Female doctor consulting young couple patients in fertility clinic about IVF or IUI for infertility.
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A test developed at the University of California (UC) San Diego School of Medicine that is based on extracellular (ex)RNAs could help improve the success rates of in-vitro-fertilization (IVF) by better predicting embryo quality prior to implantation.

Each year, more than 200,000 people in the U.S. undergo IVF to help them to conceive. Unfortunately, current estimates for live birth rates in women under the age of 40 years who undergo IVF are only 20–40%.

This relatively low success rate is largely due to it being very difficult to predict the best embryos to implant using currently available methods.

“Unfortunately, IVF success still involves a big element of chance, but that’s something we’re hoping our research can change,” said Irene Su, a professor at UC San Diego School of Medicine and co-senior investigator of the current study, in a press statement.

“Right now, the best way we have to predict embryo outcome involves looking at embryos and measuring morphological characteristics or taking some cells from the embryo to look at genetic makeup, both of which have limitations.”

The new study, which is published in Cell Genomics, proposes a new method for testing embryo quality using exRNAs. These small pieces of RNA are left in the liquid that the embryos are developed in, and Su and colleagues think they could reveal how likely an embryo is to progress to a live birth if implanted. If successful, this method has the advantage of being totally non-invasive.

“IVF is challenging enough as it is, so it was extremely important to us that our research didn’t interfere with this already-delicate process,” said Su. “What we’ve done is more akin to looking at what’s left behind at an archeological site to help us learn more about who lived there and what they did.”

The research team created a temporal extracellular transcriptome atlas (TETA) of human pre-implantation development using exRNA’s from embryos developed in the lab. The team discovered that there are around 4,000 exRNAs for each stage of embryo development.

They then used this data to train a machine learning model and use it to predict the morphology of each embryo based on the exRNAs found in the culture media. The model was able to correctly predict embryo morphology, which suggests this could be a good method to assess embryo health and quality, although more research is needed before it can be used in the clinic.

“We have data connecting healthy morphology to positive IVF outcomes, and now we’ve seen that exRNAs can be used to predict good morphology, but we still need to draw that final line before our test will be ready for primetime,” said Su.

“Once that work is done, we hope this will make the overall process of IVF simpler, more efficient, and ultimately less of an ordeal for the families seeking this treatment.”

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