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合成 DNA 加标物(SDSI)可用于在 SARS-CoV-2 测序工作流程中跟踪样本并检测样本间的污染。

Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows.

机构信息

Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Harvard Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.

出版信息

Nat Microbiol. 2022 Jan;7(1):108-119. doi: 10.1038/s41564-021-01019-2. Epub 2021 Dec 14.

Abstract

The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will increase with the expanding global production of sequences across a variety of laboratories for epidemiological and clinical interpretation, as well as for genomic surveillance of emerging diseases in future outbreaks. We present SDSI + AmpSeq, an approach that uses 96 synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination throughout the sequencing workflow. We apply SDSIs to the ARTIC Consortium's amplicon design, demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and validate them across 6,676 diagnostic samples at multiple laboratories. We establish that SDSI + AmpSeq provides increased confidence in genomic data by detecting and correcting for relatively common, yet previously unobserved modes of error, including spillover and sample swaps, without impacting genome recovery.

摘要

SARS-CoV-2 的全球传播和持续进化推动了病毒基因组监测的空前激增。基于扩增子的测序方法提供了一种敏感、低成本和快速的方法,但存在很高的污染风险,这可能会破坏实验室流程和结果。随着越来越多的实验室在全球范围内对序列进行扩展,用于流行病学和临床解释,以及未来爆发时对新兴疾病的基因组监测,这一挑战将会增加。我们提出了 SDSI+AmpSeq 方法,该方法使用 96 个合成 DNA 加标物 (SDSIs) 来跟踪样本,并在整个测序工作流程中检测样本间的污染。我们将 SDSIs 应用于 ARTIC 联盟的扩增子设计,在对疑似 SARS-CoV-2 医院聚集性病例的实时调查中证明了它们的实用性和效率,并在多个实验室的 6676 份诊断样本中进行了验证。我们确定 SDSI+AmpSeq 通过检测和纠正包括溢出和样本交换在内的以前未观察到的常见错误模式,在不影响基因组恢复的情况下,为基因组数据提供了更高的可信度。

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