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Functional analysis of single enzymes combining programmable molecular circuits with droplet-based microfluidics

Nature Nanotechnology (2024)Cite this article

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Abstract

The analysis of proteins at the single-molecule level reveals heterogeneous behaviours that are masked in ensemble-averaged techniques. The digital quantification of enzymes traditionally involves the observation and counting of single molecules partitioned into microcompartments via the conversion of a profluorescent substrate. This strategy, based on linear signal amplification, is limited to a few enzymes with sufficiently high turnover rate. Here we show that combining the sensitivity of an exponential molecular amplifier with the modularity of DNA–enzyme circuits and droplet readout makes it possible to specifically detect, at the single-molecule level, virtually any D(R)NA-related enzymatic activity. This strategy, denoted digital PUMA (Programmable Ultrasensitive Molecular Amplifier), is validated for more than a dozen different enzymes, including many with slow catalytic rate, and down to the extreme limit of apparent single turnover for Streptococcus pyogenes Cas9. Digital counting uniquely yields absolute molar quantification and reveals a large fraction of inactive catalysts in all tested commercial preparations. By monitoring the amplification reaction from single enzyme molecules in real time, we also extract the distribution of activity among the catalyst population, revealing alternative inactivation pathways under various stresses. Our approach dramatically expands the number of enzymes that can benefit from quantification and functional analysis at single-molecule resolution. We anticipate digital PUMA will serve as a versatile framework for accurate enzyme quantification in diagnosis or biotechnological applications. These digital assays may also be utilized to study the origin of protein functional heterogeneity.

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Fig. 1: Proof of principle for the detection of NBI nickase activity.
Fig. 2: Generalization of the PUMA-assisted detection assay over a dozen enzymes.
Fig. 3: Specificity of the enzymatic assays.
Fig. 4: Digital detection of DNA-related enzymes.
Fig. 5: Activity distribution is assessed from time-lapse experiments.
Fig. 6: Study of functional heterogeneity of an enzyme population.

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Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information. All nucleic acid sequences and experimental conditions are available in Supplementary Tables 1 and 3. Unprocessed data related to Figs. 1–4 are provided in the source data file. Raw data (droplet coordinates and fluorescence for each time point) from Figs. 5 and 6 can be accessed at the following publicly accessible repository: https://doi.org/10.5281/zenodo.10455918, https://doi.org/10.5281/zenodo.10455829 and https://doi.org/10.5281/zenodo.10455612.

Code availability

The Mathematica code used for droplet analysis is available via GitHub at the following link: https://github.com/GuGi75/Droplet-analysis

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Acknowledgements

We thank the R&D department of New England Biolabs for providing us with the enzyme concentrations. We thank N. Lobato-Dauzier, A. Genot and the platform FEMTO-ST (CNRS, Besançon) for providing the silicon-made incubation chambers. This work was supported by the European Research Council under the framework program H2020 for research and innovation (grant projects MoP-MiP, number 949493 and ProFF, number 647275) and ANR grant number 243063 MoBiDYC.

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Authors and Affiliations

  1. Laboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, Paris, France

    Guillaume Gines, Rocίo Espada, Adèle Dramé-Maigné, Nicolas Larrouy & Yannick Rondelez

  2. LIMMS, IRL 2820 CNRS-Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Alexandre Baccouche

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  1. Guillaume Gines
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Contributions

G.G. and Y.R. conceived the study and contributed to the design of experiments. G.G., R.E. A.D.-M. and N.L. performed the experiments. A.B. designed the spy.Cas9 assay and produced the sgRNA. G.G., Y.R. and R.E. contributed to data analysis and interpretation. G.G. drafted the paper and all authors provided feedback.

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Correspondence to Guillaume Gines.

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Competing interests

G.G. and Y.R. are listed as inventors on a patent assigned to the CNRS, INSERM, ESPCI Paris, Université de recherche PSL and Université Paris Cité. All other authors declare no competing interests.

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Nature Nanotechnology thanks Hans-Heiner Gorris, Fabrice Gielen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary information

Supplementary Notes 1–3, Figs. 1–33 and Tables 1–4.

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Source data

Source Data Fig. 1

Unprocessed amplification time traces.

Source Data Fig. 2

Unprocessed amplification time traces.

Source Data Fig. 3

Unprocessed amplification time traces.

Source Data Fig. 4

Droplets analysis source data.

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Gines, G., Espada, R., Dramé-Maigné, A. et al. Functional analysis of single enzymes combining programmable molecular circuits with droplet-based microfluidics. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01617-1

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