CRISPR-Cas9—The Potential “Holy Grail” for Generating Biomedically Relevant Cells through Cell Fate Engineering
Bibliographic record
Abstract
Background: The main goal of cellular therapy is to effectively engineer the fate of stem/progenitor/terminally differentiated cells into desired lineages to promote therapeutic tissue regenerative effects. Numerous methods such as ectopic transgene overexpression, small molecules, micro RNAs, and CRISPR-Cas9 have been used to engineer cell fate. Objective: In this review, we have attempted to highlight various cell fate engineering strategies with a particular emphasis on transdifferentiation that involves CRISPR-Cas9-based approaches in what appear to be the most promising and medically relevant preclinical models. Methods: A large number of recent publications involving the application of CRISPR-Cas9-based gene regulation strategies in modulating the identities of different cell types for promoting efficacious tissue regeneration were reviewed. Results: From the literature, it appears that the ability to manipulate endogenous gene expression programs has dramatically increased with the help of CRISPR-Cas9-based gene activation/repression/knockout strategies. These approaches have also enabled the generation of cells that closely resemble their true cellular counterparts. Also, in most cases, the efficacy of cell fate engineering through the CRISPR-Cas9-based technology is quite comparable to other methods of cell fate manipulation and in some instances superior. Conclusion: The reviewed studies demonstrate novel ways in manipulating cellular identities for regenerative medicine applications using the CRISPR-Cas9-based genome editing tool. Transdifferentiating certain cell types into another using CRISPR-Cas9 seems to have enjoyed more success in comparison to conventional methods. These findings highlight the favorable attributes of the CRISPR-Cas9-based technology in cell-based therapies and their potential use in the near future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".