Visualization and quantitative analysis of extrachromosomal telomere-repeat DNA in individual human cells by Halo-FISH
Bibliographic record
Abstract
Current methods for characterizing extrachromosomal nuclear DNA in mammalian cells do not permit single-cell analysis, are often semi-quantitative and frequently biased toward the detection of circular species. To overcome these limitations, we developed Halo-FISH to visualize and quantitatively analyze extrachromosomal DNA in single cells. We demonstrate Halo-FISH by using it to analyze extrachromosomal telomere-repeat (ECTR) in human cells that use the Alternative Lengthening of Telomeres (ALT) pathway(s) to maintain telomere lengths. We find that GM847 and VA13 ALT cells average ∼80 detectable G/C-strand ECTR DNA molecules/nucleus, while U2OS ALT cells average ∼18 molecules/nucleus. In comparison, human primary and telomerase-positive cells contain <5 ECTR DNA molecules/nucleus. ECTR DNA in ALT cells exhibit striking cell-to-cell variations in number (<20 to >300), range widely in length (<1 to >200 kb) and are composed of primarily G- or C-strand telomere-repeat DNA. Halo-FISH enables, for the first time, the simultaneous analysis of ECTR DNA and chromosomal telomeres in a single cell. We find that ECTR DNA comprises ∼15% of telomere-repeat DNA in GM847 and VA13 cells, but <4% in U2OS cells. In addition to its use in ALT cell analysis, Halo-FISH can facilitate the study of a wide variety of extrachromosomal DNA in mammalian cells.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".