journal article·2026-06-01Open access

Micro-comb 3D printing: rapid fabrication of tissue-guiding substrates using micro-embossed nozzles

Sarkhan Butdayev, Stefan Leone, Shayla Nikzad, Janko Kajtez, Katrine Bech Lauritzen, Moises Di Sante, Francesco S Pasqualini, Kirstine Calloe, Rodolphe Marie, Stephan S Keller, Anne Z Eriksen, Johan U Lind

Biofabrication· Volume 18· Issue 2

Abstract

Abstract To replicate the function of native tissue in cell cultures, one must reproduce the structure of the native tissue. This can be achieved using tissue‐guiding architectures with cell‐scale dimensions, typically ranging from single to tens of microns. However, this spatial resolution exceeds the capabilities of many common fabrication methods, including extrusion‐based 3D printing. Indeed, although increasingly popular in bioengineering, extrusion-based 3D printing is not only limited by the properties of the print materials, but also by the inherent trade-off that smaller features require smaller nozzles. This, in turn, results in more toolpaths and longer build times. To overcome this limitation, we introduce nozzles with micro-scale structures at their orifice, fabricated through straightforward hot embossing of commercial polypropylene nozzles. This approach enables microstructure printing using large (⩾0.4 mm inner diameter) nozzles. Specifically, we demonstrate rapid printing of microstructured soft substrates, capable of guiding skeletal and cardiac muscle cell cultures into physiomimetic, anisotropic tissues for electrophysiological assays and drug studies. Furthermore, we show that axonal growth in neuronal tissue cultures can also be directed. Thus, our approach may serve as a scalable and easily accessible method for fabricating human cell cultures and tissue models with enhanced physiological relevance.
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Research context

OpenAlex topics: 3D Printing in Biomedical Research · Nanofabrication and Lithography Techniques · Advanced Materials and Mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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