Hybrid bioprinting of hierarchical vascular networks at capillary-scale resolution

Replicating the intricate hierarchical architecture of natural vascular networks, especially at capillary-scale resolution, remains a pivotal challenge in organ fabrication. Here we present a machine learning-enhanced hybrid bioprinting strategy that combines high-resolution aerosol jet printing of sacrificial materials and high-throughput extrusion printing of tissue matrices. This integrated approach enables sub-10 μm of resolution, achieving capillary-like channels and allowing on-demand modulation of vessel diameters in real time. Constrained Bayesian optimization rapidly identify optimal printing parameters, ensuring reliable, high-fidelity attainment of target channel sizes without exhaustive trial-and-error. This streamlined workflow supports the fabrication from 1D conduits to 3D multibranch hierarchical networks with tunable geometries. Endothelial cells seeded into these channels form continuous, functional monolayers, significantly reducing permeability while maintaining high cell viability and proliferation. By transcending the resolution limits of conventional sacrificial printing, this bioprinting method establishes a new route for producing biomimetic vasculature. Its unique combination of rapid optimization, real-time tunability, and microcapillary-scale precision holds exceptional promise for tissue engineering, regenerative medicine, and drug discovery.