System for unitary automation of library preparation

文库制备统一自动化系统

• 批准号: 10395282
• 负责人: Jay Kenneth Fisher
• 金额: $ 29.28万
• 依托单位: REDBUD LABS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-17 至 2023-02-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395282
• 关键词: Automation; Bar Codes; Bioinformatics; Communicable Diseases; Complex; Computer software; Data; Decentralization; Development; Devices; Diagnostic Equipment; Diagnostic tests; Disease Outbreaks; Disease Surveillance; Ebola; Environment; Evolution; Formulation; Future; Genomic DNA; Genomics; Goals; Gold; Hour; Letters; Libraries; Library Automation; Ligation; Liquid substance; Manuals; Microfluidic Microchips; Microfluidics; Molecular; Nucleic Acids; Outcome; Pathogen detection; Performance; Phase; Preparation; Reaction; Reagent; Resource-limited setting; Risk; Running; Sampling; Severities; Small Business Innovation Research Grant; Speed; System; Tail; Technology; Testing; Time; Training; Tube; Variant; Virus; base; cDNA Library; cloud based; cost; diagnostic platform; flexibility; genomic epidemiology; innovation; instrument; invention; laptop; nanopore; operation; pandemic disease; pathogen; portability; repaired; response; success; transmission process

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.

抽象的 基因组流行病学对于疫情监测和应对至关重要。测序数据让我们追踪病毒 实时进化，从而表征传输链、识别新出现的变异并预测未来 传播。这些信息不能仅从诊断测试中推断出来，但可以告知严重性和 爆发成为大流行之前的速度。为了根据测序数据采取纠正措施， 结果必须在几小时内获得，而不是几天。 理想情况下，自动化文库制备可以通过易于使用的单按钮设备来实现，该设备可以 作为按需传染病监测/诊断的一部分部署在资源有限的环境中 系统。然而，鉴于测序应用的多样性，不可能有一种一刀切的解决方案 图书馆准备。相反，我们认为正确的策略是自动化统一的文库制备操作， 其间定义良好的断点，使用户能够根据应用程序混合和匹配这些操作 需求。该策略将减少动手时间和操作员负担，同时保持工作流程的灵活性。 在这个第一阶段项目中，我们重点展示自动化三个单一文库制备的可行性 我们平台上的操作。这些操作是：末端修复/dA 加尾、条形码和接头连接。每个 步骤包括孵化和尺寸选择。我们的方法保护手册的可定制性 工作流程，同时消除高达 95% 的移液器步骤。该项目的成果将是一个解决方案 在资源匮乏的环境中进行分析前准备。