The project is led by neuroscientist Davi Bock of UVM’s Robert Larner, M.D. College of Medicine, with co-principal investigators Doug Taatjes, director of UVM’s Microscopy Imaging Center, and Mark Ellisman at the University of California San Diego. Together the partners will integrate three complementary capabilities: state-of-the-art high-throughput electron microscopy at the Larner College of Medicine, advanced sample preparation and multimodal imaging expertise at UC San Diego’s National Center for Microscopy and Imaging Research, and large-scale data management at the San Diego Supercomputer Center. The ambition is to design a complete pipeline that carries a preserved biological sample all the way to analyzed, explorable scientific data.

Bock has spent two decades helping to build the science of connectomics, the mapping of nervous systems down to individual neurons and the synapses that link them. His laboratory acquired the electron microscopy images that an international collaboration used to produce the first complete wiring map of an adult fruit fly brain, a landmark achievement published in Nature in 2024. That accomplishment demanded enormous gains in imaging speed and computational power, because the hard problem has never been magnification alone. Electron microscopes already resolve astonishingly small structures; the challenge lies in assembling and aligning millions of images to follow the branching paths of neurons through substantial volumes of tissue, and in converting those image stacks into usable maps.

Having built that capacity, Bock argues the moment has come to widen both its reach and its range. “We built a searchlight to look at one type of problem, and now, with a purpose-built facility for advanced work like this, we will use it to look at many, many different problems,” Bock says. “This new national center will be globally unique in pointing high throughput volume microscopes at the full spectrum of biology. Right now, that doesn’t exist anywhere else.” The observatory’s name is deliberate: just as an astronomical observatory gives many researchers shared access to the heavens, NCUVO will give biologists a shared window onto structures far too small for ordinary light microscopes, the fine details known as ultrastructure that fill the interiors of cells and the spaces between them.

The technical workflow will unfold across three institutions. Tissue preserved in resin will be shaved into thousands of ultrathin slices, each captured at nanometer-scale resolution by a high-throughput electron microscope housed in UVM’s Firestone Medical Research Building. Computers at the San Diego Supercomputer Center will then assemble the slices into coherent three-dimensional volumes. Artificial intelligence will shoulder much of the interpretive burden, identifying cell boundaries, tracing individual neurons through dense tissue, and distinguishing organelles and other features within cells. Researchers will retain tools to verify the automated reconstructions, label structures of interest, and interrogate the data with their own questions. At the San Diego microscopy center, selected regions can be re-imaged at even higher resolution, connecting tiny details to the larger anatomical context mapped in Vermont.

The data volumes will be staggering, measured in petabytes, or millions of gigabytes, per dataset. UVM’s Vermont Advanced Computing Center will support storage and computation, while Ilkay Altintas, chief data science officer at the San Diego Supercomputer Center and principal investigator of the NSF-funded National Data Platform, will guide data curation, drawing on her team’s expertise in organizing, processing and sharing massive scientific datasets and linking the observatory to national cyberinfrastructure. “You can’t just drop a petabyte of data on someone and say mission accomplished,” Bock says. “You have to give them tools to analyze the data.” Ellisman frames the design philosophy the same way: the goal is to transform the latest sample preparation, multimodal imaging and computational technologies into a shared national capability rather than a specialty reserved for a few, empowering scientists to compare many samples at levels of detail ranging from whole tissues down to individual synapses.

Four pilot projects will stress-test the pipeline against different organisms, sample sizes and scientific demands. The first will examine how connections in the mouse hippocampus, a brain region essential for memory, change after learning, in collaboration with Anton Maximov’s laboratory at Scripps Research and Ellisman’s team at UC San Diego; researchers will trace circuits involving neurons flagged as active during a learning task. The second will compare retinal circuitry across species to probe how visual systems adapt to different environments, pairing David Berson’s laboratory at Brown University with Tom Baden’s laboratory at the University of Sussex. Bock points to sharks navigating murky versus clear water as the kind of comparative question these cross-species maps could finally answer. The work will generate datasets large enough, and samples numerous enough, for statistical comparison of how brain structures differ with learning, development or disease.

The third and fourth pilots reach beyond classic neuroscience entirely. Parasitologist Bruno Martorelli Di Genova’s laboratory at UVM will join Christopher Moore’s laboratory at Brown University to study how the parasite Toxoplasma gondii crosses the blood-brain barrier to cause toxoplasmosis, a disease spread through contaminated food and cat litter. “This is an amazing organism because it causes rodents to lose their fear of cat urine,” Bock explains. “It changes the behavior of cat prey to be less frightened of cats. And there’s some evidence that infected humans also are less risk averse.” The fourth pilot will image protists, single-celled organisms with surprisingly elaborate internal architecture, in partnership with Omaya Dudin’s laboratory at the University of Geneva and Gautam Dey’s laboratory at the European Molecular Biology Laboratory in Heidelberg, both recipients of the 2026 EMBO Gold Medal for their work in evolutionary cell biology.

Education is built into the observatory’s mission as well. UVM neuroscience assistant professor Molly Stanley will coordinate a collaboration with Connectomes for Undergraduate Neuroscience Education and Learning, whose leadership includes Andrew Bellemer at Appalachian State University, Kenneth Colodner at Mount Holyoke College and Divya Sitaraman at California State University, East Bay, to develop undergraduate teaching materials from the pilot datasets. The project also plans virtual-reality materials for high school classrooms built on the syGlass platform, along with training opportunities for UVM biomedical engineering master’s students, ensuring that the resource cultivates the next generation of researchers even before the full facility exists.

The five-year effort will deliver a tested design, pilot datasets and an implementation plan for a subsequent national facility, with approximately $15.68 million supporting Bock’s work at UVM and $3.32 million funding UC San Diego’s contributions, including the supercomputer center. The research is fundamentally curiosity-driven basic biology, but the team expects it could illuminate the structural changes that accompany neurodegenerative disease and neural repair, and even inform new approaches to artificial intelligence. “You can’t predict what we’ll find. And that’s the point,” Bock says, invoking Antony van Leeuwenhoek’s first glimpses of microscopic life, observations that seemed abstract at the time but ultimately led to the germ theory of disease. “Anything that you can think of, any tissue, any species, any organism we can look at, we should,” he says. “That’s democratization, broadening the spectrum of questions that can be asked and answered by our national research community.”

Subject of Research: A National Science Foundation-funded connectomics and ultrastructure imaging observatory

Article Title: University of Vermont launches $19 million national research center to unlock secrets of neurobiology

Article References: University of Vermont launches $19 million national research center to unlock secrets of neurobiology. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: connectomics, electron microscopy, neuroscience, National Science Foundation, University of Vermont, three-dimensional imaging, artificial intelligence, ultrastructure, petabyte data, Toxoplasma gondii, brain mapping, scientific observatory

The funding comes from the Advanced Research Projects Agency for Health, or ARPA-H, a federal agency within the U.S. Department of Health and Human Services that backs pioneering, high-impact medical research. The anticipated five-year initiative, known as ReSCUED—Reprogramming Severe Critical Illness Using Extensible Digital Twins—is part of ARPA-H’s Critical Illness Immunological Reprogramming and Control Point Learning Engine, or CIRCLE, program. If the technology performs as hoped, researchers believe it could shorten intensive care unit stays by at least 25 percent. The stakes are enormous: each year, 4.6 million Americans are treated in ICUs, at a cost of up to $70 billion annually.

The project targets one of medicine’s most stubborn problems: the disordered immune response that drives the defining diseases of critical care, including severe trauma, burns, and sepsis. Sepsis, a life-threatening reaction to infection, occurs when the body’s immune system triggers widespread toxic inflammation that damages healthy tissues and can rapidly progress to organ failure. Treating it demands urgent ICU care with intravenous fluids, blood pressure support, antibiotics, and often machines such as ventilators or dialysis to prop up failing organs. Yet even with these interventions, many patients never recover their immune balance.

“Sepsis is a huge health care problem, and one that will only get bigger as the population gets older and we get better at keeping people alive,” said An, the project’s principal investigator and a professor in the Department of Surgery at UVM’s Larner College of Medicine. He explained that before intensive care units existed, patients simply died, but modern organ support technologies now keep people alive longer even when their bodies cannot escape what he calls the immune dysfunction hole. “The multidimensional dynamics of immune dysfunction is too complex for a person, even an expert, to comprehend. But we can train a computational model to do that,” An said.

The concept of a digital twin is borrowed from engineering. A digital twin is a mathematical model of a real-world object, process, or system that uses real-time data to predict performance, identify problems, and guide decision-making. The aerospace industry offers a familiar illustration: sensors on an aircraft engine continuously stream performance information to a computational model that forecasts how that specific engine will behave over time, allowing operators to optimize maintenance and operations. Health care researchers now envision the same principle applied to human physiology, with personalized models forecasting how a disease will progress in an individual patient as molecular and clinical data are continuously added and analyzed.

Under the ReSCUED approach, the process begins with intensive data collection. A critically ill patient’s blood will be drawn every six hours, with measurements taken of the critical cells, proteins, and molecules circulating in the sample. Physiological monitoring adds another stream of information. These data feed into the patient’s digital twin, which generates a real-time picture of the immune response and continuously refines its forecast as the patient’s condition evolves. The model then becomes the testing ground: clinicians can evaluate potential treatment strategies using FDA-approved medications virtually, before administering anything to the actual patient.

The ultimate product is an AI-based “virtual consultant” designed to analyze highly complex biological interactions that exceed human cognitive capacity. The system would suggest intervention strategies tailored to a specific patient, simulate their effects, and present the results to the treating physician. “The complexity of critical illness exceeds what any individual can interpret in real time. Artificial intelligence gives us a way to evaluate those complex biological dynamics and determine how existing treatments might be used more effectively for a particular patient,” An said. “Our goal is to give clinicians a more precise understanding of what is happening within an individual patient and provide information that could help them select the right treatment at the right time.”

The initiative is deliberately multidisciplinary and multi-institutional. UVM’s Larner College of Medicine will serve as the lead institution, with An’s team focusing on developing the computational models and the digital twin platform. Patient data will be collected at three clinical sites: Wake Forest University School of Medicine and the University of Alabama at Birmingham Heersking School of Medicine, both participating through the Quantum Leap Healthcare Collaborative, and Washington University School of Medicine. Two private-sector partners bring specialized technologies. The DNA Medicine Institute of Cambridge, Massachusetts, will contribute a bedside molecular testing platform originally developed for the International Space Station, while InflammaSense, a California-based medical technology company, will provide wearable monitors that measure activity in the vagus nerve, a key regulator of inflammation.

The project is structured as a milestone-based initiative lasting up to five years. During the first three years, researchers will develop and validate the digital twin and demonstrate its ability to predict patient outcomes computationally. Only if those milestones are achieved will subsequent phases test the technology in additional experimental settings and, eventually, in clinical trials involving critically ill patients. By the end of the project, the team hopes to deliver an integrated platform combining physiology, molecular testing, computational modeling, and artificial intelligence to support clinical decision-making, with the goals of improving treatment selection, reducing ICU length of stay, and improving outcomes. The work is also expected to create new computational modeling jobs at Larner and strengthen Vermont’s growing biotechnology and AI research ecosystem.

Beyond the ICU, the implications could reach far wider. If successful, the technology developed through ReSCUED could establish a foundation for treating a broad range of diseases in which patients respond differently to the same therapies—a central challenge of modern medicine. “This is the kind of work that can redefine what is possible in medicine. By combining artificial intelligence with deep expertise in biology and clinical care, UVM researchers are helping build a future in which treatment can be informed by the unique biology of each patient,” said Kirk Dombrowski, UVM’s vice president for research and economic development. University leadership has framed the award as a historic milestone, with President Marlene Tromp noting that the level of federal investment reflects the potentially life-saving impact of digital twins in medical diagnosis and treatment. The research is funded in part by ARPA-H, and the views expressed by the researchers do not necessarily represent the official policies of the United States Government.

Subject of Research: AI-powered digital twins for personalized treatment of critically ill ICU patients

Article Title: Your AI twin could save your life: UVM awarded up to $38 million to build “digital twins” for treating critically ill patients

Article References: Your AI twin could save your life: UVM awarded up to $38 million to build “digital twins” for treating critically ill patients. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: digital twins, artificial intelligence, critical care, sepsis, ICU, ARPA-H, immune response, computational modeling, precision medicine, University of Vermont, clinical decision support, biotechnology

The central problem the observatory addresses is one of access. The most advanced volume imaging technologies, capable of resolving cellular and subcellular architecture in three dimensions, are today concentrated in a handful of specialized laboratories around the world. Researchers who lack proximity to such facilities often cannot pursue questions that depend on high-resolution structural data, no matter how important those questions may be. NCUVO is designed to change that equation by integrating advanced high-performance electron microscopy, artificial intelligence, advanced computing and powerful data networks into a single coordinated system. The goal is a pipeline that takes a biological sample from preparation through imaging, processing and analysis, delivering interpretable datasets to investigators who study biology, medicine and the life sciences.

Leadership of the project is split between the two institutions. Neuroscientist Davi Bock, PhD, at the University of Vermont will lead and coordinate the effort, with UC San Diego Distinguished Professor of Neurosciences and Bioengineering Mark Ellisman, PhD, serving as co-principal investigator alongside Doug Taatjes, PhD, director of UVM’s Microscopy Imaging Center. Of the $19 million in new funding, approximately $15.68 million will directly support the procurement of advanced tools and the recruitment of new staff at UVM, while $3.32 million will fund the use of shared advanced imaging and high-performance computing resources at UC San Diego. The division of labor reflects the complementary strengths each campus brings to the observatory design.

Coordinated from UVM’s Larner College of Medicine, the effort will establish state-of-the-art high-throughput electron microscopy in Vermont, while UC San Diego’s National Center for Microscopy and Imaging Research, known as NCMIR, will contribute deep expertise in sample preparation and complementary advanced microscopy techniques. Data management and processing will be handled in conjunction with the San Diego Supercomputer Center, or SDSC, at UC San Diego’s Halıcıoğlu School of Data Science and Computing. Together, these three nodes form the skeleton of the observatory: instruments that image, expertise that prepares and contextualizes samples, and cyberinfrastructure that stores, curates and distributes the resulting data.

Ellisman, who directs NCMIR, framed the initiative as a deliberate shift in how the nation treats its most powerful imaging technologies. In his view, the observatory’s design is about transforming the latest sample preparation, multimodal imaging and computational technologies into a shared national capability rather than a specialty available only to a few. At NCMIR, his team will help provide the means to turn raw microscopy image data into something that can be interpreted to advance understanding of biological processes. By combining those capabilities with UVM’s new imaging instruments and SDSC’s resources for advanced cyberinfrastructure, the project aims to empower scientists around the country to complete studies that compare many samples at different levels of detail, a task that has historically been impractical outside the largest research groups.

The data challenge is enormous. Once the observatory is established, it will generate millions of gigabytes of imagery, volumes that dwarf what most laboratories can organize and analyze on their own. Guiding the project’s data curation efforts is Ilkay Altintas, PhD, chief data science officer at the San Diego Supercomputer Center and principal investigator of the NSF-funded National Data Platform. Her team will bring expertise in organizing, processing and sharing large scientific datasets, connecting the observatory’s images with NSF-supported national computing resources so that the data do not simply accumulate but become actively usable by the research community.

Altintas emphasized that the infrastructure is intended to open new windows into how living systems process information, making advanced biological imaging more accessible than ever before. Drawing on the expertise of the SDSC and the capabilities of the National Data Platform, UC San Diego will help the imaging community design and validate scalable, reproducible workflows that connect imaging data with the computing power needed to turn remarkable images into new discoveries. Reproducibility is a recurring theme in the project’s design: by standardizing how samples are prepared, imaged and processed, the observatory hopes to produce datasets that other scientists can trust, reuse and build upon.

To test whether the system can handle the diversity of real-world research, four pilot projects will exercise the observatory across different organisms, sample sizes and scientific needs. One will examine how connections in the mouse hippocampus, a brain region critical for memory, change following learning. Working with Ellisman’s team at UC San Diego and Anton Maximov’s laboratory at The Scripps Research Institute, scientists will trace circuits involving neurons and their synapses marked as active in establishing a new memory, effectively watching the structural footprint of learning take shape. Another pilot will compare retinal circuitry across species to investigate how visual systems are suited to different environments; Bock points to sharks navigating murky versus clear water as the kind of comparative question such data could address. The third and fourth projects focus on single-celled organisms, including a study of how the parasite Toxoplasma gondii crosses the blood-brain barrier to cause toxoplasmosis, a parasitic disease spread through contaminated food and cat litter.

The five-year effort is structured to culminate in a tested design, pilot datasets and an implementation plan for operating the infrastructure as a facility for the nation, a true national observatory in the tradition of shared astronomical resources but pointed inward at the machinery of life itself. Education is also a key component, with undergraduate teaching materials and training opportunities planned around the pilot data, ensuring that these new scientific resources are not merely available but that scientists know how to use them. Bock argues that the scale of the undertaking is precisely the point: there is much about the natural world that remains incompletely understood, and unlocking those secrets requires research efforts occurring at a far greater scale than any individual laboratory can manage alone.

The potential payoff extends well beyond the pilot questions. While the initial projects are expected to produce fundamental breakthroughs in basic biology, the discoveries that follow could reshape understanding of neurodegenerative diseases and, eventually, help scientists discover tomorrow’s medicines. Bock is candid about the uncertainty at the heart of the enterprise, noting that no one can predict what the observatory will find, and that unpredicted discoveries are exactly why the country has historically invested in basic research. If the NCUVO succeeds, the smallest structures of life, from the synaptic connections of a learning mouse to the invasive machinery of a single-celled parasite, will become visible to a far broader community of scientists than ever before, and the next generation of biological insight may come from anywhere in the country rather than from a privileged few institutions.

Subject of Research: Construction of a National Connectomics and Ultrastructure Volume Observatory for high-resolution 3D biological imaging

Article Title: UC San Diego and University of Vermont share $19 million from NSF to build National Biological Observatory

Article References: UC San Diego and University of Vermont share $19 million from NSF to build National Biological Observatory. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: National Science Foundation, connectomics, electron microscopy, UC San Diego, University of Vermont, NCUVO, neuroscience, San Diego Supercomputer Center, biological imaging, data curation, Toxoplasma gondii, hippocampus