Artificial intelligence‐assisted automatic and index‐based microbial single‐cell sorting system for One‐Cell‐One‐Tube

作者：Zhidian Diao, Lingyan Kan, Yilong Zhao, Huaibo Yang, Jingyun Song, Chen Wang, Yang Liu, Fengli Zhang, Teng Xu, Rongze Chen, Yuetong Ji, Xixian Wang, Xiaoyan Jing, Jian Xu, Yuandong Li, Bo Ma · 发表于：mLife · 年份：2022 · DOI：10.1002/mlf2.12047 · 被引用次数：19 · 研究领域：Microfluidic and Bio-sensing Technologies、Innovative Microfluidic and Catalytic Techniques Innovation、3D Printing in Biomedical Research

Abstract Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in‐depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)‐assisted object detection model for cell phenotype screening and a cross‐interface contact method for single‐cell exporting, we developed an automatic and index‐based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, “One‐Cell‐One‐Tube” manner. The target cell is automatically identified based on an AI‐assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross‐interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single‐cell culture or sequencing. The efficiency of the system for single‐cell printing is >93%. The throughput of the system for single‐cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI‐assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single‐cell proliferation assays. The automation, index maintenance, and vitality ...