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

作者： Rafael Badain1, Daniel S. C. Damineli1,2, Maria Teresa Portes3, José Feijó4, Stefano Buratti5, Giorgia Tortora6, Hugo Neves de Oliveira1, Roberto M. Cesar Jr1 1Institute of Mathematics and Statistics,University of São Paulo, 2Center for Mathematics, Computing and Cognition,Federal University of ABC, 3Department of Botany, Institute of Biosciences,University of São Paulo, 4Cell Biology and Molecular Genetics Department,University of Maryland, 5Department of Biosciences,University of Milan, 6Department of Physics,Politecnico di Milano

简介：

Overview

This manuscript presents a novel image analysis pipeline designed to automate the midline extraction of polarized single cells and quantify their spatiotemporal behavior. The user-friendly online interface enhances the analysis of time-lapse data, addressing the limitations of manual methods.

Key Study Components

Area of Science

• Cellular dynamics
• Image analysis
• Fluorescent imaging

Background

• Current methods for analyzing polarized cells are often manual.
• Manual analysis can introduce biases and is time-consuming.
• Automation in image analysis can improve scalability and standardization.
• This study focuses on a new approach to streamline this process.

Purpose of Study

• To automate the extraction of midlines in polarized cells.
• To quantify spatiotemporal dynamics from time-lapse imaging.
• To provide a user-friendly interface for researchers.

Methods Used

• Utilization of Jupyter Notebook for data analysis.
• Accessing time-lapse files through Google CoLab or local setup.
• Preparation of input and output directories for data management.
• Execution of setup and file input code blocks for data processing.

Main Results

• The method effectively quantifies dynamics of polarized cells.
• Automation reduces subjective biases in analysis.
• Scalability allows for handling larger datasets efficiently.
• Results can be visualized and interpreted through the interface.

Conclusions

• The proposed pipeline enhances the analysis of polarized cell dynamics.
• It offers a standardized approach to image analysis.
• This method can significantly improve research efficiency in cellular studies.

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