Protocol for autofluorescence removal in microglia by photobleaching in free-floating immunofluorescent staining of mouse brain tissue
Bibliographic record
Abstract
Autofluorescence in brain tissue poses a challenge in immunofluorescent staining by obscuring antibody-labeled proteins, mainly within microglia. Here, we describe a cost-effective protocol for removing autofluorescence in mouse brain sections by photobleaching. We outline steps to collect and section brain tissue, apply a photobleaching step using a light-emitting diode (LED), and perform immunofluorescent staining with primary and secondary antibodies. This protocol enables clearer visualization of microglial markers, particularly in mouse models of neurodegenerative diseases and aging. • Simple construction of a cost-effective photobleaching apparatus • Step-by-step techniques for photobleaching and immunofluorescent staining • Guidance on interpreting immunofluorescent images of disease-associated microglia Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Autofluorescence in brain tissue poses a challenge in immunofluorescent staining by obscuring antibody-labeled proteins, mainly within microglia. Here, we describe a cost-effective protocol for removing autofluorescence in mouse brain sections by photobleaching. We outline steps to collect and section brain tissue, apply a photobleaching step using an LED light, and perform immunofluorescent staining with primary and secondary antibodies. This protocol enables clearer visualization of microglial markers, particularly in mouse models of neurodegenerative diseases and aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".