Suppr超能文献

用于细菌定量、生长监测及超高通量微流控分离的生长相关液滴收缩

Growth-Associated Droplet Shrinkage for Bacterial Quantification, Growth Monitoring, and Separation by Ultrahigh-Throughput Microfluidics.

作者信息

Geersens Émilie, Vuilleumier Stéphane, Ryckelynck Michael

机构信息

Université de Strasbourg, CNRS, Architecture et Réactivité de l'ARN, UPR 9002, 67000 Strasbourg, France.

Université de Strasbourg, CNRS, Génétique Moléculaire, Génomique, Microbiologie, UMR 7156, 67000 Strasbourg, France.

出版信息

ACS Omega. 2022 Mar 30;7(14):12039-12047. doi: 10.1021/acsomega.2c00248. eCollection 2022 Apr 12.

Abstract

Microbiology still relies on en masse cultivation for selection, isolation, and characterization of microorganisms of interest. This constrains the diversity of microbial types and metabolisms that can be investigated in the laboratory also because of intercellular competition during cultivation. Cell individualization by droplet-based microfluidics prior to experimental analysis provides an attractive alternative to access a larger fraction of the microbial biosphere, miniaturizing the required equipment and minimizing reagent use for increased and more efficient analytical throughput. Here, we show that cultivation of a model two-strain bacterial community in droplets significantly reduces representation bias in the grown culture compared to batch cultivation. Further, and based on the droplet shrinkage observed upon cell proliferation, we provide proof-of-concept for a simple strategy that allows absolute quantification of microbial cells in a sample as well as selective recovery of microorganisms of interest for subsequent experimental characterization.

摘要

微生物学仍然依赖大规模培养来筛选、分离和鉴定感兴趣的微生物。这限制了实验室中可研究的微生物类型和代谢的多样性,这也是由于培养过程中的细胞间竞争所致。在实验分析之前,通过基于微滴的微流控技术实现细胞个体化,为获取更大比例的微生物生物圈提供了一种有吸引力的替代方法,可使所需设备小型化,并减少试剂使用量,以提高分析通量和效率。在这里,我们表明,与分批培养相比,在微滴中培养模型双菌株细菌群落可显著降低培养物中的代表性偏差。此外,基于细胞增殖时观察到的微滴收缩,我们为一种简单策略提供了概念验证,该策略可对样品中的微生物细胞进行绝对定量,并选择性回收感兴趣的微生物用于后续的实验表征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1893/9016821/d68d3874fdb3/ao2c00248_0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验