Soft-lithographically defined template for arbitrarily patterned acoustic bioassembly
Source abstract
Abstract
Abstract Acoustic bioassembly is recently regarded as a highly efficient biofabrication tool to generate functional tissue mimics. Despite their capacity of directly patterning live cells with close intercellular proximity, most acoustic bioassembly techniques are currently limited to generate some specific simple types of periodic and symmetric patterns, which represents an urgent challenge to emulate geometrically complex cytoarchitecture in human tissue. To address this challenge, we herein demonstrate a soft-lithographically defined acoustic bioassembly (SLAB) technique that enables to assemble live cells into geometrically defined arbitrary multicellular structures. Particularly, we employed a widely accessible soft lithography technique to fabricate a polydimethylsiloxane (PDMS) construct that works as an amplitude modulation template to define the pressure distribution of near-field acoustic waves. We found that zero pressure areas of the near-field acoustic waves at the PDMS surface distribute above the air-filling regions of the PDMS construct when both the PDMS top layer and air layer are approximately one-tenth of the acoustic wavelength. Using this technique, bioparticles can be assembled into symmetrical or asymmetrical patterns. Specifically, we have demonstrated the SLAB of endothelial spheroids and hepatic cells into liver tissue mimics (LTMs). The functional analysis further indicates that the formed LTMs displayed liver-specific functions, including albumin secretion, urea synthesis, glucose metabolism, and lipid storage. We expect this SLAB technique will be broadly used to construct complex functional tissues for tissue engineering and regenerative medicine.
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Research context
OpenAlex topics: Microfluidic and Bio-sensing Technologies · Microfluidic and Capillary Electrophoresis Applications · 3D Printing in Biomedical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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