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Baccouche A, Okumura S, Sieskind R, Henry E, Aubert-Kato N, Bredeche N, Bartolo JF, Taly V, Rondelez Y, Fujii T, Genot AJ. Massively parallel and multiparameter titration of biochemical assays with droplet microfluidics. Nat Protoc 2017; 12:1912-1932. [PMID: 28837132 DOI: 10.1038/nprot.2017.092] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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.
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Affiliation(s)
- Alexandre Baccouche
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.,Earth Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan
| | - Shu Okumura
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.,CIBIS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Rémi Sieskind
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.,Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, Paris, France
| | - Elia Henry
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.,Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, Paris, France
| | - Nathanaël Aubert-Kato
- Earth Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan.,Department of Information Science, Ochanomizu University, Tokyo, Japan.,Sorbonne Universités, UPMC Univ. Paris 06, CNRS, Institute of Intelligent Systems and Robotics (ISIR), Paris, France
| | - Nicolas Bredeche
- Sorbonne Universités, UPMC Univ. Paris 06, CNRS, Institute of Intelligent Systems and Robotics (ISIR), Paris, France
| | | | - Valérie Taly
- INSERM UMR-S1147, CNRS SNC5014, Paris Descartes University, Equipe labellisée Ligue Nationale Contre le Cancer, Paris, France
| | - Yannick Rondelez
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.,Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, Paris, France
| | - Teruo Fujii
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan.,CIBIS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Anthony J Genot
- LIMMS, CNRS-Institute of Industrial Science, UMI 2820, The University of Tokyo, Tokyo, Japan
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Tabatabaei Shafiei M, Carvajal Gonczi CM, Rahman MS, East A, François J, Darlington PJ. Detecting glycogen in peripheral blood mononuclear cells with periodic acid schiff staining. J Vis Exp 2014:52199. [PMID: 25548935 PMCID: PMC4354478 DOI: 10.3791/52199] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Periodic acid Schiff (PAS) staining is an immunohistochemical technique used on muscle biopsies and as a diagnostic tool for blood samples. Polysaccharides such as glycogen, glycoproteins, and glycolipids stain bright magenta making it easy to enumerate positive and negative cells within the tissue. In muscle cells PAS staining is used to determine the glycogen content in different types of muscle cells, while in blood cell samples PAS staining has been explored as a diagnostic tool for a variety of conditions. Blood contains a proportion of white blood cells that belong to the immune system. The notion that cells of the immune system possess glycogen and use it as an energy source has not been widely explored. Here, we describe an adapted version of the PAS staining protocol that can be applied on peripheral blood mononuclear immune cells from human venous blood. Small cells with PAS-positive granules and larger cells with diffuse PAS staining were observed. Treatment of samples with amylase abrogates these patterns confirming the specificity of the stain. An alternate technique based on enzymatic digestion confirmed the presence and amount of glycogen in the samples. This protocol is useful for hematologists or immunologists studying polysaccharide content in blood-derived lymphocytes.
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Affiliation(s)
- Mahdieh Tabatabaei Shafiei
- Department of Biology, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University
| | - Catalina M Carvajal Gonczi
- Department of Biology, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University
| | - Mohammed Samiur Rahman
- Department of Chemistry and Biochemistry, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University
| | - Ashley East
- Department of Exercise Science, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University
| | - Jonathan François
- Department of Exercise Science, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University
| | - Peter J Darlington
- Department of Biology, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University; Department of Exercise Science, Centre for Structural and Functional Genomics, PERFORM Centre, Concordia University;
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