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 intel...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.展开更多
The isolation chip method(iChip)provides a novel approach for culturing previously uncultivable microorganisms;this method is currently limited by the user being unable to ensure single-cell loading within individual ...The isolation chip method(iChip)provides a novel approach for culturing previously uncultivable microorganisms;this method is currently limited by the user being unable to ensure single-cell loading within individual wells.To address this limitation,we integrated flow cytometry-based fluorescence-activated cell sorting with a modified iChip(FACS-iChip)to effectively mine microbial dark matter in soils.This method was used for paddy soils with the aim of mining uncultivable microorganisms and making preliminary comparisons between the cultured microorganisms and the bulk soil via 16S rRNA gene sequencing.Results showed that the FACS-iChip achieved a culture recovery rate of almost 40%and a culture retrieval rate of 25%.Although nearly 500 strains were cultured from 19 genera with 8 FACS-iChip plates,only six genera could be identified via 16S rRNA gene amplification.This result suggests that the FACS-iChip is capable of detecting strains in the currently dead spaces of PCR-based sequencing technology.We,therefore,conclude that the FACS-iChip system provides a highly efficient and readily available approach for microbial‘dark matter’mining.展开更多
基金the National Key R&D Program of China(Grant No.2021YFC2101100).
文摘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.
基金This research was financially supported by the National Natural Science Foundation of China(41991334)the Zhejiang Provincial Natural Science Foundation of China(LD19D060001,LQ20C030006)the China Postdoctoral Science Foundation(2019M652097).
文摘The isolation chip method(iChip)provides a novel approach for culturing previously uncultivable microorganisms;this method is currently limited by the user being unable to ensure single-cell loading within individual wells.To address this limitation,we integrated flow cytometry-based fluorescence-activated cell sorting with a modified iChip(FACS-iChip)to effectively mine microbial dark matter in soils.This method was used for paddy soils with the aim of mining uncultivable microorganisms and making preliminary comparisons between the cultured microorganisms and the bulk soil via 16S rRNA gene sequencing.Results showed that the FACS-iChip achieved a culture recovery rate of almost 40%and a culture retrieval rate of 25%.Although nearly 500 strains were cultured from 19 genera with 8 FACS-iChip plates,only six genera could be identified via 16S rRNA gene amplification.This result suggests that the FACS-iChip is capable of detecting strains in the currently dead spaces of PCR-based sequencing technology.We,therefore,conclude that the FACS-iChip system provides a highly efficient and readily available approach for microbial‘dark matter’mining.
