In-vitro fertilization (IVF) is a widely used fertility treatment that offers hope to many individuals struggling to conceive. However, the complexity of the multi-step process coupled with the uncertainty of predicting the success of lab-grown embryos often results in low live birth rates. To address this issue, researchers at the University of California San Diego School of Medicine have developed a noninvasive approach that utilizes genetic material particles, known as exRNAs, left behind in the liquid media used to cultivate embryos. By analyzing exRNAs, the quality of lab-grown embryos can be better predicted. The findings of the study have been published in Cell Genomics.
The current success rate of IVF treatments remains dependent on chance, and the research conducted aims to refine this approach. H. Irene Su, MD, professor in the Department of Obstetrics, Gynecology, and Reproductive Sciences at UC San Diego School of Medicine and a reproductive endocrinologist at UC San Diego Health, stated, “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.”
Rather than relying on visual characteristics or biopsies of embryos, the noninvasive approach operates similarly to a blood test by analyzing molecules in the fluid sample. Instead of collecting samples directly from the embryos, researchers study the leftover medium used for their growth. Consequently, this noninvasive approach does not require any additional steps from the patient, ensuring a less intrusive process. Su further explained, “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. What we’ve done is more akin to looking at what’s left behind at an archaeological site to help us learn more about who lived there and what they did.”
RNA, a form of genetic material, works with DNA to execute cellular development and functionality. While most RNA remains within cells, a small fraction, known as exRNAs, is released into the immediate environment during cellular activities. Although the specific biological function of exRNAs remains uncertain, their discovery has catalyzed biomedical research, offering potential insights into cell-to-cell communication, disease processes, and diagnostic and therapeutic applications.
During the study, researchers analyzed exRNA in the culture media of embryos at five different developmental stages, identifying approximately 4,000 distinct exRNA molecules per stage. These exRNAs corresponded to the genes expressed by embryos at each specific stage. Sheng Zhong, Ph.D., professor in the Department of Bioengineering at UC San Diego Jacobs School of Engineering, noted, “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. 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.”
Based on this collection of data, the researchers trained a machine learning model to predict embryo morphology using exRNAs as indicators. Results showed that the model could replicate morphological measurements used in existing embryo tests, suggesting that exRNAs could serve as a promising predictor of embryo quality.
However, it is important to note that further research is required to determine whether this test can directly predict positive IVF outcomes, such as successful births. Su emphasized, “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 prime time. 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.”
The study’s co-authors include Qiuyang Wu and Zhangming Yan from UC San Diego, Zixu Zhou from Genomo Inc., and Megan Connel, Gabriel Garzo, Analisa Yeo, and Wei Zhang from Reproductive Partners San Diego.
