Optically Active Bionanomachine Interfaces Build Therapeutic Nanonetworks for Glioblastoma Multiforme
- Resource Type
- Conference
- Authors
- Hamidieh, Avraam El; Dietis, Nikolaos; Samoylenko, Anatoliy; Meiser, Ina; Nicolaou, Niovi; Abdelrady, Eslam; Zhyvolozhnyi, Artem; Vainio, Seppo; Odysseos, Andreani D.
- Source
- 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT) Medical Information and Communication Technology (ISMICT), 2022 IEEE 16th International Symposium on. :1-6 May, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Proteins
Wireless communication
Biomedical optical imaging
Optical design
Optical propagation
Optical fiber networks
Optical receivers
- Language
- ISSN
- 2326-8301
The evolution of Glioblastoma Multiforme (GBM) is defined by the dynamics of growing bionanomachine networks in an interplay between "senders" or "transceivers" and "receivers". Central to this process are the inter-communications between sub-cellular bionanomachines secreted by GBM cells in the form of exosomes. Herein we present a dynamic cell-based therapeutic nanonetwork of genetically engineered optically active bionanomachines. The communication paradigm is defined by the interaction between neural stem cell-derived exosomes expressing Enhanced Green Fluorescent Protein and GBM cells expressing Tandem Dimer Tomato protein (tdT), based on the dynamic transfer of energies of excited state bionanomachines in resonance. With EGFP serving as energy donor and tdT as energy acceptor we provide multilevel evidence validating a Förster Resonance Energy Transfer - mediated interaction between GBM cells and exosomes, therefore documenting their sustainable and close proximity. Such an approach has the potential to enable wireless communication between optically active bionanomachines via quantifiable interfaces within channel networks, further enabling mechanistic and therapeutic models.