Accelerated Formulation Screening of Multi-Component Bioinks via a Deep Learning-Integrated High-Throughput Gradient Printing Platform.

The development of performant multi-component bioinks is severely bottlenecked by the astronomical size of the possible design space and the inefficiency of empirical, trial-and-error experimentation. Existing high-throughput screening (HTS) methods, such as microarrays and droplet microfluidics, remain limited by discrete sampling, cell-unfriendly environments, and a lack of integrated analytics. Herein, a high-throughput gradient printing (HTGP) platform is employed to overcome these barriers by integrating continuous combinatorial bioprinting with deep learning-powered phenotyping. The HTGP system rapidly generates bioink libraries with gradients within a native three-dimensional hydrogel environment, preserving cell viability and enabling in situ, direct interrogation of cell-material interactions. Coupled with an automated neural network pipeline, the platform quantitatively deciphers complex relationships between bioink composition and cellular responses from massive images. Under the tested in vitro conditions, it identified a formulation that enhanced human umbilical vein endothelial cell (HUVEC) proliferation and spreading, as well as a candidate bioink that exhibited osteo-inductive potential by promoting osteogenic differentiation of mesenchymal stem cells (BMSCs). Critically, the formulation identified through deep learning-assisted screening showed an enhanced early regenerative response in a rat periodontal bone defect model compared with the controls. The HTGP platform shifts from discrete testing to intelligent, continuous exploration, poised to accelerate the discovery of advanced biomaterials for personalized tissue engineering.