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

Area of Science:

• Biomaterials Science
• Surface Chemistry
• Cell Biology

Background:

• Cell behavior is influenced by the density of molecules on biomaterial surfaces.
• Traditional methods for optimizing these surfaces are inefficient and low-throughput.
• Investigating and optimizing combinational densities presents significant challenges.

Purpose of the Study:

• To develop a high-throughput screening setup for biomaterial surface functionalization.
• To identify optimal surface combinational densities for specific cell behaviors.
• To translate identified surface modifications to medical device applications.

Main Methods:

• Integrated photo-controlled thiol-ene surface chemistry with machine learning-based label-free cell identification.
• Utilized a high-throughput screening approach to analyze cell responses to varying surface functionalization densities.
• Translated the optimal surface composition into a coating formula for nickel-titanium alloy.

Main Results:

• Identified a specific combinational density of polyethylene glycol (PEG) and arginine-glutamic acid-aspartic acid-valine peptide (REDV) that promotes endothelial cell (EC) selectivity over smooth muscle cells (SMC).
• Demonstrated that the identified coating formula improves EC competitiveness and induces endothelialization on medical nickel-titanium alloy surfaces.
• Successfully validated the high-throughput method for investigating co-cultured cell behaviors on combinatorially modified surfaces.

Conclusions:

• A novel high-throughput method enables efficient investigation and optimization of biomaterial surface functionalization.
• Specific PEG and REDV peptide densities can be engineered to control cell selectivity for improved biomaterial performance.
• This approach holds promise for advancing the development of next-generation medical devices through tailored surface modifications.