A new computational method could dramatically accelerate efforts to map the body’s cells in space, according to a study published in Nature Genetics. Spatial multi-omics technologies—often described as ultra-high-resolution maps of tissues—allow scientists to see not only which genes or proteins are active in a cell, but exactly where that activity occurs. That spatial context is critical for understanding complex organs such as the brain, immune tissues and developing embryos.

Unfortunately, capturing multiple molecular layers at once remains expensive and technically challenging, said David Gate, Ph.D., assistant professor in the Ken and Ruth Davee Department of Neurology’s Division of Behavioral Neurology, who was a co-author of the study.

“In practice, investigators end up with ‘mosaic’ datasets: different slices or batches that each capture only some of the layers, often from different technologies or labs, with batch effects and missing pieces,” said Gate, who also leads the Abrams Research Center on Neurogenomics.

A new tool to unify data

The new computational method, dubbed SpaMosaic, was designed to solve this growing problem. Developed by a collaborative team led by computational investigators, the tool uses artificial intelligence to align and integrate spatial datasets.

To create the new tool, investigators combined contrastive learning—which helps AI models learn meaningful similarities and differences across datasets—with graph neural networks that account for spatial relationships between neighboring cells. The result is a shared dataset that allows RNA, protein, chromatin accessibility and histone modification data to be analyzed together, even when individual datasets measure only a subset of these features.

Performance across tissues and species

In benchmarking experiments, SpaMosaic consistently outperformed existing integration methods on both simulated data and real-world datasets spanning mouse brain development, mouse embryos and human immune tissues such as lymph node and tonsil. The investigators found that the tool excelled at identifying biologically meaningful spatial domains—regions of tissue with shared functional identity—even when datasets came from different technologies or developmental stages.

“SpaMosaic is also effective at removing technical ‘batch effects’ (like differences in how samples were processed) while keeping the real biology intact,” Gate said.

Predicting missing molecular layers

One of SpaMosaic’s most novel capabilities is its ability to predict molecular layers that were never directly measured. In a large mosaic dataset of the mouse brain, the tool inferred histone modification patterns in regions where only transcriptomic data were available.

“SpaMosaic filled in the gaps and actually revealed stronger links between gene activity and epigenetic regulation than the directly measured chromatin data sometimes did,” Gate said.

Implications for atlases and neuroscience

The findings suggest the method can uncover regulatory relationships between molecular layers, offering an alternative to costly, technically demanding experiments. Instead of being limited by what a single experiment can measure, investigators can now combine data across studies, platforms and labs, Gate said.

“This is a real game-changer for building true multi-omics ‘atlases’ of tissues,” he said. “For neuroscience (our focus), this means better maps of brain development, neuroinflammation, and eventually disease states like Alzheimer’s or ALS, where spatial relationships and multi-layer regulation are critical. It accelerates discovery without requiring every lab to re-do perfect multi-modal experiments on every sample.”

Next steps for SpaMosaic development

The team is already exploring next steps, including scaling SpaMosaic to even larger datasets. Additionally, Gate and his collaborators will further test the method to assess how reliable the predicted data are, he said.

“This project is a great example of what happens when computational innovators and experimental biologists work closely together,” Gate said. “Tools like SpaMosaic are going to democratize spatial multi-omics, letting more labs contribute to and benefit from large-scale tissue atlases.”

The UK-led OpenBind initiative has reached a major milestone with the release of its first publicly available dataset and predictive AI model, a groundbreaking step toward accelerating the discovery of new medicines using artificial intelligence.

The release showcases how engineering the production of AI-ready data is not only feasible but essential to evolving AI tools for scientific fields, which all suffer from a lack of data. With this OpenBind release, both high-quality, standardized experimental data, and a newly trained predictive model, OpenBind v1, become freely accessible to researchers worldwide, for immediate use in therapeutic discovery and to drive the next generation of AI models.

While AI has introduced a step-change in predictive accuracy for protein structures, its impact on drug discovery has remained muted, limited above all by the global shortage of reliable experimental data measuring in atomic detail how molecules of drug discovery bind to disease-related proteins. OpenBind aims to fill this critical gap.

Led by Diamond Light Source, the collaboration of structural biologists and AI specialists—supported in its foundation phase by the Department for Science, Innovation and Technology (DSIT)—is the first initiative to generate these essential datasets at an industrial scale, openly and continuously, and designed specifically for AI.

This first release demonstrates that OpenBind’s pipeline is now operational, having generated 800 high-quality measurements in only seven months—in the past, such large datasets took years to be produced and released.

This integrated operation combines automated chemistry, robust binding measurements and high throughput crystallography at Diamond’s XChem Fragment Screening facility with an engineered data release process and AI model training using the UK’s Isambard-AI compute cluster.

It lays the groundwork for transformative progress in drug discovery, with future data tranches planned to address global-health challenges such as COVID-19, malaria, dengue, Zika, and cancer, where rapid development of new treatments remains vital.

Professor Mohammed Alquraishi of Columbia University said, “AlphaFold2 revolutionized protein structure prediction by leveraging decades of experimental data on protein structures in the PDB. The equivalent of such a dataset for protein-drug complexes does not yet exist, but OpenBind aims to create it, and in the process create the next generation of computational tools for modeling interactions between drugs and proteins.”

The initial dataset also reflects invaluable learning from the initiative’s early experimental cycles. Standardized workflows, strong metadata practices and high levels of automation have proven crucial in ensuring the consistency and reproducibility required for AI, while highlighting opportunities to further streamline data handling and release frequency.

Dr. Fergus Imrie of the University of Oxford said, “High-quality experimental data is essential for developing new and improved AI models, and this first data release shows that OpenBind now has this foundation in place. We’re enabling AI to improve model performance and guide future experiments, helping to accelerate discovery.

“The lessons from these early cycles are already helping us improve the speed, consistency, and reproducibility of the pipeline, which will be critical as OpenBind grows.”

Professor Frank von Delft, principal beamline scientist at Diamond Light Source, said, “We couldn’t have made such rapid progress without the contributions of our consortium members and operational team. Their expertise and commitment have enabled us to reach this ambitious milestone. We will now implement the lessons from this foundation phase to ramp up a long-term operation that links high-volume production of AI data with active discovery projects.”

Building on this foundation, OpenBind will expand to include many more targets, larger chemical series and deeper datasets, alongside community-blind challenges that will validate AI models for newly generated experimental data. Ultimately, OpenBind aims to create a global, open data engine capable of supporting the development of faster, more accurate and more equitable therapeutics.

A review in Quantitative Biology demonstrates that scientists can now reliably build and combine very large pieces of DNA, making it much easier to redesign microbes such as yeast and bacteria to act as efficient “cell factories.” With these advances, whole biological pathways, and even extra chromosomes, can be assembled and inserted into cells, allowing microbes to produce complex products like medicines, fuels, and chemicals more efficiently than before.

The review highlights recent progress and makes clear that the field has reached a turning point. The ability to assemble large DNA segments quickly and accurately opens possibilities with relevance for health care, sustainable manufacturing, agriculture, and industrial biotechnology.

The methods described are relevant to ongoing global debates about how to reduce reliance on fossil fuel-based production, improve the sustainability of manufacturing, and scale up biotechnological solutions safely.

“As large DNA assembly technologies increasingly integrate with automated platforms and AI-driven design, the development cycle of microbial cell factories is poised to accelerate dramatically,” said corresponding author Yue Shen, Ph.D., Chief Scientist of Synthetic Biology of BGI Research, in China. “This technological leap is unlocking their true potential as practical, sustainable platforms for global biomanufacturing.”

Imagine a sea of glowing blue lights pulsing to the beat of the music. But instead of glow sticks filled with toxic chemicals, the luminescence comes from living algae, shimmering on demand. In a new study published in Science Advances, researchers at the University of Colorado Boulder and collaborators unveil a new technology that could make it possible.

They’ve successfully turned on the “light switch” in algae and kept them lit up using simple chemical solutions. The finding opens the door for future technologies such as autonomous robots that can operate in dark environments and living sensors for water quality.

“This project was a moonshot idea,” said Wil Srubar, professor in the Department of Civil, Environmental and Architectural Engineering. “I was curious if we could create a world in which we don’t use electricity but rather use biology to produce light. This discovery really paves the way for engineering other living light materials and devices.”

The science of natural bioluminescence

In the natural world, a wide range of animals, from fireflies to anglerfish and even certain mushrooms, produce their own light, a phenomenon known as bioluminescence. In the deep ocean, as much as 90% of creatures may be able to glow and glitter through chemical reactions inside their cells.

Pyrocystis lunula, a type of bioluminescent algae, is one of the organisms that emit an icy blue glow sometimes seen in ocean waves. Subsisting only on seawater, sunlight, and carbon dioxide (CO₂), these photosynthetic organisms flash when they are agitated by crashing tides or passing boats, for example.

But those flashes last only milliseconds. Srubar and his team wondered if they could keep the lights on with chemistry instead. Previous research has suggested that exposure to different chemical compounds could activate P. lunula’s bioluminescent reaction.

Using chemistry to sustain the glow

So the team exposed the algae to an acidic solution with a pH of 4, similar to that of tomato juice, and a basic solution with a pH of 10, comparable to mild soap.

They found that both environments could trigger light production in P. lunula. In the acidic condition, the algae could stay aglow for as long as 25 minutes, with light appearing bright and concentrated. In the basic condition, the glow was more diffused and short-lived.

“It was a very exciting moment when we found the right chemical stimulant that allowed the light to stay on for a long time,” says Giulia Brachi, the first author and research associate in the Department of Civil, Environmental and Architectural Engineering. “This is the first time we have figured out how to sustain luminescence.”

3D-printing living light structures

To turn these glowing algae into usable materials, the researchers embedded them into a naturally derived hydrogel, a type of water-based gel material. They then used 3D printing to shape the material into structures and shapes, from a crescent pattern to a CU Buffalo logo.

By exposing the structures to the acidic or basic solution, they prompted the P. lunula inside to emit light, illuminating the entire structure in a blue glow.

Inside these printed structures, the algae remained alive for weeks. The acidic condition worked best, with P. lunula in these 3D-printed structures retaining 75% of their brightness even after four weeks.

Potential applications and environmental impact

The findings could have wide applications beyond making eye-catching designs. These living materials could someday help light up autonomous robots for deep-sea or space exploration without the need for batteries.

Next, the team is exploring whether P. lunula may respond to other chemicals. If so, they could also serve as a tool for water quality monitoring and light up when toxins are present.

Beyond their ability to light up spaces, P. lunula also offers an environmental benefit. Because these algae are photosynthetic, they convert carbon dissolved in seawater into energy.

“We’re storing carbon while we’re producing light, whereas conventionally, we emit carbon to light up spaces,” Srubar said.

And yes, future rave scenes could someday glow with light powered by living algae.