PhagoStat is a Python-based pipeline designed to streamline the quantification, analysis and interpretation of phagocytic activity in dynamic, unstained cells under real-world experimental conditions.
Research Themes
Project Overview
PhagoStat provides researchers with an efficient and reliable method for evaluating cellular phagocytosis, supporting the study of immune mechanisms, cellular interactions and neurodegenerative disease processes.
The framework combines image preprocessing, data quality assessment, shift correction, aggregate quantification, instance-level cell analysis and trajectory estimation. Its design emphasizes scalability, interpretability and usability in realistic experimental settings.
Scientific Motivation
Quantifying phagocytic activity is essential for understanding immune response, cellular dynamics and neurodegenerative mechanisms. However, real-world microscopy data often include noise, drift, heterogeneous acquisition conditions and limited manual annotations.
PhagoStat addresses these challenges through an automated and interpretable workflow that supports robust quantification while preserving the ability to inspect and validate intermediate results.
Resources
Demonstrations
Data Quality Check and Shift Correction
This module ensures that dynamic microscopy data are reliable and accurately aligned before downstream quantification.
Real-Time Aggregate Quantification
This module enables automated quantification of aggregate dynamics over time, supporting the analysis of phagocytic activity at population scale.
Real-Time Cellular Quantification and Trajectory Estimation
This module estimates cellular instances and trajectories, enabling dynamic analysis of individual cells and their interactions with aggregates.
