Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. Highthroughput metabarcoding using DNA extracted from community samples (bulk) or from water orsediment (environmenta...Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. Highthroughput metabarcoding using DNA extracted from community samples (bulk) or from water orsediment (environmental DNA) has revolutionized biomonitoring, given the capability to assess biodiversity across the tree of life rapidly with feasible effort and at a modest price. DNA metabarcoding canbe upscaled to process hundreds of samples in parallel. However, while automated high-throughputanalysis workflows are well-established in the medical sector, manual sample processing still predominates in biomonitoring laboratory workflows limiting the upscaling and standardization for routinemonitoring applications. Here we present an automated, scalable, and reproducible metabarcodingworkflow to extract DNA from bulk samples, perform PCR and library preparation on a liquid handler.Key features are the independent sample replication throughout the workflow and the use of manynegative controls for quality assurance and quality control. We generated two datasets: i) a validationdataset consisting of 42 individual arthropod specimens of different species, and ii) a routine monitoringdataset consisting of 60 stream macroinvertebrate bulk samples. As a marker, we used the mitochondrialCOI gene. Our results show that the developed single-deck workflow is free of laboratory-derivedcontamination and produces highly consistent results. Minor deviations between replicates are mostlydue to stochastic differences for low abundant OTUs. Thus, we successfully demonstrated that roboticliquid handling can be used reliably from DNA extraction to final library preparation on a single deck,thereby substantially increasing throughput, reducing costs, and increasing data robustness for biodiversity assessments and monitoring.展开更多
基金This study is a part of the GeDNA project funded by the German Environment Agency(FKZ 3719242040)All members are part of COST Action DNAqua-Net(CA15219)+1 种基金D..B is supported by a grant of the German Research Foundation(DFG,LE 2323/9-1)We thank Kristin Stolberg(LfU)for discussions and permission to use the metabarcoding data.
文摘Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. Highthroughput metabarcoding using DNA extracted from community samples (bulk) or from water orsediment (environmental DNA) has revolutionized biomonitoring, given the capability to assess biodiversity across the tree of life rapidly with feasible effort and at a modest price. DNA metabarcoding canbe upscaled to process hundreds of samples in parallel. However, while automated high-throughputanalysis workflows are well-established in the medical sector, manual sample processing still predominates in biomonitoring laboratory workflows limiting the upscaling and standardization for routinemonitoring applications. Here we present an automated, scalable, and reproducible metabarcodingworkflow to extract DNA from bulk samples, perform PCR and library preparation on a liquid handler.Key features are the independent sample replication throughout the workflow and the use of manynegative controls for quality assurance and quality control. We generated two datasets: i) a validationdataset consisting of 42 individual arthropod specimens of different species, and ii) a routine monitoringdataset consisting of 60 stream macroinvertebrate bulk samples. As a marker, we used the mitochondrialCOI gene. Our results show that the developed single-deck workflow is free of laboratory-derivedcontamination and produces highly consistent results. Minor deviations between replicates are mostlydue to stochastic differences for low abundant OTUs. Thus, we successfully demonstrated that roboticliquid handling can be used reliably from DNA extraction to final library preparation on a single deck,thereby substantially increasing throughput, reducing costs, and increasing data robustness for biodiversity assessments and monitoring.
