Quantifying Early Electromechanical Integration of Cardiomyocytes Using a Minimalist PCL Nanofiber Platform
Year: 2025
Type: article
Abstract: A critical obstacle in cardiac cell therapy is the unpredictable and poorly understood initial electrophysiological integration of grafted cardiomyocytes into the host tissue, a process that dictates therapeutic success and arrhythmic risk. Current models fail to capture the earliest stages of functional coupling formation. Here, we employed a tailored bioengineering platform, where single cardiomyocytes were stabilized on minimalist electrospun polycaprolactone (PCL) nanofibers, to model the "graft-host" interface and study the dynamics of excitation wave transmission in real-time. Using high-speed optical mapping enhanced by a custom SUPPORT neural network, we achieved the first quantitative insights into the efficiency of nascent intercellular contacts. We determined that within the first 3 h, these initial connections are 39-44 times less effective at conducting excitation than mature contacts within the native monolayer, explaining the observed partial (46%) synchronization of grafted cells. This work provides the first direct measurement of the functional deficit during the initial minutes and hours of graft integration. It establishes that simple, inert polymer fibers can act as a catalytic scaffold to enable this fundamental biological process, offering a powerful strategy to deconstruct and ultimately control the integration of engineered tissues (or cells) for safer cell therapies.
Source: Polymers
Institutions: Ministry of Health of the Russian Federation, Centre of Advanced Studies, Meshalkin National Medical Research Center, V.I.Shumakov Federal Research Center of Transplantology and Artificial Organs, Novosibirsk State Medical University, Moscow Regional Scientific Research Clinical Institute. MF Vladimirsky
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,258
Award: G2829585735
OpenAlex ID: W7116692764
