Generation and Utilization of Human Induced Pluripotent Stem Cells (iPS) from Primary Endothelial Cells for Studying Endothelial Gene Regulation
Bibliographic record
Abstract
The mechanism(s) that establishes endothelial cells (EC) phenotype is not known. We aimed to study endothelial gene regulation by generating induced pluripotent stem cells (iPS) from human umbilical vein EC (HUVEC) and differentiating the resulting iPS back into EC. This model provides a system with a homogenous genetic background to explore how EC phenotype is revoked (HUVEC to iPS), and reestablished (iPS to EC). iPS colonies were generated using originally reported transcription factors. Quantitative RT-PCR, immunofluorescence and microarray analysis were used to characterize iPS and cell lineages derived from iPS. Pluripotency of the iPS were demonstrated by generation of embryoid bodies (EB) and detection of the three germ layer markers, as well as direct differentiation to functional specific cell types (including neurons, cardiomyocytes, and EC). We examined the mechanism of activation and repression of a highly EC-specific gene, von Willebrand factor (VWF), in HUVEC, iPS, EB and iPS that was differentiated into EC (EC-diff). We explored whether the expression pattern of transcription factors that regulate VWF are associated with establishment of EC phenotype. The expression levels of both VWF activators and repressors are reduced in iPS and EB but detected to similar levels in HUVEC and EC-diff, suggesting a more prominent role for activators in establishing EC gene activation and consequently EC phenotype. This system provides an opportunity for exploring endothelial gene regulation and consequently enables us to develop new therapeutic approaches for vascular diseases.
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 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".