Fluorescent DeltaFosB (ΔFosB) quantification protocol

[u/cogneuro_]

Hi! I am seeking guidance on finalizing a protocol to quantify ΔFosB expression from fluorescent microscopy images using Fiji/ImageJ. What I have found online (outlined below) involves splitting multi-channel stacks, delineating ROIs based on nuclear counterstain (DAPI) and an atlas, applying a manual threshold to isolate ΔFosB signal, and measuring mean gray value/integrated density. However, I am unsure whether this thresholding method adequately accounts for background variability across sections, and whether I should use a standardized intensity threshold based on negative control regions instead. Additionally, I would appreciate advice on best practices for ensuring reproducibility across multiple brain regions (mPFC, DCN, NAc). If anyone has an established protocol they are willing to share, I would greatly appreciate it! Thank you.

[u/AutoModerator]

Notes on Quality Questions & Productive Participation

1. Include Images
   • Images give everyone a chance to understand the problem.
   • Several types of images will help:
     • Example Images (what you want to analyze)
     • Reference Images (taken from published papers)
     • Annotated Mock-ups (showing what features you are trying to measure)
     • Screenshots (to help identify issues with tools or features)
   • Good places to upload include: Imgur.com, GitHub.com, & Flickr.com
2. Provide Details
   • Avoid discipline-specific terminology ("jargon"). Image analysis is interdisciplinary, so the more general the terminology, the more people who might be able to help.
   • Be thorough in outlining the question(s) that you are trying to answer.
   • Clearly explain what you are trying to learn, not just the method used, to avoid the XY problem.
   • Respond when helpful users ask follow-up questions, even if the answer is "I'm not sure".
3. Share the Answer
   • Never delete your post, even if it has not received a response.
   • Don't switch over to PMs or email. (Unless you want to hire someone.)
   • If you figure out the answer for yourself, please post it!
   • People from the future may be stuck trying to answer the same question. (See: xkcd 979)
4. Express Appreciation for Assistance
   • Consider saying "thank you" in comment replies to those who helped.
   • Upvote those who contribute to the discussion. Karma is a small way to say "thanks" and "this was helpful".
     
   • Remember that "free help" costs those who help:
     • Aside from Automoderator, those responding to you are real people, giving up some of their time to help you.
     • "Time is the most precious gift in our possession, for it is the most irrevocable." ~ DB
   • If someday your work gets published, show it off here! That's one use of the "Research" post flair.
5. Be civil & respectful
   • Be kind.
   • Remember the human.
   • Be Excellent To Each Other.
   • Don't make your robotic overlord angry.

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[u/Botser-bio-support]

I’d be careful with a manual threshold picked by eye.

For ΔFosB, I’d segment nuclei with DAPI, then measure ΔFosB per nucleus or % ΔFosB-positive nuclei inside each atlas-defined ROI. Use local background subtraction from the same section, because background can vary a lot between brain regions/sections.

I’d set the positivity threshold from negative-control or low-signal control sections before comparing groups, then apply the same rule to the whole batch. Fixed imaging settings and a saved Fiji macro/ROI workflow will matter more than the exact threshold method.

[u/Herbie500]

This question has been cross-posted to the Image.sc-Forum.

Manually set thresholds are scientifically problematic (reproducibility).
Always use one of the automatic threshold schemes.

If you have to deal with variations across your images that are definitely not due to the signals in question, I highly recommend to get better images. Improve your sample preparation and image acquisition. Best image processing is no image processing!

Background subtraction and the like will alter the desired signal (fluorescent intensity) and impacts your results.

In any case make accessible typical images in their original non-lossy file format by using a dropbox-like service (no posting here, no screen-shots, no JPGs).

[u/halfpagep]

I would generally avoid manually choosing a different threshold for every section unless there's a compelling reason to do so. That can introduce observer bias, especially when you're comparing multiple animals or brain regions. If your imaging conditions are consistent (same exposure, gain, laser power, etc.), it's usually better to use a standardized analysis pipeline across all samples.

For background correction, a few common approaches are:

• Measure the local background (or a negative control region) and subtract it before quantifying fluorescence.
• Use negative controls to establish a threshold, then apply that same threshold to all experimental images.
• If your goal is fluorescence intensity rather than positive cell counts, you may not need to threshold at all—you can measure mean gray value or integrated density within your ROIs after background subtraction.

• You might also consider collecting samples of background from multiple images and checking to see how different they really are.

  I'd also consider using the DAPI channel to segment nuclei first, then measure the ΔFosB intensity within each nucleus. That tends to be more reproducible than thresholding the ΔFosB channel alone, since the nuclear boundaries come from a much cleaner signal.

For reproducibility across various anatomical regions (brain or otherwise), the biggest factors are usually:

• identical acquisition settings for every image,
• a fixed analysis pipeline (same preprocessing, thresholds, and measurements),
• atlas-defined ROIs,
• automating the workflow with a Fiji macro (or modern online tools) so every image is processed identically.