Convolutional Neural Networks-Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps

Trapalyzer: a computer program for quantitative analyses in fluorescent live-imaging studies of neutrophil extracellular trap formation

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.

Emerging Role of Neutrophil Extracellular Traps in Gastrointestinal Tumors: A Narrative Review

Neutrophil extracellular traps (NETs) are extracellular fibrous networks consisting of depolymerized chromatin DNA skeletons with a variety of antimicrobial proteins. They are secreted by activated neutrophils and play key roles in host defense and immune responses. Gastrointestinal (GI) malignancies are globally known for their high mortality and morbidity. Increasing research suggests that NETs contribute to the progression and metastasis of digestive tract tumors, among them gastric, colon, liver, and pancreatic cancers. This article explores the formation of NETs and reviews the role that NETs play in the gastrointestinal oncologic microenvironment, tumor proliferation and metastasis, tumor-related thrombosis, and surgical stress. At the same time, we analyze the qualitative and quantitative detection methods of NETs in recent years and found that NETs are specific markers of coronavirus disease 2019 (COVID-19). Then, we explore the possibility of NET inhibitors for the treatment of digestive tract tumor diseases to provide a new, efficient, and safe solution for the future therapy of gastrointestinal tumors.

Neutrophil Extracellular Traps (NETs) in Cancer Invasion, Evasion and Metastasis

Simple Summary

This review focuses on the pro-tumorigenic action of neutrophil extracellular traps (NETs). NETs were found in various samples of human and animal tumors. The role of the NETs in tumor development increasingly includes cancer immunoediting and interactions between immune system and cancer cells. NETs awake dormant cancer cells, play a key regulatory role in the tumor microenvironment, and exacerbate tumor aggressiveness by enhancing cancer migration and invasion capacity. Furthermore, NETs induce the epithelial to mesenchymal transition in tumor cells. NET proteinases can also degrade the extracellular matrix, promoting cancer cell extravasation. Moreover, NETs can entrap circulating cancer cells and, in that way, facilitate metastasis. A better understanding of the crosstalk between cancer and NETs can help to devise novel approaches to the therapeutic interventions that block cancer evasion mechanisms and prevent metastatic spread.

Abstract

The present review highlights the complex interactions between cancer and neutrophil extracellular traps (NETs). Neutrophils constitute the first line of defense against foreign invaders using major effector mechanisms: phagocytosis, degranulation, and NETs formation. NETs are composed from decondensed nuclear or mitochondrial DNA decorated with proteases and various inflammatory mediators. Although NETs play a crucial role in defense against systemic infections, they also participate in non-infectious conditions, such as inflammation, autoimmune disorders, and cancer. Cancer cells recruit neutrophils (tumor-associated neutrophils, TANs), releasing NETs to the tumor microenvironment. NETs were found in various samples of human and animal tumors, such as pancreatic, breast, liver, and gastric cancers and around metastatic tumors. The role of the NETs in tumor development increasingly includes cancer immunoediting and interactions between the immune system and cancer cells. According to the accumulated evidence, NETs awake dormant cancer cells, causing tumor relapse, as well as its unconstrained growth and spread. NETs play a key regulatory role in the tumor microenvironment, such as the development of distant metastases through the secretion of proteases, i.e., matrix metalloproteinases and proinflammatory cytokines. NETs, furthermore, directly exacerbate tumor aggressiveness by enhancing cancer migration and invasion capacity. The collected evidence also states that through the induction of the high-mobility group box 1, NETs induce the epithelial to mesenchymal transition in tumor cells and, thereby, potentiate their invasiveness. NET proteinases can also degrade the extracellular matrix, promoting cancer cell extravasation. Moreover, NETs can entrap circulating cancer cells and, in that way, facilitate metastasis. NETs directly trigger tumor cell proliferation through their proteases or activating signals. This review focused on the pro-tumorigenic action of NETs, in spite of its potential to also exhibit an antitumor effect. NET components, such as myeloperoxidase or histones, have been shown to directly kill cancer cells. A better understanding of the crosstalk between cancer and NETs can help to devise novel approaches to the therapeutic interventions that block cancer evasion mechanisms and prevent metastatic spread. This review sought to provide the most recent knowledge on the crosstalk between NETs and cancer, and bring more profound ideas for future scientists exploring this field.

SDoF-Net: Super Depth of Field Network for Cell Detection in Leucorrhea Micrograph

Accompanied with the rapid increase of the demand for routine examination of leucorrhea, efficiency and accuracy become the primary task. However, in super depth of field (SDoF) system, the problem of automatic detection and localization of cells in leucorrhea micro-images is still a big challenge. The changing of the relative position between the cell center and focus plane of microscope lead to variable cell morphological structure in the two-dimensional image, which is an important reason for the low accuracy of current deep learning target detection algorithms. In this paper, an object detection method based on Retinanet in state of super depth of field is proposed, which can achieve high precision detecting of leucorrhea components by the SDoF feature aggregation module. Compared with the current mainstream algorithms, the mean average accuracy (mAP) index has been improved significantly, the mAP index is 82.7% for SDoF module and 83.0% for SDoF+ module, with an average increase of more than 10%. These improved features can significantly improve the efficiency and accuracy of the algorithm. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.

Competitive fitness analysis using Convolutional Neural Network

We developed a procedure for estimating competitive fitness by using Caenorhabditis elegans as a model organism and a Convolutional Neural Network (CNN) as a tool. Competitive fitness is usually the most informative fitness measure, and competitive fitness assays often rely on green fluorescent protein (GFP) marker strains. CNNs are a class of deep learning neural networks, which are well suited for image analysis and object classification. Our model analyses involved image classification of nematodes as wild-type vs. GFP-expressing, and counted both categories. The performance was analyzed with (i) precision and recall parameters, and (ii) comparison of the wild-type frequency calculated from the model against that obtained by visual scoring of the same images. The average precision and recall varied from 0.79 to 0.87 and from 0.84 to 0.92, respectively, depending on worm density in the images. Compared with manual counting, the model decreased counting time at least 20-fold while preventing human errors. Given the rapid development in the field of CNN, the model, which is fully available on GitHub, can be further optimized and adapted for other image-based uses.