Nathan Swami
University of Virginia, VA·Biomedical Engineering
Cancer cells that break away from a tumor and travel in the blood, called circulating tumor cells (CTCs), carry clues about how a cancer is spreading and whether treatment is working. They are hard to count and classify because they vary so much.
- Fields
Biomedical Engineering
Engineering
Biophysics
- Topics
circulating tumor cells
pancreatic ductal adenocarcinoma
pancreatic cancer
- Grants
- 2 known grants, through Feb 2030
What they work on
- Generate pancreatic cancer cells spanning a range of hybrid epithelial–mesenchymal states by co-culturing them with cancer-associated fibroblasts in a perfusable system under shear stress and hypoxia, validated by immunofluorescence.
- Measure how single PDAC cells deform and recover under microfluidic viscoelastic extensional flow, using microgels of known stiffness as co-flowing reference standards.
- Train multilayer perceptron neural networks to classify the EMT state of hybrid circulating tumor cells from deformability metrics, in samples resembling portal vein blood.
- Build an impedance-activated recognition, tracking and sorting (IARTS) system that uses neural networks to link image-based cell shape data with fast electrical impedance signals to trigger label-free sorting.
- Optimize IARTS throughput by testing deformation geometries, flow rates, trigger methods and sorting parameters on model samples before applying it to complex mixed cell samples.
More about the research
The same problem appears with many cell types that change their behavior over time. Standard methods tag cells with fluorescent antibodies, but tags miss cells whose surface markers shift. These projects instead use physical properties of single cells, such as how easily a cell squeezes and springs back, measured in microfluidic channels (tiny fluid channels on a chip). The projects combine these measurements with neural networks to recognize, classify and sort cells without labels.
Why it matters
Pancreatic cancer patients could benefit from better blood-based tests that track metastasis and drug resistance. Classifying circulating tumor cells by stiffness may catch hybrid cells that fluorescent tagging misses, and could add prognostic information from portal vein liquid biopsies. A label-free, real-time cell sorter could also give researchers a way to isolate changing cell subpopulations for molecular study.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Emanuele Cocucci
Ohio State University, OH·Biology
Perform an unbiased calibration of how the number of circulating extracellular vesicles relates to their cells of origin, using enCD63, an engineered version of the vesicle protein CD63, expressed in specific cells of genetically engineered mice.
Michael King
Rice University, TX·Biomedical Engineering
Build a high-throughput shear stress device based on a BioJet printer and multiwell plates, and use it to measure calcium influx, membrane and mitochondrial damage, and cell death in prostate cancer cells.
Saunjoo Yoon
University of Florida, FL·Pharmacology
Test the effectiveness of 4NSGnpcetu, anti-EGFR antibody-coated nanoparticles carrying a modified gemcitabine, in patient-derived pancreatic cancer organoids with stroma and in primary tumor cells from patients.
The school
University of Virginia: 43 labs on PI Pages, $814M a year on research, 413 research doctorates a year.
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