Canonical Wnt signaling promotes pacemaker cell specification of cardiac mesodermal cells derived from mouse and human embryonic stem cells
Bibliographic record
Abstract
Cardiac differentiation of embryonic stem cells (ESCs) can give rise to de novo chamber cardiomyocytes and nodal pacemaker cells. Compared with our understanding of direct differentiation toward atrial and ventricular myocytes, the mechanisms for nodal pacemaker cell commitment are not well understood. Taking a cue from the prominence of canonical Wnt signaling during cardiac pacemaker tissue development in chick embryos, we asked if modulations of Wnt signaling influence cardiac progenitors to bifurcate to either chamber cardiomyocytes or pacemaker cells. Omitting an exogenous Wnt inhibitor, which is routinely added to maximize cardiac myocyte yield during differentiation of mouse and human ESCs, led to increased yield of spontaneously beating cardiomyocytes with action potential properties similar to those of native sinoatrial node pacemaker cells. The pacemaker phenotype was accompanied by enhanced expression of genes and gene products that mark nodal pacemaker cells such as Hcn4, Tbx18, Tbx3, and Shox2. Addition of exogenous Wnt3a ligand, which activates canonical Wnt/β-catenin signaling, increased the yield of pacemaker-like myocytes while reducing cTNT-positive pan-cardiac differentiation. Conversely, addition of inhibitors of Wnt/β-catenin signaling led to increased chamber myocyte lineage development at the expense of pacemaker cell specification. The positive impact of canonical Wnt signaling on nodal pacemaker cell differentiation was evidenced in direct differentiation of two human ESC lines and human induced pluripotent stem cells. Our data identify the Wnt/β-catenin pathway as a critical determinant of cardiac myocyte subtype commitment during ESC differentiation: endogenous Wnt signaling favors the pacemaker lineage, whereas its suppression promotes the chamber cardiomyocyte lineage.
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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".