da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells.
Jacques Augenstreich1, Anushka Poddar1, Ashton T Belew1,2
1Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
Biology Open
|August 23, 2024
Summary
We developed an automated workflow for analyzing cell and pathogen dynamics using time-lapse microscopy. This tool enables high-throughput quantification of cellular processes and pathogen interactions, aiding infectious disease research.
Area of Science:
- Cell biology
- Microscopy
- Infectious disease research
Background:
- Time-lapse microscopy is vital for studying dynamic cellular processes.
- Quantifying signals within tracked cells, especially around pathogens, is challenging.
- Current single-phagosome analysis often relies on manual methods.
Purpose of the Study:
- To develop a near-fully automated workflow for high-throughput cell and pathogen tracking and quantification.
- To overcome limitations in manual tracking for single-phagosome analysis.
- To provide a versatile tool for studying host-pathogen interactions in infectious diseases.
Main Methods:
- Utilized PyImageJ to integrate Fiji functionality into Python.
- Combined Cellpose (deep learning) for segmentation with Trackmate for tracking.
- Developed the 'da_tracker' workflow for multi-channel, z-stack, time-lapse confocal microscopy data.
Main Results:
- Achieved near-fully automated, high-throughput cell segmentation and tracking.
- Enabled quantitative tracking of single cells and single bacteria/phagosomes.
- Provided versatile measurements at single-cell (e.g., velocity, bacterial burden) and single-phagosome (e.g., maturation) levels.
Conclusions:
- The 'da_tracker' workflow offers a flexible, open-source solution for detailed cellular and pathogen quantification.
- Facilitates deeper insights into bacterial pathogenicity and virulence mechanisms.
- Aids in developing novel therapeutic strategies for infectious diseases.
