New embryo quality test could make it easier to get pregnant by IVF

Researchers say they have developed a noninvasive method to better predict the quality of embryos created through in vitro fertilization — they hope it makes it easier to get pregnant.

“Unfortunately, IVF success still involves a big element of chance, but that’s something we’re hoping our research can change,” Dr. H. Irene Su, a professor at the University of California San Diego and co-senior study author, said in a statement. 

The key to the UCSD approach is detecting small particles of genetic material known as exRNAs in the liquid where the embryos were grown.

The study findings were published Wednesday in the journal Cell Genomics.

IVF accounts for 1.6% of live births in the US, according to a 2022 report.

Couples often turn to IVF when other methods, like timed intercourse or artificial insemination, don’t work.

The process works like this: Eggs are retrieved from female patients and fertilized with sperm in a laboratory. The resulting embryos are transferred into the uterus with the hopes they implant and lead to a birth.

UCSD scientists pointed out that the live birth rate from IVF in the US is only 20% to 40% for women younger than 40.

Higher-quality embryos have a better chance of implantation — but quality can be difficult to measure.

“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,” Su explained.

So, the UCSD researchers turned their efforts to exRNAs, which are molecules found in bodily fluids.

They avoid disturbing the delicate embryo by using the leftover liquid it was grown in.

The scientists were stunned to find about 4,000 different exRNA fragments for each of the five developmental stages they studied.

“We were surprised by how many exRNAs were produced so early in embryonic development, and how much of that activity we could detect using such a minute sample,” said Sheng Zhong, a UCSD bioengineering professor and co-senior study author.

“This is an approach where we can analyze a sample from outside a cell and gain an incredible amount of insight into what’s happening inside it,” he continued.

The researchers trained a machine learning model to predict the embryo’s morphology (its structure as it develops) based on the exRNAs it produced.

They found the exRNAs to be a “promising predictor” of embryo quality.

“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,” Su said.

She added: “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.”