When it comes to brain function, neurons get a lot of the glory. But healthy brains depend on the cooperation of many kinds of cells. The most abundant of the brain’s non-neuronal cells are astrocytes, star-shaped cells with a lot of responsibilities. Astrocytes help shape neural circuits, participate in information processing, and provide nutrient and metabolic support to neurons. Individual cells can take on new roles throughout their lifetimes, and at any given time, the astrocytes in one part of the brain will look and behave differently than the astrocytes somewhere else.

After an extensive analysis by researchers at MIT, neuroscientists now have an atlas detailing astrocytes’ dynamic diversity. Its maps depict the regional specialization of astrocytes across the brains of both mice and marmosets — two powerful models for neuroscience research — and show how their populations shift as brains develop, mature, and age. 

The open-access study, reported in the Nov. 20 issue of the journal Neuron, was led by Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences at MIT. This work was supported by the Hock E. Tan and K. Lisa Yang Center for Autism Research, part of the Yang Tan Collective at MIT, and the National Institutes of Health’s BRAIN Initiative.

“It’s really important for us to pay attention to non-neuronal cells’ role in health and disease,” says Feng, who is also the associate director of the McGovern Institute for Brain Research and the director of the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT. And indeed, these cells — once seen as mere supporting players — have gained more of the spotlight in recent years. Astrocytes are known to play vital roles in the brain’s development and function, and their dysfunction seems to contribute to many psychiatric disorders and neurodegenerative diseases. “But compared to neurons, we know a lot less — especially during development,” Feng adds.

Probing the unknown

Feng and Margaret Schroeder, a former graduate student in his lab, thought it was important to understand astrocyte diversity across three axes: space, time, and species. They knew from earlier work in the lab, done in collaboration with Steve McCarroll’s lab at Harvard University and led by Fenna Krienen in his group, that in adult animals, different parts of the brain have distinctive sets of astrocytes.

“The natural question was, how early in development do we think this regional patterning of astrocytes starts?” Schroeder says.

To find out, she and her colleagues collected brain cells from mice and marmosets at six stages of life, spanning embryonic development to old age. For each animal, they sampled cells from four different brain regions: the prefrontal cortex, the motor cortex, the striatum, and the thalamus.

Then, working with Krienen, who is now an assistant professor at Princeton University, they analyzed the molecular contents of those cells, creating a profile of genetic activity for each one. That profile was based on the mRNA copies of genes found inside the cell, which are known collectively as the cell’s transcriptome. Determining which genes a cell is using, and how active those genes are, gives researchers insight into a cell’s function and is one way of defining its identity.

Dynamic diversity

After assessing the transcriptomes of about 1.4 million brain cells, the group focused in on the astrocytes, analyzing and comparing their patterns of gene expression. At every life stage, from before birth to old age, the team found regional specialization: astrocytes from different brain regions had similar patterns of gene expression, which were distinct from those of astrocytes in other brain regions.

This regional specialization was also apparent in the distinct shapes of astrocytes in different parts of the brain, which the team was able to see with expansion microscopy, a high-resolution imaging method developed by McGovern colleague Edward Boyden that reveals fine cellular features.

Notably, the astrocytes in each region changed as animals matured. “When we looked at our late embryonic time point, the astrocytes were already regionally patterned. But when we compare that to the adult profiles, they had completely shifted again,” Schroeder says. “So there’s something happening over postnatal development.” The most dramatic changes the team detected occurred between birth and early adolescence, a period during which brains rapidly rewire as animals begin to interact with the world and learn from their experiences.

Feng and Schroeder suspect that the changes they observed may be driven by the neural circuits that are sculpted and refined as the brain matures. “What we think they’re doing is kind of adapting to their local neuronal niche,” Schroeder says. “The types of genes that they are up-regulating and changing during development points to their interaction with neurons.” Feng adds that astrocytes may change their genetic programs in response to nearby neurons, or alternatively, they might help direct the development or function of local circuits as they adopt identities best suited to support particular neurons.

Both mouse and marmoset brains exhibited regional specialization of astrocytes and changes in those populations over time. But when the researchers looked at the specific genes whose activity defined various astrocyte populations, the data from the two species diverged. Schroeder calls this a note of caution for scientists who study astrocytes in animal models, and adds that the new atlas will help researchers assess the potential relevance of findings across species.

Beyond astrocytes

With a new understanding of astrocyte diversity, Feng says his team will pay close attention to how these cells are impacted by the disease-related genes they study and how those effects change during development. He also notes that the gene expression data in the atlas can be used to predict interactions between astrocytes and neurons. “This will really guide future experiments: how these cells’ interactions can shift with changes in the neurons or changes in the astrocytes,” he says.

The Feng lab is eager for other researchers to take advantage of the massive amounts of data they generated as they produced their atlas. Schroeder points out that the team analyzed the transcriptomes of all kinds of cells in the brain regions they studied, not just astrocytes. They are sharing their findings so researchers can use them to understand when and where specific genes are used in the brain, or dig in more deeply to further to explore the brain’s cellular diversity.

Two current MIT affiliates and seven additional alumni are among those named to the 2025 cohort of AI2050 Fellows.  

Zongyi Li, a postdoc in the MIT Computer Science and Artificial Intelligence Lab, and Tess Smidt ’12, an associate professor of electrical engineering and computer science (EECS), were both named as AI2050 Early Career Fellows. 

Seven additional MIT alumni were also honored. AI2050 Early Career Fellows include Brian Hie SM '19, PhD '21; Natasha Mary Jaques PhD '20; Martin Anton Schrimpf PhD '22; Lindsey Raymond SM '19, PhD '24, who will join the MIT faculty in EECS, the Department of Economics, and the MIT Schwarzman College of Computing in 2026; and Ellen Dee Zhong PhD ’22. AI2050 Senior Fellows include Surya Ganguli ’98, MNG ’98; and Luke Zettlemoyer SM ’03, PhD ’09. 

AI2050 Fellows are announced annually by Schmidt Sciences, a nonprofit organization founded in 2024 by Eric and Wendy Schmidt that works to accelerate scientific knowledge and breakthroughs with the most promising, advanced tools to support a thriving planet. The organization prioritizes research in areas poised for impact including AI and advanced computing, astrophysics, biosciences, climate, and space — as well as supporting researchers in a variety of disciplines through its science systems program. 

Li is postdoc in CSAIL working with associate professor of EECS Kaiming He. Li's research focuses on developing neural operator methods to accelerate scientific computing. He received his PhD in computing and mathematical sciences from Caltech, where he was advised by Anima Anandkumar and Andrew Stuart. He holds undergraduate degrees in computer science and mathematics from Washington University in St. Louis. 

Li's work has been supported by a Kortschak Scholarship, PIMCO Fellowship, Amazon AI4Science Fellowship, Nvidia Fellowship, and MIT-Novo Nordisk AI Fellowship. He has also completed three summer internships at Nvidia. Li will join the NYU Courant Institute of Mathematical Sciences as an assistant professor of mathematics and data science in fall 2026.

Smidt, associate professor of electrical engineering and computer science (EECS), is the principal investigator of the Atomic Architects group at the Research Laboratory of Electronics (RLE), where she works at the intersection of physics, geometry, and machine learning to design algorithms that aid in the understanding of physical systems under physical and geometric constraints, with applications to the design both of new materials and new molecules. She has a particular focus on symmetries present in 3D physical systems, such as rotation, translation, and reflection.

Smidt earned her BS in physics from MIT in 2012 and her PhD in physics from the University of California at Berkeley in 2018. Prior to joining the MIT EECS faculty in 2021, she was the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory, and a software engineering intern on the Google Accelerated Sciences team, where she developed Euclidean symmetry equivariant neural networks that naturally handle 3D geometry and geometric tensor data. Besides the AI2050 fellowship, she has received an Air Force Office of Scientific Research Young Investigator Program award, the EECS Outstanding Educator Award, and a Transformative Research Fund award.

Conceived and co-chaired by Eric Schmidt and James Manyika, AI2050 is a philanthropic initiative aimed at helping to solve hard problems in AI. Within their research, each fellow will contend with the central motivating question of AI2050: “It’s 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome?”