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.
Biorxiv : the Preprint Server for Biology
|April 22, 2024
Summary
We developed an automated workflow for analyzing cell biology microscopy videos. This tool quantifies signals within single cells and phagosomes, aiding infectious disease research.
Area of Science:
- Cell biology
- Microscopy
- Infectious disease research
Background:
- Time-lapse microscopy is vital for understanding dynamic cellular processes.
- Quantifying signals in tracked cells, especially around pathogens, is challenging.
- Current single-phagosome analysis relies on limited manual tracking.
Approach:
- Developed a near-fully automated workflow using PyImageJ for Python integration.
- Combined deep learning (Cellpose) for segmentation with tracking algorithms (Trackmate).
- Enabled high-throughput cell and bacterium/phagosome segmentation and tracking in multi-channel, z-stack, time-lapse confocal microscopy videos.
Key Points:
- Workflow performs high-throughput, low-bias cell and single bacterium/phagosome quantification.
- Measures single-cell parameters (e.g., velocity, bacterial burden).
- Assesses single-phagosome maturation over time.
Conclusions:
- The open-source workflow offers versatile, flexible single-cell and single-phagosome quantification.
- Facilitates deciphering bacterial pathogenicity and virulence factor mechanisms.
- Aids development of novel therapeutic strategies for infectious diseases.
