Probing the Mitotic History and Developmental Stage of Hematopoietic Cells Using Single Telomere Length Analysis (STELA)
Bibliographic record
Abstract
Abstract In most somatic cells, telomeres shorten with each round of cell division. As a result telomere length can be used to assess the mitotic history of cells with some important caveats: the telomere length at birth is highly variable (presumably reflecting different alleles of genes regulating telomere length in the germline), telomere losses can be compensated by telomerase and the overall decline in telomere length includes sporadic, variable losses of telomere repeats resulting from damage to telomeric DNA and/or replication errors. The first caveat can be circumvented by testing cells from the same individual and sporadic telomere losses can be analyzed by single telomere length analysis (STELA). It is more difficult to exclude the effect of telomerase on telomere length but we have previously shown that, despite readily detectable expression of telomerase, hematopoietic stem and progenitor cells show a progressive decline in telomere length with cell division and with age. The clinical relevance of telomere shortening is illustrated in several recent studies linking very short telomeres to bone marrow failure and pulmonary fibrosis. We now show that purified human hematopoietic populations from mobilized peripheral blood (MPB) and cord blood (CB) enriched for stem cells (Lin−CD34+CD38−Rho−) and successively more mature cells display progressively shorter telomeres, pointing to the utility of this method for studies of the mitotic relationship between various stem and progenitor cells. Ultra-short telomeres were readily observed (and found to be significantly more frequent) in terminally differentiated cell populations of MPB, suggesting that sporadic telomere losses occur more frequently during differentiation. When 1000 Lin−CD34+CD38−Rho− cord blood cells were transplanted into two immuno-deficient mice, the most primitive human hematopoietic cells with a CD34+CD38− phenotype lost 3970 and 2790 bp respectively following regeneration in vivo, indicative of ~ 30–80 cell divisions assuming a telomere loss of 50–100 bp/division. Further losses in more differentiated cells were similar to those observed in cells before transplantation. These results illustrate the power of STELA for analysis of telomeres in rare cells and point to a novel strategy to study the turnover and replicative history of cells. Furthermore, these data demonstrate that self-renewal divisions in stem cells rather than additional cell divisions in downstream progenitors are the primary cause of telomere loss following transplantation.
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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) | 0.003 | 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".