Affiliation:
1. Neuroelectronics Munich Institute of Biomedical Engineering Department of Electrical Engineering TUM School of Computation Information and Technology Technical University of Munich Hans‐Piloty‐Strasse 1 85748 Garching Germany
2. Medical & Health Informatics Laboratories NTT Research Incorporated 940 Stewart Dr. Sunnyvale CA 94085 USA
3. Disease Biophysics Group Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Science and Engineering Complex, 150 Western Ave. Boston MA 02134 USA
4. Wyss Institute for Biologically Inspired Engineering Harvard University 201 Brookline Ave. Boston MA 02115 USA
5. Department of Mechanical Engineering Keio University Hiyoshi, Kohoku‐ku Yokohama 223‐8522 Japan
Abstract
Studying the behavior of electroactive cells, such as firing dynamics and chemical secretion, is crucial for developing human disease models and therapeutics. Following the recent advances in cell culture technology, traditional monolayers are optimized to resemble more 3D, organ‐like structures. The biological and electrochemical complexity of these structures requires devices with adaptive shapes and novel features, such as precise electrophysiological mapping and stimulation in the case of brain‐ and heart‐derived tissues. However, conventional organ‐on‐chip platforms often fall short, as they do not recreate the native environment of the cells and lack the functional interfaces necessary for long‐term monitoring. Origami‐on‐a‐chip platforms offer a solution for this problem, as they can flexibly adapt to the structure of the desired biological sample and can be integrated with functional components enabled by chosen materials. In this review, the evolution of origami‐on‐a‐chip biointerfaces is discussed, emphasizing folding stimuli, materials, and critical findings. In the prospects, microfluidic integration, functional tissue engineering scaffolds, and multi‐organoid networks are included, allowing patient‐specific diagnoses and therapies through computational and in vitro disease modeling.