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The recording of the action potentials in electrogenic cells plays a central role in many fields of research spanning from neuroscience to cardiology, cell biology and pharmacology. To this aim, many techniques have been developed both in research and in clinical applications. Broadly speaking they can be divided into two categories: direct electrical measurements performed with micro-electrodes (such as patch-clamp and multi-electrode arrays (MEAs) and optical measurements collected from fluorescent reporters administrated to the cell cultures. However, regardless of the field of application some fundamental needs are still not met. Among them the ability of accurate monitoring combined with zero-invasiveness and large spatial scalability (investigation of networks).
In this talk, we will show a radically new concept for monitoring action potentials in human cells that overcomes the mentioned limitations [1]. It bases on the concept of “mirror charge” in classical electrodynamics: electric charges placed in proximity of a conductor affect its spatial charge distribution thus generating mirror charges into the conductor itself. Hence, by monitoring the dynamics of the mirror charges one can monitor the dynamics of the “source charges” and the related electric potential, i.e. the action potential. The latter can be done by using charged dyes whose dynamics can be monitored by conventional optical methods. However, being the dyes placed in microfluidic chip, separated from the cell culture, the cells are subjected neither to dye contact, nor to direct light illumination, but are in a perfectly unperturbed physiological state. Remarkably, the optical signal perfectly resembles an action potential (AP) even without the need of cell membrane poration as for conventional electrical recording. We reported a set of experiments on human-derived cardiomyocytes that uniquely support our statements. Such an innovative configuration could inspire a new disruptive class of electro-optical sensors or hybrid devices for optical computing.
[1] A. Barbaglia et al., Mirroring Action Potentials. Advanced Materials 2021, 33, 2004234.
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