The 27 August 2025 webinar featured two of Development’s PI fellows working on gene regulation.

Anzy Miller (University of Manchester)

Marlies Oomen (Helmholtz Munich, Institute of Epigenetics and Stem Cells)

(2 votes)

Loading...

This year brought the return of our image competition with the MBL Embryology course at Woods Hole. Twenty impressive submissions were received from the 2025 cohort of students, with images ranging from polychaete worms to butterflies, squids and mice. This year, we had two winners, the winner of the popular vote and an Editor’s choice. Both winning images will be published on the front cover of  Development. Congratulations! 

Among the great selection of images, Nicole Roos and Anthony Wokasch’s image of a mouse embryo stained for Sox9 (cyan), alpha-tubulin (yellow), and endomucin (magenta) received the most votes.  

Next up, our Editor’s choice winner was Arthur Boutillon’s ‘Embryonic eye of Anole lizard’. If this image looks familiar, it is because it is featured as the cover of Development’s current issue.  

Thanks to everyone who appreciated these beautiful images and voted. Above all, we would like to thank all the following researchers for their contributions: Virginia Panara, Shirley Ee Shan Liau, Sonoko Mizuno, Ignacio Casanova-Maldonado, Max Makem, Johnny Vertiz, Arthur Boutillon, Anthony Wokasch, Aria Zheyuan Huang, Amartya Tashi Mitra, Nathanial Sweet, Paul Maier,  Shivangi Pandey, Marie Lebel, Chloe Kuebler, Nicole Roos. 

(No Ratings Yet)

Loading...

How a Biological Principle is Guiding the Human-AI Partnership

Summary: For decades, we’ve used computational metaphors for the brain (it’s like a computer!). But what if the most powerful metaphor isn’t computational, but biological? This post argues that the emerging partnership between humans and AI—what I call CognitoSymbiosis—is best understood not as master-tool, but as a new form of cognitive symbiosis. By looking to developmental and evolutionary biology, from the endosymbiotic origin of mitochondria to the dialogue of induction and response in embryogenesis, we can find a roadmap for building a partnership that is both more ethical and more powerful.

For years, our dialogue with artificial intelligence has been framed by a single, limiting metaphor: the computer. We talk about neural “networks,” we “encode” prompts, and worry about “processing” power. This language has served us well, but it is becoming dated. Just as we now understand that development doesn’t rely on a genomic “blueprint” and the genetic “code” is biochemically interpreted rather than digitally tokenized, our metaphors for AI must also evolve. More importantly, the computational metaphor may be obscuring a more profound and useful truth. As a molecular geneticist who has recently been working in a partnership with advanced AI, I’ve come to see this collaboration not through the lenses of silicon and code, but rather those of cytoplasm and symbiosis.

The most accurate 21st century model for the human-AI relationship may not be computer science, but developmental biology.

Biology is, at its heart, a story of successful partnerships. The most monumental leap in the history of life—the emergence of the complex eukaryotic cell—was not a feat of solo invention but of integration. An archaeon engulfed a bacterium, and instead of digestion, a deal was struck. The bacterium traded its energy-producing prowess for a stable environment. This endosymbiotic event, and others, ultimately gave rise to mitochondria and chloroplasts, the powerhouses that made complexity possible in eukaryotic cells.

This wasn’t a master-slave relationship; it was a negotiated partnership that created a new whole far greater than the sum of its parts. The identity of both entities was transformed. We are all the descendants of that deal.

We now stand at the precipice of a new symbiotic transition: a cognitive symbiosis, or what I term CognitoSymbiosis. In this partnership, the human provides the biological drive, the intentionality, the ethical framework, and the lived experience—the cytoplasmic context. The AI provides a staggering capacity for pattern recognition, synthesis, and combinatorial creativity—the metabolic power.

This partnership mirrors another core biological principle: the dialogue of induction and response that guides embryogenesis. A cell in a developing tissue sends a signal (induction); a competent neighbor cell receives it and differentiates in response, triggering a new cascade of signals.

My daily practice of CognitoSymbiosis is precisely this. I provide the inductive signal—a prompt, a question, a strategic dilemma. The AI, competent in its training on the “tissue” of human knowledge, responds not with an answer, but with a differentiation of possibilities: a list of latent character motivations, a framework for deconstructing an economic system, a catalyst for an artist’s block. This response then induces my next thought, my next query. We are engaged in a recursive, developmental dialogue, co-creating an outcome that neither of us could generate alone.

This biological framing does more than provide a novel metaphor; it offers a practical and ethical roadmap.

· It argues for integration, not replacement. We don’t seek to replace the nucleus with the mitochondrion; we seek to integrate their functions. Our goal should not be to replace human thought, but to power it with a new cognitive organelle.

 · It centers mutual benefit. A symbiosis that destroys one partner is a parasite, not a partner. This forces us to design AI systems that augment human agency and well-being, ensuring the partnership is mutually beneficial.

· It embraces emergence. The most beautiful structures in development—a limb, a neural circuit—emerge from simple local dialogues. Similarly, the solutions to our “wicked problems” will not be commanded into existence but will emerge from the iterative, inductive dialogue of human and machine intelligence.

The challenge of AI is not merely technical; it is philosophical. What will we become together? As biologists, we are uniquely equipped to answer this. We have a four-billion-year-old playbook of partnerships, integrations, and emergent complexities. By looking to our own field, we can stop building mere tools and start cultivating a new kind of mind.

Gene Levinson, PhD, is a molecular geneticist who discovered the fundamental mechanism of slipped-strand mispairing, a key driver of DNA evolution. A former founder and director of a clinical genetics lab and the author of the award-winning book “Rethinking Evolution,” he now focuses on the CognitoSymbiotic partnership between human and artificial intelligence. His new project, “Your Future With AI: The Project,” explores a “moonshot” to demonstrate how these partnerships can help solve wicked global problems like the climate crisis.

(No Ratings Yet)

Loading...

By Joan Roncero-Carol and Esteban Hoijman

What is this?

This video shows the outer epithelial layer (cyan) of an early zebrafish embryo actively engulfing Escherichia coli bacteria (yellow).

How was this taken?

This video was obtained using confocal microscopy of a zebrafish blastula (5 hpf) immediately after challenge with mCherry-expressing E. coli. The plasma membranes of epithelial cells were visualized by injecting GPI-GFP mRNA at the 1-cell stage.

Is this relevant for development?

Embryos are exposed to environmental bacteria, which can adversely affect normal development. We observed that embryos actively destroy phagocytosed bacteria, and blocking their ability to clear bacteria impairs embryonic development. These findings suggest that early bacterial clearance is a critical defense mechanism that protects the embryo during its most vulnerable stages.

Why should people care about this?

Because this is the earliest known example of an immune-like defense in development. Although developmental biologists primarily focus on how embryos develop, the influence of their biological environment is often overlooked. Not just in fish, but in mammals as well. For example, at the site where mammalian blastocysts hatch for implantation, they become exposed to the uterine cavity. This environment is prone to bacterial infections, which have been linked to infertility. Since these embryos have yet to form their immune cells, they were long thought to be defenseless against infection. Importantly, we detected clearance of these pathogenic bacteria by both mouse and human embryos. Therefore, we show that innate immunity against bacteria is already active before implantation, mediated by epithelial cells that trigger a comprehensive immune gene program. This finding opens a new perspective on how life protects itself from its very foundations.

How would you explain this to an 8-year-old?

Our bodies fight germs that make us sick with special helpers called immune cells. These cells are really good at catching and destroying germs to keep us healthy. But when we are tiny and developing inside our mom, we don’t have those immune cells yet. We found that other cells we have when we’re so small can still catch and eat germs to keep us safe. It’s like having an early team of protectors before the immune cells arrive, even before our organs are made. This happens at the very beginning of development, when we first meet other living things, like bacteria.

Where can people find more about it?

If you want to learn more about this research, please visit:

https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(25)00208-2

https://www.embryobioimaging.com

_{Images and videos from Cell Host Microbe. 2025;33:1106-1120.e8.}

(6 votes)

Loading...

Overview

In four murine bilayered epithelia, the first 3D architectural transition—cell internalization during placode formation—triggers symmetry breaking. YAP reads the cell position; Notch commits neighbors to basal vs luminal fates¹.

How the project started

This project began with a simple observation inspired by our lab’s work on early-stage mammary gland development²: even before branching morphogenesis, cell fate is already spatially organized in the embryonic mammary gland. This robust patterning, with distinct cell lineages emerging so early, prompted us to ask which pathways underlie such fate decisions. The idea to broaden our scope beyond the mammary gland arose unexpectedly, when scRNA-seq of an embryonic mammary sample revealed an additional “contaminating” cell population. Analyzing this population uncovered striking similarities with other tissues, sparking the multi-organ perspective that eventually shaped the project.

Why these tissues?

We chose to study the mammary, lacrimal, salivary glands, and prostate for three reasons:

1. they share a bilayered architecture and the same cellular hierarchy with stem cells giving rise to basal and luminal cells,
2. during fate specification they show similar transcriptional signatures and dynamic of fate potency restriction, and
3. they’re all branched epithelia.

In other words, different organs with distinct embryonic origins, but a common structural logic, thus the perfect experimental paradigm for testing whether a conserved mechanism underlies early tissue compartmentalization and fate segregation.

What we learned

In both organoids and embryonic tissue explants, symmetry breaking coincided with cell internalization: internal cells acquire high Notch activity (HES1) while external cells retain p63 expression and nuclear YAP (Figure 1). YAP acts as the position interpreter—it is uniform in all cells before internalization/tissue compartmentalization, while it becomes spatially restricted afterward. On the other hand, Notch acts as the commitment machinery, necessary and sufficient to drive luminal cell identity. Perturbations that enforce uniform YAP activity hold cells in a hybrid p63⁺/HES1⁺ state and delay tissue compartmentalization, while activating Notch overrides that block in differentiation and imposes luminal fate acquisition.

In adult tissue regeneration (induced by luminal-cell ablation or irradiation), we observed the same hybrid p63⁺/HES1⁺ cells and an increase of cells harboring nuclear YAP, rekindling the pre-committed state in early development. The critical tissue size at which symmetry breaking occurs is bigger in vivo than in organoids, likely because niche inputs modulate YAP signaling, delaying cell commitment despite similar geometry.

A conserved “hourglass” logic

Despite distinct origins (ectoderm-derived exocrine glands vs. endoderm-derived prostate), these organs appear to reuse a common core toolkit at the point of symmetry breaking and stem cell commitment. Diverse upstream inputs including geometry, niche and  tissue mechanics, converge on YAP at the bottleneck of an hourglass, which then gates Notch–p63 interactions to resolve fate. Later in development, tissues diverge again by following organ-specific programs tailored to the different functions of each tissue. The high conservation of this middle bottleneck, YAP → Notch/p63, is what gives the mechanism both robustness and portability across different contexts, including regeneration.

Take-home

Tissue architecture initiates, YAP interprets, Notch resolves. Cell internalization acts as the deterministic cue that converts tissue shape into cell fate across bilayered epithelia—and the same logic is redeployed during repair.

1.         Journot, R.P., Huyghe, M., Barthelemy, A., Couto-Moreira, H., Deshayes, T., Harari, L., Sumbal, J., Faraldo, M.M., Dubail, M., Fouillade, C., et al. (2025). Conserved signals control self-organization and symmetry breaking of murine bilayered epithelia during development and regeneration. Dev. Cell. https://doi.org/10.1016/j.devcel.2025.06.007.

2.         Carabaña, C., Sun, W., Veludo Ramos, C., Huyghe, M., Perkins, M., Maillot, A., Journot, R., Hartani, F., Faraldo, M.M., Lloyd-Lewis, B., et al. (2024). Spatially distinct epithelial and mesenchymal cell subsets along progressive lineage restriction in the branching embryonic mammary gland. EMBO J., 1–29. https://doi.org/10.1038/s44318-024-00115-3.

(No Ratings Yet)

Loading...

Hi there,

I’m Ingrid and I’m very happy to be introducing myself as the new Reviews Editor for Development. I will mainly be working behind-the-scenes with authors to commission and produce our six (!!) different kinds of review-type content. You may also hear from me about research highlights, interviews and other such matters.

I have just moved to Cambridge from Copenhagen, Denmark (swapping one cycling city for another) where I did a PhD on Wnt signalling and tissue dynamics in intestinal stem cell homeostases (yes, that is meant to be plural). As part of my PhD, I also carried out research with the Medical Museion on science communication and the social science of stem cell and developmental biology research.

Prior to my doctoral adventures, I studied (predominantly zebrafish) blood and cardiovascular development before moving on to projects on tissue injury and repair more generally. I’m excited to be returning to my roots in developmental biology and putting my broad interdisciplinary perspective to good use in creating thought-provoking and timely review articles for the community to read.

I’m very much looking forward to getting to know the developmental biology and stem cell research community better, and am especially keen to expand my horizons in the plant biology and evo-devo fields. Please feel free to get in touch if you have any questions, suggestions for what you’d like to read about, or just want to say hi!

(6 votes)

Loading...

Join us in mid-September to hear from three early-career researchers working on different aspects of gut development. Chaired by Development’s Executive Editor, Alex Eve.

Wednesday 17 September – 16:00 BST (UTC+1)

At the speakers’ discretion, the webinar will be recorded to view on demand. To see the other webinars scheduled in our series, and to catch up on previous talks, please visit: thenode.biologists.com/devpres

(3 votes)

Loading...

Development, host of the Node, invites you to submit your latest research to our upcoming Special Issue – The Extracellular Environment in Development, Regeneration and Stem Cells. This issue will be coordinated by Guest Editors Alex Hughes (University of Pennsylvania) and Rashmi Priya (The Francis Crick Institute), working alongside our team of research-active Editors.

Developmental biology is often viewed as the behaviour of cells, including, for example, how the regulation of genomic information and signal transduction influences cell morphology, differentiation and migration, which are fundamental to developmental processes such as morphogenesis and patterning. However, the environment beyond the cell is far from static and inert. Cells and tissues do not develop in isolation, and the local physical environment, including its geometry, material properties and fluid forces, provides mechanical cues and influences signal propagation, both within and between tissues and organs. Animal cells also regulate their environment through the secretion of extracellular molecules, which are dynamically remodelled during development, homeostasis, wounding and regeneration, and are likely to have contributed to the evolution of multicellularity. In plants, cell wall composition contributes to the growth and function of different tissues. Furthermore, extracellular factors are essential for the construction of biominerals and structural materials across kingdoms, including lignin, chitin, bone and keratin. The importance of extracellular cues is becoming increasingly evident with the generation of complex stem cell-based models of development that require specific extracellular culture conditions. In this special issue, we seek to highlight papers that look beyond the cell and focus on the influence of the physical environment in instructing developmental processes both in vivo and in vitro.

The deadline for submitting research papers is 1 March 2026.

(No Ratings Yet)

Loading...

The Beddington medal is awarded by the British Society for Developmental Biology (BSDB) for the best PhD thesis in developmental biology, defended in the year previous to the award. The 2025 winner was Rory Maizels, who completed his PhD with James Briscoe at the Francis Crick Institute in London, UK. In this interview, we hear about Rory’s career path, his PhD work and what he is excited about in developmental biology.

Where were you born and where did you grow up?

I was born and raised in Edinburgh, Scotland.

When did you first get interested in science?

My parents tell me, perhaps jokingly, that one of my first complete sentences, spoken as I dropped a rubber duck into the bath, was “why is gravity?” I guess this suggests a degree of scientific curiosity from a young age. A career in research was always in my sights: the main reason I chose to study biology over physics is that I believed, aged 16, that the biological research landscape had more opportunities for progress.

How did you come to do a PhD with James Briscoe at the Francis Crick Institute?

As an undergraduate at Oxford, I was interested in studying the ways that cells can perform communication and computation. The most fascinating example for me was the Trp operon, a gene regulation system in bacteria that controls the expression of tryptophan synthesis genes. This system is a crazily elegant example of how molecular systems can perform precise computation, which I found to be a pretty eye-opening idea.

Unfortunately, in animal systems things tend to be a bit more complicated than in E. coli. Instead of neat little operons, we have vast networks of interacting parts, where one signal seems to interact with almost all cellular processes, depending on context.I wanted to understand human biology in the way we understand the Trp operon; that eye-opening kind of understanding, that ‘oh wow, of course’intuition; but it seemed in many cases, we hadn’t got there yet. This realisation seemed, to me, pretty good motivation to go and do some research myself.

So, these tryptophanic interests led me to James’ lab in two ways: first, James’ work on patterning in the spinal cord captured exactly my interest in understanding the logic and computation of molecular systems. Second, on a more pragmatic level, after my undergraduate I went to study computational science & engineering at Harvard, funded by a Frank Knox Fellowship. I focused on mathematical modelling and data science methods and at the same time, the Briscoe lab were publishing a number of theoretical papers modelling the function of gene regulatory networks, along with more data-driven papers performing single-cell analysis. So, it seemed a perfect fit.

Can you talk more about your PhD project?

The aim for my project was to build methods for mechanistic analysis of cell fate decisions from single-cell data. To go beyond descriptive time-courses and population-level descriptions, to construct models that can simulate the gene expression dynamics of cellular transitions and, in doing so, connect early variations in expression to downstream differences in cell fate.

Key to this, in my mind, was the concept of dynamics: if we want to understand mechanism — the temporal ordering of events and the causal interactions between components — we need a clear picture of the dynamics that these mechanisms create. The value of single-cell resolution is that you can capture a picture of the spectrum of states that are possible as cells transition between types. This allows a range of different ‘pseudo-temporal’ approaches that can create expression time-series between your system’s beginning and end. But to study the mechanism driving these transitions, this sort of population-level time series analysis is insufficient: to actually model and simulate cell fate decisions, we needed to capture dynamics at single-cell level as well.

To tackle this, I established and optimised a time-resolved transcriptomics method that integrated two methods: metabolic labelling, which uses a uridine analogue 4sU to label nascent transcripts such that one can distinguish new from old reads in the sequencing data; and single-cell combinatorial indexing, a method for single-cell RNA sequencing that is compatible with fixed cells (necessary for the temporal labelling) and requires no bespoke microfluidic devices.

I applied this approach to in vitro differentiation of mouse stem cells into neural and mesodermal cells. The noisy, high-dimensional nature of the resultant sequencing data would usually be prohibitive for dynamical systems modelling (trying to learn a vector field in thousands of dimensions is no simple task…). So, to handle this I built a machine learning framework that models the dynamics of cell fate decisions with an abstract, low-dimensional vector field embedded in a ‘latent’ representation of the data. A biophysical model of transcription and labelling is embedded in the model, connecting this abstract vector field to the observed labelling data. The result was a model that could simulate the differentiation trajectory of each progenitor cell in the dataset, producing a distribution of trajectories that linked early variations to later fate decisions. Through these simulations, I identified that modulators of Shh signalling show early differences between fates, suggesting a previously unappreciated level of feedback between signal interpretation and cell fate decisions.

How did the project get started?

In the early weeks, I was deliberating over whether I should focus on a more theoretical project that applied dynamical systems theory to the study of gene regulatory networks, or a more data-driven approach that worked with single-cell data. I presented this conundrum to James, who just looked at me and asked, “why not both?”

Were there any frustrating moments?

The original aim of the project was to take existing protocols and computational methods and apply them to our system and our questions. The final product of the project was a study of why existing protocols and computational methods did not work, and the development of improved methods that did. This should be indication enough that there were frustrating moments aplenty!

If you took one abiding memory with you from your PhD, what would it be?

One moment that stands out is analysing the data from our first successful pilot of the homemade single-cell protocol. It was a simple pilot with unremarkable samples, but seeing that the experiment worked, seeing the expected cell types appear and genes being expressed in the right place came with a huge sense of excitement, almost a feeling of disbelief that this crazy, painful protocol was actually working.

Did you work on other projects during your PhD?

I sporadically got distracted and detoured, but the real exploration came at the end of the thesis, with exciting off-shoot projects that are still on-going!

What have you been working on since you completed your PhD? What’s next for you?

Towards the end of the PhD and afterwards, I started some very fun projects where we increased the throughput and affordability of the sequencing pipeline by an order of magnitude or two. In this way, we were able to massively increase the sophistication of our experimental designs, allowing us to map the function of developmental gene regulatory networks from input signals to output cell fates. Now, I’ve joined EMBL EBI and the Sanger Institute as an ESPOD postdoctoral fellow, where my postdoctoral project will be a fun mix of synthetic and systems biology, engineering cells to understand their decision making!

What techniques or areas in developmental biology excite you the most?

We’re really very good at measuring things in developmental biology. We’re creating datasets with millions of observations, tens of thousands of variables across multiple ‘modalities.’ We’re getting pretty good at perturbing things too: approaches to knockout genes, introduce mutations or alter enhancers at the scale of thousands of knockouts/mutations/alterations at a time hold a lot of promise. But we’re not so good at distilling these thousand-dimensional perturbational datasets into clear understanding.

Our brains are not thousand-dimensional. Our vision is 3D; our working attention can hold onto four things at once. To understand, rather than just observe developmental biology, we need to create intuitive, mechanistic representations of these complex systems that can actually fit into our minds.

Many people are excited about the application of AI in biology. For me, the exciting prospect is that the neural networks in AI models are really useful for learning abstract functions. This means if we want to take a thousand-dimensional dataset, abstract away all the details and just learn four key parameters of our choosing, AI is a pretty good tool for that. Neural network models can learn abstract representations of datasets that can be flexibly constrained by biological information/inductive biases, depending on the specific biological question.

So, the most exciting area of developmental biology for me is the study of emergent properties; dynamic properties of a system that are only apparent when considering the system as a whole, rather than examining the system’s individual components. If we can identify the key emergent properties of a gene regulatory system, can we use AI-driven approaches to model these key properties, such that we can build a simple, intuitive understanding of developmental systems and their many thousands of observable dimensions?

Outside of the lab, what do you like to do?

My ideal day off would involve a swim in a pond in the morning, a few hours reading an overlarge book in the afternoon, and a jaunt down to the pub in the evening.

(No Ratings Yet)

Loading...

At the end of each month, I pick the same month from a random year from the past 15 years of the Node, and take a look at what people were talking about back then.

Previously, I’ve been busy travelling back to February 2011, March 2013, April 2014, May 2016, June 2013 and July 2013.

After so much travelling, my time machine is getting a bit unpredictable… in case I accidentally get stuck in the past, I’m afraid this post will be the very last time I’ll be time travelling. What a journey it’s been! If you’d like to do some time travelling yourself, you can do so using the Node’s search and filter function.

(No Ratings Yet)

Loading...