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2026 Feb 9

- Fed on Reams of Cell Data, AI Maps New Neighborhoods in the Brain

NEUROSCIENCE | VIEW ON WEBSITE

Fed on Reams of Cell Data, AI Maps New Neighborhoods in the Brain

By Amber Dance

February 9, 2026

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.

A machine learning algorithm created maps of the mouse brain with 1,300 subregions by predicting how brain cell types tend to group together, like buildings in a neighborhood.

Irena Gajic for Quanta Magazine

Introduction

Real estate agents will tell you that a home’s most important feature is “location, location, location.” It’s similar in neuroscience: “Location is everything in the brain,” said Bosiljka Tasic, a self-described “biological cartographer.” Brain injury in one spot could knock out memory; damage in another could interfere with personality. Neuroscientists and doctors are lost without a good map.

Researchers have been mapping the brain for more than a century. By tracing cellular patterns that are visible under a microscope, they’ve created colorful charts and models that delineate regions and have been able to associate them with functions. In recent years, they’ve added vastly greater detail: They can now go cell by cell and define each one by its internal genetic activity. But no matter how carefully they slice and how deeply they analyze, their maps of the brain seem incomplete, muddled, inconsistent. For example, some large brain regions have been linked to many different tasks; scientists suspect that they should be subdivided into smaller regions, each with its own job. So far, mapping these cellular neighborhoods from enormous genetic datasets has been both a challenge and a chore.

Recently, Tasic, a neuroscientist and genomicist at the Allen Institute for Brain Science, and her collaborators recruited artificial intelligence for the sorting and mapmaking effort. They fed genetic data from five mouse brains — 10.4 million individual cells with hundreds of genes per cell — into a custom machine learning algorithm. The program delivered maps that are a neuro-realtor’s dream, with known and novel subdivisions within larger brain regions. Humans couldn’t delineate such borders in several lifetimes, but the algorithm did it in hours. The authors published their methods in Nature Communications in October.

Bosiljka Tasic, a neuroscientist, genomicist, and self-described “biological cartographer,” used AI to sort through reams of cell data and build new maps of the mouse brain.

Erik Dinnel/Allen Institute

By applying the same technique to other animals and eventually to humans, researchers hope not only to detail the brain’s finer-grained layout but also to generate and test hypotheses about how the organ’s parts operate in health and disease.

“We want to understand how the cells are organized in 3D space,” said Claudia Doege, a neuroscientist at Columbia University who wasn’t involved in the study. “Only if we know how they are organized can we figure out how they can potentially work with each other.”

Neural Cartography

Brain mapping is an old science, dating back to the early 1900s when the German neuroscientist Korbinian Brodmann defined regions of the cerebral cortex — the outer, thinking part of the brain. He stained human brain slices with a dye that turned genetic material violet and then studied them under the microscope, where the densities and arrangements of brain cells produced different, observable textures. He traced the borders to create a map of 52 regions, known as Brodmann areas, some of which are still recognized today.

I have always been fascinated and intrigued by how we can leverage AI to understand cellular organization in the brain.

Reza Abbasi-Asl, University of California, San Francisco

For decades, brain-mapping scientists wielded tools little more advanced than Brodmann’s, said Yongsoo Kim, a neuroanatomist at Penn State College of Medicine. “What anatomists used to do is, they have a pencil, and they draw the line” between different-looking regions on brain images, he said. One such map, the Allen Mouse Brain Common Coordinate Framework, which was published in 2020, was based on data from 1,675 mouse brains and includes more than 1,000 different areas. Such maps are undeniably valuable but also inevitably subjective: When Kim asked senior scientists to impart the secrets of their methods, he said the answer was often, “It’s all in my head.”

Recently, more advanced molecular techniques have allowed neuro-cartographers to investigate individual cells. Under this framework, a cell’s identity is determined by which of its tens of thousands of genes are turned on, something that can be represented by the sequences of RNA molecules (copies of active DNA regions) present in the cell. Thus, scientists can slice up a brain, measure the RNAs from each cell, and then map those genetic patterns back to the cells’ original locations.

This approach has distinguished thousands of individual types of brain cells, many more than previously known. The Allen Institute’s latest mouse brain atlas, published in 2023, includes more than 5,000 different cell types. The first-draft Human Brain Cell Atlas, based on 3 million cells from the brains of three deceased people, defines 3,313 cell types.

But those massive datasets didn’t yield the kind of brain cartography that Tasic sought. The resulting maps generated regions that weren’t always “biologically meaningful,” she said. That’s because most brain regions aren’t defined by a single cell type, and many cell types aren’t limited to one region. Instead, each area contains a mixture of cell types, including different kinds of nerve cells plus the brain’s support and immune cells.

“Only if we know how [cells] are organized can we figure out how they can potentially work with each other,” said Claudia Doege, a neuroscientist at Columbia University.

Courtesy of Claudia Doege

For comparison, imagine an airplane passenger looking out the window and trying to identify neighborhood boundaries within a city below. If the passenger focuses on just one building at a time, they can’t discern its surroundings. To identify neighborhoods, they need to focus on how different building types group together: One neighborhood might be crowded with brownstones and playgrounds, another could be populated by mostly larger apartment buildings and bodegas, a third might be full of high-rise office complexes and restaurants.

To map the brain’s subregions, Tasic needed to analyze how different cell types grouped together. That’s not something her human brain, for all its glorious complexity, could do on its own by studying the RNA data.

Tasic needed better computational tools — and a research partner.

Neighborhood Watch

Tasic found the perfect collaborator in Reza Abbasi-Asl, a computational neuroscientist at the University of California, San Francisco. “I have always been fascinated and intrigued by how we can leverage AI to understand cellular organization in the brain,” he said.

To define cellular neighborhoods, Abbasi-Asl and his graduate student Alex Lee started with RNA profiles collected from 3.9 million cells in a single mouse brain. They programmed a machine learning algorithm to choose one cell; its identity and gene expression would be masked. Then the AI, which they called CellTransformer, would predict that cell’s gene expression and type based on those of its neighbors, check if it had guessed right, and update its algorithm based on the result. By repeating this process millions of times, the algorithm learned how and where different types of brain cells group together. From there, it could build a high-resolution map of those groups.

Reza Abbasi-Asl (left) and Alex Lee at UCSF programmed a machine learning algorithm to find out how brain cell types tend to cluster together for neurobiological mapmaking.

Abbasi Lab at the University of California, San Francisco

Returning to that airborne city observer, what CellTransformer does is the equivalent of holding up a thumb to the window to block one building, and then predicting its type. The surroundings provide clues as to what kind of structure fits into the neighborhood.

Approaching brain mapping as relationships between nearby cells was the “secret sauce,” Abbasi-Asl said, that allowed the algorithm to map out meaningful neural neighborhoods, each made of a blend of different cell types. Depending on the level of granularity the scientists asked for, it could define anywhere from 25 to 1,300 neighborhoods in the mouse brain, though that’s not necessarily the upper limit of brain regions. With AI, “we see things that a human eye cannot see,” Tasic said.

Using single-cell RNA data from four additional mouse brains — including ones from male or female mice, and ones sliced from left to right or front to back — CellTransformer produced similar maps. This, Doege said, is excellent evidence that the technique is reliable.

While the algorithm used its predictions to group cells, it wasn’t generating wholly new maps, so it couldn’t hallucinate as some generative AI models can. Nonetheless, it was essential to compare CellTransformer’s novel output to known brain maps. As a trusted comparator, the team returned to the hand-drawn Allen Mouse Brain Common Coordinate Framework. The CellTransformer map was a good match, laying out similar structures such as the layers in the cortex.

Mouse brain maps created by the AI CellTransformer (left column) and by human scientists with the Mouse Brain Common Coordinate Framework (right column). Both located more than 1,000 cellular subregions in the brain (the AI found more).

Abbasi Lab at the University of California, San Francisco

The algorithm was also able to identify new neighborhoods, regions that previous neuroscience methods, including the Allen Mouse Brain Common Coordinate Framework, had missed. Take the striatum, a striped, vaguely C-shaped structure near the middle of the brain. In maps of the mouse brain, where the striatum is called the caudoputamen, “you just see one huge structure,” said Hourig Hintiryan, a neuroanatomist at the University of California, Los Angeles who wasn’t involved in the new project. It’s known to participate in movement, reward, and overall brain management. How could one piece of brain perform such disparate tasks?

CellTransformer’s explanation is that it’s not one uniform brain region after all. The map confirmed that the caudoputamen is, in fact, subdivided into smaller areas, although researchers have not yet matched each region to a function. Moreover, the new subdivisions corresponded nicely to a map that Hintiryan and colleagues published in 2016 based on an entirely different technique, which traced connections between the caudoputamen and other regions.

Identifying such subregions across the brain, Hintiryan said, could resolve debates between neuroscientists who assign vastly different functions to the same large brain region. It seems likely that “they’re both correct, they’re just looking at different areas,” she said.

Abbasi-Asl and Tasic were thrilled with CellTransformer’s ability to accurately match known brain cartography, and even more excited that the algorithm mapped novel subdivisions. For example, the brainstem’s midbrain reticular nucleus, which is involved in initiating movement, is a fairly underexplored region, Abbasi-Asl said. CellTransformer picked out four new neighborhoods there. Each of those neighborhoods featured particularly prevalent cell types and specifically activated genes. They also had several cell types that earlier analyses had placed in an entirely different part of the brain.

A Map in Hand

The Nature Communications paper serves mainly to introduce the CellTransformer method and show that it can find novel regions; the thousand-plus new neighborhoods still require validation. As with any exploration of new territory, drawing the map is just the beginning. What’s most exciting is what scientists may be able to do with it. “The more granular our understanding of structure, the more specific we can get with our interrogations and interventions,” Hintiryan said.

Emerging questions center on the functions of all these neural neighborhoods. To pinpoint what each bit does, scientists could eliminate or activate these newly identified regions in lab animals and then check for behavioral changes.

Related:

1. New Cell Atlases Reveal Untold Variety in the Brain and Beyond

2. Once Thought To Support Neurons, Astrocytes Turn Out To Be in Charge

3. Cell by Cell, Scientists Map the Genetic Steps as Eggs Become Animals

4. Why the First Drawings of Neurons Were Defaced

The real prize will be to apply CellTransformer to human brains. Doege suspects that some neighborhoods will match well between mice and people, while others will diverge. Unfortunately, the quantity of data the algorithm needs to make accurate predictions isn’t available from human brains — at least, not yet. While the mouse brain contains about 100 million cells, the human brain has around 170 billion, and that menagerie is still undergoing genetic analysis. When sufficient amounts of that data become available, Abbasi-Asl and Tasic think CellTransformer will be up to the challenge.

They are also interested in incorporating other technologies, such as the connection tracing used by Hintiryan, into CellTransformer. This would be like adding streets and highways to the city neighborhoods. And beyond the brain, the same algorithm could offer detailed cell maps of other organs, allowing scientists to compare, for example, healthy versus diabetic kidneys.

Human scientists simply can’t sort out these details on their own. “I see AI as kind of a helper for the human,” Kim said. “Discovery will be accelerated in a dramatic way.”

2026 Feb 6

- Long-Sought Proof Tames Some of Math’s Unruliest Equations

PARTIAL DIFFERENTIAL EQUATIONS | VIEW ON WEBSITE

Long-Sought Proof Tames Some of Math’s Unruliest Equations

By Paulina Rowińska

February 6, 2026

Mathematicians finally understand the behavior of an important class of differential equations that describe everything from water pressure to oxygen levels in human tissues.

To study the flow of air around an airplane’s wing, the distribution of stress on a bridge, or various other situations, researchers use elliptic partial differential equations. These equations are notoriously difficult to understand.

Kristina Armitage; Michael Kanyongolo/Quanta Magazine

Introduction

The trajectory of a storm, the evolution of stock prices, the spread of disease — mathematicians can describe any phenomenon that changes in time or space using what are known as partial differential equations. But there’s a problem: These “PDEs” are often so complicated that it’s impossible to solve them directly.

Mathematicians instead rely on a clever workaround. They might not know how to compute the exact solution to a given equation, but they can try to show that this solution must be “regular,” or well-behaved in a certain sense — that its values won’t suddenly jump in a physically impossible way, for instance. If a solution is regular, mathematicians can use a variety of tools to approximate it, gaining a better understanding of the phenomenon they want to study.

But many of the PDEs that describe realistic situations have remained out of reach. Mathematicians haven’t been able to show that their solutions are regular. In particular, some of these out-of-reach equations belong to a special class of PDEs that researchers spent a century developing a theory of — a theory that no one could get to work for this one subclass. They’d hit a wall.

Now, two Italian mathematicians have finally broken through, extending the theory to cover those messier PDEs. Their paper, published last summer, marks the culmination of an ambitious project that, for the first time, will allow scientists to describe real-life phenomena that have long defied mathematical analysis.

Naughty or Nice

During a volcanic eruption, a scorching, chaotic river of lava flows over the ground. But after hours or days (or perhaps even longer), it cools enough to enter a state of equilibrium. Its temperature is no longer changing from moment to moment, although it still varies from place to place across the vast expanse of space the lava covers.

Mathematicians model systems that change in space but not in time — the temperature of a lava flow at equilibrium, the distribution of nutrients in tissues, the shape of a soap film — using elliptic partial differential equations.

From top: Giles Laurent/Creative Commons;

Mikael Häggström/Creative Commons; Ted Kinsman/Science Source

Mathematicians describe situations like this using what are called elliptic PDEs. These equations represent phenomena that vary across space but not time, such as the pressure of water flowing through rock, the distribution of stress on a bridge, or the diffusion of nutrients in a tumor.

But solutions to elliptic PDEs are complicated. The solution to the lava PDE, for instance, describes its temperature at every point, given some initial conditions. It depends on a lot of interacting variables.

Researchers want to approximate such a solution even when it’s impossible to write it down. But the methods they use only work well if the solution is regular — meaning that it doesn’t have any sudden jumps or kinks. (There won’t be sharp spikes in the lava’s temperature from place to place.) “If something goes wrong, it’s probably because of the [lack of] regularity,” said Makson Santos of the University of Lisbon.

In the 1930s, the Polish mathematician Juliusz Schauder sought to establish the minimal conditions an elliptic PDE must satisfy to guarantee that its solutions will be regular. He showed that in many cases, all you have to prove is that the rules baked into the equation — such as the rule for how quickly heat will spread in lava — do not change too abruptly from point to point.

In the decades since Schauder’s proof, mathematicians have shown that this condition is enough to ensure that any PDE that describes a nice, “uniform” material has regular solutions. In such a material, there’s a limit on how extreme the underlying rules can get. For example, if you assume your lava is uniform, heat will always flow within certain speed limits, never too quickly or slowly.

But lava is actually a diverse mix of molten rock, dissolved gases, and crystals. In such a nonuniform material, you can’t control the extremes, and you might get more drastic differences in how quickly heat can spread, depending on your location: Some regions in the lava might conduct heat extremely well, and others extremely poorly. In this case, you’ll use a “nonuniformly elliptic” PDE to describe the situation.

For decades, no one could prove that Schauder’s theory held for this kind of PDE.

Unfortunately, “the real world is nonuniformly elliptic,” said Giuseppe Mingione, a mathematician at the University of Parma in Italy. That meant mathematicians were stuck. Mingione wanted to understand why.

Time Machine

In August 2000, Mingione — 28 years old and fresh off his Ph.D. — found himself in a dilapidated old resort in Russia, attending a conference on differential equations. One evening, with nothing better to do, he started reading papers by Vasiliĭ Vasil’evich Zhikov, a mathematician he’d met on the trip, and he realized that nonuniformly elliptic PDEs that seem well behaved can have irregular solutions even when they satisfy the condition Schauder had identified. Schauder’s theory wasn’t simply harder to prove in the nonuniform case. It needed an update.

Giuseppe Mingione helped prove a conjecture he made 20 years ago. The final proof, he said, was “a miracle by desperation.”

Giampiero Palatucci

Returning to Italy, he teamed up with two colleagues and proposed that nonuniformly elliptic PDEs needed to satisfy an additional condition to guarantee that their solutions would be regular. Not only did the rules governing heat flow have to change gradually from point to point, but these changes had to be tightly controlled to account for the lava’s nonuniformity. In particular, the mathematicians posited, the more uneven the material, the tighter this control would have to be. They represented this condition as an inequality, giving a precise threshold for how much nonuniformity a system could tolerate.

They showed that for PDEs where the inequality did not hold, they could no longer guarantee that the solutions would be regular. But they couldn’t prove that the inequality precisely marked the point where solutions would go from being regular to potentially irregular. Mingione spent years on the problem, to no avail. He eventually abandoned the effort.

Almost 20 years passed. Then in 2017, a first-year graduate student named Cristiana De Filippis heard about the quest to extend Schauder’s theory to nonuniformly elliptic equations. More experienced mathematicians warned her against pursuing the problem, but she ignored their advice and reached out to Mingione. Over a late-night Skype call, she told him that she had some ideas for how to prove his conjecture and was determined to pick up where he had left off.

Cristiana De Filippis has been developing a broad theory to better understand the solutions to partial differential equations, setting her sights on more and more complicated cases.

Giampiero Palatucci

“It was like a time machine,” Mingione said. “It was like meeting myself of 20 years before and knocking at the door of my own mind.”

According to him, it was De Filippis’ “new energy and enthusiasm and faith that this could be done” that persuaded him to revive his long-dormant attempt to prove his conjecture.

Miracles

The key to proving that the solution to a PDE is regular is to show that it always changes in a controlled way. Mathematicians do this by looking at a special function that describes how fast the solution changes at each point. They want to show that this function, which is called the gradient, can’t get too big.

But just as it’s usually impossible to directly compute the solution to a PDE, it’s also usually impossible to calculate its gradient.

The Polish mathematician Juliusz Schauder sought to understand when models of physical systems will provide a nice picture of reality, and when they won’t.

Public Domain

Instead, De Filippis and Mingione derived what they called a “ghost equation” from the original PDE, a shadow of what they actually needed.

This was where Mingione had gotten stuck decades earlier. But De Filippis had an idea for how to hone the ghost equation so that it could give a crisper view of the PDE. Using a long, multistep procedure, the pair was able to gain enough information from the ghost equation to recover the gradient.

“It’s kind of far-fetched to do it like this,” said Simon Nowak of Bielefeld University in Germany. “But it works, and it’s quite beautiful.”

Now they had to figure out how to show that the gradient they’d recovered couldn’t get too large. They split it into smaller pieces and proved that each piece couldn’t exceed a specific size. This took an enormous amount of effort: Even a tiny measurement error on a single piece would throw off their estimate of the gradient, taking them away from the threshold they were aiming to prove.

In a 2022 preprint, they were able to tame all these pieces well enough to show that most nonuniformly elliptic PDEs that satisfy Mingione’s inequality have to have regular solutions. But some PDEs were still missing. To prove the full conjecture, the mathematicians had to get even better bounds on the sizes of the gradient’s pieces. There was absolutely no wiggle room. This required starting over many times — “a never-ending game,” De Filippis said. But eventually, they were able to prove that the threshold Mingione had predicted decades earlier was exactly right.

Related:

1. Using AI, Mathematicians Find Hidden Glitches in Fluid Equations

2. The Hidden Math of Ocean Waves Crashes Into View

3. Mathematicians Coax Fluid Equations Into Nonphysical Solutions

It was “a miracle by desperation,” he said.

De Filippis and Mingione haven’t just completed a century-long project. They’ve also made it possible for mathematicians to study complicated real-life processes that until now had to be modeled using unrealistically simplified equations.

Researchers are also excited to apply their techniques to understand other kinds of partial differential equations, including ones that change in both space and time. “The magical part is that they were bringing all this deep theory under one umbrella and then squeezing out the proof,” said Tuomo Kuusi of the University of Helsinki.

PDEs have always been almost prohibitively difficult to analyze mathematically. Now they’ve gotten just a little bit easier. Behind them, De Filippis said, “there is an enormous reality” waiting to be explained.

2026 Feb 4

- Expansion Microscopy Has Transformed How We See the Cellular World

IMAGE GALLERY | VIEW ON WEBSITE

Expansion Microscopy Has Transformed How We See the Cellular World

By Molly Herring

February 4, 2026

Physically magnifying objects using a key moisture-absorbing ingredient in diapers has opened an unprecedented view of the microbial world.

Expansion microscopy images of the dinoflagellate Karenia papiloniacea. Centrin is in green, nucleus in blue, and in red what appear to be intracellular crystals that could be compartments for metabolite storage.

DeyLab/DudinLab/CentrioleLab

When you slip a slide under a microscope, a system of glass lenses magnifies the object of your attention — a microbe, for example. But even with the largest zoom on a classic compound optical system, scientists struggle to make sense of finer details, which can be further obscured when tough cell walls make it difficult to inject dyes that help identify structures.

Now, rather than invest in more powerful and more expensive technologies, some scientists are using an alternative technique called expansion microscopy, which inflates the subject using the same moisture-absorbing material found in diapers.

“It’s cheap, it’s easy to learn, and indeed, on a cheap microscope, it gives you better images,” said Omaya Dudin, a cell biologist at the University of Geneva who studies multicellularity.

Expansion microscopy was developed by Ed Boyden at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology in 2015. Boyden and colleagues successfully expanded a biological sample by infusing it with a hydrogel made of sodium acrylate. A key ingredient layered in diapers to keep babies dry, the compound can absorb hundreds of times its weight in water while retaining its overall structure.

Omaya Dudin (top) and Gautam Dey

In expansion microscopy, specific biomolecules such as proteins are anchored to the gel. As the gel absorbs added water, its weblike matrix swells, and the space between the web’s anchor points dilates. Ideally, the overall structure remains, allowing researchers to visualize extra-tiny anatomy or see inside cells with tough barriers.

Dudin had spent six frustrating years trying to force antibodies through his target cells’ sturdy walls to bind to specific proteins and visualize their internal structures, and he was only able to do so through a complex freeze-and-thaw protocol that destroyed most of the final product. Desperate, he struck up a Covid-era collaboration with the lab next door that was using expansion microscopy.

“That moment was just magical. All the cells were expanded, everything stained, we could see,” Dudin said. “It very rapidly became clear that we should aim for the sky with this one.”

Gautam Dey, a cell biologist at the European Molecular Biology Laboratory in Heidelberg who studies mitosis, found that the method worked just as well in his lab. The samples were clearer, and the dyes and antibodies penetrated cells more effectively, so the two labs struck up a collaboration to visualize species they had never studied before. They are working to chart the landscape of cytoskeletal diversity, visualizing complex cytoskeletal structures that have never been seen in such detail.

Perhaps most importantly, expansion microscopy is possible for any lab with a basic microscope and the hydrogel. “People have talked about democratizing microscopy before. This is it, it’s happening,” Dey said. “I think it’s just a matter of time before any cell biology lab in the world is doing it. A basic fluorescence microscope is never too far away.”

The first species that Dudin’s lab imaged using expansion microscopy was the unicellular eukaryote Sphaeroforma arctica, a protist that has multiple nuclei in a single cell. This image shows cells at different stages of their life cycle. Nuclei in the two upper cells are undergoing mitosis, or nuclear division. The lower cells are in late mitosis, with the nuclei switching back to a growth stage.

DudinLab

The predatory ciliate Lacrymaria is a Dudin lab favorite and has a feeding structure formed from a vortex of microtubules. Although it is retracted here, Lacrymaria can rapidly extend the tubule structure to capture escaping prey.

DeyLab/DudinLab/CentrioleLab

Expansion microscopy has allowed Dudin and Dey to see all sorts of unexpected structures. For instance, Rhinomonas is a unicellular photosynthetic alga with two flagella, found predominantly in marine environments. The appearance of the specialized protein centrin (green) decorating regularly spaced arrays was previously unreported for this lineage.

DeyLab/DudinLab/CentrioleLab

Diatoms are particularly difficult to image through their protective glass shells, called frustules. Dey’s lab decided to use an alternative expansion microscopy protocol, in which the sample is expanded and its proteins tagged with fluorescent antibodies; rapid freezing then locks everything in place.

DeyLab/Vincent Lab

Centrin is a small calcium-binding protein that co-polymerizes with larger proteins to produce networks of structural filaments in a cell. Centrin-containing fibers are especially abundant in ciliates; this intricate mesh (yellow) is located just below the cell membrane in this expanded Vorticella cell.

DeyLab/DudinLab/CentrioleLab

In freshwater ciliates known as Vorticella, the researchers spotted “an amazing centrin cage, dotted tubulin flagellar components, and an insane mouth apparatus [at far left, green],” Dudin said. In the middle, a spindle (green) appears to be organizing as cells prepare for division. Green dots of the structural protein tubulin are visible throughout the protist’s distinctive stalk (at right).

DeyLab/DudinLab/CentrioleLab

A dinoflagellate identified in Antarctica’s Ross Sea is known to steal organelles called plastids from the microalga Phaeocystis antarctica. Expansion microscopy of the engulfing process revealed that the host microbe retained more than just plastids. The alga’s nucleus (blue) and other features such as a protein complex crucial for photosynthesis (green) were also observed inside the host. These interactions offer insight into early evolutionary steps of eukaryotes but are still not fully understood.

DudinLab/DecelleLab

Expansion microscopy of the dinoflagellate Lingulodinium polyedra reveals the chromosomal organization (DNA in blue) and internal axostyle (center, green) — a microtubule structure that helps the cell move. “We have imaged more than 60 species across the dinoflagellate tree, allowing us to map cytoskeletal diversity within the whole lineage,” Dudin said.

DeyLab/DudinLab/CentrioleLab

2026 Feb 2

- How Modern and Antique Technologies Reveal a Dynamic Cosmos

ASTRONOMY | VIEW ON WEBSITE

How Modern and Antique Technologies Reveal a Dynamic Cosmos

By Liz Kruesi

February 2, 2026

Today’s observatories document every pulse and flash in the sky each night. To understand how the cosmos has changed over longer periods, scientists rely on a more tactile technology.

Glass plates taken at the Armagh-Dunsink-Harvard Telescope in Bloemfontein, South Africa, in 1950 and 1951 were annotated by hand.

Kayleigh MacDonald for Quanta Magazine

Introduction

In early 2007, René Hudec was in Building D of the Harvard-Smithsonian Center for Astrophysics, thumbing through roomfuls of floor-to-ceiling cabinets that look more like a vast record collection than an academic archive. Each paper sleeve holds a glass plate, most of which are 8 by 10 inches, a historic photographic record of the cosmos from before the age of sophisticated digital detectors. Hudec, an astrophysicist at the Astronomical Institute of the Czech Academy of Sciences in Ondřejov, was searching for a specific pattern of stars that he had memorized, a region that features a binary system of mammoth yet compact objects. There, two supermassive black holes and their surrounding accretion disks are locked in a dance that will eventually merge them into one. Hudec was tracking when this system, known as OJ 287, flared in brightness.

Hudec’s archival detective work has been a success: He has found the black hole pair on more than 2,000 glass plates. The earliest with usable data was from 1896. With a magnifying glass, he examined each plate to estimate the system’s luminosity as compared to other stars in the field of view. Astronomers knew about a 1913 flare and more recent events, but Hudec ended up discovering multiple flares that were unknown to astronomy, including a major one in 1900.

With this new — well, old — data, Hudec and his colleagues could better model the system and begin to understand the physics that leads to the flares. “The discoveries and exact timing of historical outbursts,” he said, help to “tune the model parameters.” One of the black holes is substantially more massive than the other. As the less massive one passes through the other’s accretion disk, it creates fireworks that coincide with the historical flares he spotted.

OJ 287 and other active black holes are just one type of celestial object or system that varies in brightness over time. But the universe is full of objects that fluctuate periodically or that quickly burst forth and then disappear. The night sky, for all its apparent consistency, simmers and sparks. In fact, “every astrophysical object can be considered a transient or a variable in a different timescale,” said Rosaria Bonito, an astrophysicist at the Palermo Astronomical Observatory of the National Institute for Astrophysics in Italy. Any attempt to understand what we see in the cosmos relies on knowing how it changes, night after night.

Glass plates from the Bruce Telescope at Harvard are annotated with different colors, markings, and stickers that indicate different types of imagery, kind of plate, and plates of interest.

Kayleigh MacDonald for Quanta Magazine

The plate on the left contains annotations by the first Curator of Astronomical Photographs, Williamina Fleming, while the one on the right shows the Orion Nebula.

Kayleigh MacDonald for Quanta Magazine

The Harvard Plate Stacks also contain contact prints made from glass plates, so a single image could be annotated in a variety of ways. These show the Magellanic Clouds.

Kayleigh MacDonald for Quanta Magazine

The Harvard Plate Stacks contains a variety of card catalogs and archival materials maintained by individual astronomers that refer to plates in the collection.

Kayleigh MacDonald for Quanta Magazine

Wooden crates were used to ship and transport plates from Harvard to observatories, libraries and universities. The plates here date to 1897, and depict the Orion Nebula and the Andromeda Galaxy.

Kayleigh MacDonald for Quanta Magazine

The collection includes a lantern slide collection used for teaching at the Harvard College Observatory. Drawers here are labeled by astronomical subjects: galaxies, nebulae, sun, planets, and the moon.

Kayleigh MacDonald for Quanta Magazine

This notion that the sky above is ever-changing led to the development of the newest large astronomical observatory. The Vera C. Rubin Observatory, high in the Chilean Andes, is beginning, in early 2026, a 10-year survey that will monitor all things that change and move, to reveal what astronomers call the time-domain universe. With data from previous telescopes — in both digital form and on glass — the project promises a brilliantly dynamic view. The value of the Rubin telescope lies not just in the reams of data it will create, but in its potential to connect today’s observations to more than a century of meticulous work. Within vast analog collections, many of which are at risk of disappearing, lie unexplored connections to today’s changing cosmos. Pulling together the modern and the historical could reveal a century-long movie of the sky above us.

The Only Constant Is Change

Far from light-polluted cities, one can see meteors racing across the night sky. Jupiter and Mars move faster than distant stars on that same nightly view. There are many, many other changes that human eyes aren’t sensitive enough to perceive.

“Everything in the cosmos is changing, nothing is static,” said Elizabeth Griffin, an astronomer at the Herzberg Astronomy and Astrophysics Research Center in Victoria, Canada. “The changes may be quite quick, quite periodic; may be explosive, may be sudden; or they may be terribly, terribly slow.”

All these variations comprise the dynamic universe that astronomers are working to reveal. Matthew Graham, an astronomer at the California Institute of Technology, separates the objects studied by time-domain astronomy into two groups: things that go bang, and things that fluctuate. The former are also known as transients, and they change quickly. These include the largest blasts in the universe, such as supernova explosions and gamma-ray bursts.

A photographic plate at the Neils Bohr Institute in Copenhagen captures the 1919 solar eclipse that was used to confirm Einstein’s theory of general relativity.

Niels Bohr Institute, Copenhagen University

The second category, variable objects, includes a wide variety of solo stars including the Cepheids, which fluctuate at a rate that reveals their intrinsic brightness (making them very valuable for measuring galactic distances). Systems of binary stars also fall into this category, as their observed brightness changes depending on where each star is, if one is passing in front of the other, and if one is siphoning material from the other.

The actively accreting black holes at the cores of galaxies also vary over time, “across all wavelengths and across all sorts of timescales,” said Graham, who studies this variability. These phenomena can tell astronomers and astrophysicists how physics operates within the most extreme environments. Any theoretical model that describes how those active galactic nuclei (AGNs) emit flares and produce other brightness variations must match the actual observations. That’s where time-domain astronomy comes in: Tracking the changes over a year, a decade, or a century gives astrophysicists specific markers for a model to match.

The full scope of celestial changes helps set boundaries on astronomers’ theoretical understanding: Exploding stars help cosmologists measure extragalactic distances, while variations in stellar brightness can reveal details of how stars evolve.

Astronomer Percival Lowell peers through the eyepiece of the telescope at the observatory that bears his name in Flagstaff, Arizona. It was there that, in 1930, Clyde Tombaugh found Pluto in a time series of photographs.

Historic Images/Alamy

To find these changes across time, astronomers use software to quickly scour data captured via modern telescopes and their powerful digital cameras. This has been the method of choice for some 40 years. But prior to the development of digital imaging detectors, astronomers used analog photography and glass plates. To find changes in the dynamic sky, human eyes carefully compared plate images captured at different times. Both collecting the data and discovering celestial variations took a great deal more time during the century of analog astrophotography.

New Eye on the Sky

The Vera C. Rubin Observatory will study nearly the entire sky visible from atop Cerro Pachón in Chile with its wide-angle camera system. Its aim is to reveal millions of new objects, both in the solar system and much farther beyond, by comparing images taken at different times. It is the same technique that Clyde Tombaugh used to discover Pluto at the Lowell Observatory in Arizona nearly 100 years ago. Tombaugh took thousands of hourlong photographs over the course of months and examined them visually to document the faint object on the edge of the solar system.

The Rubin Observatory, on the other hand, boasts a camera that is the largest ever built for any purpose. In one 15-second exposure, Rubin can reveal objects more than 2,500 times fainter than what the Pluto scope saw, and some 40 million times fainter than what unaided human eyes can see. Its Legacy Survey of Space and Time (LSST) project will tile the sky, repeatedly moving from each 3.5-degree-wide field (seven full moons side by side) to the next. Over the next 10 years, it will image the southern sky some 1,000 times in six color filters, from ultraviolet through visible light and into infrared, bringing ever-fainter objects into view. Rubin will show a constantly changing cosmos, filled with moving objects, stars that vary in brightness, and stellar explosions. Comparing the view of the sky before Rubin to after, Bonito likens it to music, “from a single note to chords.”

The Vera C. Rubin Observatory (top) is about to begin a 10-year survey that will image the entire southern sky 1,000 times. Early images from its telescope (bottom) are already leading to discoveries.

NSF–DOE Rubin Observatory/P.J. Assuncao Lago (top); NSF–DOE Vera C. Rubin Observatory

Once the survey has ramped up, the Rubin team estimates that the observatory will collect 20 terabytes of data every evening. Software automatically compares the just-captured images to previous ones of the same location. If anything has changed, it sends an alert within a minute. “It’s basically a small packet of information that [tells] you when, where, and by how much a source has changed its luminosity,” said Igor Andreoni, an astrophysicist at the University of North Carolina, Chapel Hill who co-chairs the Rubin variable stars and transients group.

Pulling together the modern and the historical could reveal a century-long movie of the sky above us.

There will be a lot of alerts; the Rubin team expects to send more than 20 billion of them over the 10-year survey. Anyone can receive them, but no one can expect to comb through them all. To manage the torrent, researchers are writing their own code to sort the alerts. For example, Andreoni is looking for anything that has shown brightness changes in at least three images in a row, and specifically cases of a star that’s stretched and changed by getting too close to a black hole — a tidal disruption event.

The Rubin Observatory will capture these changes over hours and days and eventually a decade. But many cosmic variations happen over much longer time spans, and there have certainly been countless transients in decades past that were unnoticed or undocumented in their time. If researchers want to access time-domain astronomy beyond the past few decades, the antique technology Tombaugh used remains vital. But there are no alerts for glass photographic plates.

The Glass Sky

Astronomical photography before around 1980 looked very different than it does in the digital age. Scientists such as Tombaugh imaged the sky on rectangular panes of glass coated on one side in a biochemical emulsion. As starlight passed through a telescope, it hit the emulsion on the plate. When scientists developed the plates, any spots where starlight hit the emulsion would be dark. The density of the darkness corresponded to brighter starlight.

This 1934 plate of Andromeda, captured at the Oak Ridge Observatory at Harvard, was so heavily marked up because it was used to count galaxies.

Plate MC27415. September 11, 1934. Harvard Plate Stacks. Center for Astrophysics | Harvard & Smithsonian, Cambridge, Mass.

Hudec, the Czech astrophysicist, who has visited more than 70 plate collections across the world, estimates that there could be 10 million plates still in existence. If all those plates were carefully digitized, they would amount to thousands of terabytes of data. They hold more than a century of astronomical observations. And though they are scattered around the world and unwieldy to work with, they’ve provided today’s researchers with more than 100 modern discoveries.

The collection at the Harvard-Smithsonian Center for Astrophysics is the largest and best-studied astronomical plate archive in the world, with some 550,000. The collection covers both the northern and southern skies, with observations from 1849 through 1992. And some 430,000 of these are now in digital and searchable form thanks to a 20-year digitization project led by Jonathan Grindlay, an astrophysicist at Harvard University. “It seemed like a very obvious thing to do,” he said.

The Digital Access to a Sky Century at Harvard, or DASCH, has been cited in close to 100 scientific papers and talks, for discoveries covering all areas of time-domain astronomy. And that was the point, Grindlay said: “to be able to do time-domain astrophysics in a way that’s never been done before.”

The Photographic Plate Archive at Harvard College Observatory in the 1890s. It would grow for another century.

Science History Images/Alamy

While most of Harvard’s plates are digitized, most plates in other archives are not. In addition, some astronomers who study those plates prefer to use the physical, tangible objects, so they can be sure that there are no artifacts from the scanning process. Time-domain differences can be subtle.

Rajka Jurdana-Šepić, an astrophysicist at the University of Rijeka in Croatia, always begins her plate searches with a set of 10 or more “wish-list” objects. While some astronomers, such as Hudec and Graham, might be looking for a few specific, active, variable supermassive black holes, others such as Jurdana-Šepić study bursting stars. The goal is to better understand the physical processes and mechanisms that cause brightness to change.

At plate stacks, such as the collection at the Asiago Astrophysical Observatory in Italy and the one at Harvard, Jurdana-Šepić pulls the plates she has targeted off the shelf. She carefully removes them from their paper jackets and places one plate at a time on a light table. Then comes the hard part: peering through a microscope to find her half-inch-wide field among thousands of black dots. Usually she can find the area within 20 minutes. Then she begins measuring. She prefers opera for this part: “Puccini, Verdi, or something in my ears, and thousands of plates on the table,” she said.

Her measurements involve comparing, by eye, her stellar target and bright stars of known magnitude in that same tiny field. “For example, seven grades from A, three grades from B,” she said. At the end of several weeks, she’ll have recorded a few hundred magnitude measurements across hundreds or even thousands of plates to build up a light curve for the object that spans decades.

René Hudec has visited more than 70 plate collections around the world, and estimates there could be 10 million surviving glass plates.

Courtesy of Rene Hudec

Bradley Schaefer, an astronomer at Louisiana State University, focuses on cataclysmic variable stars, objects that vary in brightness over time due to some type of major turmoil. His favorites are recurrent novas — binary systems in which a massive white dwarf siphons so much material from its partner that its surface becomes dense enough and hot enough to undergo nuclear fusion, resulting in a dramatic increase in brightness at least twice per century. More common novas work in a similar way, but their white dwarfs are smaller, meaning their bursts are much less frequent.

Scientists think there could be a connection between recurrent novas and Type Ia supernovas. These supernovas are critically important for measuring the expansion rate of the universe. Schaefer is testing the theory that recurrent novas could evolve into Type Ia supernovas.

By measuring their brightness over years, potentially over multiple fusion bursts, Schaefer could observe patterns and changes in these orbital systems. He’s been able to track the orbital periods of more than a dozen systems, and he has ruled out a few of them as potential precursors to a special type of supernova explosion.

For this work, “you need many decades’ worth of data,” Schaefer said. “Archival data [is] the only game in town.”

Historical Outbursts

If and when something in the cosmos goes bang, astronomers want to know its history. Older data lets them observe earlier blips, flares, or other activity, and “that then helps the interpretation, and maybe suggests a different model,” Graham said.

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The AGNs that Graham studies are active supermassive black holes and their surrounding accretion disks. These vary randomly, so describing their brightness changes is complex. While scientists have 60 years of targeted data and 5 million time series images of AGNs, he said, “we still really don’t understand the mechanisms by which they are variable.”

Graham is co-leader and project scientist at the Zwicky Transient Facility (ZTF), another sky survey looking for changing cosmic objects. ZTF uses the 48-inch Oschin Schmidt telescope at Mount Palomar in California. The telescope bridges the eras of observational astronomy. Today it boasts sophisticated digital detectors, but it was also used for foundational surveys in the days of glass photographic plates. Graham recently acquired 20 terabytes of historical data: the digitized versions of the plates captured during the Palomar Observatory Sky Survey (POSS), first conducted in the 1940s and 1950s, and its successor POSS-II, which spanned the 1980s and 1990s.

This won’t be the first time he has combined data from multiple surveys, although in the past he’s focused on putting together digital surveys only. Combining datasets from multiple telescopes and observations requires calibration to account for different optics, filters, and detectors.

A physical plate, or a scanned image of one, adds further complications, Griffin said. The photographic emulsion’s density — those dark marks — relates in a very specific way to the incoming starlight’s intensity.

But the astronomers who know how to read the plates can combine their measurements with ones from more recent surveys for a longer-term view. Schaefer uses data from multiple space telescopes, but they’re not suited to his purpose unless paired with ground-based datasets and the more historical forms of observation. “It’s just one point,” he said of the space telescope images, “where the story is told over the century.”

Race Against Time

Every telescope that astronomers used to document the sky during the century before 1980 used glass plate photography. The plates, if stored in stable conditions and not piled up with weight on top of them, can endure for centuries.

Unfortunately, some collections were misplaced when university departments switched buildings. Others have even been thrown into the garbage. Of the many that have survived, their condition can be a concern; some glass plates are cracked or little more than shards. In other cases, the emulsion is peeling due to humidity swings or growing mold from water exposure.

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Both Hudec and Griffin are strong proponents of the value in these historical artifacts. Both have also seen collections destroyed and data saved.

Griffin spoke of an experience at a collection overseas. She reached into a box filled with plate envelopes to pull out a plate with the spectrum of the bright star Arcturus. “The emulsion fell off,” she said. “Only the glass came up.” All the plates in the box were the same. They had apparently been moved too quickly from high humidity to low humidity.

Surviving plate collections can be a constant source of discovery. And with careful scanning, the data they hold can be invaluable, full of hidden surprises from a century of astronomical flares, bursts, and more. The Rubin Observatory will lead to many incredible discoveries; its early images already have. But the lifetimes of cosmic objects are long, and understanding today’s data relies on yesterday’s, in the form of stacks and stacks of glorious glass. That’s why, Griffin said, “it’s so desperately important to keep them.”

2026 Jan 30

- Once Thought To Support Neurons, Astrocytes Turn Out To Be in Charge

NEUROSCIENCE | VIEW ON WEBSITE

Once Thought To Support Neurons, Astrocytes Turn Out To Be in Charge

By Ingrid Wickelgren

January 30, 2026

New experiments reveal how astrocytes tune neuronal activity to modulate our mental and emotional states. The results suggest that neuron-only brain models, such as connectomes, leave out a crucial layer of regulation.

Waves of calcium, revealed by a fluorescent dye, ripple across a bed of astrocytes, showing that the cells actively signal. Warmer colors represent higher calcium concentration. The video was sped up from the original. Its resolution was enhanced using AI with the creator’s consent.

R. Douglas Fields/NHIH

Introduction

The human brain is a vast network of billions of neurons. By exchanging signals to depress or excite each other, they generate patterns that ripple across the brain up to 1,000 times per second. For more than a century, that dizzyingly complex neuronal code was thought to be the sole arbiter of perception, thought, emotion, and behavior, as well as related health conditions. If you wanted to understand the brain, you turned to the study of neurons: neuroscience.

But a recent body of work from several labs, published as a trio of papers in Science in 2025, provides the strongest evidence yet that a narrow focus on neurons is woefully insufficient for understanding how the brain works. The experiments, in mice, zebra fish, and fruit flies, reveal that the large brain cells called astrocytes serve as supervisors. Once viewed as mere support cells for neurons, astrocytes are now thought to help tune brain circuits and thereby control overall brain state or mood — say, our level of alertness, anxiousness, or apathy.

Astrocytes, which outnumber neurons in many brain regions, have complex and varied shapes, and sometimes tendrils, that can envelop hundreds of thousands or millions of synapses, the junctions where neurons exchange molecular signals. This anatomical arrangement perfectly positions astrocytes to affect information flow, though whether or how they alter activity at synapses has long been controversial, in part because the mechanisms of potential interactions weren’t fully understood. In revealing how astrocytes temper synaptic conversations, the new studies make astrocytes’ influence impossible to ignore.

“We live in the age of connectomics, where everyone loves to say [that] if you understand the connections [between neurons], we can understand how the brain works. That’s not true,” said Marc Freeman, the director of the Vollum Institute, an independent neuroscience research center at Oregon Health and Science University, who led one of the new studies. “You can get dramatic changes in firing patterns of neurons with zero changes in [neuronal] connectivity.”

Marc Freeman’s experiments on fruit fly astrocytes are leading the way in revealing how the cells can rapidly shift the brain’s state. Unfortunately, he said, 99% of neuroscientists don’t consider astrocytes in their studies.

Courtesy of Marc Freeman

Astrocytes do not engage in the rapid-fire signaling typical of neurons at synapses. Instead, they monitor and tune higher-level network activity, dialing it up or down to maintain or switch the brain’s overall state. This function, termed neuromodulation, may cause an animal’s brain to switch between dramatically different states, such as by gauging when an action is futile and prompting the animal to give up, one of the new papers shows.

Neuromodulation is necessary for keeping the brain’s activity level in a functional range, preventing it from either flatlining or erupting in seizures. “No neural circuit would work at all without continual fine-tuning by these things we call neuromodulators, [the molecules that mediate the adjustments],” said Stephen Smith, an emeritus professor of neuroscience at Stanford University who conducted pioneering experiments in astrocyte signaling in the late 1980s and early 1990s and was not involved in the new research.

For many years, that fine-tuning was thought to be conducted by neurons themselves. While previous work has implicated astrocytes in some cellular signaling, the latest experiments use “advanced techniques to really pinpoint and satisfy beyond a doubt that astrocytes are having a key role in neuromodulation in the brain,” said Douglas Fields, an emeritus neuroscientist at the National Institutes of Health who was not involved in the new research.

In that role, astrocytes could be major participants in sleep or psychiatric disorders that broadly disrupt the state of the brain. “We have to think about what this means for neuropsychiatric disease,” Freeman said.

A Star Is Born

Astrocytes are a type of glial cell, a class of non-neuronal nervous system cells that tile the brain, filling the space between neurons like packing peanuts. Greek for “glue,” the name “glia” reflects the mid-18th-century idea that the cells’ purpose was simply to hold the brain together.

By the 1950s, researchers knew that astrocytes did more than that. In experiments, the cells sucked up excess neurotransmitters, buffered potassium, and secreted substances that neurons require for energy. Like cellular alchemists, astrocytes seemed to be monitoring and adjusting the broth of the brain, keeping conditions favorable for neurons. But scientists considered them relatively passive regulators until the late 1980s, when Smith built a new microscope for his neuroscience lab at Yale University.

The neuroscientist Stephen Smith, pictured here with Banjo the Havanese dog, built a microscope in the late 1980s that prompted pioneering research into astrocyte signaling.

Lyn Flaim Healy

Smith’s novel digital video fluorescence microscope was designed to take movies of neuronal activity using fluorescent light. When a neuron fires, calcium rushes into the cell. So the researchers put fluorescent sensors into brain cells that glowed when they encountered calcium. The microscope could detect the light as it brightened and dimmed over space and time, revealing the cells’ firing patterns. “We had probably the most advanced, sensitive, coolest setup going,” Smith said.

One day in 1989, Smith’s graduate student Steve Finkbeiner (now a neurologist at the nonprofit Gladstone Institutes in San Francisco) was using the microscope to explore the potentially toxic effects of the neurotransmitter glutamate, the molecule most neurons in the brain use to communicate. Finkbeiner was not interested in astrocytes, but because they help keep neurons alive, he put them in his cell culture. Then he added glutamate.

“He’s all of a sudden yelling and screaming from his microscope setup: ‘Hey boss, come here! You’ve got to see this!’” Smith recalled. “They [the astrocytes] went completely nuts.” Fluorescence rippled across the bed of astrocytes in waves, hopping from one cell to the next. These calcium waves showed coordinated activity, as if the astrocytes were communicating with each other. And because the cells responded to glutamate, it was only logical that they would also respond to neurons. In their 1990 paper describing the experiment, the researchers boldly proposed that “networks of astrocytes may constitute a long-range signaling system within the brain.” Other teams soon showed that astrocytes in dishes, brain slices, and even anesthetized animals responded to various neurotransmitters.

This video, taken in 1989, shook up the field of neuroscience. Fluorescence reveals waves of calcium, hopping from one astrocyte to the next, in response to glutamate, the molecule many neurons use to communicate. Glutamate was added at 0 seconds.

Stephen J Smith

Many neuroscientists at the time likened astrocytes’ newfound properties to those of neurons, but in retrospect the differences seem glaring. For one thing, astrocytes occupy relatively massive territory: One astrocyte covers a large expanse of tissue, reaching as many as 2 million synapses in the human brain. Astrocytes work on longer timescales than neurons do. Their calcium waves spread over a period ranging from seconds to minutes — much longer than the milliseconds it takes for neurons to propagate signals down their axons and release neurotransmitters.

To study how this surprising new view of astrocytes related to behavior, research groups turned to animal models. Researchers tried to activate astrocytes in lab mice by bombarding them with sensory stimuli, such as by shining light in their eyes or touching their whiskers; they looked for a response through a cranial window under a fluorescent microscope. Sometimes the cells responded, sometimes they didn’t. Then, in 2013 and 2014, two independent research teams reported a sure-fire way to get astrocytes’ attention: They startled the mice by surprising them with a puff of air or by abruptly turning on a treadmill under their feet. The startle response is a largely unconscious defense mechanism and a sudden switch in brain state, found throughout the animal kingdom.

When vertebrate animals are startled, neurons in a brainstem region called the locus coeruleus release norepinephrine, a neuromodulator associated with arousal, along fibers that fan out across the brain. Instead of sending a specific message, as neurotransmitters do, neuromodulators dial brain activity up or down and change the brain’s overall state like a dial on a radio. The studies indicated that norepinephrine was the trigger for the astrocyte waves, implicating astrocytes in neuromodulation in some capacity.

A single rat astrocyte is splayed on a specialized nanowire structure. In its native environment, the cell would envelop hundreds of thousands of synapses, enabling it to monitor and adjust neuronal signaling.

Johns Hopkins University

Still, so much about astrocyte signaling remained mysterious. The cells were known to have norepinephrine receptors, but no one knew how the binding of norepinephrine led to the calcium waves. And there was still the question of what signal those waves sent to downstream neurons. Some researchers thought astrocytes produced their own “gliotransmitter” molecules that acted upon neurons, but others disputed that notion. At meetings, researchers engaged in loud, heated debates over how much — indeed, whether — astrocytes shape the flow of information in the brain.

A student in Freeman’s lab, Zhiguo Ma, then at the University of Massachusetts Medical School, sought to settle the issue in a fruit fly brain. “Please don’t,” Freeman recalled warning him. “It’s such a mess.” Ma forged ahead. He replicated the startle response in fruit flies by suddenly flipping them upside down. Using the delicate tools of molecular biology, he traced the chemical relay: The fly versions of norepinephrine activated astrocytes by opening a channel in the cell membrane, causing the release of a gliotransmitter — likely adenosine — that squelched neuronal signaling. It was critical to characterize such neuron–astrocyte interactions, “as they would represent a potentially widespread mechanism for controlling brain function,” Freeman’s team wrote in Nature in 2016.

To some, the experiment provided the first proof that astrocytes are integral parts of neural circuits. But one fruit fly paper was not enough to sway skeptics. Nearly a decade later, eerily parallel findings in a vertebrate would tip the scales.

When To Give Up

Although we don’t often think of it this way, the act of giving up reflects a sudden shift in brain activity. It represents a change in mental state from hope to hopelessness that, like being startled, has profound effects on behavior. Researchers led by the neuroscientist Misha Ahrens were studying what made zebra fish larvae give up when they made a discovery about how astrocytes mediate such a sudden change in mood.

Misha Ahrens showed that astrocytes modulate the switch in brain state from hope to hopelessness — specifically, to the state of giving up after engaging in a futile effort.

© HHMI, photo by Toby Hayman

What does it look like when a zebra fish gives up? In the wild, if a zebra fish wants to stay put in flowing water, it will swim against the current. In the lab at the Howard Hughes Medical Institute’s Janelia Research Campus in Virginia, Ahrens’ team used virtual reality to create a simulation of a current in the zebra fish tank, so that a fish would think it was slipping backward no matter how furiously it swam. The fish would swim harder at first, but after about 20 seconds, it would typically give up. A little while later, it would try again.

All the while, the researchers monitored neurons and astrocytes in the zebra fish’s brain using advanced whole-brain imaging techniques. As the fish fruitlessly fought the current, neurons that release norepinephrine fired; in response, calcium built up in astrocytes. The buildup paralleled the number of attempts the fish made to fight the current, as if the astrocytes were keeping track — until at some point they issued a stop signal, and the zebra fish gave up.

When Ahrens’ team disabled the astrocytes using a laser, the fish never stopped swimming. And if the astrocytes were artificially activated, the fish stopped right away. “It was the first time that it was shown that astrocytes had a role in behavioral state switching,” Ahrens said.

In an ensuing Science paper, published in 2025, the researchers revealed how astrocytes caused these changes in behavior. Using fluorescent sensors for various molecules, they found that when enough calcium builds up in astrocytes, they release the energy molecule ATP, short for adenosine triphosphate. Outside the cell, the ATP is converted into adenosine, which acts on neurons — in this case, by exciting neurons that inhibit swimming and suppressing swim neurons. This sequence echoes what Ma and Freeman observed in the fruit fly.

For a long time, astrocytes (seen here in a fluorescence light micrograph of human tissue) were considered mere support and scaffolding for all-important neurons. The new experiments reveal in great detail the cells’ influence over neuronal signaling in the brain.

Science Source

The same molecular chain of events also showed up in the mouse brain, according to research led by Thomas Papouin at Washington University School of Medicine and published in the same Science issue. Papouin’s team was studying changes at synapses that alter communication between neurons, a form of neuroplasticity that underlies ongoing shifts in thought and behavior. Norepinephrine was thought to produce these shifts by acting directly on neurons. But to Papouin’s surprise, norepinephrine’s effects were apparent even when its receptors on neurons had been removed. The process depended solely on astrocytes.

“We did expect that, in large part, the effect of norepinephrine on synapses would be mediated by astrocytes,” Papouin said. “But we did not expect all of it to be!”

The finding of parallel molecular pathways in such distinct species as fruit flies, zebra fish, and mice points to “an evolutionarily conserved way in which astrocytes can profoundly affect neural circuits,” Freeman said.

The results suggest a gaping hole in previous theories of neuromodulation. “In the past, neuroscientists studied neuromodulators and knew they were important in regulating neural circuit function, but none of their thinking, none of their diagrams, none of their models had anything in them other than neurons,” Fields said. “Now we see that they missed a big part of the story.”

Thomas Papouin discovered that a type of neuroplasticity, which underlies shifts in thought and behavior, is mediated entirely by astrocytes, neurons not required.

Courtesy of Thomas Papouin

The fruit fly research by Freeman’s team indicates the next steps in research in vertebrates; in the same Science issue the group reported that norepinephrine changes how astrocytes respond to input from neurons. Freeman’s postdoc Kevin Guttenplan doused a dissected fly brain with the fly versions of norepinephrine. “All of a sudden, the astrocytes went from responding to none of the other neurotransmitters to responding to all of them,” Guttenplan said. Norepinephrine and its analogues in the fly seem to enable astrocytes to “hear” neurons’ molecular messages and then modulate their activity.

This dynamic helps explain how astrocytes can rapidly switch the brain from one state to another. “If there’s low norepinephrine, which would mean low arousal, astrocytes don’t listen much at all to other synapses,” Freeman said. “But as soon as you arouse the animal, and there is norepinephrine around, now astrocytes can listen to every synapse, and they can turn around and change how neurons fire in response to that.”

The results reveal a new complexity to how the brain processes information, Guttenplan said. “On top of the already complicated connectome [network of neurons], you have this whole other layer of regulation.”

Mood Meter

Although the details of astrocytes’ signaling mechanism are coming into focus, they still lag behind what is known about neurotransmission. “It’s an exciting time,” said Alex Chen, a student at Harvard Medical School and first author of the zebra fish paper. “For the astrocyte field, at least conceptually, we are not very far ahead of where people were for neurons” at the onset of modern neuroscience in the 1950s.

Working in zebra fish, Alex Chen observed the first known instance of astrocytes mediating a rapid transition between brain states.

Courtesy of Harvard_MCB

Meanwhile, researchers are homing in on critical brain functions that astrocytes mediate. Some research suggests that astrocytes’ ability to accumulate information over time (as happened with the zebra fish’s swim attempts) extends to the sleep-wake cycle. Astrocytes appear to keep track of people’s increasing sleep debt throughout the day, likely through a buildup of calcium, and secrete sleep-inducing molecules that alter brain activity.

“We see astrocytes implicated in behaviors associated with big state transitions — like sleep, hunger, arousal — where you need multiple types of circuits over a very large area to get turned on and off, especially on a slower timescale,” Guttenplan said.

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Those behaviors may reflect mental health conditions. Last year, researchers revealed a neuron-astrocyte brain circuit that was triggered by stress and produced behavior resembling depression in mice. It’s possible that some mental health disorders are disorders of astrocyte signaling. People’s moods change relatively slowly, Ahrens said, in a process partly driven by neuromodulators. Astrocytes’ role in neuromodulation points to their promise as a drug target.

“Neuroscience has only cared about neurons for a century now, and we don’t yet have a cure for a single brain disorder,” Papouin said. The way to change that, he said, is to accept the existence and influence of non-neuronal cells such as astrocytes, and to include them in models and experiments.

Most neuroscientists haven’t received that memo, Freeman said. “Ninety-nine percent of people who are out there doing experiments on circuits don’t even think about what the astrocyte might be doing. And it could have really profound effects on how that circuit functions.”

Table of Contents

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2026 Jan 30

Once Thought To Support Neurons, Astrocytes Turn Out To Be in Charge