Inferring and simulating a gene regulatory network for the sympathoadrenal differentiation from single-cell transcriptomics in human

Neuroblastoma is a malignant childhood cancer with significant inter- and intrapatient heterogeneity, arising from abnormal differentiation of neural crest cells into sympathetic neurons. The lack of actionable mutations limits therapeutic options, highlighting the need to better understand the molecular mechanisms driving this differentiation. While RNA velocity has provided some insights, it is limited in modeling regulatory relationships.

To address this, we applied our integrated Gene Regulatory Networks (GRNs) inference and simulation tools using a published single-cell RNAseq dataset from human sympathoadrenal differentiation. Our analysis identified a 97-gene GRN driving the transition from Schwann cell precursors to chromaffin cells and sympathoblasts, highlighting dynamic interactions like self-reinforcing loops and toggle switches.

To showcase the model’s ability to predict the impact of perturbations, we conducted in silico knockouts (KOs) and overexpressions (OVs) of few selected genes. This analysis revealed that certain perturbations had a notably significant effect on the differentiation process, either acing specifically on one ligneage choice, or preventing cells to exit from a stem compartment. Altogether, these findings demonstrate the utility of our GRN model framework in predicting gene perturbations after inferring the GRN structure.