Three dimensional dual labelled DNA fluorescent in situ hybridization analysis in fixed tissue sections
Bibliographic record
Abstract
Emerging studies demonstrate that three-dimensional organization of chromatin in the nucleus plays a vital role in regulating the genome. DNA fluorescent in situ hybridization (FISH) is a common molecular technique used to visualize the location of DNA sequences. The vast majority of DNA FISH studies are conducted on cultured cells due to the technical difficulties encountered using fixed tissue sections. However, the use of cultured cells poses important limitations that could yield misleading results, making in vivo analysis a far superior approach. Here we present a protocol for multiplexed three dimensional DNA FISH in mouse brain sections, which is also applicable to other tissues. Paraffin-embedded tissues could be used but the embedding and preparation of the samples is time-consuming and often associated with poor antigenicity. To overcome this problem we:•developed a FISH technique using fixed, frozen cryosections;•provide specific instructions for tissue processing for proper fixation and freezing, including equilibration in sucrose gradients to maintain proper cellular structure;•include optimized permeabilization and washing steps to achieve specific signal and to limit background fluorescence in tissue sections.
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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 | 0.001 |
| 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) | 0.004 | 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".