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DNA Area and NETosis Analysis (DANA): a High-Throughput Method to Quantify Neutrophil Extracellular Traps in Fluorescent Microscope Images.

ABSTRACT: Background:Neutrophil extracellular traps (NETs), extracellular structures composed of decondensed chromatin and antimicrobial molecules, are released in a process called NETosis. NETs, which are part of normal host defense, have also been implicated in multiple human diseases. Unfortunately, methods for quantifying NETs have limitations which constrain the study of NETs in disease. Establishing optimal methods for NET quantification holds the potential to further elucidate the role of NETs in normal and pathologic processes. Results:To better quantify NETs and NET-like structures, we created DNA Area and NETosis Analysis (DANA), a novel ImageJ/Java based program which provides a simple, semi-automated approach to quantify NET-like structures and DNA area. DANA can analyze many fluorescent microscope images at once and provides data on a per cell, per image, and per sample basis. Using fluorescent microscope images of Sytox-stained human neutrophils, DANA quantified a similar frequency of NET-like structures to the frequency determined by two different individuals counting by eye, and in a fraction of the time. As expected, DANA also detected increased DNA area and frequency of NET-like structures in neutrophils from subjects with rheumatoid arthritis as compared to control subjects. Using images of DAPI-stained murine neutrophils, DANA (installed by an individual with no programming background) gave similar frequencies of NET-like structures as the frequency of NETs determined by two individuals counting by eye. Further, DANA quantified more NETs in stimulated murine neutrophils compared to unstimulated, as expected. Conclusions:DANA provides a means to quantify DNA decondensation and the frequency of NET-like structures using a variety of different fluorescent markers in a rapid, reliable, simple, high-throughput, and cost-effective manner making it optimal to assess NETosis in a variety of conditions.

SUBMITTER: Rebernick R

PROVIDER: S-EPMC5878938 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The COVID-19 pandemic has caused substantial losses worldwide in people's lives, health, and property. Currently, COVID-19 is still prominent worldwide without any specific drug treatment. The SARS-CoV-2 pathogen is the cause of various systemic diseases, mainly acute pneumonia. Within the pathological process, neutrophils are recruited to infected sites, especially in the lungs, for the first stage of removing invading SARS-CoV-2 through a range of mechanisms. Macroautophagy/autophagy, a conserved autodegradation process in neutrophils, plays a crucial role in the neutrophil phagocytosis of pathogens. NETosis refers to neutrophil cell death, while auto-inflammatory factors and antigens release NETs. This review summarizes the latest research progress and provides an in-depth explanation of the underlying mechanisms of autophagy and NETosis in COVID-19. Furthermore, after exploring the relationship between autophagy and NETosis, we discuss potential targets and treatment options. This review keeps up with the latest research on COVID-19 from neutrophil autophagy and NETosis with a new perspective, which can guide the urgent development of antiviral drugs and provide guidance for the clinical treatment of COVID-19.Abbreviations: AKT1: AKT serine/threonine kinase 1; AMPK: AMP-activated protein kinase; AP: autophagosome; ARDS: acute respiratory distress syndrome; ATG: autophagy related; BECN1: beclin 1; cfDNA: cell-free DNA; COVID-19: coronavirus disease 2019; CQ: chloroquine; DMVs: double-membrane vesicles; ELANE/NE: elastase, neutrophil expressed; F3: coagulation factor III, tissue factor; HCQ: hydroxychloroquine; MAP1LC3/LC3: microtubule associated protein 1 light chain of 3; MPO: myeloperoxidase; MTORC1: mechanistic target of rapamycin kinase complex 1; NETs: neutrophil traps; NSP: nonstructural protein; PI3K: class I phosphoinositide 3-kinase; PtdIns3K: class III phosphatidylinositol 3-kinase; PtdIns3P: phosphatidylinositol-3-phosphate; ROS: reactive oxygen species; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; SKP2: S-phase kinase associated protein 2; TCC: terminal complement complex; ULK1: unc-51 like.

Project description:BackgroundMacrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively.ResultsWe show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest.ConclusionsThe computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

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DNA Area and NETosis Analysis (DANA): a High-Throughput Method to Quantify Neutrophil Extracellular Traps in Fluorescent Microscope Images.

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