bioRxivBioinformaticsPreprint
Engineering a pacemaker-driven human mini-heart guided by spatial multi-omics of sinoatrial node development
Jiajun ZhuiD, Zhaowei HaniD, Roberto De GregorioiD, Chang KeviniD, Zheyu ZhangiD, Xue DongiD, Kalpita BanerjeeiD, Kai LiuiD, Martha Rea-MorenoiD, Mikal Kizilbash, Alicia AlonsoiD, Jie Liu, Su‐Yi TsaiiD, Ya‐Wen CheniD, Todd EvansiD, Shuibing CheniD
Abstract
The human sinoatrial node (SAN) functions as the primary pacemaker of the heart and coordinates the hierarchical electrical activity that drives cardiac contraction. However, experimental systems capable of reconstructing pacemaker-driven cardiac organization in human tissues remain limited. Here we integrate spatial multi-omics of the human fetal SAN with stem-cell engineering to generate pacemaker organoids ("Sinoids") and assemble them into a pacemaker-driven human mini-heart composed of sinoatrial, atrial and ventricular cardiac modules. High-resolution spatial transcriptomics and single-nucleus multi-omic analyses of human fetal SAN tissues identify regulatory pathways guiding pacemaker lineage specification, which we leverage to engineer human pluripotent stem cell-derived SAN organoids with robust pacemaker identity and electrophysiological activity. When integrated with atrial and ventricular cardioids, Sinoids initiate and coordinate electrical activation across assembled cardiac tissues, establishing directional propagation of electrophysiological signals within structured mini-heart organoids. Combining AI-guided perturbation modeling with functional validation further identifies conserved regulatory pathways controlling pacemaker specification and regionalization, including YAP-TEAD and NRG-ERBB signaling. Together, these results establish a multi-omic-guided strategy for engineering pacemaker tissues and reconstructing cardiac conduction hierarchy in vitro. The pacemaker-driven mini-heart platform provides a modular human cardiac system for studying pacemaker biology, modeling arrhythmia mechanisms and enabling electrophysiological drug discovery.
- organism: human
- assay: spatial transcriptomics
- assay: multiome
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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