EasyQuant: A machine-learning-assisted imaging-based tool for quantifying neutrophil extracellular traps (NETs)

Neutrophil extracellular traps (NETs) play a crucial role in host defense, yet their dysregulation is associated with inflammation and tissue damage. Consequently, researching NETs in vitro is essential for understanding their role in both health and disease. However, accurate quantification of NETs in vitro remains a challenge due to limitations in manual methods and semi-automated tools, which often require extensive user input and/or are time-consuming. Moreover, some methods identify neutrophils and NETs based on a single fixed parameter, such as size, which may lead to inaccuracies in recognizing neutrophils due to the cells' biological complexity. In this study, we developed a user-friendly framework for NET quantification using LABKIT, an ImageJ plugin, together with a custom Jython-based script that streamlines image processing and NET classification. By combining fluorescence microscopy with a machine learning tool, this approach enables rapid, scalable analysis of large multi-dimensional datasets with improved flexibility, reliability, and accessibility. Leveraging LABKIT for quantification ensures objective and consistent segmentation of NETs and neutrophils, reducing user bias and processing time while supporting reproducible data curation. Overall, EasyQuant provides a foundation for a freely accessible pipeline that can be integrated into diverse research workflows, herein specifically applied to improve the accuracy and throughput of neutrophil quantification.