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. 2020 Feb 24;9(2):508.
doi: 10.3390/cells9020508.

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

Aneta Manda-Handzlik  1   2 , Krzysztof Fiok  3 , Adrianna Cieloch  1 , Edyta Heropolitanska-Pliszka  4 , Urszula Demkow  1
Affiliations

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

Aneta Manda-Handzlik et al. Cells. .

Abstract

Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections. Currently applied methods for NETs release quantification are often limited by the use of unspecific dyes and technical difficulties. Therefore, we aimed to develop a fully automatic image processing method for the detection and quantification of NETs based on live imaging with the use of DNA-staining dyes. For this purpose, we adopted a recently proposed Convolutional Neural Network (CNN) model called Mask R-CNN. The adopted model detected objects with quality comparable to manual counting-Over 90% of detected cells were classified in the same manner as in manual labelling. Furthermore, the inhibitory effect of GW 311616A (neutrophil elastase inhibitor) on NETs release, observed microscopically, was confirmed with the use of the CNN model but not by extracellular DNA release measurement. We have demonstrated that a modern CNN model outperforms a widely used quantification method based on the measurement of DNA release and can be a valuable tool to quantitate the formation process of NETs.

Keywords: automatic image analysis; chronic granulomatous disease; convolutional neural networks (CNN), mask R-CNN; neutrophil extracellular traps (NETs) quantification; neutrophils; nitric oxide; peroxynitrite; reactive nitrogen species.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Representative examples of objects manually assigned as unstimulated, decondensed, neutrophil extracellular trap (NET)-releasing or dead cells. Isolated human neutrophils were seeded into plates, pre-incubated with or without neutrophil elastase inhibitor (NEi) for 30 min and stimulated with phorbol 12-myristate 13-acetate (PMA), S-nitroso-N-acetyl-dl-penicillamine (SNAP) or peroxynitrite or left unstimulated. After 1–3 hours of incubation, cells were simultaneously stained with Hoechst 33342 and SYTOX Green. Samples were visualized with inverted fluorescent microscope and 300 images were gathered at 40× magnification to create a NETs dataset. The observed objects were manually assigned into four categories: unstimulated, decondensed, NET-producing, and dead cells. In this figure representative examples of objects assigned into aforementioned categories are shown. Bar = 50 μm.
Figure 2
Figure 2
The adopted model trained on our preliminary dataset is able to detect objects with quality comparable to manual labeling. Images of unfixed neutrophils stained with Hoechst 33342 and SYTOX Green were analyzed by the CNN model (right) in parallel with manual analysis (left). To facilitate the assessment of labeling, colored dots are shown at one of the four vertexes of a rectangle surrounding the object. Grey dots are unstimulated cells (un), blue dots are decondensed cells (dec), violet dots are NETs, yellow dots are dead cells. Numerical values represent model’s confidence in the given cell class prediction—1 is maximum confidence, and the scale bar is 100 μm.
Figure 3
Figure 3
The adopted CNN model, contrary to DNA release measurement, confirmed the inhibitory effect of NEi on NETs release. Peripheral blood neutrophils were isolated from peripheral blood of six healthy donors, samples were pretreated with NEi for 30 min and/or stimulated with peroxynitrite for 2 h. Negative control—Unstimulated cells, incubated only with RPMI 1640. (a) Representative images of samples pretreated with NEi and/or stimulated with peroxynitrite using live imaging with Hoechst 33342 and SYTOX Green; (b) representative images of immunofluorescently-labeled samples pretreated with NEi and/or stimulated with peroxynitrite; (c, d) for each patient, 10 images of unfixed cells stained with Hoechst 33342 and SYTOX Green were taken per each one of four experimental conditions (negative control; cells pretreated with NEi; cells stimulated with peroxynitrite; cells pretreated with NEi and stimulated with peroxynitrite). The images were analyzed using the adopted model and the results were analyzed as percentage of objects of different classes and compared between groups. (d) n = 6, 2-way ANOVA with post-hoc Tukey’s test. (e) At the indicated timepoint, NETs formation was assessed fluorometrically using SYTOX green after detachment of DNA with MNase, n = 6, 1-way ANOVA with posthoc Bonferroni’s test.
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