Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5, Canada.
School of Natural Sciences, Bangor University, Bangor, LL57 2DG, UK.
Microb Cell Fact. 2021 Sep 23;20(1):184. doi: 10.1186/s12934-021-01675-3.
Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.
In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.
Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.
微生物可以通过代谢工程进行改造，以生产广泛的商业重要化学品。用于菌株设计的计算策略和用于构建设计菌株的合成生物学技术的进步，促进了大量潜在候选物库的产生，这些候选物库可用于化学生产。因此，需要高通量实验室规模的技术来对这些候选物进行特性分析和筛选，以选择用于在大规模发酵过程中进一步研究的菌株。几种小规模发酵技术与实验室自动化相结合，提高了酶和菌株表型实验的通量。然而，这种高通量实验通常需要大量的运营成本，并产生大量的实验室塑料废物。
在这项工作中，我们开发了一种环保的自动化工作流程，该流程可以有效地校准和消毒固定点移液系统，以减少吸头浪费。我们还研究了在微孔板中建立厌氧条件的廉价方法，以进行高通量厌氧表型分析。为了验证我们的表型分析平台，我们进行了两项案例研究-厌氧酶筛选和微生物表型筛选。我们使用我们的自动化平台来研究在 0.5 L 生物反应器和我们缩小的发酵平台中，几种大肠杆菌菌株在相同条件下表现出相同表型的条件。我们还提出了使用 t 分布随机邻居嵌入 (t-SNE) 进行维度降低，并结合我们的表型分析平台，有效地在生物反应器规模上对表现相似的菌株进行聚类。
固定点移液系统可以显著减少生物实验室产生的塑料废物量，我们的去污和校准方案可以促进这些系统的广泛采用。此外，在我们的自动化平台中结合使用 t-SNE 可以作为生物反应器发酵的有效缩小模型。最后，通过整合内部数据分析管道，我们能够加快代谢工程设计-构建-测试-学习循环的“测试”阶段。
