20 years of stemness: From stem cells to hypertranscription and back
Bibliographic record
Abstract
Transcriptional profiling of stem cells came of age at the beginning of the century with the use of microarrays to analyze cell populations in bulk. Since then, stem cell transcriptomics has become increasingly sophisticated, notably with the recent widespread use of single-cell RNA sequencing. Here, we provide a perspective on how an early signature of genes upregulated in embryonic and adult stem cells, identified using microarrays over 20 years ago, serendipitously led to the recent discovery that stem/progenitor cells across organs are in a state of hypertranscription, a global elevation of the transcriptome. Looking back, we find that the 2002 stemness signature is a robust marker of stem cell hypertranscription, even though it was developed well before it was known what hypertranscription meant or how to detect it. We anticipate that studies of stem cell hypertranscription will be rich in novel insights in physiological and disease contexts for years to come.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".