System for unitary automation of library preparation

摘要 基因组流行病学对疫情监测和应对至关重要。测序数据让我们追踪病毒 在真实的时间演变，从而表征传输链，识别新出现的变种，并预测未来 传播.这些信息不能单独从诊断测试中推断出来，它们告知了严重程度， 在它成为大流行之前的爆发速度。为了从测序数据中通知纠正措施， 必须在数小时内而不是数天内取得结果。 理想情况下，自动化文库制备将通过易于使用的单个按钮装置来实现， 部署在资源有限的环境中，作为需要时传染病监测/诊断的一部分 系统然而，考虑到测序应用的多样性，不可能有一个一刀切的解决方案 相反，我们认为正确的策略是自动化单一的图书馆准备操作， 定义良好的断点，使用户能够混合和匹配这些操作作为应用程序 要求。该策略将减少操作时间和操作员负担，同时保持工作流程的灵活性。 在这个第一阶段的项目中，我们重点展示了自动化三个单一的图书馆准备的可行性， 在我们的平台上运行。操作是：末端修复/dA加尾、条形码化和衔接子连接。每个 步骤包括孵育，然后进行大小选择。我们的方法保护了手动 工作流程，同时消除高达95%的移液步骤。该项目的成果将是一个解决方案， 在低资源环境下进行分析前准备。

ABSTRACT Genomic epidemiology is crucial to outbreak surveillance and response. Sequencing data lets us track virus evolution in real time and thus characterize transmission chains, identify emerging variants, and predict future spread. This information, which cannot be deduced from diagnostic testing alone, informs the severity and speed of an outbreak before it becomes a pandemic. In order to inform corrective action from sequencing data, results must be obtained within hours, instead of days. Ideally, automated library preparation would be achieved with an easy-to-use, single button device that can be deployed in resource-limited settings as part of a point-of-need infectious disease surveillance/diagnostic system. However, given the diversity of sequencing applications, there cannot be a one-size-fits-all solution to library prep. Instead, we believe the correct strategy is to automate unitary library prep operations, with well-defined breakpoints in between, enabling the user to mix and match these operations as the application demands. This strategy will reduce hands-on time and operator burden while maintaining workflow flexibility. In this Phase I project, we focus on demonstrating the feasibility of automating three unitary library prep operations on our platform. The operations are: end repair/dA-tailing, barcoding, and adapter ligation. Each step involves an incubation followed by size selection. Our approach protects the customizability of manual workflows while eliminating up to 95% of pipette steps. The outcome of this project will be a solution for pre-analytical preparation in low-resource settings.