Accurate estimate of fetal maturity could provide individualized guidance for delivery of complicated pregnancies. However, current methods are invasive, have low accuracy, and are limited to fetal lung maturation. To identify diagnostic gestational biomarkers, we performed transcriptomic profiling of lung and brain, as well as cell-free RNA from amniotic fluid of preterm and term rhesus macaque fetuses. These data identify potentially new and prior-associated gestational age differences in distinct lung and neuronal cell populations when compared with existing single-cell and bulk RNA-Seq data. Comparative analyses found hundreds of genes coincidently induced in lung and amniotic fluid, along with dozens in brain and amniotic fluid. These data enable creation of computational models that accurately predict lung compliance from amniotic fluid and lung transcriptome of preterm fetuses treated with antenatal corticosteroids. Importantly, antenatal steroids induced off-target gene expression changes in the brain, impinging upon synaptic transmission and neuronal and glial maturation, as this could have long-term consequences on brain development. Cell-free RNA in amniotic fluid may provide a substrate of global fetal maturation markers for personalized management of at-risk pregnancies.
Augusto F. Schmidt, Daniel J. Schnell, Kenneth P. Eaton, Kashish Chetal, Paranthaman S. Kannan, Lisa A. Miller, Claire A. Chougnet, Daniel T. Swarr, Alan H. Jobe, Nathan Salomonis, Beena D. Kamath-Rayne
Usage data is cumulative from August 2022 through October 2022.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.