Fertility
Unlocking new potential in stem cell-based embryo models

Researchers have identified Nr1h2, a critical transcription factor essential for early embryo development. The findings enhance our understanding of gene regulation during blastoid formation and hold promise for regenerative medicine, fertility treatments, and developmental biology research.
At the earliest stages of life, the blastocyst—a highly organised structure critical for implantation—begins to form. Its development is tightly controlled by genetic and epigenetic programmes.
Stem cell-based embryo models, such as blastoids, serve as models of the blastocyst and are invaluable tools for studying embryogenesis and early human development. However, the variability in blastoid induction has limited their utility, due to a limited understanding of the genetic drivers of blastoid formation.
The researhers addressed this gap by uncovering the role of Nr1h2 in regulating stem cell fate and driving high-quality blastoid formation.
Using a loss-of-function screen, the researchers pinpointed Nr1h2 as a key transcription factor conserved across mammalian species. Nr1h2 activation was sufficient to enhance the functional and genetic fidelity of stem cell-derived embryo models.
To test Nr1h2’s potential, the team treated embryonic stem cells with the small-molecule agonist T0901317. The treated cells, termed NrESCs, exhibited expanded pluripotency, expressing canonical markers and generating both embryonic and extra-embryonic lineages.
Transcriptomic and epigenetic analyses showed that NrESC-derived blastoids closely resembled natural blastocysts, surpassing current EPSC-derived models in genetic and physiological fidelity.
“Nr1h2 activation rewires embryonic stem cells into an expanded pluripotent state, creating a robust platform to study early developmental processes and identify therapeutic targets,” said Dr Jonathan Yuin-Han Loh of the A*STAR Institute of Molecular and Cell Biology (IMCB).
Therapeutic potential
The discovery also has profound implications for reproductive health. Trophectoderm cells, essential for implantation, were more physiologically faithful in NrESC-derived blastoids.
When transferred into mice, these blastoids achieved significantly higher implantation rates compared to EPSC-derived counterparts. Nr1h2 activation also enhanced blastocyst generation in both mice and pigs, suggesting a highly conserved mechanism across species.
Nr1h2’s identification as a master regulator of early embryogenesis opens new avenues for developmental biology. By refining stem cell-based embryo models, this discovery supports the design of targeted therapies, advances regenerative medicine, and improves our ability to explore the earliest stages of life.
The team’s work sets the stage for future research into transcriptional networks and their role in lineage determination.
“Stem cell-based embryo models are revolutionising drug discovery and reproductive biology,” Dr Loh said.
“Our findings demonstrate that activating Nr1h2 enhances the fidelity of these models, providing an innovative approach to tackle developmental disorders, infertility, and beyond.”
Fertility
Gum disease may impair female fertility and egg quality – study
Cancer
AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
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