Intelligent optofluidic analysis for ultrafast single bacterium profiling of cellulose production and morphology

Abstract

Bacterial cellulose (BC), a renewable type of cellulose, has been used in the manufacture of foods, cosmetics, and biomedical products. To produce BC, a high-throughput single-bacterium measurement is necessary to identify the functional bacteria that can produce BC with sufficient amount and desirable morphology. In this study, a continuous-flow intelligent optofluidic device was developed to enable high-throughput single-bacterium profiling of BC. Single bacteria were incubated in agarose hydrogel particles to produce BC with varied densities and structures. An intelligent convolutional neural network (CNN) computational method was developed to analyze the scattering patterns of BC. The BC production and morphology were determined with a throughput of ∼35 bacteria per second. A total of ∼10⁵ single-bacterium BC samples were characterized within 3 hours. The high flexibility of this approach facilitates high-throughput comprehensive single-cell production analysis for a range of applications in engineering biology.