A paradigm for high-throughput screening of cell-selective surfaces coupling orthogonal gradients and machine learning-based cell recognition.

The combinational density of immobilized functional molecules on biomaterial surfaces directs cell behaviors. However, limited by the low efficiency of traditional low-throughput experimental methods, investigation and optimization of the combinational density remain daunting challenges. Herein, we report a high-throughput screening set-up to study biomaterial surface functionalization by integrating photo-controlled thiol-ene surface chemistry and machine learning-based label-free cell identification and statistics. Through such a strategy, a specific surface combinational density of polyethylene glycol (PEG) and arginine-glutamic acid-aspartic acid-valine peptide (REDV) leads to high endothelial cell (EC) selectivity against smooth muscle cell (SMC) was identified. The composition was translated as a coating formula to modify medical nickel-titanium alloy surfaces, which was then proved to improve EC competitiveness and induce endothelialization. This work provided a high-throughput method to investigate behaviors of co-cultured cells on biomaterial surfaces modified with combinatorial functional molecules.