A semi-automated imaging and analysis pipeline for NET quantification and temporal-profiling of NETosis.

Area of Science:

• Immunology
• Cell Biology
• Biotechnology

Background:

• Neutrophil extracellular trap (NET) formation, or NETosis, is a key innate immune process.
• Dysregulated NETosis contributes to tissue damage and organ injury.
• Advanced tools are needed to precisely characterize NETosis and screen its modulators.

Purpose of the Study:

• To present a novel live-cell imaging and analysis pipeline for quantifying NETosis.
• To enable in-depth analysis and machine learning-based classification of NETosis stages.
• To facilitate high-throughput screening of NETosis modulators.

Main Methods:

• Development of a CellProfiler pipeline for live-cell imaging and analysis of NETosis.
• Application of machine learning for NETosis stage characterization.
• Validation in differentiated HL-60 cells and primary mouse neutrophils.

Main Results:

• The pipeline enables large-scale, automated tracking of NETosis progression.
• Dose- and stimulus-dependent responses to NETosis inducers and inhibitors were confirmed.
• The approach demonstrated flexibility and scalability for high-throughput analysis.

Conclusions:

• The novel pipeline offers a flexible and scalable method for assessing NETting dynamics.
• This tool is instrumental for screening potential NETosis-modulating agents.
• The approach will aid in developing therapies for NET-induced tissue injury.