Batch Immunostaining for Large-Scale Protein Detection in the Whole Monkey Brain
Bibliographic record
Abstract
Immunohistochemistry (IHC) is one of the most widely used laboratory techniques for the detection of target proteins in situ. Questions concerning the expression pattern of a target protein across the entire brain are relatively easy to answer when using IHC in small brains, such as those of rodents. However, answering the same questions in large and convoluted brains, such as those of primates presents a number of challenges. Here we present a systematic approach for immunodetection of target proteins in an adult monkey brain. This approach relies on the tissue embedding and sectioning methodology of NeuroScience Associates (NSA) as well as tools developed specifically for batch-staining of free-floating sections. It results in uniform staining of a set of sections which, at a particular interval, represents the entire brain. The resulting stained sections can be subjected to a wide variety of analytical procedures in order to measure protein levels, the population of neurons expressing a certain protein.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".