Telomere length measurements in leukocyte subsets by automated multicolor flow‐FISH
Bibliographic record
Abstract
BACKGROUND: Telomeres are essential protein-DNA structures at the end of chromosomes which are implicated in genome stability and cell replication. The average length of telomere repeats can be measured by in situ hybridization and flow cytometry [flow-FISH]. Such telomere length values reflect telomere shortening (resulting from cell divisions, oxidative damage and other causes) and telomere elongation (mainly resulting from telomerase activity) of the chromosome-specific telomere length inherited in the gametes. Here we report improvements in flow-FISH methodology that enable measurements of telomere length in subsets of human nucleated blood cells. METHODS AND RESULTS: In order to measure the telomere length in granulocytes, naive T cells, memory T cells, B cells and natural killer (NK)/NKT cells within a blood sample, we combined flow-FISH with antibody-staining (Multicolor flow-FISH). Most steps in the staining protocol were automated using a 96-well microdispenser device. The minimum detectable difference in telomere length and the reproducibility of the method are in the range of 0.2-0.5 kb and measurements can be made with as few as a thousand cells. CONCLUSIONS: Automated multicolor flow-FISH will greatly facilitate studies of telomere length regulation in subsets of nucleated blood cells, especially when only few cells are available and when differences in telomere length are small.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".