Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
Centre of New Technologies, University of Warsaw, Warsaw, Poland.
Front Immunol. 2023 Jun 8;14:1021638. doi: 10.3389/fimmu.2023.1021638. eCollection 2023.
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.
中性粒细胞胞外陷阱（NETs）是由中性粒细胞将其 DNA 排出到环境中形成的捕捉病原体的结构，被认为在免疫和自身免疫性疾病中发挥重要作用。近年来，人们越来越关注开发软件工具来量化荧光显微镜图像中的 NETs。然而，目前的解决方案需要大量的、手动准备的训练数据集，对于没有计算机科学背景的用户来说难以使用，或者功能有限。为了克服这些问题，我们开发了 Trapalyzer，这是一种用于自动量化 NETs 的计算机程序。Trapalyzer 分析用细胞通透性和非通透性染料双重染色的样本的荧光显微镜图像，例如 Hoechst 33342 和 SYTOX™ Green 的流行组合。该程序的设计重点是软件人体工程学，并附有逐步教程，使其使用简单直观。未经培训的用户安装和配置软件不到半小时。除了 NETs，Trapalyzer 还可以检测、分类和计数处于 NET 形成不同阶段的中性粒细胞，从而更深入地了解这一过程。它是第一个无需大量训练数据集即可实现此功能的工具。同时，它的分类精度可与最先进的机器学习算法相媲美。作为一个示例应用，我们展示了如何使用 Trapalyzer 来研究中性粒细胞-细菌共培养物中的 NET 释放。在这里，经过配置后，Trapalyzer 在大约三分钟内在个人计算机上处理了 121 张图像并检测和分类了 16000 个 ROI。软件和使用教程可在 https://github.com/Czaki/Trapalyzer 上获得。
