Inline impedance-activated recognition, tracking and sorting system on single-cell biophysical metrics - phenotypic plasticity is common for cell types with multiple functions, but there are few…

The National Institute of General Medical Sciences awarded $1.092 million to the Rector & Visitors of the University of Virginia on March 15, 2026, under the Biomedical Research and Research Training program (CFDA 93.859) to develop an impedance-activated recognition, tracking, and sorting (IARTS) system for label-free, near-real-time identification and isolation of phenotypically plastic cells based on multiparametric biophysical metrics.

The IARTS system integrates neural networks with impedance signal processing to extract cellular metrics of deformability, viability, and electrical physiology in real-time, coupling the accuracy of image data for discerning cell shapes under deformation to the speed of impedance processing. The system will be validated with model samples to optimize recognition and sorting throughputs by determining optimal deformation geometries, flow rates, trigger methods, and sorting parameters, with application to complex heterogeneous samples for label-free selection and molecular profiling of phenotypically plastic cells.

Work is performed in Virginia with performance extending through February 28, 2030. University of Virginia serves as the recipient and principal investigator institution, operating under its role as a state-controlled public research university delivering basic biomedical research and scientific investigation to federal agencies.

Inline impedance-activated recognition, tracking and sorting system on single-cell biophysical metrics - phenotypic plasticity is common for cell types with multiple functions, but there are few label-free tools capable of dynamic tracking of cell phenotypes to quantify their heterogeneity and sort their subpopulations. Biophysical phenotypes undergo dynamic alterations as cells navigate through their functions, but their utilization to monitor plasticity is limited by the lack of high throughput tools for multiparametric cell recognition and activated sorting. For advancing near real-time single-cell recognition based on multiparametric biophysical metrics and activated sorting to isolate specific phenotypes, this application will integrate neural networks to couple the accuracy of image data for discerning intricate cell shapes under deformation, to the speed of impedance signal processing, so that identifying multiparametric cellular metrics of deformability, viability, and electrical physiology can be extracted in near-real-time to activate sorting. This impedance-activated recognition, tracking, and sorting (iarts) system will be validated with model samples to optimize recognition and sorting throughputs by determining optimal deformation geometries, flow rates, trigger methods and sorting parameters, for application to complex heterogeneous samples for label-free selection and molecular profiling of phenotypically plastic cells.