Evidence map›Paper›PMID 28837132›Full record

ArticleNature protocols2017

Massively parallel and multiparameter titration of biochemical assays with droplet microfluidics.

Alexandre Baccouche, Shu Okumura, Rémi Sieskind, Elia Henry, Nathanaël Aubert-Kato, Nicolas Bredeche, Jean-François Bartolo, Valérie Taly, Yannick Rondelez, Teruo Fujii and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
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

16 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
  2. Droplet-Based Microfluidics Methods for Detecting Enzyme Inhibitors.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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

11 authors at 8 institutions in 2 countries.

Alexandre BaccoucheLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Shu OkumuraLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Rémi SieskindLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Elia HenryLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Nathanaël Aubert-KatoEarth Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan.
Nicolas BredecheSorbonne Universités, UPMC Univ. Paris 06, CNRS, Institute of Intelligent Systems and Robotics (ISIR), Paris, France.
Jean-François BartoloLCAMB, UMR 7199, CNRS/Université de Strasbourg, Illkirch, France.
Valérie TalyINSERM UMR-S1147, CNRS SNC5014, Paris Descartes University, Equipe labellisée Ligue Nationale Contre le Cancer, Paris, France.
Yannick RondelezLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Teruo FujiiLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Anthony J GenotLIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.
Laboratory for Integrated Micro-Mechatronic Systems · JPUniversité Paris Sciences et Lettres · FRESPCI Paris · FRSorbonne Université · FRThe University of Tokyo · JPTokyo Institute of Technology · JPUniversité de Strasbourg · FRUniversité Paris Cité · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biochemical systems in which multiple components take part in a given reaction are of increasing interest. Because the interactions between these different components are complex and difficult to predict from basic reaction kinetics, it is important to test for the effect of variations in the concentration for each reagent in a combinatorial manner. For example, in PCR, an increase in the concentration of primers initially increases template amplification, but large amounts of primers result in primer-dimer by-products that inhibit the amplification of the template. Manual titration of biochemical mixtures rapidly becomes costly and laborious, forcing scientists to settle for suboptimal concentrations. Here we present a droplet-based microfluidics platform for mapping of the concentration space of up to three reaction components followed by detection with a fluorescent readout. The concentration of each reaction component is read through its internal standard (barcode), which is fluorescent but chemically orthogonal. We describe in detail the workflow, which comprises the following: (i) production of the microfluidics chips, (ii) preparation of the biochemical mixes, (iii) their mixing and compartmentalization into water-in-oil emulsion droplets via microfluidics, (iv) incubation and imaging of the fluorescent barcode and reporter signals by fluorescence microscopy and (v) image processing and data analysis. We also provide recommendations for choosing the appropriate fluorescent markers, programming the pressure profiles and analyzing the generated data. Overall, this platform allows a researcher with a few weeks of training to acquire ∼10,000 data points (in a 1D, 2D or 3D concentration space) over the course of a day from as little as 100-1,000 μl of reaction mix.

Indexed as

Biological AssayEquipment DesignFluorescent DyesImage Processing, Computer-AssistedMicrofluidic Analytical TechniquesSurface-Active AgentsTitrimetryFluorescent DyesSurface-Active Agents

Identifiers

PMID28837132
OpenAlexW2745393282

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.