Single-molecule imaging of telomerase RNA reveals a Recruitment – Retention model for telomere elongation
Bibliographic record
Abstract
Abstract Extension of telomeres is a critical step in the immortalization of cancer cells. This complex reaction requires proper spatio-temporal coordination of telomerase and telomeres, and remains poorly understood at the cellular level. To understand how cancer cells execute this process, we combined CRISPR genome editing and MS2 RNA-tagging to image single-molecules of telomerase RNA (hTR). Real-time dynamics and photoactivation experiments of hTR in Cajal bodies (CBs) reveal that hTERT controls the exit of hTR from CBs. Single-molecule tracking of hTR at telomeres shows that TPP1-mediated recruitment results in short telomere-telomerase scanning interactions, then base-pairing between hTR and telomere ssDNA promotes long interactions required for stable telomerase retention. Interestingly, POT1 OB-fold mutations that result in abnormally long telomeres in cancers act by enhancing this retention step. In summary, single-molecule imaging unveils the life-cycle of telomerase RNA and provides a framework to understand how cancer-associated mutations mechanistically drive defects in telomere homeostasis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".