Plug and play microfluidics for frontier Agri-Tech assays

开发即插即用的芯片实验室技术，用于农药代谢降解的高通量分析：农药行业的最大瓶颈之一是缺乏对农药如何被给定物种代谢的定量和预测性理解（包括期望的和不期望的）。这是该行业面临的许多尚未解决的挑战的核心，这些挑战围绕着以下问题：我们如何使用更少的农药，从而最大限度地减少环境暴露？我们怎样才能设计出对目标害虫更有针对性的杀虫剂，避免非目标物种？我们如何使用预测工具在开发管道的上游做出决策，从而降低将农用化学品推向市场的成本？因此，了解农药如何被感兴趣的物种代谢，对于化合物开发和确定其生物利用度至关重要。目前探测农药代谢的方法缓慢、费力且通量低：化合物基本上是一个接一个地测试，大大降低了测试新分子的可能性。该项目将汇集我们在微流控、分析化学、代谢组学和农业科学方面的专业知识，开发新的最先进的平台，使我们能够正面应对这一挑战。我们的技术将融合机器人采样、连续流微流控和微滴生产，以生成一个活性成分（AI）库，并与模型病原体一起孵育。每个液滴内AI的代谢降解将使用耦合的快速亚30秒分析分离和/或质谱直接在线测量，以在单次运行中产生数千个数据点。当与大数据分析相结合时，快速筛选将使我们能够建立一套预测模型，将化学结构与代谢降解特征相关联。我们的芯片实验室设置将是当前方法的一步变化，当前方法依赖于人工处理孔板。重要的是，我们的技术将是使能技术：一旦建立，它们可以重新用于检测多种细胞类型，并在未来的迭代中扩展到转录组学，蛋白质组学和脂质组学分析。

Development of plug and play lab-on-chip technologies for high-throughput profiling of pesticide metabolic degradation: One of the greatest bottlenecks in the agrichemical industry is the lack of a quantitative and predictive understanding of how pesticides are metabolised by a given species (both desired and undesired). This goes to the heart of many unresolved challenges the industry faces, which centre around the following questions: How can we use fewer pesticides, thus minimising environmental exposure? How can we design pesticides that are more specific to the target pest, avoiding non-target species? How can we reduce the cost of getting an agrichemical to market using predictive tools to make decisions upstream of the development pipeline? Understanding how a pesticide is metabolised by a species of interest therefore of central importance for compound development, and determination of its bioavailability. Current methods to probe pesticide metabolism are slow, laborious, and low-throughput: compounds are essentially tested one-by-one, considerably reducing possibilities to test new molecules. This project will bring together our expertise in microfluidics, analytical chemistry, metabolomics, and agri-science to develop new state-of-the-art platforms, allowing us to tackle this challenge head-on. Our technologies will fuse robotic sampling, continuous flow microfluidics, and microdroplet production to generate a library of active ingredients (AIs) that will be incubated with a model pathogen. Metabolic degradation of the AIs within each droplet will be measured online using coupled rapid sub-30-second analytical separation and/or mass spectrometry directly, to yield thousands of data points within a single run. When coupled to big-data analysis, rapid screening will allow us to build a suite of predictive models to correlate chemical structure with metabolic degradation characteristics. Our lab-on-chip setup will be a step-change from current methods, which rely on manual handling of well plates. Importantly, our technologies will be enabling ones: once established, they can be repurposed to assay multiple cell types, and extended to transcriptomic, proteomic & lipidomic analysis in future iterations.