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

NETosis is a distinct form of neutrophil cell death involved in innate immunity and characterized by the release of DNA, that when dysregulated contributes to tissue damage and target-organ injury. As our understanding of the mechanisms governing this process continues to advance, there is a growing need for refined tools that can precisely characterize NETosis and enable efficient screening of its modulators. Here, we present a novel live-cell imaging and analysis pipeline for quantifying NETosis in cultured cells. We have generated a CellProfiler pipeline that enables in-depth analysis of cell and NET features, allowing for subsequent characterization and classification of NETosis stages using machine learning. Coupled with high throughput live cell imaging, this approach allows for large-scale and automated tracking of NETosis progression. The pipeline was validated in promyelocytic HL-60 cells differentiated into granulocyte-like cells, as well as primary neutrophils from the bone marrow of mice. We further confirmed dose and stimulus-dependent responses to common stimuli and pharmacological inhibitors of NETosis. As a flexible and scalable high-throughput imaging pipeline, our novel approach allows for the assessment of NETting dynamics and the screening of potential NETosis-modulating agents, which will be instrumental in developing therapies for NET-induced tissue injury.