Newt A1 cell-derived extracellular vesicles promote mammalian nerve growth.

Newts have the extraordinary ability to fully regenerate lost or damaged cardiac, neural and retinal tissues, and even amputated limbs. In contrast, mammals lack these broad regenerative capabilities. While the molecular basis of newts' regenerative ability is the subject of active study, the underlying paracrine signaling factors involved remain largely uncharacterized. Extracellular vesicles (EVs) play an important role in cell-to-cell communication via EV cargo-mediated regulation of gene expression patterns within the recipient cells. Here, we report that newt myogenic precursor (A1) cells secrete EVs (A1EVs) that contain messenger RNAs associated with early embryonic development, neuronal differentiation, and cell survival. Exposure of rat primary superior cervical ganglion (SCG) neurons to A1EVs increased neurite outgrowth, facilitated by increases in mitochondrial respiration. Canonical pathway analysis pinpointed activation of NGF/ERK5 signaling in SCG neurons exposed to A1EV, which was validated experimentally. Thus, newt EVs drive neurite growth and complexity in mammalian primary neurons.

A comprehensive method for identification of suitable reference genes in extracellular vesicles.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is one of the most sensitive, economical and widely used methods for evaluating gene expression. However, the utility of this method continues to be undermined by a number of challenges including normalization using appropriate reference genes. The need to develop tailored and effective strategies is further underscored by the burgeoning field of extracellular vesicle (EV) biology. EVs contain unique signatures of small RNAs including microRNAs (miRs). In this study we develop and validate a comprehensive strategy for identifying highly stable reference genes in a therapeutically relevant cell type, cardiosphere-derived cells. Data were analysed using the four major approaches for reference gene evaluation: NormFinder, GeNorm, BestKeeper and the Delta Ct method. The weighted geometric mean of all of these methods was obtained for the final ranking. Analysis of RNA sequencing identified miR-101-3p, miR-23a-3p and a previously identified EV reference gene, miR-26a-5p. Analysis of a chip-based method (NanoString) identified miR-23a, miR-217 and miR-379 as stable candidates. RT-qPCR validation revealed that the mean of miR-23a-3p, miR-101-3p and miR-26a-5p was the most stable normalization strategy. Here, we demonstrate that a comprehensive approach of a diverse data set of conditions using multiple algorithms reliably identifies stable reference genes which will increase the utility of gene expression evaluation of therapeutically relevant EVs.