Multi-material Gradient Printing Using Meniscus-enabled Projection Stereolithography (MAPS)

Cellnet technology to generate 3D, functional, single-cell networks in custom architectures within collagen

Cells possess the remarkable ability to generate tissue-specific 3D interconnected networks and respond to a wide range of stimuli. Understanding the link between the spatial arrangement of individual cells and their networks' emergent properties is necessary for the discovery of both fundamental biology as well as applied therapeutics. However, current methods spanning from lithography to 3D photo-patterning to acoustofluidic devices are unable to generate interconnected and organized single cell 3D networks within native extracellular matrix (ECM). To address this challenge, we report a novel technology coined as Cellnet. This involves the use of natural collagen crosslinked within three-chambered microfluidic chips followed by femtosecond laser-assisted cavitation to generate user-defined 3D microchannel networks. Model cells, seeded within side chamber of the chip, migrate within microchannel networks within hours, self-organize and form viable, interconnected, 3D single-cell networks in custom architectures such as square grid, concentric circle, parallel lines, and spiral patterns. Heterotypic Cellnets can also be generated by seeding multiple cell types in side-chambers of the chip. The functionality of cell networks can be studied by monitoring the real-time calcium signaling response of individual cells and signal propagation within Cellnets when subjected to flow stimulus alone or a sequential combination of flow and biochemical stimuli. Furthermore, user-defined disrupted Cellnets can be generated by lethally injuring target cells within the 3D network and analyzing the changes in their signaling dynamics. As compared to the current self-assembly based methods that exhibit high variability and poor reproducibility, Cellnets can generate organized 3D single-cell networks and their real-time signaling responses to a range of stimuli can be accurately captured using simple cell seeding and easy-to-handle microfluidic chips. Cellnet technology, agnostic of cell types, ECM formulations, 3D cell-connectivity designs, or location and timing of network disruptions, could pave the way to address a range of fundamental and applied bioscience applications.