Sergis, V. | Search bioRxiv

bioRxiv2026

Dityrosine photocrosslinking of native collagen bioinks for controlled shape-fidelity of bioprinted cardiac tissue constructs: probing the interplay between fibrillogenesis and covalent bond formation

Collagen bioinks are widely used in biofabrication, but their relatively soft mechanical properties can lead to structural instabilities under cell-generated contraction forces. While synthetic functional groups can be conjugated for covalent crosslinking, these methods often disrupt natural protein fibrillogenesis, thereby compromising collagen fibre architecture. This work presents a strategy for the direct covalent stabilisation of native collagen bioinks with dityrosine bonds via visible-light photocrosslinking with ruthenium (Ru) and sodium persulfate (SPS), avoiding the need for polymer pre-functionalisation. Multimodal characterisation, including high-resolution microscopy, spectroscopy, mass spectrometry, and nanoindentation, identified photocrosslinking conditions that enhance collagen fibrillogenesis and reduce off-target polymer oxidation. Interestingly, the biofabrication process itself affected ultimate collagen fibre architecture, with shear-induced alignment during extrusion enhancing fibril proximity and self-assembly, overcoming inhibitory effects the crosslinkers had on fibrillogenesis via ionic and electrostatic interactions. Leveraging these insights, embedded bioprinting was used to fabricate cardiac constructs with high cell viability (>80%), where dityrosine crosslinking could be tuned to modulate geometric shape changes under cell-generated forces (1-15% shrinkage). Finally, the platform was used to bioprint anatomically accurate double-ventricle human heart models with robust shape fidelity. This research establishes a versatile photocrosslinking framework for bioprinting cardiac constructs with tunable shape stability using native collagen bioinks.

bioengineering↗

bioRxiv2024

Autonomous control of extrusion bioprinting using convolutional neural networks

Extrusion bioprinting technology suffers from reproducibility challenges due to the open-loop nature of current hardware systems. Here, we present a novel AI-powered extrusion bioprinting platform with integrated real-time quality monitoring and automated error correction capabilities. To achieve this, we engineered a custom bioprinting system with an integrated camera for continuous process monitoring and trained convolutional neural networks (CNNs) to classify the extrusion process in real-time. The CNN models, including Xception and ResNet, were trained on a combination of real and synthetic data to classify extrusion quality (good, over, or under) across various printing scenarios, including single-line and infill patterns. Notably, transfer learning, utilizing synthetic data for initial training followed by refinement with real-world data enhanced classification accuracy, with the Xception model displaying 90% accuracy for single-line extrusion and 75% for infill extrusion. This intelligent monitoring system was then coupled with a closed-loop control system that dynamically adjusted extrusion parameters on-the-fly to correct errors. The platform successfully corrected both over- and under-extrusion errors for alginate and collagen bioinks with varying rheological properties, demonstrating adaptability to unseen materials. Importantly, extrusion errors were corrected within [~]10 seconds. This novel closed-loop bioprinting platform represents a significant advance over traditional open-loop systems.

bioengineering↗