An integrated 3D fluidic device with bubble guidance mechanism for long-term primary and secondary cell recordings on multi-electrode array platform

Incubator-independent perfusion system integrated with microfluidic device for continuous electrophysiology and microscopy readouts

Advances in primary and stem cell derived neuronal cell culture techniques and abundance of available neuronal cell types have enabledin vitroneuroscience as a substantial approach to modelin vivoneuronal networks. Survival of the cultured neurons is inevitably dependent on the cell culture incubators to provide stable temperature and humidity and to supply required CO₂levels for controlling the pH of culture medium. Therefore, imaging and electrophysiology recordings outside of the incubator are often limited to the short-term experimental sessions. This restricts our understanding of physiological events to the short snapshots of recorded data while the major part of temporal data is neglected. Multiple custom-made and commercially available platforms like integrated on-stage incubators have been designed to enable long-term microscopy. Nevertheless, long-term high-spatiotemporal electrophysiology recordings from developing neuronal networks needs to be addressed. In the present work an incubator-independent polydimethylsiloxane-based double-wall perfusion chamber was designed and integrated with multi-electrode arrays (MEAs) electrophysiology and compartmentalized microfluidic device to continuously record from engineered neuronal networks at sub-cellular resolution. Cell culture media underwent iterations of conditioning to the ambient CO₂and adjusting its pH to physiological ranges to retain a stable pH for weeks outside of the incubator. Double-wall perfusion chamber and an integrated air bubble trapper reduced media evaporation and osmolality drifts of the conditioned media for two weeks. Aligned microchannel-microfluidic device on MEA electrodes allowed neurite growth on top of the planar electrodes and amplified their extracellular activity. This enabled continuous sub-cellular resolution imaging and electrophysiology recordings from developing networks and their growing neurites. The on-chip versatile and self-contained system provides long-term, continuous and high spatiotemporal access to the network data and offers a robustin vitroplatform with many potentials to be applied on advanced cell culture systems including organ-on-chip and organoid models.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.