A Simple Method for Immunohistochemical Staining of Zebrafish Brain Sections for <i>c-fos</i> Protein Expression
Bibliographic record
Abstract
Immediate early genes (IEGs) are transcription factors whose own transcription is initiated rapidly, for example, in the brain in response to environmental stimuli. c-fos is an IEG often used as a marker of neuronal activation. c-fos mRNA expression has started to be quantified and localized in the zebrafish brain following environmental manipulations but analysis of the expression of c-fos protein in the zebrafish brain has rarely been attempted. Here, we describe an immunofluorescence staining method for quantifying c-fos protein expression in different regions of the zebrafish brain. In addition, we expose zebrafish to caffeine, a positive control for c-fos activation in the brain. To confirm cell nucleus specific binding of the c-fos antibody, we counterstained brain sections with the nuclear fluorescent stain DAPI. Furthermore, we describe a method for reducing background autofluorescence often observed in zebrafish brain tissue. Our analysis showed that exposure to caffeine increased the number of c-fos protein-positive cells in specific zebrafish brain regions detected by the immunofluorescence method. Our results demonstrate the feasibility of immunofluorescence-based methods in the analysis of neuronal activation in the zebrafish brain, and reinforce the utility of the zebrafish in behavioral neuroscience research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".