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

Abstract

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 (<i>e.g.</i>, impedance amplitude, phase and opacity) to characterize single cells instead of intrinsic biophysical metrics (<i>e.g.</i>, radius <i>r</i>, cytoplasm conductivity <i>σ</i>_i and specific membrane capacitance <i>C</i>_sm). 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 (<i>r</i>, <i>σ</i>_i and <i>C</i>_sm) can be obtained online and in real-time <i>via</i> 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 <i>per se</i> 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.