A Flexible Transgene Integration ‘Landing-Pad’ Toolkit in Human Induced Pluripotent Stem Cells Enables Facile Cellular Engineering, Gene Zygosity Control, and Parallel Transgene Integration
Bibliographic record
Abstract
Abstract Development of a repeatable method for delivering transgene payloads to human induced pluripotent stem cells (hiPSCs) without risking unintended off-target effects is not fully realized. Yet, such methods are indispensable to fully unlocking the potential for applying synthetic biological approaches to regenerative medicine, delivering quantum impacts to cell-based therapeutics development. Here we present a toolkit for engineering hiPSCs centred on the development of two core ‘landing-pad’ cell-lines, facilitating rapid high-efficiency delivery of transgenes to the AAVS1 safe-harbour locus using the Bxb1 large-serine recombinase. We developed two landing-pad cell lines expressing green and red fluorescent reporters respectively, both retaining stemness whilst fully capable of differentiation into all three germ layers. A fully selected hiPSC population can be isolated within 1-2 weeks after landing-pad recombinase-mediated cassette exchange. We demonstrate the capability for investigator-controlled homozygous or heterozygous transgene configurations in these cells. As such, the toolkit of vectors and protocols associated with this landing-pad hiPSC system has the potential to accelerate engineering workflows for researchers in a variety of disciplines.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".