Absolute scaling of single-cell transcriptomes identifies pervasive hypertranscription in adult stem and progenitor cells
Bibliographic record
Abstract
Hypertranscription supports biosynthetically demanding cellular states through global transcriptome upregulation. Despite its potential widespread relevance, documented examples of hypertranscription remain few and limited to early development. Here, we demonstrate that absolute scaling of single-cell RNA-sequencing data enables the estimation of total transcript abundances per cell. We validate absolute scaling in known cases of developmental hypertranscription and apply it to adult cell types, revealing a remarkable dynamic range in transcriptional output. In adult organs, hypertranscription marks activated stem/progenitor cells with multilineage potential and is redeployed in conditions of tissue injury, where it precedes bursts of proliferation during regeneration. Our analyses identify a common set of molecular pathways associated with both adult and embryonic hypertranscription, including chromatin remodeling, DNA repair, ribosome biogenesis, and translation. These shared features across diverse cell contexts support hypertranscription as a general and dynamic cellular program that is pervasively employed during development, organ maintenance, and regeneration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".