Biomedical Coating Combination Optimized by High Throughput Biochip and Cell Image Machine Learning

Hongye Hao, Junjie Huang, Yunfan Xue, Jian Ji*

MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, PR China.

ABSTRACT: The combinatorial actions of multiple biomolecules in extracellular matrix (ECM) play a key role in selectively inducing cell growth and regulating tissue generation. These include the combinations of nonspecific resistance caused by highly hydrated molecules and specific recognition produced by active ligands, which achieved high selectivity in complex human body environment. However, most of current studies were confined to qualitative investigation of single components, which were unable to reveal the basic regularity and optimize the combination. Thus, it is urgent to develop new platforms for efficient fabrication and characterization of combinational bio-interface.

High throughput screening has thrown us a new sight for analysis of the interactions between cells and vast number of biomaterials by using platforms with high integration degree. In this work, a high throughput biochip and cell image machine learning were combined to investigae the combined effect of nonspecific resistance and specific recognition towards cell competitiveness between ECs and SMCs, which is essential to design the biomedical coating for cardiovascular system.

Figure 1. Overview of Label-free identification of co-cultured cells via machine learning

Keywords: machine learning; co-culture; cell identification