In a game-changing move for the fertility world, scientists at the University of California San Diego School of Medicine have developed a noninvasive test that could help pinpoint the best embryos for in-vitro fertilization (IVF), potentially boosting pregnancy success rates. This novel method analyzes genetic material, known as exRNAs, in the culture media where embryos are grown, as detailed in a study published in Cell Genomics on January 10, 2023.

IVF is often a strenuous and uncertain path for many trying to have a child, with live birth rates after treatment only clocking in between 20-40% for females under 40. Determining which embryos have the highest chances of success has been a sticking point for physicians, leading to multiple rounds of emotionally and financially taxing treatments. Until now, assessments were based primarily on an embryo's physical characteristics or genetic biopsies, both of which have their drawbacks.

Enter the team at UC San Diego, who've sidestepped these invasive techniques in favor of a liquid-based "biopsy." The new approach offers a significant departure from more intrusive practices. "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," H. Irene Su, MD, a reproductive endocrinologist at UC San Diego Health, told UC San Diego News Center.

The study's scientists discovered that by evaluating the leftover media from the embryo growth environment, they could identify around 4,000 exRNA molecules at different stages of embryonic development. This approach reveals a wealth of information about the genetic activity taking place inside the embryos without a needing to disturb them directly. "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, PhD, a co-senior author of the study.

Using these findings, researchers have developed a machine learning model that can predict the embryo's morphology based on the exRNAs, essentially replicating current embryo assessment methods noninvasively. Despite the promising results, the scientists warn that more research is needed before the test can be confirmed as a reliable predictor of IVF success.

"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 explained. If proven effective, this method would simplify the IVF process and potentially alleviate some of the emotional and financial hardship for those seeking fertility treatments.

Alongside Su and Zhong, the research was conducted by Qiuyang Wu and Zhangming Yan at UC San Diego, Zixu Zhou at Genomo Inc., and Megan Connel, Gabriel Garzo, Analisa Yeo, and Wei Zhang at Reproductive Partners San Diego, with funding from the National Institutes of Health and a Kruger research grant.