Fulka Lab

Ever wondered about the function of nucleoli in zygotes and early embryos, and their interaction with the embryonic genome? Mark your calendars! #Embryology #Genomics

It took me a while to realize that signs of oocyte aging aren't uniform (why would they be, right?): Two mouse strains, similar in age, completely different spindle traits. How much does this apply to human individuals? #OocyteAging #GeneticDiversity

@BoianiMichele Congratulations! Nice paper!

After sharing the advanced publication access few weeks ago, I am now pleased to share the final typeset version, open access, of my group’s latest contribution in Molecular Human Reproduction doi.org/10.1093/molehr… In a nutshell, we report that the first two blastomeres are reciprocally different in terms of epiblast production, for reasons that seem to depend on the site where the spermatozoon penetrated the oocyte, in mice. Our half-cell proteomics data supports that this difference is possibly due to heterogeneities in ooplasm composition and how they are apportioned into the blastomeres depending on the orientation of the first zygotic division. Thanks to the co-Authors, to the Reviewers, to the Journal, and I hope you like the paper!

@HAIYANGWANG6 @NatureAging @MBIsg @ACRLE_NUS @NUSingapore Cool paper! And a cool method!

My paper on rejuvenating aged oocytes has been selected as the cover story for the latest issue of @NatureAging! 🌟 Grateful for this recognition! @MBIsg @ACRLE_NUS @NUSingapore #Oocyte #IVF #ReproductiveLongevity #AgingResearch x.com/NatureAging/st… nature.com/articles/s4358…

@ControllingLMNT @EwelinaBolcunF For sure. Successful maturation does not guarantee a "good" egg, and a euploid egg does not guarantee a viable embryo, right? Still don´t know where the reduced maturation rates in AMA oocytes I find in papers come from...

@FulkaLab @EwelinaBolcunF After oocytes reach a dev stage, it’s almost impossible for them not to undergo spontaneous maturation when granulosa cell cGMP transfer is lost. Gross maturation is essentially meaningless, no? Better to look at egg activation and cleavage IMHO.

How does one identify an oocyte with good developmental potential? Contrary to the often-reported decrease in maturation rate with advancing age, we never observed any difference. Has anyone truly witnessed this, or is it just the result of pressure to meet expectations/publish?

@zaffagg3 I have been working with AMA mice (>10months) for something like two years and don't see a difference. Sometimes, I would say they "mature" even slightly better than young ones. So I am wondering where the data comes from, I found it in so many papers.

@FulkaLab Idk if I have quantitative data but yes, in my hands aged oocytes (10-12mo) generally matured well (at least in terms of PB extrusion)

@zaffagg3 Yeah, in mice. It is in a lot of papers, but I just never saw it (not even in the same strain)... like, never.

@FulkaLab In mice you mean?

Our first attempt with media tailored for oocytes from older females shows promising results: significantly reduced DNA damage and a 50% increase in the number of euploid oocytes! But will this work for boosting blastocyst formation rates as well? 🤔

Happy Sunday! Don't miss the latest issue of #cebooc featuring the newst papers on oocyte and embryo biology! biomed.news/bims-cebooc/20… Highlight: pubmed.ncbi.nlm.nih.gov/41721019/ Thanks @Bims_BiomedNews for preselecting!

Is the role of the spindle and non-chromosomal egg elements in aneuploidy underestimated? Research shows that placing young germinal vesicles (Y GVs) into the cytoplasm of aged (AMA) oocytes increases aneuploidy rates. For more details, see: onlinelibrary.wiley.com/doi/10.1111/ac…

Tracking the movement of nucleoli in parental pronuclei could be instrumental in selecting top-quality human embryos. This movement could potentially be driven by nuclear actin, which facilitates the motion of the nucleoli: academic.oup.com/reproduction/a…

@zaffagg3 Relatable! ... in the middle of a manuscript preparation 😆

Raw data vs submitted manuscript

Are standard in vitro fertilization and culture media truly "one-size-fits-all"? Research shows that embryos created from eggs of older females are more sensitive to laboratory environments. Can we craft a more personalized approach?

Creating a healthy embryo requires more than just an euploid egg: Research shows that although half of the eggs from older mice have the correct chromosome count, only a quarter of the embryos reach the blastocyst stage, and no live animals were produced. onlinelibrary.wiley.com/doi/10.1111/ac…

Here we go, networking in full swing! 🗃

Not all sperm are made equal 👇 📢 Want to improve ICSI outcomes in domestic animals? Check out our new paper on sperm sensitivity to freezing and developmental competence: onlinelibrary.wiley.com/share/author/H… #publishedarticle @wileyinresearch

PhD student tackling their first project without supervision

Everyone’s hyped about “AI for Science.” in 2025! At the end of the year, please allow me to share my unease and optimism, specifically about AI & biology. After spending another year deep in biological foundation models, healthcare AI, and drug discovery, here are 3 lessons I learned in 2025. 1. Biology is not “just another modality.” The biggest misconception I still see: “Biology is text + images + graphs. Just scale transformers.” No. Biology is causal, hierarchical, stochastic, and incomplete in ways that language and vision are not. Tokens don’t correspond cleanly to reality. Labels are sparse, biased, and often wrong. Ground truth is conditional, context-dependent, and sometimes unknowable. We’ve made real progress—single-cell, imaging, genomics, EHRs are finally being modeled jointly—but the hard truth is this: Most biological signals are not supervised problems waiting for better loss functions. They are intervention-driven problems. They demand perturbations, counterfactuals, and mechanisms, beyond just prediction. Scaling obviously helps. But without causal structure, scaling mostly gives you sharper correlations. 2025 reinforced my belief that biological foundation models must be built around perturbation, uncertainty, and actionability, not just representation learning. 2. Benchmarks are holding biology back more than compute is. Let’s be honest: Benchmarking in AI & biology is still broken. Everyone reports SOTA. Everyone picks a different dataset slice. Everyone tunes for a different metric. Everyone avoids prospective validation. We’ve imported the worst habits of ML benchmarking into a domain where stakes are much higher. In biology and healthcare, a 1% gain that doesn’t transfer is worse than useless—it’s misleading. What’s missing isn’t more benchmarks. It’s hard benchmarks: •Prospective, not retrospective •Perturbation-based, not static •Multi-site, not single-lab •Failure-aware, not leaderboard-optimized If your model only works on the dataset that created it, it’s not a foundation model—it’s a dataset artifact. In 2026, we need fewer flashy plots and more humility, rigor, and negative results. 3. “Reasoning” in biology is not chain-of-thought. There’s a growing tendency to directly apply the word reasoning onto biological LLMs. Let’s be careful. Biological reasoning isn’t verbal fluency, longer context windows, or prettier explanations. Those are surface-level improvements. Real reasoning in biology shows up elsewhere: in forming hypotheses, deciding which experiments to run, updating beliefs when perturbations fail, and constantly trading off cost, risk, and uncertainty. A model that explains a pathway beautifully but can’t decide which experiment to run next is not reasoning, it’s narrating. 2025 convinced me that the future lies in agentic biological AI: systems that couple foundation models with experimentation, simulation, and decision-making loops. Closing thought: AI & biology is not lagging behind AI for code or language. It’s just playing a harder game. The constraints are real. The data is messy. The feedback loops are slow. The consequences matter. If 2025 clarified anything for me, it’s this: We won’t make progress by treating biology like text. We’ll make progress by building AI that behaves more like a scientist : skeptical, iterative, and willing to be wrong. Onward to 2026.