Telomere analysis using 3D fluorescence microscopy suggests mammalian telomere clustering in hTERT-immortalized Hs68 fibroblasts
Bibliographic record
Abstract
Telomere length and dynamics are central to understanding cell aging, genomic instability and cancer. Currently, there are limited guidelines for analyzing telomeric features in 3D using different cellular models. Image processing for telomere analysis is of increasing interest in many fields, however a lack of standardization can make comparisons and reproducibility an issue. Here we provide a user's guide for quantitative immunofluorescence microscopy of telomeres in interphase cells that covers image acquisition, processing and analysis. Strategies for determining telomere size and number are identified using normal human diploid Hs68 fibroblasts. We demonstrate how to accurately determine telomere number, length, volume, and degree of clustering using quantitative immunofluorescence. Using this workflow, we make the unexpected observation that hTERT-immortalized Hs68 cells with longer telomeres have fewer resolvable telomeres in interphase. Rigorous quantification indicates that this is due to telomeric clustering, leading to systematic underestimation of telomere number and overestimation of telomere size.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".