experimental data.

Main Results:

• DeepFRAP achieves parameter estimation orders of magnitude faster than traditional least squares methods.
• Neural network estimations demonstrate strikingly similar performance and excellent agreement with least squares methods.
• The DeepFRAP framework provides accurate diffusion coefficient estimates, validated by experimental data.

Conclusions:

• DeepFRAP offers a substantial speed-up for FRAP data analysis without compromising accuracy.
• The trained neural networks can also serve as excellent initial guesses, accelerating least squares optimization.
• This machine learning approach enhances experimental efficiency and has broad applicability across various scientific disciplines utilizing FRAP.