Highly sensitive and quantitative detection of rare pathogens through agarose droplet microfluidic e

来源 :第八届全国微全分析系统学术会议、第三届全国微纳尺度生物分离分析学术会议暨第五届国际微化学与微系统学术会议 | 被引量 : 0次 | 上传用户:grandbill
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  Genetic alternations can serve as highly specific biomarkers to distinguish fatal bacteria or cancer cells from their normal counterparts.However,these mutations normally exist in very rare amount in the presence of a large excess of non-mutated analogs.Taking the notorious pathogen E.coli O157:H7 as the target analyte,we have developed an approach that combined the agarose droplets microfluidics,digital PCR,with high-throughput flow cytometry technique for highly sensitive,specific and quantitative detection of rare pathogens in the high background of normal bacteria.The agarose emulsion droplet microfluidic technology employed agarose with low melting and gelling temperature,which was coupled with PCR reverse primer using Schiff-based reaction.E.coli K12 and E.coli O157:H7 were compartmentalized in droplets at single-molecule level,in which the concentration of O157 cell varied from 0.5 to 0.00005 cell per droplet(cpd),while keeping the K12 cell density as 50 cpd.Each droplet contained two kinds of fluorescein-labeled forward primers.One is specific for E.coli O157:H7 which generated red fluorescent signals,and the other is for E.coli K12 which generated green fluorescent signals.After PCR,the droplets were cooled and converted to microbeads carrying the amplified products.The ratio of E.coli O157:H7 to E.coli K12 was determined by counting the ratio of red to green beads through flow cytometric analysis.Massively parallel singleplex and multiplex PCR at the single-cell level in agarose droplets have been successfully established and the sensitive and quantitative analysis of rare pathogen has been achieved with the sensitivity of a single E.coli O157:H7 cell in the high background of 100 000 excess normal E.coli K12 cells.Such a multiplex single-cell agarose droplet amplification method enables ultra-high throughput and multi-parameter genetic analysis of large population of cells at the single-cell level to uncover the stochastic variations in biological systems.
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