The transcriptome of human CD34 <sup>+</sup> hematopoietic stem-progenitor cells
Bibliographic record
Abstract
Studying gene expression at different hematopoietic stages provides insights for understanding the genetic basis of hematopoiesis. We analyzed gene expression in human CD34(+) hematopoietic cells that represent the stem-progenitor population (CD34(+) cells). We collected >459,000 transcript signatures from CD34(+) cells, including the de novo-generated 3' ESTs and the existing sequences of full-length cDNAs, ESTs, and serial analysis of gene expression (SAGE) tags, and performed an extensive annotation on this large set of CD34(+) transcript sequences. We determined the genes expressed in CD34(+) cells, verified the known genes and identified the new genes of different functional categories involved in hematopoiesis, dissected the alternative gene expression including alternative transcription initiation, splicing, and adenylation, identified the antisense and noncoding transcripts, determined the CD34(+) cell-specific gene expression signature, and developed the CD34(+) cell-transcription map in the human genome. Our study provides a current view on gene expression in human CD34(+) cells and reveals that early hematopoiesis is an orchestrated process with the involvement of over half of the human genes distributed in various functions. The data generated from our study provide a comprehensive and uniform resource for studying hematopoiesis and stem cell biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".