Self‐Evolving Discovery of Carrier Biomaterials with Ultra‐Low Nonspecific Protein Adsorption for Single Cell Analysis

Abstract Carrier biomaterials used in single‐cell analysis face a bottleneck in protein detection sensitivity, primarily attributed to elevated false positives caused by nonspecific protein adsorption. Toward carrier biomaterials with ultra‐low nonspecific protein adsorption, a self‐evolving discovery is developed to address the challenge of high‐dimensional parameter spaces. Automation across nine self‐developed or modified workstations is integrated to achieve a “can‐do” capability, and develop a synergy‐enhanced Bayesian optimization algorithm as the artificial intelligence brain to enable a “can‐think” capability for small‐data problems inherent to time‐consuming biological experiments, thereby establishing a self‐evolving discovery for carrier biomaterials. Through this approach, carrier biomaterials with an ultra‐low nonspecific protein adsorption index of 0.2537 are successfully discovered, representing an over 80% decrease, while achieving a 10 000‐fold reduction in experiment workload. Furthermore, the discovered biomaterials are fabricated into microfluidic‐used carriers for protein‐analysis applications, showing a 9‐fold enhancement in detection sensitivity compared to conventional carriers. This is the very demonstration of a self‐evolving discovery for carrier biomaterials, paving the way for advancements in single‐cell protein analysis and further its integration with genomics and transcriptomics.