AUTHOR=Ciach Michał Aleksander , Bokota Grzegorz , Manda-Handzlik Aneta , Kuźmicka Weronika , Demkow Urszula , Gambin Anna TITLE=Trapalyzer: a computer program for quantitative analyses in fluorescent live-imaging studies of neutrophil extracellular trap formation JOURNAL=Frontiers in Immunology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1021638 DOI=10.3389/fimmu.2023.1021638 ISSN=1664-3224 ABSTRACT=

Neutrophil extracellular traps (NETs), pathogen-ensnaring structures formed by neutrophils by expelling their DNA into the environment, are believed to play an important role in immunity and autoimmune diseases. In recent years, a growing attention has been put into developing software tools to quantify NETs in fluorescent microscopy images. However, current solutions require large, manually-prepared training data sets, are difficult to use for users without background in computer science, or have limited capabilities. To overcome these problems, we developed Trapalyzer, a computer program for automatic quantification of NETs. Trapalyzer analyzes fluorescent microscopy images of samples double-stained with a cell-permeable and a cell-impermeable dye, such as the popular combination of Hoechst 33342 and SYTOX™ Green. The program is designed with emphasis on software ergonomy and accompanied with step-by-step tutorials to make its use easy and intuitive. The installation and configuration of the software takes less than half an hour for an untrained user. In addition to NETs, Trapalyzer detects, classifies and counts neutrophils at different stages of NET formation, allowing for gaining a greater insight into this process. It is the first tool that makes this possible without large training data sets. At the same time, it attains a precision of classification on par with state-of-the-art machine learning algorithms. As an example application, we show how to use Trapalyzer to study NET release in a neutrophil-bacteria co-culture. Here, after configuration, Trapalyzer processed 121 images and detected and classified 16 000 ROIs in approximately three minutes on a personal computer. The software and usage tutorials are available at https://github.com/Czaki/Trapalyzer.