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Record W4402334127 · doi:10.1101/2024.09.06.611657

Single-cell transcriptome analysis reveals CD34 as a novel marker of human sinoatrial node pacemaker cardiomyocytes

2024· preprint· en· W4402334127 on OpenAlexaff
Amos A. Lim, Delaram Pouyabahar, Mishal Ashraf, Kate Huang, Michelle Lohbihler, Matthew L. Chang, Brandon M. Murareanu, Thinh Huy Tran, Amine Mazine, Gary D. Bader, Zachary Laksman, Stephanie Protze

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsSinai Health SystemPrincess Margaret Cancer CentreLunenfeld-Tanenbaum Research InstituteSt. Paul's HospitalStem Cell NetworkUniversity Health NetworkUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsSinoatrial nodeTranscriptomeNode (physics)CellInternal medicineCardiologyBiologyMedicineGeneGene expressionEngineeringGeneticsHeart rate

Abstract

fetched live from OpenAlex

Abstract The sinoatrial node (SAN) regulates the heart rate throughout life. Failure of this primary pacemaker results in life-threatening, slow heart rhythm. Despite its important function, the cellular and molecular composition of the human SAN is not completely resolved. Particularly, no cell surface marker to identify and isolate SAN pacemaker cells has been reported to date. Here we used single-nuclei/cell RNA sequencing of fetal and human pluripotent stem cell (hPSC)- derived SAN cells and show that the SAN consists of three subtypes of pacemaker cells, including Core SAN, SAN, and Transitional Cells. Our study identified a host of novel Core SAN markers including MYH11, BMP4, and the cell surface antigen CD34. We demonstrate that sorting for CD34 + cells from cardiac hPSC differentiations enriches for SAN cells with a functional pacemaker phenotype. This novel SAN pacemaker cell surface marker is highly valuable for future hPSC- based disease modelling, drug discovery, cell replacement therapies, as well as the delivery of therapeutics to SAN cells in vivo using antibody-drug conjugates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.231
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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