Impedance flow cytometry empowered by ConvNet algorithm to differentiate bladder cancer cells based on electro-mechanical characteristics

Bladder cancer (BC) is a common malignancy and among the leading causes of cancer death worldwide. Analysis of BC cells is of great significance for clinical diagnosis and disease treatment. Current approaches rely mainly on imaging-based technology, which requires complex staining and sophisticated instrumentation. In this work, we develop a label-free method based on artificial intelligence (AI)-assisted impedance-based flow cytometry (IFC) to differentiate between various BC cells and epithelial cells at single-cell resolution. By applying multiple-frequency excitations, the electrical characteristics of cells, including membrane and nuclear opacities, are extracted, allowing distinction to be made between epithelial cells, low-grade, and high-grade BC cells. Through the use of a constriction channel, the electro-mechanical properties associated with active deformation behavior of cells are investigated, and it is demonstrated that BC cells have a greater capability of shape recovery, an observation that further increases differentiation accuracy. With the assistance of a convolutional neural network-based AI algorithm, IFC is able to effectively differentiate various BC and epithelial cells with accuracies of over 95%. In addition, different grades of BC cells are successfully differentiated in both spiked mixed samples and bladder tumor tissues.