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Hanson, E.

Publications and source records attributed to Hanson, E..

2 recordsLinked to original sources

Hedgehog signaling controls progenitor differentiation timing during heart development

Heterochrony, defined as differences in the timing of developmental processes, impacts organ development, homeostasis, and regeneration. The molecular basis of heterochrony in mammalian tissues is poorly understood. We report that Hedgehog signaling activates a heterochronic pathway that controls differentiation timing in multiple lineages. A differentiation trajectory from second heart field cardiac progenitors to first heart field cardiomyocytes was identified by single-cell transcriptional profiling in mouse embryos. A survey of developmental signaling pathways revealed specific enrichment for Hedgehog signaling targets in cardiac progenitors. Removal of Hh signaling caused loss of progenitor and precocious cardiomyocyte differentiation gene expression in the second heart field in vivo. Introduction of active Hh signaling to mESC-derived progenitors, modelled by transient expression of the Hh-dependent transcription factor GLI1, delayed differentiation in cardiac and neural lineages in vitro. A shared GLI1-dependent network in both cardiac and neural progenitors was enriched with FOX family transcription factors. FOXF1, a GLI1 target, was sufficient to delay onset of the cardiomyocyte differentiation program in progenitors, by epigenetic repression of cardiomyocyte-specific enhancers. Removal of active Hh signaling or Foxf1 expression from second heart field progenitors caused precocious cardiac differentiation in vivo, establishing a mechanism for resultant Congenital Heart Disease. Together, these studies suggest that Hedgehog signaling directly activates a gene regulatory network that functions as a heterochronic switch to control differentiation timing across developmental lineages.

developmental biology

Messenger RNA biomarker signatures for forensic body fluid identification revealed by targeted RNA sequencing

The recovery of a DNA profile from the perpetrator or victim in criminal investigations can provide valuable source level information for investigators. However, a DNA profile does not reveal the circumstances by which biological material was transferred. Some contextual information can be obtained by a determination of the tissue or fluid source of origin of the biological material as it is potentially indicative of some behavioral activity on behalf of the individual that resulted in its transfer from the body. Here, we sought to improve upon established RNA based methods for body fluid identification by developing a targeted multiplexed next generation mRNA sequencing assay comprising a panel of approximately equal sized gene amplicons. The multiplexed biomarker panel includes several highly specific gene targets with the necessary specificity to definitively identify most forensically relevant biological fluids and tissues (blood, semen, saliva, vaginal secretions, menstrual blood and skin). In developing the biomarker panel we evaluated 66 gene targets, with a progressive iteration of testing target combinations that exhibited optimal sensitivity and specificity using a training set of forensically relevant body fluid samples. The current assay comprises 33 targets: 6 blood, 6 semen, 6 saliva, 4 vaginal secretions, 5 menstrual blood and 6 skin markers. We demonstrate the sensitivity and specificity of the assay and the ability to identify body fluids in single source and admixed stains. A 16 sample blind test was carried out by one lab with samples provided by the other participating lab. The blinded lab correctly identified the body fluids present in 15 of the samples with the major component identified in the 16th. Various classification methods are being investigated to permit inference of the body fluid/tissue in dried physiological stains. These include the percentage of reads in a sample that are due to each of the 6 tissues/body fluids tested and inter-sample differential gene expression revealed by agglomerative hierarchical clustering.

genomics