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 a sufficiently large cell pellet.</li>
	<li>Mix the blood with one volume of 2% dextran in saline (0.9% NaCl) and allow to sediment at room temperature for 30 min in a sterile 50 mL conical centrifuge tube.</li>
	<li>Aspirate the supernatant into a sterile 50 mL conical centrifuge tube and centrifuge at 200 x g for 10 min at 4 &#176;C.</li>
	<li>From this step onwards continue isolation at 4 &#176;C or on ice.</li>
	<li>Resuspend the pellet in 5 mL of ice cold saline and layer on top of 5 mL of leukocyte isolation gradient (9.1% sodium diatrizoate with 5.7% dextran, w/v) in a sterile 15 mL conical centrifuge tube.</li>
	<li>Centrifuge for 30 min at 400 x g at 4 &#176;C.</li>
	<li>Aspirate the supernatant and discard it.</li>
	<li>Lyse red blood cells by resuspending the pellet in 3 mL of ice-cold water for 30 s. Immediately add 1 mL of 3.6% NaCl and then fill up with 10 mL of ice cold saline.</li>
	<li>Centrifuge the cell suspension for 10 min at 350 x g.</li>
	<li>Remove the supernatant, collect the cell pellet and resuspend it in 1 mL of saline. Set aside 10 &#181;L in a microcentrifuge tube for assessment of the cell number and viability using trypan blue in a B&#252;rker chamber.</li>
	<li>Add 10 &#181;L of cell suspension to 90 &#181;L of 0.4% trypan blue solution. Take 10 &#181;L of the cell suspension in a B&#252;rker chamber. Count the cells in the 4 squares bound by 3 lines at each corner of the chamber. Cells that appear dark blue to the uptake of dye are non-viable, exclude them from the total cell number.</li>
	<li>Express the cell number as cells/mL as defined by the equation below. 
	<img alt="Equation" src="/files/ftp_upload/58528/58528eq1.jpg" /> 
	Note: Here chamber factor was 10,000, dilution factor was 10 and the total number of squares was 4.</li>
	<li>Dilute the remaining cell suspension to 10 mL for a final washing step.</li>
	<li>Centrifuge for 5 min at 200 x g.</li>
	<li>Resuspend the neutrophils in RPMI-1640 with 2 mg/mL heat-inactivated human serum albumin (HSA) at a concentration of 5 x 105 cells/mL.</li>
</ol>
2. Preparation of Coverslips and Stimulation of Neutrophils
<ol>
	<li>Place one coverslip (10 mm, #1) in each well of a 12-well plate and coat the coverslip by adding 200 &#181;L of 0.01% poly-L-lysine solution and leave it at 37 &#176;C overnight.</li>
	<li>Wash coverslips with 300 &#181;L phosphate buffer saline (PBS) once and leave to dry.</li>
	<li>Add 400 &#181;L of 5 x 105 neutrophils/mL to each well and incubate at room temperature for 15 min.</li>
	<li>Move the plate containing neutrophils to an incubator at 37 &#176;C with 5% CO2 for 15 min.</li>
	<li>Remove the supernatant. Add 400 &#181;L of prewarmed RPMI-1640 medium with 2 mg/mL HSA to controls. Add 300 &#181;L pre-warmed RPMI with 20 nM phorbol 12-myristate 13-acetate (PMA) for stimulation.</li>
	<li>Stimulate neutrophils for 150 min at 37 &#176;C with 5% CO2.</li>
</ol>
3. Visualization of NETs
<ol>
	<li>Remove the supernatant and wash the samples 2x with 200 &#181;L of PBS.</li>
	<li>Fix samples by adding 200 &#181;L of 4% paraformaldehyde (PFA) in PBS for 20 min at 37 &#176;C. 
	Note: PFA is toxic and must be handled with care.</li>
	<li>Wash samples 3x with 200 &#181;L of PBS.</li>
	<li>Permeabilize the samples by adding 50 &#181;L of 0.5% Triton X-100 for 30 s.</li>
	<li>Wash the samples 3x with 200 &#181;L of PBS.</li>
	<li>Block the samples with 5% goat serum in PBS for 1 h at 37 &#176;C.</li>
	<li>Add 300 &#181;L of primary rabbit anti-human neutrophil elastase in blocking solution at a dilution of 1:500 for 90 min at 37 &#176;C.</li>
	<li>Wash the samples 3x with 300 &#181;L of PBS.</li>
	<li>Add 300 &#181;L of secondary goat anti-rabbit fluorescent antibody at a dilution of 1:1000 for 90 min at 37 &#176;C.</li>
	<li>Wash the coverslips 3x with 300 &#181;L of PBS.</li>
	<li>Remove the coverslips from the wells and mount the coverslip with 10 &#181;L mounting medium containing DAPI. Store overnight at room temperature in the dark to dry the samples. 
	Note: The staining of DNA with DAPI is certainly a critical step in the method. The users can also troubleshoot by adding exogenous DAPI solution at a final concentration range of 0.1&#8210;0.5 &#181;g/mL for 2&#8210;3 min, followed by 3 washing steps with 300 &#181;L PBS.</li>
	<li>Acquire images with a wide-field fluorescence microscope using a 20X objective.</li>
</ol>
4. Analysis and Quantification of NETs using NETQUANT
Note: NETQUANT can be downloaded by clicking the installation file found on the Zenodo Github archive or the Nordenfelt Lab website (<a href="https://nordlab.med.lu.se/?page_id=34" target="_blank">https://nordlab.med.lu.se/?page_id=34</a>).
<ol>
	<li>Importing datasets for analysis, naming of channels and conversion of images
	<ol>
		<li>Open the Setup tab in NETQUANT.</li>
		<li>Choose the source folder for analysis by clicking the Get path option in the source menu and select the folder containing the image sequences to be analyzed.</li>
		<li>Click on the Get Path option in the target menu and select the folder for saving the data following the image analysis.</li>
		<li>Name the channels so that "DNA channel" corresponds with the DNA staining (e.g, DNA or DAPI) and "NET-channel" depicts the NET-bound protein staining (e.g, NET, neutrophil elastase) in the images. For the smooth functioning of the software, (recommended) name the folder containing the control image files as "control". 
		Note: The NET channel refers to the NET-bound granule protein marker staining only.</li>
		<li>Feed the image metadata into the software by clicking the Load image information button on the Image information sub-menu.</li>
		<li>Select the correct channel order contained in the images in the Channel order sub-menu. This option has been included as a fail-safe to prevent accidental mismatches.</li>
		<li>Acquire primary image properties from the raw data and convert the images by clicking the Prepare data button. The converted images appear in the Sample type sub-menu. Click on the Sample type menu for displaying and selecting all datasets acquired for analysis.</li>
		<li>Select an image from the Sample type sub menu and click on the Display image data button to display the images split into the DNA and NET channel respectively.</li>
	</ol>
	</li>
	<li>Segmentation of Cells in the DNA channel and the NET channel
	<ol>
		<li>Select the segmentation method by clicking on the Method sub menu in both the DNA channel and the NET channel. 
		Note: The default method of segmentation is set to adaptive and is the recommended setting. Other options are also available including global, edge and Chan-Vese. A watershed option is also included to help distinguish between closely placed cells or NETs.</li>
		<li>Enter the Segmentation tab to segment control cells first in both channels by clicking on the Segment control samples option.</li>
		<li>Select PMA from the sample type sub-menu and click on the Batch option (recommended) to begin segmentation of all images included in the data set. Select the images in the sample type menu and click on the Display image data button to visualize and validate the binary image masks (DNA mask and NET mask) generated post-segmentation.</li>
	</ol>
	</li>
	<li>Single-cell Analysis of Identifiable Properties
	<ol>
		<li>Enter the analysis tab and analyze the control samples by clicking on the Determine threshold button.</li>
		<li>Change the sample type to PMA and click on the Get cell properties button to complete the analysis of stimulated samples.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Comparison of Cell properties to Identify NET-forming Cells
	<ol>
		<li>Select sample from the sample type sub-menu and click on the Analyze NETs button to complete the analysis. 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 (recommended).</li>
		<li>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. 
		Note: The NET criteria can then be used across all images in the data set. Any changes in the NET-criteria are applied simultaneously across all control samples. This limits the possibility of any potential differences that may arise due to over-fitting of the NET parameters. The settings in the NET criteria can be adjusted according to the user&#39;s requirements. The relationship between false discovery rates and NETQUANT has been explored previously13. Typical ranges for area increase are 2&#8210;4, circularity to be 0.7&#8210;0.9 and the DNA/NET ratio to be 0.6&#8211;2.0.</li>
		<li>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. 
		Note: The total percentage of NETs in the entire dataset is displayed by the "NET-gauge". The total image count, cell count, percentage of NETs in the sample (NETs%) and NETs% in the control sample are displayed in the summary statistics table below the NET-gauge. We recommend that the control data are reported alongside the data obtained from stimulated samples.</li>
	</ol>
	</li>
	<li>Result Outputs
	<ol>
		<li>Enter the Output tab to select and view result outputs.</li>
		<li>Explore and compare the various data outputs generated from the analysis of control and PMA by selecting the form of the output and clicking on the Output results button. 
		Note: All data generated post-analysis for both controls and stimulations is saved in the analysis folder as chosen in the target sub-menu. Data are saved in either .csv or .pdf formats.</li>
		<li>Launch the Method file to obtain the version of the software and NET-criteria used for the analysis (to be included in methods section for publication purposes).</li>
		<li>Click on the Results data table to visualize the individual data points in a given sample.</li>
		<li>Visualize the NET area distribution and DNA:NET ratio in the samples. The red line indicates the threshold value in the graphs.</li>
		<li>Determine the NET area versus shape of DNA by clicking on the Bivariate distribution file.</li>
	</ol>
	</li>
	<li>Loading Previous Analysis and Batch All Steps
	<ol>
		<li>Load previously successful analysis settings into NETQUANT using the Load previous analysis button.</li>
		<li>Use the Batch all steps button included in the Setup menu to run steps 5&#8210;12 (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5) directly to obtain the final output.</li>
	</ol>
	</li>
</ol>

<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>

TM and PN have a patent pending related to the algorithms used in NETQUANT.

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 the advantage of being able to detect cells forming NETs at a single-cell level and can therefore better reduce the background signal. Manual methods of quantification can be exhaustive, slow and subject to user bias. A few semi-automatic options exist that have provided users with reliable analysis11,12. However, they might require significant technical skill for operation or may require several manual steps for quantification. This is challenging especially to previously non-experienced users. In a recent publication, a fully automatic computational method has been used to quantify NET formation using flow cytometry-based imaging as well as thin tissue sections. However, it can only be applied to specific scenarios and also requires a significant knowledge of programming for operation16. Presently, fully automated image quantification software dedicated towards general NET quantification that is easy to use and possesses single-cell resolution capacity is unavailable to researchers.
Here we present NETQUANT, a fully automated NET quantification software that is freely available. This software has been developed specifically to quantify NET formation with high stringency using immunofluorescence microscopy images, and with the ability to perform single-cell analysis and quantification. Apart from considering the increase in the area of staining under NETs, NETQUANT also takes into account nuclear deformation due to chromatin decondensation and an increase in the DNA/NET-marker protein co-localization to define NET formation in a given sample. This allows for distinguishing cells that have only undergone nuclear decondensation from cells that have undergone complete NET formation. The NET definition criteria included in the NETQUANT algorithm allows for higher stringency in quantification in comparison to many earlier quantification approaches that rely solely upon an increase in the area of NET staining.
The GUI is aimed to be user-friendly and simple to use. Each step of the analysis is numbered to ensure that users can follow the workflow. One of the key features provided in the software is the segmentation tool/tab that allows NETQUANT to perform single-cell analysis in images, which is the highest possible sensitivity that can be achieved. This makes NETQUANT one of the few available options that can perform single-cell quantification to quantitate NET formation. In addition, Mohanty et al. have demonstrated that the software can also adapt to image variations and acquisition parameters13. Due to the flexibility offered in the segmentation and NET-definition criteria, donor and plate variations can be accommodated. NETQUANT can handle large data sets adequately and can be used for reliable high-throughput image analysis13.
Although we found the workflow to yield robust results from multiple donors, we recommend that the user should decide on the appropriate values based on the individual samples. In theory, the data generated post-analysis could also be influenced by high cell density. Also, excessive aggregation of NETs can result in the reduction of the number of NETs detected. This is because the segmentation algorithm cannot distinguish entangled NETs into separate events. A similar problem with segmentation may also arise when unstimulated neutrophils are trapped within the NETs. The use of images acquired at 20X and 40X is currently recommended as NETQUANT&#39;s performance has not been tested at other magnification levels. Still, as long as the pixel size is set correctly (either manually or from metadata in the load image info section) the software should adapt area calculations accurately to higher or lower magnifications. Proper staining of the DNA and the granule marker is a critical factor. Poor staining or poor image quality in either channel will result in suboptimal results. Therefore, it is recommended that the user should determine a suitable cell number, staining procedure and antibody titer to yield optimal results.
NETQUANT has only been tested on NETs derived from human neutrophils in vitro. NETs from other species can also quantified with proper corrections in the criteria. NET quantification can be performed on fixed samples in chamber slides or multi-well glass bottom plates. High quality images of samples stained for DNA and granule protein markers are critical. NETQUANT can also be adapted to quantify other forms of extracellular traps (ET)-osis, e.g., as exhibited by eosinophils. Quantifying ETs in non-granulocytes such as monocytes is currently not possible as cytoplasmic granule staining area is required for the segmentation algorithm.
In conclusion, NETQUANT is a simple, automatic and rapid tool for accurately identifying NET formation that allows for single-cell analysis and quantification. We believe this software may lower the barrier for performing automatic NET quantification and that the research community will benefit from this freely available app.

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 epifluorescence microscope and a 20X (NA = 0.75) objective. Sample data used for the test analysis in the manuscript can be downloaded by following the link <a href="https://nordlab.med.lu.se/?page_id=34" target="_blank">https://nordlab.med.lu.se/?page_id=34</a> and clicking on the sample dataset icon.
Here we describe the ability of the NETQUANT workflow (Figure 1) to quantify NET formation in a given sample. The software tool can be installed as an app in MATLAB and can be used without any previous knowledge of this interface. The GUI of NETQUANT was used to analyze the datasets as described above. All images used for analysis by NETQUANT are always left unaltered in the parent directory. The GUI is divided into 4 tabs &#8212; setup tab, segmentation tab, analysis tab and results output tab. The samples were prepared for analysis in the setup tab (Figure 2). The file paths (1), naming conventions (2), image information (3) and channel orders (4) are defined by the user. The prepare data option (5) ensures that the conversion and organization in a standardized fashion throughout the experiment. The segmentation parameters were the set in the segmentation tab (Figure 3) to identify individual cells in the samples (6). The control samples are always segmented first (7) prior to the segmentation of stimulated samples (8). All recommended values for segmentation are indicated in the software tab. The properties that define unstimulated control cells are first acquired (9) and then compared to PMA stimulated cells (10) (Figure 4). NETs are then analyzed in the samples based upon the NET criteria (11) and displayed with total image counts, cell counts and corresponding NETs% in the control samples. The final data outputs from the analysis were selected and then visualized (Figure 5). An option for loading and using a previously successful analysis, and a batch-all option to process groups of steps 5-12 (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5) have also been included in the setup tab.
During the analysis, a total of 2619 cells were analyzed in the experiment. The PMA stimulated neutrophils were able to generate 90.59% NETs (NETs%) in contrast to 25.87 NETs% in the control samples (Figure 6). The image analysis was completed within 10 min of initiating the workflow on the software. In summary, NETQUANT provides a rapid and convenient option to quantify NET formation in images that have been acquired using immunofluorescence microscopy.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58528/58528fig1.jpg" /> 
Figure 1: Overview of the NETQUANT workflow. Fluorescent micrographs of human neutrophils double stained with anti-elastase and DAPI (DNA) are first processed and converted. The images are then segmented, followed by analysis of cell properties, detection of NETs and result outputs. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58528/58528fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58528/58528fig2.jpg" /> 
Figure 2: Setup tab options in NETQUANT. File paths for the datasets to be analyzed and data output folders are entered in the setup tab first (1). The naming conventions are then filled in to define channel names and the control folder name (2). The image information is then acquired (3) followed by naming the correct channel order (4). The proper setup of all fields is recommended prior to the processing of the datasets (5) for standardized image conversion. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58528/58528fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58528/58528fig3.jpg" /> 
Figure 3: Segmentation tab parameters in NETQUANT. The parameters used for segmentation of both DNA channel and NET-protein marker include method, sensitivity, iterations, minimum area and watershed (6). The use of an adaptive segmentation method and watershed is recommended. Also, other settings provided as presets in the software can be used without modification for most purposes. Control samples are first segmented (7) and then the stimulated samples are segmented (8). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58528/58528fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58528/58528fig4.jpg" /> 
Figure 4: Detection of NETs in the analysis tab. The cell properties defining unstimulated cells are analyzed and acquired from control samples (9). This is followed by the analysis of cell properties in the experimental samples (10). The NET-definition criteria (fold increase in cellular area, nuclear deformation (values 0 to 1) and DNA staining area/NET-marker staining area) are applied to both control and stimulated samples to define NET formation (11). The quantity of NETs, total cell count and number of images in both samples is displayed in the summary statistics section. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58528/58528fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58528/58528fig5.jpg" /> 
Figure 5: Visualization of result output files in NETQUANT. Results obtained post-analysis can be selected by the individual user. The result outputs that can be generated with NETQUANT includes a .csv file containing data points from individual cells. Also, histograms depicting distribution of cells undergoing increase in NET (cellular) area, deformation of nucleus due to chromatin decondensation and DNA:NET-marker ratio, and a 3D graph of NET area versus nuclear deformation are also generated in the analysis. All outputs are automatically saved in the analysis folder chosen in the setup tab as .csv, .pdf and .txt files. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58528/58528fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58528/58528fig6.jpg" /> 
Figure 6: Analysis of NET formation. (A) The NETs%, total cell count and number of images from the test data set as observed in the summary statistics for controls and PMA-stimulated (20 nM) neutrophils. (B) A side-by-side comparison of histograms depicting increase in the area, nuclear deformation (values 0 to 1) and DNA/NET ratio in control and PMA-stimulated samples. The red line in the histograms threshold values for the NET-definition criteria. (C) Comparison of 3D graph of NET area versus nuclear deformation generated by NETQUANT analysis of control and PMA-stimulated samples. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58528/58528fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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. ...

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

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

6.6K Views.  Lund University. 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.

Video: Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Neutrophil Extracellular Traps: How to Generate and Visualize Them

Neutrophil granulocytes are the most abundant group of leukocytes in the peripheral blood. As professional phagocytes, they engulf bacteria and kill them intracellularly when their antimicrobial granules fuse with the phagosome. We found that neutrophils have an additional way of killing microorganisms: upon activation, they release granule proteins and chromatin that together form extracellular fibers that bind pathogens. These novel structures, or Neutrophil Extracellular Traps (NETs), degrade virulence factors and kill bacteria1, fungi2 and parasites3. The structural backbone of NETs is DNA, and they are quickly degraded in the presence of DNases. Thus, bacteria expressing DNases are more virulent4. Using correlative microscopy combining TEM, SEM, immunofluorescence and live cell imaging techniques, we could show that upon stimulation, the nuclei of neutrophils lose their shape and the eu- and heterochromatin homogenize. Later, the nuclear envelope and the granule membranes disintegrate allowing the mixing of NET components. Finally, the NETs are released as the cell membrane breaks. This cell death program (NETosis) is distinct from apoptosis and necrosis and depends on the generation of Reactive Oxygen Species by NADPH oxidase5. 
Neutrophil extracellular traps are abundant at sites of acute inflammation. NETs appear to be a form of innate immune response that bind microorganisms, prevent them from spreading, and ensure a high local concentration of antimicrobial agents to degrade virulence factors and kill pathogens thus allowing neutrophils to fulfill their antimicrobial function even beyond their life span. There is increasing evidence, however, that NETs are also involved in diseases that range from auto-immune syndromes to infertility6.
We describe methods to isolate Neutrophil Granulocytes from peripheral human blood7 and stimulate them to form NETs. Also we include protocols to visualize the NETs in light and electron microscopy.

Neutrophil Extracellular Traps (NETs) are an important innate immune mechanism to fight pathogenic bacteria, fungi and parasites. Here we describe methods to isolate neutrophil granulocytes from human blood and to activate them to form NETs. We present preparation techniques to visualize NETs in light and electron microscopy.

Neutrophil granulocytes are the most abundant group of leukocytes in the peripheral blood. As professional phagocytes, they engulf bacteria and kill them ...

Concurrent Quantification of Cellular and Extracellular Components of Biofilms

Confocal laser scanning microscopy (CLSM) is a powerful tool for investigation of biofilms. Very few investigations have successfully quantified concurrent distribution of more than two components within biofilms because: 1)&#160;selection of fluorescent dyes having minimal spectral overlap is complicated, and 2)&#160;quantification of multiple fluorochromes poses a multifactorial problem. Objectives: Report a methodology to quantify and compare concurrent 3-dimensional distributions of three cellular/extracellular components of biofilms grown on relevant substrates. Methods: The method consists of distinct, interconnected steps involving biofilm growth, staining, CLSM imaging, biofilm structural analysis and visualization, and statistical analysis of structural parameters. Biofilms of Streptococcus mutans (strain UA159) were grown for 48 hr on sterile specimens of Point 4 and TPH3 resin composites. Specimens were subsequently immersed for 60 sec in either Biot&#232;ne PBF (BIO) or Listerine Total Care (LTO) mouthwashes, or water (control group; n=5/group). Biofilms were stained with fluorochromes for extracellular polymeric substances, proteins and nucleic acids before imaging with CLSM. Biofilm structural parameters calculated using ISA3D image analysis software were biovolume and mean biofilm thickness. Mixed models statistical analyses compared structural parameters between mouthwash and control groups (SAS software; &#945;=0.05). Volocity software permitted visualization of 3D distributions of overlaid biofilm components (fluorochromes). Results: Mouthwash BIO produced biofilm structures that differed significantly from the control (p&#60;0.05) on both resin composites, whereas LTO did not produce differences (p&#62;0.05) on either product. Conclusions: This methodology efficiently and successfully quantified and compared concurrent 3D distributions of three major components within S. mutans biofilms on relevant substrates, thus overcoming two challenges to simultaneous assessment of biofilm components. This method can also be used to determine the efficacy of antibacterial/antifouling agents against multiple biofilm components, as shown using mouthwashes. Furthermore, this method has broad application because it facilitates comparison of 3D structures/architecture of biofilms in a variety of disciplines.

A protocol for the concurrent quantification and comparison of three cellular and extracellular components within biofilms is presented. The methodology involves the use of confocal laser scanning microscopy, biofilm structural analysis and visualization software, and statistical analysis software.

Confocal laser scanning microscopy (CLSM) is a powerful tool for investigation of biofilms. Very few investigations have successfully quantified ...

Simplified Human Neutrophil Extracellular Traps (NETs) Isolation and Handling

Neutrophil Extracellular Traps (NETs) have been recently identified as part of the neutrophil&#8217;s antimicrobial armamentarium. Apart from their role in fighting infections, recent research has demonstrated that they may be involved in many other disease processes, including cancer progression. Isolating purified NETs is a crucial element to allow the study of these functions.
In this video, we demonstrate a simplified method of cell free NET isolation from human whole blood using readily available reagents. Isolated NETs can then be used for immunofluorescence staining, blotting or various functional assays. This enables an assessment of their biologic properties in the absence of the potential confounding effects of neutrophils themselves.
A density gradient separation technique is employed to isolate neutrophils from healthy donor whole blood. Isolated neutrophils are then stimulated by phorbol 12-myristate 13-acetate (PMA) to induce NETosis. Activated neutrophils are then discarded, and a cell-free NET stock is obtained.
We then demonstrate how isolated NETs can be used in an adhesion assay with A549 human lung cancer cells. The NET stock is used to coat the wells of a 96 well cell culture plate O/N, and after ensuring an adequate NET monolayer formation on the bottom of the wells, CFSE labeled A549 cells are added. Adherent cells are quantified using a Nikon TE300 fluorescent microscope. In some wells, 1000U DNAse1 is added 10 min before counting to degrade NETs

Simplified Human Neutrophil Extracellular Traps NETs Isolation and Handling

In the following protocol, we describe a very simple way to isolate Neutrophil Extracellular traps (NETs) from human whole blood using readily available reagents. We then demonstrate how the isolated NETs can be used in an in vitro adhesion assay with cancer cells.

Neutrophil Extracellular Traps (NETs) have been recently identified as part of the neutrophil&#8217;s antimicrobial armamentarium. Apart from their role ...

Methods to Study Lipid Alterations in Neutrophils and the Subsequent Formation of Neutrophil Extracellular Traps

Lipid analysis performed by high performance thin layer chromatography (HPTLC) is a relatively simple, cost-effective method of analyzing a broad range of lipids. The function of lipids (e.g., in host-pathogen interactions or host entry) has been reported to play a crucial role in cellular processes. Here, we show a method to determine lipid composition, with a focus on the cholesterol level of primary blood-derived neutrophils, by HPTLC in comparison to high performance liquid chromatography (HPLC). The aim was to investigate the role of lipid/cholesterol alterations in the formation of neutrophil extracellular traps (NETs). NET release is known as a host defense mechanism to prevent pathogens from spreading within the host. Therefore, blood-derived human neutrophils were treated with methyl-&#946;-cyclodextrin (M&#946;CD) to induce lipid alterations in the cells. Using HPTLC and HPLC, we have shown that M&#946;CD treatment of the cells leads to lipid alterations associated with a significant reduction in the cholesterol content of the cell. At the same time, M&#946;CD treatment of the neutrophils led to the formation of NETs, as shown by immunofluorescence microscopy. In summary, here we present a detailed method to study lipid alterations in neutrophils and the formation of NETs.

Lipids are known to play an important role in cellular functions. Here, we describe a method to determine the lipid composition of neutrophils, with emphasis on the cholesterol level, by using both HPTLC and HPLC to gain a better understanding of the underlying mechanisms of neutrophil extracellular trap formation.

Lipid analysis performed by high performance thin layer chromatography (HPTLC) is a relatively simple, cost-effective method of analyzing a broad range of ...

A Simple Fluorescence Assay for Quantification of Canine Neutrophil Extracellular Trap Release

Neutrophil extracellular traps are networks of DNA, histones and neutrophil proteins released in response to infectious and inflammatory stimuli. Although a component of the innate immune response, NETs are implicated in a range of disease processes including autoimmunity and thrombosis. This protocol describes a simple method for canine neutrophil isolation and quantification of NETs using a microplate fluorescence assay. Blood is collected using conventional venipuncture techniques. Neutrophils are isolated using dextran sedimentation and a density gradient using conditions optimized for dog blood. After allowing time for attachment to the wells of a 96 well plate, neutrophils are treated with NET-inducing agonists such as phorbol-12-myristate-13-acetate or platelet activating factor. DNA release is measured by the fluorescence of a cell-impermeable nucleic acid dye. This assay is a simple, inexpensive method for quantifying NET release, but NET formation rather than other causes of cell death must be confirmed with alternative methods.

Neutrophil extracellular traps (NETs) are networks of DNA, histones and neutrophil proteins. Although a component of the innate immune response, NETs are implicated in autoimmunity and thrombosis. This protocol describes a simple method for canine neutrophil isolation and quantification of NETs using a microplate fluorescence assay.

Neutrophil extracellular traps are networks of DNA, histones and neutrophil proteins released in response to infectious and inflammatory stimuli. Although ...

Visualizing Macrophage Extracellular Traps Using Confocal Microscopy

A primary method used to define the presence of neutrophil extracellular traps (NETs) is confocal microscopy. We have modified established confocal microscopy methods to visualize macrophage extracellular traps (METs). These extracellular traps are defined by the presence of extracellular chromatin with co-expression of other components such as granule proteases, citrullinated histones, and peptidyl arginase deiminase (PAD). The expression of METs is generally measured after exposure to a stimulus and compared to un-stimulated samples. Samples are also included for background and isotype control. Cells are analyzed using well-defined image analysis software. Confocal microscopy may be used to define the presence of METs both in vitro and in vivo in lung tissue.

Macrophage extracellular traps are a newly described entity. This article will concentrate on confocal microscopy methods and how they are visualized in vitro and in vivo from lung samples.

A primary method used to define the presence of neutrophil extracellular traps (NETs) is confocal microscopy. We have modified established confocal ...

In Vitro Canine Neutrophil Extracellular Trap Formation: Dynamic and Quantitative Analysis by Fluorescence Microscopy

In response to invading pathogens, neutrophils release neutrophil extracellular traps (NETs), which are extracellular networks of DNA decorated with histones and antimicrobial proteins. Excessive NET formation (NETosis) and citH3 release during sepsis is associated with multiple organ dysfunction and mortality in mice and humans but its implications in dogs are unknown. Herein, we describe a technique to isolate canine neutrophils from whole blood for observation and quantification of NETosis. Leukocyte-rich plasma, generated by dextran sedimentation, is separated by commercially available density gradient separation media and granulocytes collected for cell count and viability testing. To observe real-time NETosis in live neutrophils, cell permeant and cell impermeant fluorescent nucleic acid stains are added to neutrophils activated either by lipopolysaccharide (LPS) or phorbol 12-myristate 13-acetate (PMA). Changes in nuclear morphology and NET formation are observed over time by fluorescence microscopy. In vitro NETosis is further characterized by co-colocalization of cell-free DNA (cfDNA), myeloperoxidase (MPO) and citrullinated histone H3 (citH3) using a modified double-immunolabelling protocol. To objectively quantify NET formation and citH3 expression using fluorescence microscopy, NETs and citH3-positive cells are quantified in a blinded manner using available software. This technique is a specific assay to evaluate the in vitro capacity of canine neutrophils to undergo NETosis.

In Vitro Canine Neutrophil Extracellular Trap Formation: Dynamic and Quantitative Analysis by Fluorescence Microscopy

We describe methods to isolate canine neutrophils from whole blood and visualize NET formation in live neutrophils using fluorescence microscopy. Also described are protocols to quantify NET formation and citrullinated histone H3 (citH3) expression using immunofluorescence microscopy.

In response to invading pathogens, neutrophils release neutrophil extracellular traps (NETs), which are extracellular networks of DNA decorated with ...

A High-throughput Assay to Assess and Quantify Neutrophil Extracellular Trap Formation

Neutrophil extracellular traps (NETs) are immunogenic extracellular DNA structures that can be released by neutrophils upon a wide variety of triggers. NETs have been demonstrated to serve as an important host defense mechanism that traps and kills microorganisms. On the other hand, they have been implicated in diverse systemic autoimmune diseases. NETs are immunogenic and toxic structures that contain a pool of relevant autoantigens including anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) and systemic lupus erythematosus (SLE). Different forms of NETs can be induced depending on the stimulus. The amount of NETs can be quantified using different techniques including measuring DNA release in supernatants, measuring DNA-complexed with NET-molecules like myeloperoxidase (MPO) or neutrophil elastase (NE), measuring the presence of citrullinated histones by fluorescence microscopy, or flow cytometric detection of NET-components which all have different features regarding their specificity, sensitivity, objectivity, and quantity. Here is a protocol to quantify ex vivo NET formation in a highly-sensitive, high-throughput manner by using three-dimensional immunofluorescence confocal microscopy. This protocol can be applied to address various research questions about NET formation and degradation in health and disease.

This protocol describes a highly sensitive and high throughput neutrophil extracellular trap (NET) assay for the semi-automated quantification of ex vivo NET formation by immunofluorescence three-dimensional confocal microscopy. This protocol can be used to evaluate NET formation and degradation after different stimuli and can be used to study potential NET-targeted therapies.

Neutrophil extracellular traps (NETs) are immunogenic extracellular DNA structures that can be released by neutrophils upon a wide variety of triggers. ...

Identification of Neutrophil Extracellular Traps in Paraffin-Embedded Feline Arterial Thrombi using Immunofluorescence Microscopy

Neutrophil extracellular traps (NETs), composed of cell-free DNA (cfDNA) and proteins like histones and neutrophil elastase (NE), are released by neutrophils in response to systemic inflammation or pathogens. Although NETs have previously been shown to augment clot formation and inhibit fibrinolysis in humans and dogs, the role of NETs in cats with cardiogenic arterial thromboembolism (CATE), a life-threatening complication secondary to hypertrophic cardiomyopathy, is unknown. A standardized method to identify and quantify NETs in cardiogenic arterial thrombi in cats will advance our understanding of their pathological role in CATE. Here, we describe a technique to identify NETs in formaldehyde-fixed and paraffin-embedded thrombi within the aortic bifurcation, extracted during necropsy. Following deparaffinization with xylene, aortic sections underwent indirect heat-induced antigen retrieval. Sections were then blocked, permeabilized, and ex vivo NETs were identified by colocalization of cell-free DNA (cfDNA), citrullinated histone H3 (citH3), and neutrophil elastase (NE) using immunofluorescence microscopy. To optimize the immunodetection of NETs in thrombi, autofluorescence of tissue elements was limited by using an autofluorescence quenching process prior to microscopy. This technique could be a useful tool to study NETs and thrombosis in other species and offers new insights into the pathophysiology of this complex condition.

We describe a method to identify neutrophil extracellular traps (NETs) in formaldehyde-fixed and paraffin-embedded feline cardiogenic arterial thrombi using heat-induced antigen retrieval and a double immunolabeling protocol.

Neutrophil extracellular traps (NETs), composed of cell-free DNA (cfDNA) and proteins like histones and neutrophil elastase (NE), are released by ...

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Glomerular cell death is a pathological feature of myeloperoxidase anti neutrophil cytoplasmic antibody associated vasculitis (MPO-AAV). Extracellular deoxyribonucleic acid (ecDNA) is released during different forms of cell death including apoptosis, necrosis, necroptosis, neutrophil extracellular traps (NETs) and pyroptosis. Measurement of this cell death is time consuming with several different biomarkers required to identify the different biochemical forms of cell death. Measurement of ecDNA is generally conducted in serum and urine as a surrogate for renal damage, not in the actual target organ where the pathological injury occurs. The current difficulty in investigating ecDNA in the kidney is the lack of methods for formalin fixed paraffin embedded tissue (FFPE) both experimentally and in archived human kidney biopsies. This protocol provides a summary of the steps required to stain for ecDNA in FFPE tissue (both human and murine), quench autofluorescence and measure the ecDNA in the resulting images using a machine learning tool from the publicly available open source ImageJ plugin trainable Weka segmentation. Trainable Weka segmentation is applied to ecDNA within the glomeruli where the program learns to classify ecDNA. This classifier is applied to subsequent acquired kidney images, reducing the need for manual annotations of each individual image. The adaptability of the trainable Weka segmentation is demonstrated further in kidney tissue from experimental murine anti-MPO glomerulonephritis (GN), to identify NETs and ecMPO, common pathological contributors to anti-MPO GN. This method provides objective analysis of ecDNA in kidney tissue that demonstrates clearly the efficacy in which the trainable Weka segmentation program can distinguish ecDNA between healthy normal kidney tissue and diseased kidney tissue. This protocol can easily be adapted to identify ecDNA, NETs and ecMPO in other organs.

Extracellular DNA (ecDNA) released during cell death is proinflammatory and contributes to inflammation. Measurement of ecDNA at the site of injury can determine the efficacy of therapeutic treatment in the target organ. This protocol describes the use of a machine learning tool to automate measurement of ecDNA in kidney tissue.

Glomerular cell death is a pathological feature of myeloperoxidase anti neutrophil cytoplasmic antibody associated vasculitis (MPO-AAV). Extracellular ...