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High-Throughput Steady-State Enzyme Kinetics Measured in a Parallel Droplet Generation and Absorbance Detection Platform
Open accessPublished version of a preprint
Abstract
ABSTRACT Microfluidic water-in-oil emulsion droplets are becoming a mainstay of experimental biology, where they replace the classical test tube. In most applications (e.g. in ultrahigh throughput directed evolution) the droplet content is identical for all compartmentalized assay reactions. When emulsion droplets are used for kinetics or other functional assays, though, concentration dependencies (e.g. of initial rates for Michaelis-Menten plots) are required. Droplet-on-demand systems satisfy this need but extracting large amounts of data is challenging. Here we introduce a multiplexed droplet absorbance detector which, coupled to semi-automated droplet generation, forms a tubing-based droplet-on-demand system able to generate and extract quantitative datasets from defined concentration gradients across multiple series of droplets for multiple time points. The emergence of product is detected by reading the absorbance of the droplet sets at multiple, adjustable time points (reversing the flow direction after each detection, so that the droplets pass a line scan camera multiple times). Detection multiplexing allows absorbance values at twelve distinct positions to be measured and enzyme kinetics are recorded for label-free concentration gradients (composed of about 60 droplets each, covering as many concentrations). With a throughput of around 8640 data points per hour, a 10-fold improvement compared to the previously reported single point detection method is achieved. In a single experiment, twelve full datasets of high-resolution and high accuracy Michaelis-Menten kinetics were determined to demonstrate the potential for enzyme characterization for glycosidase substrates covering a range in enzymatic hydrolysis of seven orders of magnitude in k cat /K M . The straightforward set-up, high throughput, excellent data quality, wide dynamic range that allows coverage of diverse activities suggest that this system may serve as a miniaturized spectrophotometer to for detailed analysis of study clones emerging from large-scale combinatorial experiments. Abstract Figure
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Millisecond Kinetics on a Microfluidic Chip Using Nanoliters of Reagents
Song, H., & Ismagilov, R. F. (2003) · Journal of the American Chemical Society · doi:10.1021/ja0354566 · Cited by 693
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Metadata
- Authors
- Neun, S., van Vliet, L., Hollfelder, F., & Gielen, F.
- Affiliations
- University of Cambridge (GB)
- University of Exeter (GB)
- Corresponding author
- Hollfelder, Florian
- Gielen, Fabrice
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-11-23
- Volume
- 94
- Issue
- 48
- Pages
- 16701-16710
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- PMC
- PMC9730296
- OpenAlex
- W4309809681
- Cited by
- 20
- References
- 2
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 6 · 2023: 3
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Analytical Chemistry (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- AstraZeneca
- Engineering and Physical Sciences Research Council (EP/H046593/1)
- H2020 Food (101000560)
- H2020 LEIT Biotechnology (685474)
- H2020 European Research Council (695669)
- Biotechnology and Biological Sciences Research Council (BB/T003545/1)
- Topics
- Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Innovative Microfluidic and Catalytic Techniques Innovation
- Physical Sciences › Engineering › Biomedical Engineering › Microfluidic and Capillary Electrophoresis Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- droplet microfluidics, water-in-oil emulsion droplets, enzyme kinetics, Michaelis-Menten kinetics, absorbance detection, concentration gradient, label-free detection, line-scan camera, combinatorial chemistry, directed evolution
- MeSH terms
- Biological Assay, Emulsions, Hydrolysis, Kinetics, Microfluidics, Microfluidic Analytical Techniques
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