Apr 13, 2026
Cortical Degradation Analysis with ABBA
- Joseph Martin1,
- Logan Dunn1,
- Jacob Elrod1,
- Behnaz Namdarzadeh1,
- Billy Lau1
- 1University of Tennessee, Knoxville
Protocol Citation: Joseph Martin, Logan Dunn, Jacob Elrod, Behnaz Namdarzadeh, Billy Lau 2026. Cortical Degradation Analysis with ABBA. protocols.io https://dx.doi.org/10.17504/protocols.io.3byl46wjjgo5/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: August 30, 2025
Last Modified: April 13, 2026
Protocol Integer ID: 225917
Keywords: whole-brain analysis, ChABC, ABBA, perineuronal nets, PNNs, neuroscience, cortical degradation analysis with abba, cortical degradation analysis, structural degradation in the brain, mediated perineuronal net degradation, perineuronal net degradation, allen brain atlas, interactive alignment of mouse brain section image, mouse brain section image, aligning big brain, brain region, degradation, characterization of degradation, structural degradation, brain, big brain, abba, chabc
Funders Acknowledgements:
UTK Start-Up Funds (KK, BL)
Grant ID: N/A
UTK URF AURA (JM)
Grant ID: N/A
UTK URF FRAF (JM)
Grant ID: N/A
NSF GRFP (LD)
Grant ID: N/A
Disclaimer
This protocol was written in the Krishnan lab by Joseph Martin, but all software and original protocols were developed and are available through BIOP-EPFL.
Abstract
Aligning Big Brains & Atlases (ABBA) enables interactive alignment of mouse brain section images with the Allen Brain Atlas, facilitating highly reproducible, region-specific analyses. This protocol outlines a systematic approach to quantify structural degradation in the brain (such as ChABC-mediated perineuronal net degradation) using the output from ABBA and various tools in ImageJ. The resulting data enable detailed evaluation and characterization of degradation across brain regions and can be further correlated with behavioral outcomes.
Original Documentation and Other Protocols
Original ABBA documentation can be found at https://abba-documentation.readthedocs.io/en/latest/
Another protocol involving ABBA and cell density quantification can be found at https://www.protocols.io/view/a-free-analysis-pipeline-to-coregister-3d-lightshe-8epv5rz1ng1b/v1?step=2
Videos of selected steps (also linked individually throughout the protocol): https://youtube.com/playlist?list=PLQCli8HoaKipPETVbl_tELMhf9EkPrkEm&si=AhJmpD2i15LEYHIN
Installation
Slides with installation instructions: https://docs.google.com/presentation/d/1S6U1-yMAoaHALiIuvs90rlktF-OCec8T1xMkt7ej378/edit#slide=id.g10b397560b2_0_175
Pre-Startup Checks (Before the first time using ABBA)
1. Open Fiji
2. Type “set and check” in the search box, select "set and check wrappers," and click "run"
3. Specify correct elastix and transformix paths. These should look something like this (depending on where you installed elastix):
- C:\elastix-5.0.1-win64\elastix.exe
- C:\elastix-5.0.1-win64\transformix.exe
1. Image Preparation
Goal: Obtain images from an image scanner (we used Keyence BZX-10 with a 2X objective), crop them, and name them appropriately so that each image corresponds to a tissue section.
Notes: This protocol is written specifically for images of coronal sections collected at 2X magnification, but it can also be used for sagittal sections or images collected at 10X magnification with minor modifications. Several steps below are bolded due to their critical nature. Once you move on from these steps, it is extremely difficult to make changes to images or their filenames without needing to redo a significant portion of the workflow.
1. Download stitched images
2. Crop stitched images (2X slide images only) so that each image only contains one section
3. Create a PowerPoint named with the cohort number and import stitched images, with each PowerPoint slide corresponding to one image.
4. Use crop tool on PowerPoint to crop each stitched image.
5. Critical Step: Save each image to a "Cropped" image folder, naming each image according to its position on the slide from right to left and its conditions (The top right tissue on the first slide would be 1.1.1, corresponding to slide 1, row 1, tissue 1).
- Make sure to save all images in the .TIF format
Example: #Cohort_Genotype_Condition_Stain_1.1.1
6. Order images from rostral to caudal
7. Duplicate the "Cropped" folder.
8. Create a new folder called "Ordered"
9. Move images from your duplicate of the "Cropped" folder to the "Ordered" folder based on their order along the rostral-caudal axis, adding numbers corresponding to their order to the beginning of each file name
Example: 05#Cohort_Genotype_Condition_Stain_1.1.1
10. At this point, check that images are in order and correctly oriented (as seen below)
11. Have another person check the ordered image folder before proceeding. Make sure that the stitched image powerpoint is also present for comparison.
12. Before beginning to use Qupath/ABBA, ensure that image file names are correct – you cannot change them once you put them into QuPath!
13. Generally throughout the workflow, ensure that everything you save is routed to the correct place before you click "Save" – moving things after saving can corrupt your ABBA mapping file.
Alternative Workflow Option (if you only want to run the ABBA workflow on images with obvious degradation): After ordering images but before proceeding to the next section ("2. QuPath Start-Up and Image Import"), proceed to "10. Carrying Out Degradation Analysis" and carry out steps 1 through 7 before returning to this point. Move the images you identify into a separate folder (such as "A.1 Ordered with Degradation") before proceeding with "2. QuPath Start-Up and Image Import" and carrying out the rest of the workflow.
2. QuPath Start-Up and Image Import
1. To keep things organized, create three tiers of folders as described below (following the file system of the lab):
- Tier 1: Cohort ID
- Tier 2: Conditions [(A. WT; B. Het), (A. SurWT; B. SurHet), etc…]
- Tier 3: Three separate subfolders for managing output files (name them as listed below:
- A. Ordered
- B. QuPath and ABBA Projects
- C. For Post-ABBA Analysis
2. Open QuPath.
3. Drag the "B. QuPath and ABBA Projects" folder into the sidebar (empty/black white space on the left).
This establishes it as your QuPath project folder. Click "Yes" when it prompts you to "Create project for empty directory?"
4. Open the "A. Ordered" folder and select all of your images using "Ctrl + A", making sure to click the first image first so that they stay in order
5. Drag images into the left sidebar as previously done with the empty folder.
6. Perform steps 7 through 9 within the import window before clicking "Import."
7. Under the "Image server" drop-down box, select "Bio-Formats builder."
8. Under the "Set image type" drop-down box, select "Fluorescence"
9. Ensure that the Auto-generate pyramids box is not checked.
10. Double-click on an image's filename within the list on the left. This should make it appear in the image viewer on the right side of the window.
11. Click on the "Image" tab (listed within the 5 tabs on the top left of the window)
12. Double-click on the number listed for "Pixel width"
13. Change the pixel width AND pixel height to 5 µm, then press "Ok"
14. Select the Workflow" tab at the top left, then click on the "Create Script" button on the bottom left. The "Script Editor" box will appear, and you should see the commands to set the image type to fluorescence and change the pixel scale for an image.
15. Along the top toolbar, click "Run" and then "Run for project"
16. Click the button with the ">>" symbol in the middle of the pop-up box and then click "Ok"
Note: The preceding steps should batch process all images in the same way you processed the first (by setting the image size manually). This will make things more uniform when you enter ABBA.
17. Exit the "Script Editor" box and return to the "Project" tab
18. Double click on another image's filename (other than the one you double-clicked initially) and select the "Image" tab. Under "Pixel Width" and "Pixel Height", it should say 5 µm.
19. Select "File" - "Save" and close QuPath
3. ABBA Startup and Navigation
1. Open Fiji/ImageJ
2. In the search bar, type "abba" and select "ABBA - ABBA Start" and "Start"
3. Select the appropriate atlas (we used Allen Brain Atlas V3p1) and click "Ok"
4. Select the appropriate slicing mode (we used coronal) and click "Ok"
5. To move around and select objects within ABBA:
- Left-Click: drag and select
- Mouse Wheel: zoom
- Right-Click: pan (hold and drag)
- Up and Down Keys: zoom in and out
6. The yellow sections you're seeing are the atlas.
The atlas has 3 channels:
- Channel 0 - Nissl staining
- Channel 1 - Allen Reference Atlas (ARA) - Auto Fluorescence
- Channel 2 - Borders of regions
You can turn these on and off, or increase or decrease the intensity of the layer by using the sliders and boxes on the right control panel
7. Control the displayed spacing between slices with the displayed slicing [microns] slider:
- 10 steps = 100 microns between slices
- 50 steps = 500 microns between slices
8. There are two view modes in ABBA:
Positioning Mode (all slices visible): For initial positioning/reordering/rotating of slices along the atlas axis.
Review Mode (one slice visible): For review of slice position/in plane registration
4. Importing the QuPath Project
1. In the ABBA window menu bar select "Import" > "Import QuPath Project"
2. Select the appropriate "project.qpproj" file from your "B. Qupath and ABBA Projects" folder
3. Specify an initial AP position (This is how far into the brain you want the initial position to be; 4 usually works pretty well)
4. Specify a spacing increment between slices (This is how far apart each slice is, which should roughly correlate with thickness – for example, 80 µm sections – 0.08 mm spacing)
5. In the "Create BDV Dataset" pop-up box, just click "Ok". No changes are needed here.
5. Select all slices using either "shift + click" or drawing a rectangle around all of the circles and squares in the viewing window
6. Click the "Vis." and "Ch_0" heading boxes on the table (lower right portion of the ABBA window). This should reveal all slices
5. Aligning Images to the Atlas
1. Align tissues with their corresponding atlas slices so that at least one hemisphere looks approximately correct by dragging one or multiple selected tissues left or right. Tissues should already be in order from rostral to caudal, so this process should be relatively straightforward.
2. In positioning mode, find a tissue where the hippocampus is distinctly asymmetrical.
3. Using the "Atlas Slicing" controls in the bottom right of the display, adjust the X Rotation and Y Rotation so that the hemisphere-to-hemisphere asymmetry of the atlas slices matches the asymmetry of your tissues.
4. Adjust X Rotation for dorsal-ventral asymmetry
5. Adjust Y Rotation for left-right asymmetry
6. After selecting one or several slices (if they are all "off" by a similar amount from the atlas) in review mode, Click "Align – Interactive Transform" in order to scale and transform tissues to match the atlas slices. This step is the most time-consuming part of registration in ABBA, as you will likely have to make adjustments to images one-by-one, but it is critical in order to achieve a good registration outcome.
7. Adjust rotation angle, X scale, Y scale, X translate, and Y translate as necessary
8. After interactive transformation is complete for all images, you should save the current state of the project. Name the saved state as State-1_Interactive-Transformation-Complete". Click OK.
6. Automated Slice Alignment in ABBA (Optional)
1. Enable the atlas map (channel 2) and turn off channels 0 and 1.
2. Select all tissues by clicking the first image in the image set table (on the right), and then by pushing "Ctrl + A".
Parameters for Affine Registration
- Atlas channels – use channel 2 only.
- Slices channel – use whatever channel slices are shown in (either 0 or 1).
- Registration re-sampling (micrometers) – use 15.
- Show registration results as ImagePlus – this should NOT be checked.
- Background offset value – use 0.
Note: The green circles above each image will turn red, this means the registration is in-process for this image. As they complete, the circles will go from yellow to green.
Once Affine Registration is complete, you should save the current state of the project.
Name the saved state as "State-2_Affine-Registration-Complete". Click OK.
Choose the following parameters for Spline Registration:
- Atlas channels – use channel 2 only.
- Slices channel – use whatever channel slices are shown in (either 0 or 1).
- Number of control points along X, minimum 2 – use 30.
- Registration re-sampling (micrometers) – use 15.
- Background offset value – use 0.
- Show registration results as ImagePlus – this should NOT be checked.
Note: The green circles above each image will turn red, this means the registration is in-process for this image. As they complete, the circles will go from yellow to green.
Once Spline Registration is complete, you should save the current project state.
Name the saved state as "State-3_Spline-Registration-Complete". Click OK.
Automated Slice Alignment Workflow Example (Optional)
- Affine registration on DAPI vs Atlas Nissl (Ch 0)
- Spline registration on an Autofluorescent channel vs Atlas Autofluorescent (Ch 1) (15 control points)
- Spline registration on DAPI vs Atlas Nissl (Ch 0) (15 control points)
This takes about 10 minutes for 50 slices on a laptop.
7. Saving/Exporting ROI Sets from Fiji
1. In the ABBA window, select all slices.
2. Select "Export" > "Export Regions to File" > Select the appropriate "C. For Post-ABBA Analysis" folder as the location to save all ROI file sets
3. At this point, it is safe to close both ABBA and QuPath. Leave Fiji open if continuing.
8. Preparing File Sets for Analysis
1. Open the cohort folder in which you are working and select the condition you just completed mapping for in ABBA
2. Open the "A. Ordered" folder, click on the first image, and press "Ctrl + A" followed by "Ctrl + C". Make sure to deselect any BFMEMO files that this step selects and only include the .TIF files. (This can be accomplished by holding the "Shift" key and clicking any files you wish to exclude)
3. Go up one level, open the "C. For Post-ABBA Analysis" folder, and paste the images you just copied into this folder.
4. Slowly click the first image twice; copy the image filename.
5. Create a new folder (Shift + Ctrl + N); paste the name of the first image as the new folder name.
6. Repeat this process for all newly mapped images.
7. Drag and drop each individual ROIset.zip folder into its respective subfolder within "C. For Post-ABBA Analysis". (Ensure that filenames match)
8. Drag and drop each image (.TIF format) into its respective subfolder. (Ensure that filenames match)
9. Preparing ROI Sets for Degradation Analysis
ROI Sorting Preparation
1. Open Fiji (if not already open)
2. Drag and drop the first image into Fiji to open the image.
3. Slowly click on the ROI set zipped folder for the image you just opened; before dragging and dropping into Fiji, add "...RoiSet-Complete" to the name.
Example:
Original folder name: "062321-TacWTPNN-10XW1-1.4"
New folder name: "062321-TacWT_PNN-10X_W1-1.4_RoiSet-Complete"
4. Drag and drop the ROI set into Fiji; the ROI manager will open.
5. Click "More" > "Sort" to sort each ROI alphabetically. This will now sort ROIs by hemisphere
6. Delete ROIs which are not pertinent to your current work by selecting them and clicking the "Delete" button in the ROI manager. Follow steps 9-14 for details.
7. Click on the first ROI listed in the ROI manager.
8. Scroll down until you find the first ROI which you intend to keep (Primary Motor (MOp) in this case)
9. Hold the "Shift" button on your keyboard, and click the ROI listed right before the ROI you intend to keep (for me, this would be "MO" which shows the full motor cortex subregion); this should highlight all ROIs before MOp.
10. Click the "Delete" button in the ROI manager; those ROIs should now be gone.
11. Click on the next ROI which you intend to remove from the manager (below MOp are the ROIs for the layers of this subregion).
12. Scroll until finding the next ROI you intend to keep; shift + click the ROI immediately above it and click on "Delete" in the ROI manager to remove them. Do this repeatedly until you have only the specific ROIs you want to quantify PNN expression in.
List of ROIs to keep across all z-positions (may be updated or changed to any regions in the Allen atlas):
- SSp-bfd (Primary Somatosensory Cortex – Barrel Field Subregion)
- SSp-ll (Primary Somatosensory Cortex – Lower Limb Subregion)
- SSp-m (Primary Somatosensory Cortex – Mouth Subregion)
- SSp-n (Primary Somatosensory Cortex – Nose Subregion)
- SSp-tr (Primary Somatosensory Cortex – Trunk Subregion)
- SSp-ul (Primary Somatosensory Cortex – Upper Limb Subregion)
- SSp-un (Primary Somatosensory Cortex – Undefined Subregion)
- SSs (Secondary Somatosensory Cortex)
- TEa (Temporal Association Subregion)
Deselecting ROIs
After deleting all non-useful ROIs for your analysis, ensure no ROIs are selected in the manager when the ROI file is saved by performing the following steps:
- Click on a random ROI in the ROI manager
- Click the "Deselect" button in the ROI manager
Saving ROI Files
1. Ensure you are working in the correct file to save the new ROI set (The folder listed in the "Save in:" dropdown box should match the image name you currently have open in Fiji
2. Add "-Isocortex-YourInitials" as a classifier to the file name; this will be your new ROI set.
- Example: "RoiSet-Isocortex-LD"
Splitting ROIs (into right and left ROIs)
1. Within your open ROI manager that displays the "RoiSet-Isocortex-##" ROIs, select the first ROI in the list.
2. Click "More" > "Split"
- Depending on the subregion for which you are performing this step, there could be between 2 and 25 new ROIs which appear in the manager.
- There will be a bunch of small polygons generated (that are located at the edges of ROIs within either hemisphere); we will delete these.
- The ROI for each subregion will also be generated; we will keep and re-name these.
3. Find the ROIs you intend to keep in the ROI manager; rename them to include hemisphere naming nomenclature. An example is shown below using SSp-bfd ROI.
- Hemisphere-specific ROI nomenclature: "LH SSp-bfd" and "RH SSp-bfd".
4. Delete all other ROIs generated by performing splitting, and delete the hemisphere-combined ROI for the subregion ROIs you just re-named (LH and RH).
5. Repeat this process for all ROIs and all images.
6. Click "More" > "Sort" to sort each ROI alphabetically. This will now sort ROIs by hemisphere
7. Ensure no ROIs are selected in the manager when the ROI file is saved by performing the following steps:
- Click on a random ROI in the ROI manager
- Click the "Deselect" button in the ROI manager
- Click anywhere within the open image EXCEPT for the ROIs highlighted within the image
8. Click "More" > "Save" and save this ROI set as your final version. Naming nomenclature: "RoiSet-Isocortex-Initials-Final"
9. This file set is now ready for data collection.
10. Repeat this process for all images.
10. Carrying out Degradation Analysis
Preparing for Analysis
1. Review ordered ChABC images and identify major areas of PNN depletion
A. Sample image from a Penicillinase-injected brain (Control) with no injection-induced degradation.
B. Sample image from a ChABC-injected brain with injection-induced degradation within several subregions of the primary somatosensory cortex.
2. Open an image from the "Ordered" folder
3. Identify regions of the cortex of each image where it appears that PNNs are dimmer as compared to the adjacent cortex or other hemisphere
4. Find the biPen image that corresponds to the opened ChABC image
5. Compare the intensity of dim regions of PNNs of the ChABC image with the intensity of PNNs in the corresponding region of the biPen image
6. If the PNNs are much dimmer for the ChABC image than for the biPen image, note the image’s order number
7. Repeat this process for all ChABC images until you have identified all images with depleted PNNs
8. Set-up spreadsheet for analysis (Ours is linked here: 
Degradation Analysis Spreadsheet.xlsx14.6KB )
Degradation Analysis Spreadsheet.xlsx14.6KB )9. Rename the file according to the cohort number you are working on
10. At the bottom of the screen, rename the sheet named "Cohort #" with the cohort number you are working on
11. Fill out the information in cells A4 through D4
12. Enter the cohort information in cells E4 through I4
13. Create a new row of the spreadsheet for each hemisphere (left and right) of each image in which you have identified PNN depletion, filling out all information from columns E through I
- Change column E information according to order number
- Change column H information according to section number (number used to orient tissue on slide (such as 1.1.1 being the first tissue on the first row of the first slide imaged))
- Ensure that a row exists for both the left and right hemisphere of each tissue, even if only one hemisphere has depleted PNNs
- Review the example spreadsheet page to ensure that the spreadsheet is set-up correctly
Analyzing Each Image
1. Open Fiji and drag the first image with depleted PNNs into the Fiji interface
2. Remove the scale associated with the image on Fiji – This step needs to completed each time Fiji is opened
3. Select "Analyze" – "Set Scale" – "Click to Remove Scale"
4. Check the box labeled "Global"
5. Click "Ok"
6. When opening the next image, uncheck "Disable Global Calibration" and check "Disable these Messages"
7. Drag the "RoiSet-Isocortex-Initials-Final" file corresponding to the image into the Fiji interface
- This file is found in the folder corresponding to the image you are analyzing in the "C. For Post-ABBA Analysis" folder
8. Select "Show All" to display the outlines of each cortical ROI
9. Use the polygon selection tool to draw the outline of the portion of an ROI where PNNs are dimmer (as compared to the adjacent cortex, cortex of the other hemisphere, or corresponding biPen image) by clicking to add points and dragging to draw line segments
- Begin at the outer edge of the tissue and trace along the path of the ChABC injection to the end of the dim region or ROI (whichever comes first)
- If a region of dimness intersects with the ROI outline, trace the ROI outline as closely as possible
- After "drawing" the outline, click the first point you created to complete the polygon
- Press "T" to save the outlined region that you have drawn
- In the ROI Manager window, rename the newly saved ROI (which is probably a series of numbers at this point) using the format "(L/R)H-ROI_reduced"
- Example: left hemisphere reduced SSp-bfd would be called "LHSSp-bfd_reduced"
- If the region of PNN dimness encompasses multiple ROIs, repeat the drawing and saving process for each affected ROI
10. Save the ROI file within its corresponding folder in the "C. For Post-ABBA Analysis" folder using the format "Roiset-Isocortex-Initials-Final-Analyzed"
If you forget to do this, you will have to redraw the reduced areas for each ROI before moving forward!
11. Measure the total area of the ROI where PNNs were found to be depleted
12. Select the ROI in the ROI manager window
Example: For an image where the left-sided barrel field was depleted, select "LH_SSp-bfd"
13. Select "Measure" along the right side of the ROI manager window
14. Record this value in the "Full Area" spreadsheet cell corresponding to the correct tissue, hemisphere, and ROI
15. If the region of PNN dimness encompasses multiple ROIs, repeat the measurement for each affected ROI
16. Determine the rostral-caudal axis value for each depleted ROI
17. Load the final saved state of ABBA mapping for the ChABC animal you are analyzing (found within the "B. QuPath and ABBA Projects" folder for the cohort you are working on)
Use the .ABBA file type (no other file type will load)
18. Enter review mode and navigate to the tissue that you are currently working on
19. Select "Display" > "Show mouse atlas position" from the menus along the top of the ABBA window
20. Hover your mouse over the center of the ROI you are currently analyzing, making sure that you are hovering over the correct hemisphere
21. Record the first of the three coordinates that appear when you hover over the tissue in the appropriate cell in the spreadsheet column labeled "(ROI) Map" that corresponds to the correct tissue, hemisphere and ROI.
Note: This value represents the position of the tissue along the rostral-caudal axis and changes at different points of the same tissue due to asymmetry in sectioning (which are accounted for by adjustments to the "Atlas Slicing Angle," so it is important that this value is recorded from the center of each ROI to remain consistent)
22. If the region of PNN dimness encompasses multiple ROIs, repeat the rostral-caudal axis determination for each affected ROI
23. Measure the area of the reduced ROI that you just outlined and saved
24. Select the "(L/R)H_ROI_reduced" ROI in the ROI manager
25. Select "Measure" along the right side of the ROI manager window
26. Record this value in the "Depleted Area" spreadsheet cell corresponding to the correct tissue, hemisphere, and ROI
27. If the region of PNN dimness encompasses multiple ROIs, repeat steps 1-26 for each affected ROI
28. Repeat steps 1 through 27 for each image with PNN depletion
NOTE: If you are analyzing images that fall between images with PNN depletion but do not have any depletion themselves, include a reduced area of 0 in the corresponding "Depleted Area" spreadsheet cell for that image
Calculating the percentage reduction for each depleted ROI
1. Locate the cell in the "% Depleted" part of the spreadsheet that corresponds to the tissue, hemisphere, and ROI that you have been analyzing
2. Create a formula that divides the depleted area of the ROI you calculated and recorded in step 12 by the total area of the ROI that you calculated and recorded in step 10 (to represent the proportion of the total ROI where PNNs were dimmer than "normal")
3. Use the format "=(Z16/J16)"
4. In the "Example" page of the analysis spreadsheet, this represents the proportion of an SSp-bfd ROI that was depleted
5. Ensure that the formula includes the correct total and reduced area cells
6. In the first cell of an ROI’s "% Depleted" column, click and drag down from the corner of the first cell with the manually entered formula to apply the formula to the remaining cells in the column
7. Repeat as necessary to ensure that the reduction proportion is calculated for all ROIs with PNN depletion
8. Select all cells within the "% Depleted" area of the spreadsheet then change the number format to "Percentage" within the menu options of Excel to convert the calculated number from a proportion to a percentage
9. If the region of PNN dimness encompasses multiple ROIs, repeat as needed for each affected ROI
10. Make sure that the ChABC analysis spreadsheet and all files from the ABBA process are saved to the server and checked as needed
Protocol references
Chiaruttini, N.; Castoldi, C.; Requie L. et al. ABBA+BraiAn, an integrated suite for whole-brain mapping, reveals brain-wide differences in immediate-early genes induction upon learning, Cell Reports (2025). https://doi.org/10.1016/j.celrep.2025.115876
Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports 7, 17204 (2017). https://doi.org/10.1038/s41598-017-17204-5
Wang, Q.et al. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell. (2020). 10.1016/j.cell.2020.04.007
Acknowledgements
Thanks to Behnaz Namdarzadeh for critical review of this protocol and to Alexandra McBryar, Rose Shultz, Breanna Ceesay, and Claudia Saucier for their contributions to mapping efforts with ABBA. Special thanks to Dr. Keerthi Krishnan for her mentorship and support throughout this project.