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

This method enables detection of pan ecDNA from all forms of cell death. The same method and antibodies are used for human kidney biopsy tissue (from step 4). All animal and human subjects had Ethics approval from Monash University, and Monash Health, Clayton, Victoria, Australia.

1. Staining for ecDNA with DAPI and β-Actin

1. Induce a 20 day model of anti-myeloperoxidase glomerulonephritis (anti-MPO-GN) in 8-10 week old C57/Bl6 mice, with controls⁹.
   1. Euthanize mice humanely using a CO₂ chamber.
   2. Remove kidneys by making an anterior midline incision through the skin with scissors, and then carefully cut the peritoneum and pin back on a dissection board. Using forceps push aside the anterior renal fascia and parietal peritoneum and cut the kidney free by severing the ureter and blood vessels (renal artery and vein) at the renal pelvis. Remove with forceps and cut kidneys in half (sagittal plane) with a size 22 scalpel.
   3. Place kidney in fixative (4% buffered formalin) for 16 hours at room temperature (RT). Remove and soak fixed kidney in 30% ethanol for 24 hours prior to processing.
2. Process kidneys using a standard 6-hour cycle:
   70% ethanol for 15 min
   90% ethanol for 15 min
   100% ethanol for 15 min
   100% ethanol for 20 min
   100% ethanol for 25 min
   100% ethanol for 30 min
   Xylene for 20 min
   Xylene for 30 min
   Xylene for 40 min
   Wax for 30 min
   Wax for 35 min
   Wax for 40 min
   NOTE: This is important as kidneys should not be subjected to prolonged exposure to heat in the wax, as it can destroy antigens of interest.
3. Cut 3 µm sections on a microtome and mount on commercially available glass microscope slides coated with a positive charge. Allow to dry overnight.
4. Put glass slides into a slide rack into a 60 °C oven for 60 min.
   NOTE: Steps 1.3 and 1.4 are crucial to ensure sections do not float off during the antigen retrieval step.
5. Immerse slides in two changes of solvent (xylene or substitute) for 40 min each, in a fume hood.
6. Rehydrate slides in 100% ethanol, 100% ethanol, 70% ethanol for 5 min each. Wash for 5 min in tap water.
7. Place a hot plate in a fume hood and preboil antigen retrieval solution (10 mM Tris, 1 mM EDTA pH 9.0) in a pressure cooker. As soon as the antigen retrieval solution starts to boil, place the slides in the pot horizontally using a pair of tongs and lock the lid. Boil on high (equivalent to 15 psi or 103 kPa) for 10 min.
   NOTE: This step is crucial as it retrieves the antigen of interest by unmasking the antigen epitope (cross linked via formalin fixation) to allow epitope antibody specific binding the subsequent staining will not work without it.
8. Remove pressure cooker from heat and remove lid immediately by running cold tap on top of the lid in a sink. Leave slides to equilibrate for 20 min in the antigen retrieval solution.
9. Wash slides twice for 5 min in 0.01 M phosphate buffered saline (PBS) (pH 7.4) on an orbital shaker.
10. Use a hydrophobic pen to draw circles around the kidney tissue being careful not to let any tissue dry out in this time.
11. Block tissue in 10% chicken sera in 5% bovine serum albumin (BSA)/PBS (pH 7.4, 0.01 M) for 30 min, 60 µL per section. Do not wash after this step but carefully remove the blocking solution using a 60 µL pipette, do not let the sections dry out.
12. Apply primary antibody cocktail for β-actin diluted 1/1000 in 1% BSA/PBS (pH 7.4, 0.01 M), 60 µL per section and incubate overnight in a humidity chamber at 4 °C.
13. Wash slides twice for 5 min in PBS (pH 7.4, 0.01 M) on an orbital shaker.
14. Apply secondary antibody, chicken anti rabbit 488 (1/200), 60 µL per section diluted in 1%BSA/PBS (pH 7.4, 0.01 M) for 40 min at RT.
15. Wash the slides twice for 5 min in PBS (pH 7.4, 0.01 M) on an orbital shaker.
16. Immerse the slides in 0.3% Sudan Black in 70% ethanol for 30 min in a Coplin jar.
    NOTE: This step is important as it quenches the autofluorescence caused by formalin fixation.
17. Wash the slides in tap water to remove precipitate and then immerse in PBS (pH 7.4, 0.01 M) for 10 min to prevent further Sudan Black precipitate from forming.
18. Mount slides onto confocal glass coverslips using three 60 µL drops of mounting solution with DAPI and seal the coverslips with nail polish by applying around the perimeter of the coverslip.
19. Allow slides to cure for 24 hours at RT (to allow DAPI to penetrate) and then store at 4 °C protected from light.
    NOTE: Important to keep in the dark to prevent fading of fluorescent probes.
20. Image slides using a confocal laser scanning head attached to a microscope. Excite slides with the 405 laser for DAPI and the 488 Laser for Beta Actin. Capture single plane 1024 x1024 pixel images by using line-sequential capturing and 4-line averaging, which is essential for detecting ecDNA. 40x 1.0 NA oil objective is used.
    1. Image a minimum of 20 glomeruli per sample. Save images as ND2 files.

2. DAPI and β-Actin analysis

1. Install ImageJ: https://imagej.net/Fiji/Downloads.
2. Check that the trainable Weka segmentation is available under Plugins | Segmentation | Trainable Weka Segmentation.
3. Drop the image onto the ImageJ tool bar. Click Image | Color | Split Channels. One image with DAPI in blue staining all nuclei will appear and 1 image with β-actin in green will appear, which delineates the glomeruli.
   1. Create a merged file of the single images to draw a region of interest (ROI) for the glomerulus by clicking on Color | Merge Channels. A pop-up box will ask to assign a color to each channel. After assigning each channel a color, tick the Create Composite box and the Keep Source Images box and click Ok. A merged composite image will appear.
   2. Using the β-actin staining as a guide, draw a ROI around the glomerular tuft. Keep this image aside to use the ROI at the end of this protocol.
4. Perform a Gaussian blur on the single blue DAPI image. Click Process | Filters | Gaussian Blur, put in 1-2.00. The image will now look a little blurred, but nuclei are now smooth and background softened.
   NOTE: This step is preformed to remove noise to clean up the image from artifacts that may prevent relevant detection of image attributes to be analyzed.
5. Click on Plugins | Segmentation | Trainable Weka Segmentation.
6. Using the line tool on the tool bar in ImageJ, first trace around intact nuclei using the free hand tools (there are line, circular, and square tool options available to choose from) and then add to Add Class 1 box.
7. Using the line tool on the tool bar in ImageJ delineate areas of background to Class 2 box.
8. Click on the Create New Class button label on the Weka segmentation window and label "ecDNA." Use the line tool (from ImageJ tool bar) to delineate extranuclear DNA stained by DAPI.
9. Click on the Train Classifier button in the training menu in the trainable Weka segmentation window.
   NOTE: The STOP button will appear in place of the Train Classifier button and remain until the training has finished. Do not to click on this button during training as the process will be interrupted.
10. Click on Create Result button to create an image with all the classified components, consisting of intact nuclei, the background and identified ecDNA.
11. Click on the Get Probability button. Toggle the mouse to give a black and white image of all the classes with the object of selection highlighted in white.
12. Duplicate this image by clicking on Image | Duplicate.
13. Toggle to the screen using the mouse button that contains the ecDNA. Apply a threshold to get only what has been identified as ecDNA. Click Apply when the threshold is sufficient.
14. If after thresholding, there are more areas of DNA that are not ecDNA, return to the original trained image, add more classifiers, and repeat steps 2.1-2.13.
15. Click Image | Image Adjust | Make Binary. Copy the glomerular ROI made in step 2.3 and click Ctrl+Shift+E. Activate the Weka window by Edit | Selections | Restore, which restores the selection. Click Analyze Particles, which will measure only the ecDNA particles within the glomerulus.
    NOTE: A result sheet is computed with the count of particles, the area, averages pixels and the percentage of the glomerulus containing ecDNA. These results can be exported into a spreadsheet and saved, for later statistical analysis.
16. Save the classifier as ecDNA.classifier by clicking Save Classifier from the options menu into the ImageJ app on the desktop. Then click Save Data from the options menu and save as ecDNA.arff into the ImageJ folder. The ROI will have to be manually added each time as glomerulus size and shape will change, however the program has learnt what ecDNA is.
17. To reapply the model to subsequent images, repeat steps 2.1-2.5 with a new image. Click Load Classifier from the option menu and then ecDNAmodel.classifier in the ImageJ folder from step 2.16. The log menu will pop up and run the model.
18. Once the model has run, click on Load Data in the options menu and select the ecDNA.arff file saved in the ImageJ folder from step 2.16. Click on Create Result. If the resulting image has failed to pick up all the nuclei, background or ecDNA click on re-train classifier and add more classifiers and save the new model. Complete the analysis process by repeating steps 2.9-2.16.
    NOTE: Several images can be used to generate the final model used to detect ecDNA. This is achieved by applying the model to subsequent images, adding more classifiers and saving the new model and data into the ImageJ folder. This can be particularly important when different samples have higher background. The Weka Segmentation program is also compatible with ImageJ macro language which enables many of the commands to be macro recordable to automate some of the steps.

3. Measurement of neutrophil extracellular traps and ecMPO

NOTE: This method identifies NETs by colocalization of extracellular DNA, Citrullinated Histones peptidyl arginase 4 (PAD4) and MPO.

1. Induce a 20 day model of anti-myeloperoxidase glomerulonephritis (anti-MPO-GN) in 8-10 week old C57/Bl6 mice, with controls⁹.
   1. Euthanize mice humanely using a CO₂ chamber.
   2. Remove kidneys by making an anterior midline incision through the skin with scissors, and then carefully cut the peritoneum and pin back on a dissection board. Using forceps push aside the anterior renal fascia and parietal peritoneum and cut the kidney free by severing the ureter and blood vessels (renal artery and vein) at the renal pelvis. Remove with forceps and cut kidneys in half (sagittal plane) with a size 22 scalpel.
   3. Place kidney in fixative (4% buffered formalin) for 16 hours at room temperature (RT). Remove and soak fixed kidney in 30% ethanol for 24 hours prior to processing.
2. Process kidneys using a standard 6-hour cycle:
   70% ethanol for 15 min
   90% ethanol for 15 min
   100% ethanol for 15 min
   100% ethanol for 20 min
   100% ethanol for 25 min
   100% ethanol for 30 min
   Xylene for 20 min
   Xylene for 30 min
   Xylene for 40 min
   Wax for 30 min
   Wax for 35 min
   Wax for 40 min
   NOTE: Kidneys should not be subjected to prolonged exposure to heat in the wax, as it can destroy antigens of interest.
3. Cut 3 µm sections on a microtome and mount on commercially available glass microscope slides coated with a positive charge. Allow to dry overnight
4. Put glass slides into a slide rack into a 60 °C oven for 60 min.
   NOTE: Steps 3.3-3.4 are crucial to ensure sections do not float off during the antigen retrieval step.
5. Immerse slides in two changes of solvent (xylene or substitute) for 40 min each, in a fume hood.
6. Rehydrate slides in 100% ethanol, 100% ethanol, 70% ethanol for 5 min each.
7. Wash for 5 min in tap water.
8. Place a hot plate in a fume hood and preboil antigen retrieval solution (10 mM Tris-1 mM EDTA pH 9.0) in a pressure cooker. As soon as the antigen retrieval solution starts to boil place the slides in the pot horizontally using a pair of tongs and lock the lid. Boil on high (equivalent to 15 psi or 103 kPa) for 10 min.
   NOTE: This step retrieves the antigen of interest by unmasking the antigen epitope (cross linked via formalin fixation) to allow epitope antibody specific binding the subsequent staining will not work without this step.
9. Remove the pressure cooker from heat and remove the lid immediately by running cold tap on top of the lid in a sink. Leave slides to equilibrate for 20 min in the antigen retrieval solution.
10. Wash slides twice for 5 min in phosphate buffered saline (PBS) (pH 7.4, 0.01 M) on an orbital shaker.
11. Use a hydrophobic pen to draw circles around the kidney tissue being careful not to let any tissue dry out in this time.
12. Block tissue in 10% chicken sera in 5% bovine serum albumin (BSA)/PBS (pH 7.4, 0.01 M) for 30 min, 60 µL per section. Do not wash after this step but carefully remove the blocking solution using a 60 µL pipette. Do not let the sections dry out.
13. Make a cocktail of the primary antibodies diluted in 1%BSA/PBS (pH 7.4, 0.01 M) in 1 tube. Apply 60 µL per kidney sections. The concentration of the primary antibodies are as follows:
    Rabbit anti human/mouse H3Cit 1/100
    Mouse anti human/mouse PAD4 1/50
    Goat anti human/mouse MPO 1/200
14. Incubate overnight at 4 °C in a humidity chamber.
15. Wash slides twice for 5 min in PBS (pH 7.4, 0.01 M) on an orbital shaker.
16. Make a cocktail of the secondary antibodies in 1 tube. Secondary antibodies are applied in 1% BSA/PBS, 60 µL per section as follows:
    Chicken anti rabbit 488 1/200.
    Chicken anti mouse 647 1/200.
    Chicken anti goat 594 1/200.
17. Incubate at RT for 40 min.
18. Wash slides twice for 5 min in PBS (pH 7.4, 0.01 M) on an orbital shaker.
19. Apply secondary antibody 1:200 in 1%BSA/PBS (pH 7.4, 0.01 M) for 40 min at RT 60 µL per section.
20. Wash slides twice for 5 min in PBS (pH 7.4, 0.01 M) on an orbital shaker.
21. Immerse slides in 0.3% Sudan Black in 70% ethanol for 30 min.
22. Wash slides in tap water to remove precipitate and then immerse in PBS (pH 7.4, 0.01 M) for 10 min to prevent further Sudan Black precipitate from forming.
23. Mount slides onto confocal glass coverslips using three 60 µL drops of mounting solution with DAPI. Seal the coverslips with nail polish by applying around the perimeter of the coverslip.
24. Allow slides to cure for 24 hours at RT (to allow DAPI to penetrate) and then store at 4 °C protected from light. Keep in the dark to prevent fading of fluorescent probes.
25. Image slides using a confocal laser scanning head attached to a microscope. Excite slides with the 405 laser for DAPI and the 488 Laser for H3Cit, 561 for MPO, and 647 for PAD4. Capture single plane 1024×1024 pixel images by using line-sequential capturing and 4-line averaging which is essential for detecting ecDNA. Use a 40x 1.0 NA oil objective. Image a minimum of 20 glomeruli per sample. Save images as ND2 files.

4. Neutrophil extracellular traps and ecMPO Analysis

1. Install ImageJ: https://imagej.net/Fiji/Downloads.
2. Check that the trainable Weka segmentation is available under Plugins | Segmentation | Trainable Weka Segmentation.
3. Drop the image into ImageJ. Click Image | Color | Split Channels. One image with DAPI in blue staining all nuclei will appear, one image with H3Cit in green, one image with MPO in red, and one image with PAD4 in white will appear.
   1. Create a merged file of the single images to draw a ROI for the glomerulus by clicking on Color | Merge Channels. A pop-up box will ask to assign a color to each channel. After assigning each channel a color, tick the Create Composite box and the Keep Source Images box and click Ok. A merged image appears. Unlike the protocol for ecDNA previously we will use the composite image to perform the rest of the steps.
4. Perform a Gaussian blur on the composite image. This allows smoothing out of the nuclei. Click Process | Filters | Gaussian Blur, put in 1.00-2.00. The image will now look a little blurred but nuclei are now smooth and background softened.
   NOTE: This step is preformed to remove noise to clean up the image from artifacts that may prevent relevant detection of image attributes to be analyzed.
5. Click on Plugins | Segmentation | Trainable Weka Segmentation. A whole new window will appear with the image to be analyzed and Weka segmentation specific menu tools.
6. Using the line tool on the tool bar (in ImageJ), first trace around intact nuclei not positive for all 4 NET components (MPO, PAD4, DAPI and H3Cit) using the free hand tool and then add to the Add Class 1 box.
7. Using the line tool on the ImageJ tool bar delineate areas of background to the Class 2 box.
8. Click on the Create New Class button label in the label menu and label "NETs". Use the line tool to delineate NETS identified as co-localization DAPI (blue), MPO (red), PAD4 (white) and citrullinated histones (green).
9. Click on the Create New Class button label in the label menu and label "ecMPO". Use the line tool to delineate MPO (red) that is cell free.
10. Click on the Train Classifier button in the training menu in the Trainable Weka Segmentation window.
    NOTE: The STOP button will appear in place of the Train Classifier button and remain until the training has finished. Do not to click on this button during training and the process will be interrupted.
11. Click on Create Result button in the training menu and an image is created with all the classified components, consisting of intact nuclei, the background and what was identified as ecMPO.
12. Click on the Get Probability button in the training menu. Toggle the mouse to give a black and white image of all the classes with the object of selection highlighted in white.
13. Duplicate both the NET probability and the ecMPO probability image by clicking on Image | Duplicate.
14. Toggle to the screen using the mouse button that contains NETs. Apply a threshold to ensure only highlighting of identified NETs. On the ImageJ menu tool bar, click Image | Adjust | Threshold. Move the sliding toggle bars until on the NET components are highlighted. Click Apply when satisfied with the threshold. Repeat the same steps for ecMPO.
15. If after thresholding there are still detecting areas of ecMPO or NETs, go back to the original trained image and add more classifiers and reapply steps 4.1-4.14.
16. Click Image | Image Adjust | Make Binary. Copy the glomerular ROI made in step 4.3, click Ctrl+Shift+E. Activate Weka window by Edit | Selections | Restore, which restores the selection. Click Analyze Particles to measure only the NET particles within the glomerulus.
17. Toggle to the screen using the mouse button that contains ecMPO. Apply a threshold to ensure that only identified ecMPO are obtained. Click Apply when satisfied with the threshold. Repeat step 4.16 above.
    NOTE: A result sheet is computed with the Count of particles, the area, averages pixels and the percentage of the glomerulus containing both NETs and ecMPO. These results can then be put into a spreadsheet and saved, for later statistical analysis.
18. Save the classifier as NETs.classifier by clicking Save Classifier from the options menu and save file into the ImageJ app on the desktop (ImageJ folder). Then click Save Data from the options menu and save as NETs.arff file. The glomerular ROI will have to be manually added each time as glomerulus size and shape will change, but the program has learnt what both NETs and ecMPO are.
19. To reapply the model to subsequent images, repeat steps 4.1-4.5 with a new image. Click Load Classifier from the option menu. Click on Nets.classifier in the ImageJ folder. The log menu will pop up and run model, once the model has run click on Load Data in the options menu and select the NETs.arff file.
    1. Click on Create Result. If the resulting image has failed to pick up all the nuclei, background or ecDNA click on re-train classifier and add more classifiers and save the new model. Complete the analysis process by repeating steps 4.11-4.19.
       NOTE: Several images can be used to generate the final model used to detect ecDNA, ecMPO and NETs. This is achieved by applying the model to subsequent images, adding more classifiers and saving the new model and data into the ImageJ folder. This can be particularly important when different samples have higher background. The Weka Segmentation program is compatible with ImageJ macro language which enables many of the commands to be macro recordable to automate some of the steps.