Single-cell transcriptome analysis reveals CD34 as a marker of human sinoatrial node pacemaker cardiomyocytes
Bibliographic record
Abstract
The sinoatrial node regulates the heart rate throughout life. Failure of this primary pacemaker results in life-threatening, slow heart rhythm. Despite its critical function, the cellular and molecular composition of the human sinoatrial node is not resolved. Particularly, no cell surface marker to identify and isolate sinoatrial node pacemaker cells has been reported. Here we use single-nuclei/cell RNA sequencing of fetal and human pluripotent stem cell-derived sinoatrial node cells to reveal that they consist of three subtypes of pacemaker cells: Core Pacemaker, Sinus Venosus, and Transitional Cells. Our study identifies a host of sinoatrial node pacemaker markers including MYH11, BMP4, and the cell surface antigen CD34. We demonstrate that sorting for CD34+ cells from stem cell differentiation cultures enriches for sinoatrial node cells exhibiting a functional pacemaker phenotype. This sinoatrial node pacemaker cell surface marker is highly valuable for stem cell-based disease modeling, drug discovery, cell replacement therapies, and the targeted delivery of therapeutics to sinoatrial node cells in vivo using antibody-drug conjugates. Lim et al. identify CD34 as a surface marker of the heart’s sinoatrial node pacemaker cells, enabling their isolation from stem cell differentiation cultures, advancing future disease modeling, drug discovery, and biological pacemaker applications.
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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".