Simultaneous recording of electrical and metabolic activity of cardiac cells in vitro using an organic charge modulated field effect transistor array

Author:

Spanu Andrea,Martines Laura,Tedesco Mariateresa,Martinoia Sergio,Bonfiglio Annalisa

Abstract

In vitro electrogenic cells monitoring is an important objective in several scientific and technological fields, such as electrophysiology, pharmacology and brain machine interfaces, and can represent an interesting opportunity in other translational medicine applications. One of the key aspects of cellular cultures is the complexity of their behavior, due to the different kinds of bio-related signals, both chemical and electrical, that characterize these systems. In order to fully understand and exploit this extraordinary complexity, specific devices and tools are needed. However, at the moment this important scientific field is characterized by the lack of easy-to-use, low-cost devices for the sensing of multiple cellular parameters. To the aim of providing a simple and integrated approach for the study of in vitro electrogenic cultures, we present here a new solution for the monitoring of both the electrical and the metabolic cellular activity. In particular, we show here how a particular device called Micro Organic Charge Modulated Array (MOA) can be conveniently engineered and then used to simultaneously record the complete cell activity using the same device architecture. The system has been tested using primary cardiac rat myocytes and allowed to detect the metabolic and electrical variations thar occur upon the administration of different drugs. This first example could lay the basis for the development of a new generation of multi-sensing tools that can help to efficiently probe the multifaceted in vitro environment.

Funder

Università Degli Studi di Genova

Publisher

Frontiers Media SA

Subject

Biomedical Engineering,Histology,Bioengineering,Biotechnology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing personal comfort: A machine learning approach using physiological and environmental signals measurements;Measurement;2023-08

2. Research Progress of Cell Electrophysiological Recordings;2022 16th ICME International Conference on Complex Medical Engineering (CME);2022-11-04

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