Generation of Monoclonal Antibodies Specific for Cell Surface Molecules Expressed on Early Mouse Endoderm
Bibliographic record
Abstract
The development of functional cell populations such as hepatocytes and pancreatic beta cells from embryonic stem cell (ESC) is dependent on the efficient induction of definitive endoderm early in the differentiation process. To monitor definitive endoderm formation in mouse ESC differentiation cultures in a quantitative fashion, we generated a reporter cell line that expresses human CD25 from the Foxa3 locus and human CD4 from the Foxa2 locus. Induction of these reporter ESCs with high concentrations of activin A led to the development of a CD25-Foxa3+CD4-Foxa2+ population within 4-5 days of culture. Isolation and characterization of this population showed that it consists predominantly of definitive endoderm that is able to undergo hepatic specification under the appropriate conditions. To develop reagents that can be used for studies on endoderm development from unmanipulated ESCs, from induced pluripotent stem cells, and from the mouse embryo, we generated monoclonal antibodies against the CD25-Foxa3+CD4-Foxa2+ population. With this approach, we identified two antibodies that react specifically with endoderm from ESC cultures and from the early embryo. The specificity of these antibodies enables one to quantitatively monitor endoderm development in ESC differentiation cultures, to study endoderm formation in the embryo, and to isolate pure populations of culture- or embryo-derived endodermal cells.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".