Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization.

Single-cell impedance flow cytometry (IFC) is emerging as a label-free and non-invasive method for characterizing the electrical properties and revealing sample heterogeneity. At present, most IFC studies utilize phenomenological parameters (, impedance amplitude, phase and opacity) to characterize single cells instead of intrinsic biophysical metrics (, radius , cytoplasm conductivity and specific membrane capacitance ). Intrinsic parameters are normally calculated off-line by time-consuming model-fitting methods. Here, we propose to employ neural network (NN)-enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic parameter-based cell classification at high throughput. Three intrinsic parameters (, and ) can be obtained online and in real-time a trained NN at 0.3 ms per single-cell event, achieving significant improvement in calculation speed. Experiments involving four cancer cells and one lymphocyte cell demonstrated 91.5% classification accuracy in the cell type for a test group of 9751 cell samples. By performing a viability assay, we provide evidence that the IFC test would not substantially affect the cell property. We envision that the NN-enhanced real-time IFC will provide a new platform for high-throughput, real-time and online cell intrinsic electrical characterization.