An Effective Recognition Method for Particle Coincidence in Double Differential Impedance Cytometry

As a label-free single cell analysis approach, impedance flow cytometry provides valuable information by differential signals. However, for high throughput purpose, particle coincidence is inevitable especially when the cell concentration or pumping rate is too high. In this work, we proposed the first numerical model and a novel method for particle coincidence. To investigate the multiparticle events, a double differential impedance sensor is used. From a statistic analysis of signal peaks, we could determine the threshold of particle events and then read out particle numbers. Compared with customized threshold, the data-driven cutoff line fundamentally improves the accuracy of particle recognition. Herein, the key operation parameters that influence the particle coincidence including sample concentration, driven pressure (stands for pumping rate), and sensor configuration are elucidated. Moreover, a guide to the best practices to avoid multiparticle coincidence events is suggested for real applications.

Automated summary

Impedance flow cytometry, as a label-free single cell analysis approach, provides valuable differential signals. However, particle coincidence is inevitable for high throughput detection, especially with high cell concentration or pumping rate. In this study, a numerical model and a novel method for particle coincidence were proposed to investigate multiparticle events. By statistically analyzing signal peaks, the threshold for particle events and the particle numbers were determined. Compared to customized thresholds, a data-driven cutoff line fundamentally improves the accuracy of particle recognition. Moreover, the key operational parameters and the best practices to avoid multiparticle coincidence events in real applications are elucidated.