4.8 Article

A Fully Unsupervised Compartment-on-Demand Platform for Precise Nanoliter Assays of Time-Dependent Steady-State Enzyme Kinetics and Inhibition

期刊

ANALYTICAL CHEMISTRY
卷 85, 期 9, 页码 4761-4769

出版社

AMER CHEMICAL SOC
DOI: 10.1021/ac400480z

关键词

-

资金

  1. European Research Council
  2. EPSRC
  3. EU
  4. BBSRC Enterprise fellowship
  5. EPSRC [EP/H046593/1] Funding Source: UKRI
  6. Engineering and Physical Sciences Research Council [EP/H046593/1] Funding Source: researchfish

向作者/读者索取更多资源

The ability to miniaturize biochemical assays in water-in-oil emulsion droplets allows a massive scale down of reaction volumes, so that high-throughput experimentation can be performed more economically and more efficiently. Generating such droplets in compartment-on-demand (COD) platforms is the basis for rapid, automated screening of chemical and biological libraries with minimal volume consumption. Herein, we describe the implementation of such a COD platform to perform high precision nanoliter assays. The coupling of a COD platform to a droplet absorbance detection set-up results in a fully automated analytical system. Michaelis-Menten parameters of 4-nitrophenyl glucopyranoside hydrolysis by sweet almond beta-glucosidase can be generated based on 24 time-courses taken at different substrate concentrations with a total volume consumption of only 1.4 mu L. Importantly, kinetic parameters can be derived in a fully unsupervised manner within 20 min: droplet production (S min), initial reading of the droplet sequence (5 min), and droplet fusion to initiate the reaction and read-out over time (10 min). Similarly, the inhibition of the enzymatic reaction by conduritol B epoxide and 1-deoxynojirimycin was measured, and K-i values were determined In both cases, the kinetic parameters obtained in droplets were identical within error to values obtained in titer plates, despite a >10(4)-fold volume reduction, from micro- to nanoliters.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据