Code-Multiplexed Multi-Frequency Impedance Cytometry with a Unified Deep-Unfolding Network

Abstract

Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithm to resolve overlaps in signals originating from different channels. Existing methods, however, rely on multiple task-specific networks with template-based linear fitting, which loses accuracy under nonlinear or unstable conditions common in microfluidic experiments. Prior studies have also been restricted to demultiplexing single-frequency impedance measurements. To this end, we develop a unified deep-unfolding network for analyzing code-multiplexed, multi-frequency IFC data. We unfold successive-interference cancellation (SIC) algorithm into a deep-learning network that encodes the structural priors of SIC, and enable a single multitask network to resolve overlaps while sharing information across tasks. To mitigate nonlinear signal stretching and amplification, our network recognizes events by predicting bit-level intensity and duration. For multi-frequency impedance profiling, we apply least-squares fitting to the predicted single-frequency real-impedance trace to map the real and imaginary impedance traces at other frequencies. On the cell-bead mixture evaluation dataset, our pipeline resolves overlaps from singlets to triplets reliably, reconstructs impedance-intensity distributions accurately, and enables multi-frequency impedance profiling. As a demonstration of principle, we use our pipeline to perform label-free quantification of basophil activation from code-multiplexed, multi-frequency IFC measurements. Consistent with previous study, impedance opacity correlates well with activation levels measured by fluorescence flow cytometry. In summary, our study demonstrates the feasibility and utility of a deep-unfolding network that extends code-multiplexed, multi-frequency IFC to label-free single-cell functional assays.