TALEN/CRISPR-Mediated eGFP Knock-In Add-On at the OCT4 Locus Does Not Impact Differentiation of Human Embryonic Stem Cells towards Endoderm
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) have great promise as a source of unlimited transplantable cells for regenerative medicine. However, current progress on producing the desired cell type for disease treatment has been limited due to an insufficient understanding of the developmental processes that govern their differentiation, as well as a paucity of tools to systematically study differentiation in the lab. In order to overcome these limitations, cell-type reporter hESC lines will be required. Here we outline two strategies using Transcription Activator Like Effector Nucleases (TALENs) and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-Associated protein (Cas) to create OCT4-eGFP knock-in add-on hESC lines. Thirty-one and forty-seven percent of clones were correctly modified using the TALEN and CRISPR-Cas9 systems, respectively. Further analysis of three correctly targeted clones demonstrated that the insertion of eGFP in-frame with OCT4 neither significantly impacted expression from the wild type allele nor did the fusion protein have a dramatically different biological stability. Importantly, the OCT4-eGFP fusion was easily detected using microscopy, flow cytometry and western blotting. The OCT4 reporter lines remained equally competent at producing CXCR4+ definitive endoderm that expressed a panel of endodermal genes. Moreover, the genomic modification did not impact the formation of NKX6.1+/SOX9+ pancreatic progenitor cells following directed differentiation. In conclusion, these findings demonstrate for the first time that CRISPR-Cas9 can be used to modify OCT4 and highlight the feasibility of creating cell-type specific reporter hESC lines utilizing genome-editing tools that facilitate homologous recombination.
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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 | 0.000 |
| 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".