Multiplex Immunofluorescent Batch Labeling of Marmoset Brain Sections
Bibliographic record
Abstract
PURPOSE: The common marmoset is a small nonhuman primate that has emerged as a valuable animal model in neuroscience research. Accurate analysis of brain tissue is crucial to understand marmoset neurophysiology and to model neurodegenerative diseases. Many studies to date have complemented magnetic resonance imaging (MRI) with histochemical staining rather than immunofluorescent labeling, which can generate more informative and higher resolution images. There is a need for high-throughput immunolabeling and imaging methodologies to generate resources for the burgeoning marmoset field, particularly brain histology atlases to display the organization of different cell types and other structures. METHODS AND FINDINGS: Here, we have characterized a set of marmoset-compatible fluorescent dyes and antibodies that label myelin, axons, dendrites, and the iron-storage protein ferritin, and developed a batch-style multiplex immunohistochemistry protocol to uniformly process large numbers of tissue slides for multiple cell-type specific markers. CONCLUSION: We provide a practical guide for researchers interested in harnessing the potential of marmoset models to advance understanding of brain structure, function, and pathophysiology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".