A SAGE-based comparison between glomerular and aortic endothelial cells
Bibliographic record
Abstract
Endothelial cells have many characteristics in common, but significant morphological and functional differences exist between endothelial cells from different anatomic sites. The specific glomerular endothelial (GEn) cell transcript repertoire is unknown. We sought to determine whether endothelial cells derived from bovine glomeruli display a distinct transcriptional profile compared with bovine aortic endothelium (BAE) under identical conditions. Serial analysis of gene expression (SAGE), which includes known and unknown transcripts, was used to make the comparison. The GEn and BAE SAGE libraries contain 36,844 and 26,452 total tag sequences, respectively. Among 6,524 unique tag sequences represented at least 2 times in the 2 libraries, 2,094 (32%) were matched to well-characterized bovine cDNA sequences (358 tags) or expressed sequence tags (EST). Identification of the human homolog was achieved for 1,035 of these tags. Forty-two tags were differentially expressed in GEn. For 25 of these, the bovine cDNA or EST, and for 17 the human homolog was identified. Among all transcripts with a known bovine and human tag, seven were expressed at levels more than 10-fold higher in cultured GEn cells compared with all other SAGE libraries. The transcript "DKFZp564B076" was localized by in situ hybridization to glomerular endothelium in vivo and was shown by real-time RT-PCR to be highly abundant in glomeruli compared with aortic intima. This work supports the concept that differences in the transcriptional profile of endothelial cells from distinct origins are observed under otherwise equivalent conditions. Furthermore, we have identified the first known transcript predominant in glomerular endothelium in vivo.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".