Different Concentrations of FGF Ligands, FGF2 or FGF8 Determine Distinct States of WNT-Induced Presomitic Mesoderm
Bibliographic record
Abstract
Presomitic mesoderm (PSM) cells are the precursors of the somites, which flank both sides of the neural tube and give rise to the musculo-skeletal system shaping the vertebrate body. WNT and FGF signaling control the formation of both the PSM and the somites and show a graded distribution with highest levels in the posterior PSM. We have used reporters for the mesoderm/PSM control genes T, Tbx6, and Msgn1 to investigate the differentiation of mouse ESCs from the naïve state via EpiSCs to PSM cells. Here we show that the activation of WNT signaling by CHIR99021 (CH) in combination with FGF ligand induces embryo-like PSM at high efficiency. By varying the FGF ligand concentration, the state of PSM cells formed can be altered. High FGF concentration supports posterior PSM formation, whereas low FGF generates anterior/differentiating PSM, in line with in vivo data. Furthermore, the level of Msgn1 expression depends on the FGF ligand concentration. We also show that Activin/Nodal signaling inhibits CH-mediated PSM induction in EpiSCs, without affecting T-expression. Inversely, Activin/Nodal inhibition enhances PSM induction by WNT/high FGF signaling. The ability to generate PSM cells of either posterior or anterior PSM identity with high efficiency in vitro will promote the investigation of the gene regulatory networks controlling the formation of nascent PSM cells and their switch to differentiating/somitic paraxial mesoderm. Stem Cells 2016;34:1790-1800.
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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) | 0.002 | 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".