Abstract
BACKGROUND: Cardiac development is a complex process resulting in an integrated, multilineage tissue with developmental corruption in early embryogenesis leading to congenital heart disease. Interrogation of individual genes has provided the backbone for cardiac developmental biology, yet a comprehensive transcriptome derived from natural cardiogenesis is required to gauge innate developmental milestones. METHODS AND RESULTS: Stage-specific cardiac structures were dissected from 8 distinctive mouse embryonic time points to produce genome-wide expressome analysis across cardiogenesis. With reference to this native cardiogenic expression roadmap, divergent induced pluripotent stem cell-derived cardiac expression profiles were mapped from procardiogenic 3-factor (SOX2, OCT4, KLF4) and less-cardiogenic 4-factor (plus c-MYC) reprogrammed cells. Expression of cardiac-related genes from 3-factor-induced pluripotent stem cell differentiated in vitro at days 5 and 11 and recapitulated expression profiles of natural embryos at days E7.5-E8.5 and E14.5-E18.5, respectively. By contrast, 4-factor-induced pluripotent stem cells demonstrated incomplete cardiogenic gene expression profiles beginning at day 5 of differentiation. Differential gene expression within the pluripotent state revealed 23 distinguishing candidate genes among pluripotent cell lines with divergent cardiogenic potentials. A confirmed panel of 12 genes, differentially expressed between high and low cardiogenic lines, was transformed into a predictive score sufficient to discriminate individual induced pluripotent stem cell lines according to relative cardiogenic potential. CONCLUSIONS: Transcriptome analysis attuned to natural embryonic cardiogenesis provides a robust platform to probe coordinated cardiac specification and maturation from bioengineered stem cell-based model systems. A panel of developmental-related genes allowed differential prognosis of cardiogenic competency, thus prioritizing cell lines according to natural blueprint to streamline functional applications.