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. Project Number: 1R01GM154950-01A1 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Nathan Swami | Institution: UNIVERSITY OF VIRGINIA, CHARLOTTESVILLE, VA | Award Amount: $1,092,610 | Activity Code: R01 | Study Section: Instrumentation and Systems Development Study Section[ISD] View on NIH RePORTER: https://reporter.nih.gov/project-details/11133542