bioRxivGenomicsPreprint
Time-resolved single-cell screening reveals the dynamic regulatory architecture of human cardiogenesis
Dominik LindenhoferiD, Katharina MikulikiD, Stefanie Mantz, Haeyeon JungiD, Kai UeltzhoefferiD, Moritz KueblbeckiD, Laura Rueda-GensiniiD, Ana Munoz-Verdu, Jan HaasiD, Benjamin MederiD, Daniel SchraivogeliD, Moritz MalliD, Wolfgang HuberiD, Alexandra Pettet, James Reddington, Brian ClarkeiD, Oliver StegleiD, Lars Michael SteinmetziD
Abstract
Congenital heart disease (CHD) originates during early embryogenesis, yet when disease genes act cannot be determined from existing data. Human fetal heart atlases lack critical time points, and perturbation screens that systematically assess gene function currently profile at most a few time points. Here we combined single-cell CRISPR screening of 400 disease-relevant genes in cardioids, sampled daily over seven developmental days (~975,000 cells), with a matched multiome atlas. Perturbation phenotypes were strongly dependent on developmental stage. Perturbations that reshaped cell-fate allocation or gene expression were enriched for CHD-linked genes, and most acted already during progenitor stages. Timing was largely independent of the target gene's own expression, marking instead when a cell state became functionally dependent on it, and some effects persisted after expression declined. Consensus gene regulatory networks integrating perturbation and multiome data recovered stage-specific regulators and nominated HOXB2 and HOXB3 as candidate cardiac regulators. Many perturbations converged on shared downstream programmes, which were reorganised through differentiation. Effects correlated in progenitors were maintained in differentiated cells, split into distinct programmes, converged with other perturbations or disappeared entirely, indicating that gene function is highly context dependent. Beyond advancing our understanding of the developmental regulatory logic underlying CHD, our work provides a resource for developing context-dependent models for in silico prediction of perturbation effects.
- organism: human
- assay: scRNA-seq
- assay: CRISPR screen
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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