Within every blood sample lies a hidden minority — rare cells that may quietly determine whether a patient recovers or declines, yet remain invisible to the tools we rely on most. At the University of Virginia, engineer Nathan Swami has received a four-year, $1.1 million NIH grant to build the instruments and intelligence needed to find and interrogate these elusive cells. His work sits at the intersection of microengineering, artificial intelligence, and medicine, pursuing a question as old as diagnosis itself: why do some patients respond to treatment while others do not? The answer, he beli
UVA Engineer Wins $1.1M NIH Grant to Develop Tools for Studying Rare Circulating Cells
The rare cells that matter most are often the hardest to find
Mark
Why does it matter that we can measure physical properties instead of just chemical ones?
Mimi
Because cells aren't static. They change shape, they become stiffer or more flexible, they move differently depending on what's happening in the body. A cancer cell that's about to metastasize might feel different than one that's dormant. Chemistry alone doesn't tell you that story.
Mark
So you're saying the current methods are blind to something crucial?
Mimi
Exactly. We can sequence a cell's DNA perfectly and still miss what it's actually doing. It's like knowing every ingredient in a recipe but not watching how the cook handles the pan.
Mark
How does the microfluidic device actually isolate these rare cells?
Mimi
It uses physical properties—size, stiffness, electrical charge—to sort them as they flow through tiny channels. The AI then learns which combinations of properties matter for the disease you're studying. It's pattern recognition at a scale we couldn't do by hand.
Mark
And the liquid biopsy angle—that's the part that changes patient care?
Mimi
That's the part that changes everything. No surgery, no waiting. A blood draw, a few hours of analysis, and you know what's actually happening in your patient's body right now. You can adjust treatment before the disease has time to adapt.
Mark
What's the biggest risk this technology could fail?
Mimi
The devices have to be reliable enough to work in real clinical settings, not just labs. And the AI has to generalize—what works for one cancer type might not work for another. But that's what the four years are for.
Mark
When do you think this actually reaches patients?
Mimi
The grant runs to 2030. If everything works, you might see early clinical trials in the next five to seven years. But these tools will probably reach research labs much sooner, which changes what questions scientists can even ask.
Le Pouls
- The cells most responsible for driving cancer progression and infection outcomes are so rare they vanish into the statistical noise of conventional testing — a diagnostic blind spot with life-or-death consequences.
- Existing biopsy methods capture a cell's chemistry but sacrifice its living behavior, leaving researchers unable to observe how disease-driving subpopulations actually function and adapt.
- Swami's team is engineering microfluidic chips that sort and measure individual cells by their physical properties — size, stiffness, movement — rather than chemistry alone, opening an entirely new axis of biological inquiry.
- Onboard AI and neural-network analytics will process the massive datasets these devices generate in real time, making the system practical for clinical settings rather than just research laboratories.
- If successful, the technology could replace invasive surgical biopsies with a blood draw, compressing the time between suspicion and targeted treatment from days to near-immediate.
Within every blood sample lies a hidden minority — rare cells that may quietly determine whether a patient recovers or declines, yet remain invisible to the tools we rely on most. At the University of Virginia, engineer Nathan Swami has received a four-year, $1.1 million NIH grant to build the instruments and intelligence needed to find and interrogate these elusive cells. His work sits at the intersection of microengineering, artificial intelligence, and medicine, pursuing a question as old as diagnosis itself: why do some patients respond to treatment while others do not? The answer, he believes, may lie in the cells we have not yet learned to see.
Nathan Swami, a professor of electrical and computer engineering at the University of Virginia, has received a $1.1 million NIH grant to address one of medicine's most persistent blind spots: the rare cells hidden within larger populations that may be quietly determining how disease unfolds.
When scientists examine blood or tissue, they typically sort cells into broad categories. But within those groups lurk tiny subpopulations — cells capable of shifting their physical properties and driving disease progression or shaping treatment response. Because they appear in such small numbers, conventional methods miss them entirely. Swami's research centers on this cellular plasticity, the ability of cells to adapt and behave differently from their neighbors, and its outsized role in outcomes ranging from cancer to infection.
To find and study these cells, his team will build microfluidic devices — miniature channels etched into chips — that isolate individual cells based on physical characteristics like stiffness and movement rather than chemistry alone. Current biopsy techniques reveal DNA and RNA but cannot preserve live cells for behavioral study. Alongside the hardware, the team will develop AI-powered analytics capable of processing the enormous datasets these measurements produce, integrating neural networks and decision algorithms directly onto microchips.
The clinical implications are significant. Rather than requiring invasive surgery and days of waiting, a clinician could draw blood, run it through the device, observe how cells are currently functioning, and begin targeted treatment almost immediately. The technology could identify circulating tumor cells signaling shifts in cancer or track how immune cells are responding to infection in real time.
Swami's R01 award — the NIH's most competitive independent research grant — runs through March 2030 and is funded through the National Institute of General Medical Sciences. What his lab is building is less a better microscope than a new language for asking questions about the cells that matter most: the rare ones that hold the keys to why some patients heal and others do not.
Nathan Swami, a professor of electrical and computer engineering at the University of Virginia, has just received a $1.1 million grant from the National Institutes of Health to solve a problem that has long frustrated researchers: the cells that matter most in disease are often the hardest to find.
The four-year project addresses a fundamental gap in how scientists study human cells. When researchers examine blood or tissue samples, they typically sort cells into broad categories—red blood cells, immune cells, and so on. But within those large groups hide smaller populations of cells that may be driving disease progression, determining how a patient responds to treatment, or shaping immune function. These rare cells are present in such small numbers that conventional testing methods often miss them entirely, buried in the noise of the larger population. Swami's work focuses on cellular plasticity, the ability of cells to shift their physical properties and adapt to new environments. In diseases ranging from cancer to infection, these small subpopulations can determine outcomes despite making up only a fraction of the total cell count.
The challenge is not just finding these cells but understanding how they behave. Current biopsy methods excel at revealing the chemical composition of cells—their DNA and RNA—but they don't preserve live cells in a way that allows researchers to study their physical properties and actions. Swami's team will build microfluidic devices, tiny channels etched into chips that can manipulate and analyze individual cells based on their physical characteristics rather than their chemistry alone. Alongside these devices, they will develop artificial intelligence-powered computational tools capable of processing the enormous datasets these measurements will generate. The system integrates microfabricated devices, neural-network-based analytics, and decision-making algorithms directly onto microchips—a feat that has only recently become feasible as computing power has grown fast enough to handle real-time data analysis.
The practical applications could reshape how doctors diagnose and treat disease. Liquid biopsies, which use blood or other accessible body fluids to monitor disease rather than requiring invasive surgical procedures to extract tissue, could become far more precise and accessible. Instead of a patient undergoing surgery, waiting days for lab results, and then beginning treatment, a clinician could use this device to analyze a blood sample, measure how cells are currently functioning, and begin targeted treatment immediately. The technology could help identify circulating tumor cells that signal shifts in cancer progression or reveal how immune cells are responding to infection.
Swami's grant is an R01 award, the NIH's most prestigious and competitive funding mechanism for independent, investigator-initiated research. It reflects the university's strength in bringing together engineering, medicine, and analytical science. The work runs through March 2030 and is funded through the National Institute of General Medical Sciences. What Swami and his team are building is not just a better microscope but a fundamentally new way of asking questions about the cells that matter most—the rare ones that hold the keys to understanding why some patients recover and others do not.
Citations marquantes
We develop tools that can measure at the cellular and molecular scale, but focus on the physical properties of molecules and cells. There are many functions in our body where physical properties make a huge difference.— Nathan Swami, UVA professor of electrical and computer engineering
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