Detection of Axonally Localized mRNAs in Brain Sections Using High-Resolution <em>In Situ</em> Hybridization
Bibliographic record
Abstract
mRNAs are frequently localized to vertebrate axons and their local translation is required for axon pathfinding or branching during development and for maintenance, repair or neurodegeneration in postdevelopmental periods. High throughput analyses have recently revealed that axons have a more dynamic and complex transcriptome than previously expected. These analysis, however have been mostly done in cultured neurons where axons can be isolated from the somato-dendritic compartments. It is virtually impossible to achieve such isolation in whole tissues in vivo. Thus, in order to verify the recruitment of mRNAs and their functional relevance in a whole animal, transcriptome analyses should ideally be combined with techniques that allow the visualization of mRNAs in situ. Recently, novel ISH technologies that detect RNAs at a single-molecule level have been developed. This is especially important when analyzing the subcellular localization of mRNA, since localized RNAs are typically found at low levels. Here we describe two protocols for the detection of axonally-localized mRNAs using a novel ultrasensitive RNA ISH technology. We have combined RNAscope ISH with axonal counterstain using fluorescence immunohistochemistry or histological dyes to verify the recruitment of Atf4 mRNA to axons in vivo in the mature mouse and human brains.
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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) | 0.000 | 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 | 0.002 |
| 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".