Evidence map›Paper›PMID 38409552›Full record

ArticleNature nanotechnology2024

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

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

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Article in Nature nanotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Guillaume GinesLaboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, Paris, France. guillaume.gines@espci.fr.ORCID http://orcid.org/0000-0003-1012-3250
Rocίo EspadaLaboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, Paris, France.ORCID http://orcid.org/0000-0003-3829-473X
Adèle Dramé-MaignéLaboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, Paris, France.ORCID http://orcid.org/0000-0003-3586-0361
Alexandre BaccoucheLIMMS, IRL 2820 CNRS-Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Nicolas LarrouyLaboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, Paris, France.
Yannick RondelezLaboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, Paris, France.ORCID http://orcid.org/0000-0002-2565-476X

Funding

Agence Nationale de la Recherche (French National Research Agency) 243063EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 647275EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 949493
6 · The paper itself

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.

Indexed as

MicrofluidicsDNAEnzymesStreptococcus pyogenesDNAEnzymes

Identifiers

PMID38409552

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.