Identifying gene locus associations with promyelocytic leukemia nuclear bodies using immuno-TRAP
Bibliographic record
Abstract
Important insights into nuclear function would arise if gene loci physically interacting with particular subnuclear domains could be readily identified. Immunofluorescence microscopy combined with fluorescence in situ hybridization (immuno-FISH), the method that would typically be used in such a study, is limited by spatial resolution and requires prior assumptions for selecting genes to probe. Our new technique, immuno-TRAP, overcomes these limitations. Using promyelocytic leukemia nuclear bodies (PML NBs) as a model, we used immuno-TRAP to determine if specific genes localize within molecular dimensions with these bodies. Although we confirmed a TP53 gene-PML NB association, immuno-TRAP allowed us to uncover novel locus-PML NB associations, including the ABCA7 and TFF1 loci and, most surprisingly, the PML locus itself. These associations were cell type specific and reflected the cell's physiological state. Combined with microarrays or deep sequencing, immuno-TRAP provides powerful opportunities for identifying gene locus associations with potentially any nuclear subcompartment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".