Single-Cell PCR Methods for Studying Stem Cells and Progenitors
Bibliographic record
Abstract
Knowledge of the molecular and cellular events characterizing osteoblast development is growing as new markers, including important classes of regulatory molecules such as transcription factors (e.g., Cbfa-1 [ 1 ]), are elucidated. Nevertheless, a paucity of definitive and specific markers, especially for the more primitive progenitors and the hemopoietic lineages ( 2 ). One useful model, however, has been culture of mixed populations of freshly isolated cells derived from a variety of bones (e.g., 21-d fetal rat calvaria [RC]) or bone marrow stroma under conditions that favor osteoblast development ( 2 ). For example, when such heterogeneous primary cultures are grown long-term (approx 3 wk) in medium supplemented with ascorbic acid and α-glycerophosphate, a low frequency (about 0.00001–1% of unfractionated freshly isolated populations) of osteoprogenitor cells present divide and differentiate to form 3-dimensional mineralized bone nodules ( 3 , 4 ). These infrequent cells comprise the colony forming units or colony forming cells-osteoprogenitor (CFU-Os or CFC-Os, respectively) in populations from the whole tissue and appear analogous to the nonstem cell CFU/CFCs in lineages such as the hemopoietic. Notably, the frequency of such cells can be determined by limiting dilution, and they appear to have limited capacity for self-renewal ( 3 , 4 ). On the other hand, morphological, immunohistochemical, and molecular analyses have confirmed that differentiation of CFU-Os and formation of bone nodules reproducibly recapitulates a proliferation-differentiation sequence from an early precursor cell to a mature osteoblast ( 2 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.020 |
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".