FAST-STEM: A human pluripotent stem cell engineering toolkit for rapid design-build-test-learn development of human cell-based therapeutic devices
Bibliographic record
Abstract
Very recent clinical advances in stem cell derived tissue replacement and gene therapy, in addition to the rise of artificial intelligence aided scientific discovery, have placed the possibility of sophisticated human cell based therapies firmly within reach. However, development of such cells and testing of their engineered gene circuit components, has proven highly challenging, due to the need for generating stable cell lines for each design, build, test, learn engineering cycle. Current approaches to generating stable human induced pluripotent stem cell (hiPSC) lines are highly time consuming and suffer from lack of control, poor integration efficiency, and limited functionality. Validation in clinically relevant stem cell derived tissues is also broadly lacking. Such drawbacks are prohibitive to repeatably conducting cutting edge stem cell engineering with broad application within a realistic timeframe, and will not scale with the future of regenerative medicine. We have developed FASTSTEM (Facile Accelerated Stem cell Transgene integration with SynBio Tunable Engineering Modes), a hiPSC engineering platform that drastically reduces the time to generate differentiation ready stem cell lines from several weeks to 5 days, exhibiting a ~612-fold improvement in transgene integration rate over previous methodologies. Additional FAST-STEM innovations include: (i) rapid and highly efficient transgene integration; (ii) copy number control; (iii) simultaneous or consecutive integration of multiple gene cassettes; (iv) library screen capability. In addition to this unique functional versatility, platform transportability and broad use case for stem cell engineering was confirmed by differentiation into eight different cell types across nine different laboratories. This platform dramatically lowers the bar for integration of synthetic biology with regenerative medicine, enabling experiments which were previously deemed logistically impossible, thus paving the way for sophisticated human cell device development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".