Recapitulating kidney development in vitro by priming and differentiating mouse embryonic stem cells in monolayers
Bibliographic record
Abstract
Abstract In order to harness the potential of pluripotent stem cells, we need to understand how to differentiate them to our target cell types. Here, we developed a protocol to differentiate mouse embryonic stem cells (ESCs) to renal progenitors in a step-wise manner. Microarrays were used to track the transcriptional changes at each stage of differentiation and we observed that genes associated with metanephros, ureteric bud, and blood vessel development were significantly upregulated as the cells differentiated towards renal progenitors. Priming the ESCs and optimizing seeding cell density and growth factor concentrations helped improve differentiation efficiency. Organoids were used to determine the developmental potential of the renal progenitor cells. Aggregated renal progenitors gave rise to organoids consisting of LTL+/E-cadherin+ proximal tubules, cytokeratin+ ureteric bud-derived tubules, and extracellular matrix proteins secreted by the cells themselves. Over-expression of key kidney developmental genes, Pax2, Six1, Eya1, and Hox11 paralogs, during differentiation did not improve differentiation efficiency. Altogether, we developed a protocol to differentiate mouse ESCs in a manner that recapitulates embryonic kidney development and showed that precise gene regulation is essential for proper differentiation to occur.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".