Fig. 1: Study overview.

Workflow of the analyses presented in the paper. A RNA-seq was generated on whole blood samples from MIS-C cases, pediatric COVID-19 cases, and healthy controls. B Deconvolution estimated the relative cell-type proportions in each transcriptome, which were compared between cases and controls to identify the immune cell types involved in the pathogenesis of MIS-C. C Expression of each gene in the transcriptome was tested for association with disease, resulting in a MIS-C signature that was queried to resolve the dysregulated molecular pathways. D Co-expression network construction organized genes into coherent units called modules. E and F Modules loaded with genes of the MIS-C signature were empirically identified (E), validated using DE signatures from a large Kawasaki disease (KD) gene-expression dataset (F), and functionally annotated to pathway, cell type, and other disease signatures (F). G The module with the strongest enrichment for MIS-C that also enriched for KD signatures was further annotated to pinpoint cell subtypes, and key regulators of the processes captured by this module were identified in a regulatory network built from whole blood gene expression in an independent cohort. This figure was created with BioRender.com.