Pluripotentiality and Conditional Transgene Regulation in Human Embryonic Stem Cells Expressing Insulated Tetracycline-ON Transactivator
Bibliographic record
Abstract
Conditional manipulation of gene expression by using tetracycline (TET)-ON based approaches has proven invaluable to study fundamental aspects of biology; however, the functionality of these systems in human embryonic stem cells (hESC) has not been established. Given the sensitivity of these cells to both genetic manipulation and variations of culture conditions, constitutive expression of TET transactivators might not only be toxic for hESC but might also impair their ability to self-renew or differentiate into multiple tissues. Therefore, the effect of these transactivators on the biology and pluripotentiality of hESC must first be evaluated before broad use of TET-ON methodologies is applied in these cells. Improved insulated lentivectors that display stable transgene expression and minimal insertional transactivation have been described for hESC. By using insulated lentivectors that allow simultaneous expression of TET components and fluorescent reporters, here we demonstrate that hESC constitutively expressing the TET-ON transactivator rtTA2SM2 can be derived and expanded in culture while retaining inducible transgene expression and pluripotentiality, including marker expression, a normal karyotype, and the ability to generate multiple tissues of different germ layer origin in teratomas. We also show that these cells retain the ability to control the expression of a stable integrated transgene in a doxycycline-dependent manner, which demonstrates that an insulated TET-ON lentiviral system is functional in hESC. Together, our results indicate that improved TET regulators like rtTA2SM2 in combination with insulated lentiviral-based systems offer alternative strategies for conditional gene expression in hESC. Disclosure of potential conflicts of interest is found at the end of this article.
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 | 0.001 |
| 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".