The work was funded by the Crafoord Foundation (TM and PN), Swedish Government Research grant (PN, TM), Swedish research council (PN) and Groschinsky Foundation (TM, PN).

The ethics committee of Lund University approved the collection of venous blood from healthy volunteers in accordance with the Declaration of Helsinki (2013/728). All volunteers provided their written informed consent.
1. Isolation of Peripheral Blood Neutrophils using Density-Gradient Centrifugation
<ol>
	<li>Collect human venous blood in tubes containing heparin and allow the tubes to reach room temperature. 
	Note: A minimum of 16 mL of blood from a healthy donor is required to yield...

<ol>
	<li>Nauseef, W. M., Borregaard, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neutrophils+at+work.">Neutrophils at work.</a> Nature Immunology. 15 (7), 602-611 (2014).</li><li>Borregaard, N., Cowland, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Granules+of+the+human+neutrophilic+polymorphonuclear+leukocyte.">Granules of the human neutrophilic polymorphonuclear leukocyte.</a> Blood. 89 (10), 3503-3521 (1997).</li><li>Nordenfelt, P., Tapper, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phagosome+dynamics+during+phagocytosis+by+neutrophils.">Phagosome dynamics during phagocytosis by neutrophils.</a> Journal of Leukocyte Biology. 90 (2), 271-284 (2011).</li><li>Brinkmann, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neutrophil+extracellular+traps+kill+bacteria.">Neutrophil extracellular traps kill bacteria.</a> Science. 303 (5663), 1532-1535 (2004).</li><li>Sorensen, O. E., Borregaard, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neutrophil+extracellular+traps+-+the+dark+side+of+neutrophils.">Neutrophil extracellular traps - the dark side of neutrophils.</a> Journal of Clinical Investigation. 126 (5), 1612-1620 (2016).</li><li>Yipp, B. G., Kubes, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NETosis:+how+vital+is+it?.">NETosis: how vital is it?.</a> Blood. 122 (16), 2784-2794 (2013).</li><li>Jorch, S. K., Kubes, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+emerging+role+for+neutrophil+extracellular+traps+in+noninfectious+disease.">An emerging role for neutrophil extracellular traps in noninfectious disease.</a> Nature Medicine. 23 (3), 279-287 (2017).</li><li>Fuchs, T. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+cell+death+program+leads+to+neutrophil+extracellular+traps.">Novel cell death program leads to neutrophil extracellular traps.</a> J Cell Biol. 176 (2), 231-241 (2007).</li><li>Mohanty, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+mechanism+for+NETosis+provides+antimicrobial+defense+at+the+oral+mucosa.">A novel mechanism for NETosis provides antimicrobial defense at the oral mucosa.</a> Blood. 126 (18), 2128-2137 (2015).</li><li>Hakkim, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Activation+of+the+Raf-MEK-ERK+pathway+is+required+for+neutrophil+extracellular+trap+formation.">Activation of the Raf-MEK-ERK pathway is required for neutrophil extracellular trap formation.</a> Nature Chemical Biology. 7 (2), 75-77 (2011).</li><li>Brinkmann, V., Goosmann, C., Kuhn, L. I., Zychlinsky, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+quantification+of+in+vitro+NET+formation.">Automatic quantification of in vitro NET formation.</a> Frontiers in Immunology. 3, 413 (2012).</li><li>Coelho, L. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+determination+of+NET+(neutrophil+extracellular+traps)+coverage+in+fluorescent+microscopy+images.">Automatic determination of NET (neutrophil extracellular traps) coverage in fluorescent microscopy images.</a> Bioinformatics. 31 (14), 2364-2370 (2015).</li><li>Mohanty, T., Sorensen, O. E., Nordenfelt, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NETQUANT:+Automated+Quantification+of+Neutrophil+Extracellular+Traps.">NETQUANT: Automated Quantification of Neutrophil Extracellular Traps.</a> Frontiers in Immunology. 8, 1999 (2017).</li><li>Kaplan, M. J., Radic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neutrophil+extracellular+traps:+double-edged+swords+of+innate+immunity.">Neutrophil extracellular traps: double-edged swords of innate immunity.</a> Journal of Immunology. 189 (6), 2689-2695 (2012).</li><li>Cedervall, J., Olsson, A. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunity+Gone+Astray+-+NETs+in+Cancer.">Immunity Gone Astray - NETs in Cancer.</a> Trends in Cancer. 2 (11), 633-634 (2016).</li><li>Ginley, B. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+detection+and+quantification+of+human+and+mouse+neutrophil+extracellular+traps+in+flow+cytometry+and+confocal+microscopy.">Computational detection and quantification of human and mouse neutrophil extracellular traps in flow cytometry and confocal microscopy.</a> Scientific Reports. 7 (1), 17755 (2017).</li></ol>

NET formation is a relatively recent addition to the diverse neutrophil armamentarium4 and there has been a noticeable surge of interest to study the implication of NETs in a wide array of research areas5,7,14,15. Acquisition of images using Immunofluorescence microscopy and subsequent image-based quantification is a widely used method to quantify NETs. This approach has...

Neutrophils are crucial mediators of innate host defense responses against a wide variety of microbial pathogens1. They execute their antimicrobial functions by releasing their granules containing a wide array of antimicrobial proteins2, producing reactive oxygen species (ROS) and hypochlorite1, and through phagocytosis3. In addition, Brinkmann et al.4 described neutrophil extracellular traps (NETs) as a novel mechanism by which neutrophils trap and eliminate invading pathogens. Since their discovery a little over a decade ago4, NETs have been implicated in a wide variety of infectious5,6 and non-infectious7 morbidities. NET formation is an active process and results in the extrusion of chromatin DNA coated with granule-derived antimicrobial proteins8. Some of the key changes in cellular and nuclear morphology associated with NET formation include the loss of nuclear morphology, chromatin decondensation, mobilization of granule proteins from cytoplasm to the nucleus and an increase in the nuclear and cellular diameter8,9.
The web-like NETs, which may appear as diffuse structures slightly larger than the cell or as structures several times larger than a single neutrophil are considered as indicators of NETosis5,10. Using fluorescence microscopy, NETs can be detected by probing DNA with a fluorescent probe such as 4&#39;,6-diamidino-2-phenylindole (DAPI) and by immunofluorescence staining against NET-bound proteins such as neutrophil elastase. Quantification of overlapping areas of staining for DNA and NET-bound proteins determines the total area under NETs in an image11.
A number of image analysis options are available to perform fluorescence image-based quantification of NETs11,12. But these software options present limitations in not being user-friendly and/or fully automated. In this article, we demonstrate the operation of NETQUANT13, a freely available app that can perform unbiased fully automated immunofluorescence microscopy image-based NET quantification. The app has a user friendly graphical interface (GUI) and can perform single-cell analysis. The software quantifies NETosis in an image by detecting the morphological changes in the area of DNA-NET-bound marker, chromatin decondensation associated deformation of the nucleus and increase in the DNA:NET-bound protein ratio. Taken together, the multiple NET definition criteria allows for stringent NET quantification across several data sets in an unbiased fashion.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>BD Vacutainer Heparinised plastic tubes</td>
			<td>BD Biosciences</td>
			<td>367885</td>
			<td></td>
		</tr>
		<tr>
			<td>Lymphoprep</td>
			<td>Axis-Shield</td>
			<td>114547</td>
			<td></td>
		</tr>
		<tr>
			<td>RPMI-1640 with L-Glutamine</td>
			<td>Gibco</td>
			<td>11835-030</td>
			<td></td>
		</tr>
		<tr>
			<td>50mL conical flasks</td>
			<td>Sarstedt</td>
			<td>62.547.004</td>
			<td></td>
		</tr>
		<tr>
			<td>15mL conical flasks</td>
			<td>Sarstedt</td>
			<td>62.554.002</td>
			<td></td>
		</tr>
		<tr>
			<td>12-well Tissue culture plates</td>
			<td>Falcon</td>
			<td>10626491</td>
			<td></td>
		</tr>
		<tr>
			<td>#1 Coverslips 10mm</td>
			<td>Menzel Glaser</td>
			<td>CS10100</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass slides</td>
			<td>Menzel Glaser</td>
			<td>631-0098</td>
			<td></td>
		</tr>
		<tr>
			<td>Primary anti-human elastase</td>
			<td>DAKO</td>
			<td>DAKO rabbit 1373, contract immunization</td>
			<td></td>
		</tr>
		<tr>
			<td>Secondary fluorophore conjugated goat anti-rabbit</td>
			<td>Life technologies</td>
			<td>A-11072, A-11070</td>
			<td></td>
		</tr>
		<tr>
			<td>PROLONG-Gold Antifade reagent with DAPI</td>
			<td>Life technologies</td>
			<td>P36930</td>
			<td>Mounting medium</td>
		</tr>
		<tr>
			<td>Goat serum</td>
			<td>Sigma-Aldrich</td>
			<td>G9023</td>
			<td></td>
		</tr>
		<tr>
			<td>Phorbol 12-myristate 13-acetate (PMA)</td>
			<td>Sigma-Aldrich</td>
			<td>79346</td>
			<td></td>
		</tr>
		<tr>
			<td>Paraformaldehyde</td>
			<td>Sigma-Aldrich</td>
			<td>158127</td>
			<td></td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma-Aldrich</td>
			<td>T8787</td>
			<td></td>
		</tr>
		<tr>
			<td>Nikon Ti-E Epifluorescence microscope</td>
			<td>Nikon</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>CCD camera</td>
			<td>Andor Zyla</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Plan Apochromat 20x, 40x objectives</td>
			<td>Nikon</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Windows 10</td>
			<td>Microsoft</td>
			<td></td>
			<td>Operating system</td>
		</tr>
		<tr>
			<td>macOS Sierra 10.12</td>
			<td>Apple</td>
			<td></td>
			<td>Operating system</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>Mathworks</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

automated image-based quantification of neutrophil extracellular traps using netquant

Neutrophil extracellular traps (NETs) are web-like antimicrobial structures consisting of DNA and granule derived antimicrobial proteins. Immunofluorescence microscopy and image-based quantification methods remain important tools to quantitate neutrophil extracellular trap formation. However, there are key limitations to the immunofluorescence-based methods that are currently available for quantifying NETs. Manual methods of image-based NET quantification are often subjective, prone to error and tedious for users, especially non-experienced users. Also, presently available software options for quantification are either semi-automatic or require training prior to operation. Here, we demonstrate the implementation of an automated immunofluorescence-based image quantification method to evaluate NET formation called NETQUANT. The software is easy to use and has a user-friendly graphical user interface (GUI). It considers biologically relevant parameters such as an increase in the surface area and DNA:NET marker protein ratio, and nuclear deformation to define NET formation. Furthermore, this tool is built as a freely available app, and allows for single-cell resolution quantification and analysis.

5 x 105 neutrophils/mL were seeded onto coverslips placed in a 12-well plate and stimulated with either 20 nM PMA or left unstimulated for 150 min. The samples were then stained using primary rabbit anti-human neutrophil elastase antibodies, secondary goat anti-rabbit fluorophore conjugated antibodies and DAPI - a fluorescently labelled dye that stains DNA (See the Table of Materials for details). A minimum of 5 images were then acquired using an epifluorescenc...

Watch this Scientific Journal Video about Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT at JoVE.com

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Here, we present a protocol for generating neutrophil extracellular traps (NETs) and operating NETQUANT, a fully automatic software option for quantification of NETs in immunofluorescence images.

Neutrophil extracellular traps (NETs) are web-like antimicrobial structures consisting of DNA and granule derived antimicrobial proteins. ...

Here we present NETQUANT, a fully automated approach to quantify NETS and immunofluorescence images. This will help researchers in making comparable image analysis from data generated by multiple users and conditions. The advantages of this technique are the ease of use, single-cell data analysis, multiple criteria to evaluate NET formation, and an unbiased analysis of multiple images.Install and open the analysis software. An up-to-date version can be found on the Zenodo GitHub archive, or the Nordonfelt Lab website. In the setup tab, choose a source folder for analysis by clicking the get path option in the source menu.Select the folder containing the image sequences to be analyzed. Again, click on the get path option in the target menu, and select the target folder for saving the data following image analysis. Next, when using separate channel images, name the channel folders so that the DNA channel corresponds with the nuclear DNA stain, and the NET channel depicts the neutrophil elastase stain in the images.Also, name the folder containing the control image files as control. Next, click the load image information button and load the image metadata into the software. Then in the channel order sub-menu, select the correct channel order contained in the images.This helps to prevent accidental mismatches. Now click on the prepare data button to acquire primary image properties from the raw data and convert the images. The converted images will then appear in the sample types sub-menu.Click on the sample type menu, select an image from the sub-menu and click on display image data to display the images split into the DNA and Neutrophil Extracellular Trap, or NET channel, respectively. To segment the cells into their respective channels, select the segmentation method tab, and select a method in the method sub-menu for both the DNA and the NET channels. The default method of segmentation is set to adaptive and is the recommended setting.Other options are available in the software including global edge and chanverse. A watershed option has also been included to help distinguish between closely-placed cells or NETs. Also in the segmentation tab, click on the segment control samples option.Then select PMA from sample type sub-menu and click on the batch option to begin segmentation of all images included in the data set. Next, select the images in the sample type menu and click on the display image data button to visualize and validate the binary image masks for both the DNA and NET masks generated post-segmentation. Now, in the analysis tab, click on the determine threshold button to analyze the control samples.Then, on the right hand side, change the sample type to PMA and click the get cell properties button to complete analysis of the stimulated samples. Next, select an image from the sample type sub-menu and click on the display image data button to display the overlay and the number of cells and NET forming cells in the image. Begin by selecting the sample from the sample type sub-menu.Individual images can be selected from the sample type sub-menu for analysis or the entire batch of images can be analyzed by selecting the batch option. Once selected, click on the analyze NETs button to complete the analysis. Adjust NET criteria manually to yield optimal results for a given sample.Compare identified NETs with the original images to assess the quality of identification. Once this step has been completed, the NET criteria can be applied across all images in the dataset. Any changes made to the NET criteria are also simultaneously applied to all control samples.Inspect the data summary in the cell data sub-menu, where the number of images, cell count per image, and percentage of NETs per image are displayed. Enter the output tab to select and view the result outputs. Explore and compare the various data outputs generated from the analysis of control and PMA-treated samples by selecting the form of the output and clicking on the output results button.Next, launch the results data table CSV file to explore single-cell data and click on the results PDF files to visualize the NET area distribution and DNA to NET ratio in the samples. The red line indicates the threshold value in the graphs. Finally, click on the results bivariate distribution file to determine the NET area versus shape of DNA.Here, 15 images of control neutrophils and 15 images of PMA-stimulated neutrophils were analyzed in under ten minutes. The total number of cells were counted and the percentage of cells with Neutrophil Extracellular Traps was determined using the automated quantification method described in this article. These results shows that PMA-stimulated neutrophils have a larger area, an increase in nuclear deformation, and a higher DNA to NET ratio than control samples.When combined into a 3D graph, the PMA-stimulated samples tend to show areas of tighten groupings than the control samples. While attempting the procedure, it's important to remember that although the workflow was tested using multiple donors, it is recommended that the user should decide on the software parameters appropriately based on the individual data set.

automated-image-based-quantification-neutrophil-extracellular-traps

Research

JoVE Journal

Immunology and Infection

6.6K visualizações.  Lund University. Aqui apresentamos o NETQUANT, uma abordagem totalmente automatizada para quantificar as imagens de NETS e imunofluorescência. Isso ajudará os pesquisadores a fazer análises de imagem comparáveis a partir de dados gerados por vários usuários e condições. As vantagens dessa técnica são a facilidade de uso, análise de dados unicelulares, múltiplos critérios para avaliar a formação de NET e uma análise imparcial de múltiplas imagens.Instale e abra o software de análise. U...