Speaker
Description
Microfluidic impedance cytometry (MIC) is a label-free, high-throughput technique that characterizes individual flowing particles/cells based on their interaction with a multifrequency electric field [1]. It has been successfully applied in various scenarios, including life-science research, diagnostics, and environmental monitoring. In this talk, I will discuss two emerging trends that promise to enhance the capabilities and adoption of the technique: the integration of MIC with other microfluidic tools, towards multifunctional single-cell analysis platforms [2], and the synergy with Artificial Intelligence (AI) for effective analysis of impedance data in challenging scenarios [3, 4].
MIC is a simple technique: the sensing element is just a microchannel with embedded electrodes, and the electronic acquisition system is suited for portable implementation. Accordingly, the technique lends itself to integration with other microfluidic techniques. Five categories can be identified based on the main goal of the combination: (i) improving the multiparametric characterization capability by coupling MIC with an additional sensing modality; (ii) enabling on-chip sample preparation steps to increase the accuracy of MIC measurements or to enrich selected populations prior to MIC analysis; (iii) stimulating the sample to elicit desired responses; (iv) sample carrying/confinement into droplets or microcarriers to provide tailored support or microenvironment; and (v) impedance-activated sample sorting to enable downstream analysis or reuse.
Increasing the functionalities of the microfluidic system to fulfill the lab-on-a-chip vision calls for a “brain” that controls the platform by deciding tasks based on the signals received from the sensing units. To accomplish this, AI-based solutions are highly promising, as they enable real-time perception and decision-making in complex tasks. The synergy between AI and MIC is currently a very active research field. Both signal-space and feature-space approaches have been successfully demonstrated for applications including, e.g., fast dielectric spectroscopy, coincidence arbitration, and cell population analysis. However, further research efforts are needed to address issues such as the need for target values in supervised learning and the potential fragility of the developed tools.
Acknowledgment
The research was supported under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, funded by the European Union – NextGenerationEU – Project CUP E53D23002530006.
References
[1] C. Honrado et al. Lab Chip, 2021, 21, 22-54 doi: 10.1039/d0lc00840k.
[2] M. Righetto et al., Lab Chip, 2025, 215 1316-1341 doi: 10.1039/d4lc00957f.
[3] F. Caselli et al., Lab Chip, 2022, 22, 1714-1722 doi: 10.1039/d2lc00028h.
[4] J. Jarmoshti et al., Small, 2025, 21(5), 2407212 doi: 10.1002/smll.202407212.