AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized...

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Cell polarity is crucial for biological functions like division, growth, and migration.
• Disrupted cell polarity is linked to diseases such as cancer.
• Current methods for analyzing cell polarity are time-consuming and biased.

Purpose of the Study:

• To develop a novel computational pipeline for automated and quantitative analysis of cell polarity.
• To improve the accuracy and reduce bias in spatiotemporal dynamics measurements.
• To provide a tool for investigating cytosolic ion dynamics and growth in polarized cells.

Main Methods:

• A three-step algorithm was developed to process ratiometric images.
• Image segmentation using thresholding, cell midline tracing via skeletonization.
• Generation of ratiometric timelapse and kymograph for quantitative analysis.

Main Results:

• The pipeline automates the quantification of spatiotemporal dynamics in polarized cells.
• Demonstrated faster, less biased, and more accurate analysis compared to manual methods.
• Successfully benchmarked using fluorescent reporter data from growing pollen tubes.

Conclusions:

• The novel computational pipeline significantly advances the quantitative toolkit for studying cell polarity.
• Enables more efficient and reliable investigation of intracellular dynamics.
• The AMEBaS Python source code is publicly available for research use.