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

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 crossinterface 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 preservation of EasySort AUTO suggest its excellent application potential for single-cell sorting.

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing

By directly converting solar energy and carbon dioxide into biobased products,cyanobacteria are promising chassis for photosynthetic biosynthesis.To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales,exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance.To simplify and accelerate the screening for fast-growing cyanobacteria strains,a method called Individual Cyanobacteria Vitality Tests and Screening(iCyanVS)was established.We show that the ^(13)C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations,thus greatly accelerating the process screening for fastest-growing cells.The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing.Therefore,this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.