Quantifying the nuclear localization of fluorescently tagged proteins

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Cells dynamically respond to intra- and extracellular signals, but measuring individual cell responses is challenging.
• Traditional reporters for gene expression are slow, necessitating alternative methods like monitoring protein localization.
• Quantifying nuclear localization of proteins, a rapid indicator of cellular signaling, lacks a standardized approach.

Purpose of the Study:

• To develop and validate a machine learning model for accurate quantification of nuclear localization in single cells.
• To improve the analysis of dynamic cellular responses to extracellular stimuli.
• To establish a more consistent and accurate method for single-cell biological analyses.

Main Methods:

• Developed a convolutional neural network (CNN) for nuclear localization analysis using fluorescence and bright-field microscopy images.
• Generated ground-truth data in budding yeast by tagging a transcription factor and a nuclear protein with fluorescent markers.
• Trained and evaluated the CNN against seven established methods using time-series data of single-cell responses.

Main Results:

• The CNN-based approach significantly outperformed seven previously published methods in quantifying nuclear localization.
• The model demonstrated superior performance in predicting single-cell time series, crucial for understanding cellular responses.
• The study highlights the effectiveness of machine learning for automated image processing in single-cell analysis.

Conclusions:

• Machine learning, specifically CNNs, provides a robust and accurate method for quantifying nuclear localization.
• Automated image processing using AI consistently surpasses ad hoc approaches in single-cell analyses.
• Adoption of these methods can enhance both accuracy and consistency in single-cell studies, with potential for transfer learning applications.