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Development of a low-volume, highly sensitive microimmunoassay using computational fluid dynamics-driven multiobjective optimization

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Abstract

Immunoassays are one of the most versatile and widely performed biochemical assays and, given their selectivity and specificity, are used in both clinical and research settings. However, the high cost of reagents and relatively large sample volumes constrain the integration of immunoassays into many applications. Scaling the assay down within microfluidic devices can alleviate issues associated with reagent and sample consumption. However, in many cases, a new device is designed and empirically optimized for each specific analyte, a costly and time-consuming approach. In this paper, we report the development of a microfluidic bead-based immunoassay that, using antibody-coated microbeads, can potentially detect any analyte or combination of analytes for which antibody-coated microbeads can be generated. We also developed a computational reaction model and optimization algorithm that can be used to optimize the device for any analyte. We applied this technique to develop a low-volume IL-6 immunoassay with high sensitivity (358 fM, 10 pg/mL) and a large dynamic range (four orders of magnitude). This device design and optimization technique can be used to design assays for any protein with an available antibody and can be used with a large number of applications including biomarker discovery, temporal in vitro studies using a reduced number of cells and reagents, and analysis of scarce biological samples in animal studies and clinical research settings.

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Acknowledgments

This work was partially funded by the National Institute of Health Grants P41EB002503 and UH2TR000503, the National Institute of Health Rutgers Biotechnology Training Program (T32GM008339), and the National Science Foundation Integrated Science and Engineering of Stem Cells Program (DGE0801620). The authors would like to thank Dr. Sara Salahi, Dr. Kellie Anderson, and Anwesha Chaudhury for productive conversations regarding computational and optimization issues, Dana Barrasso for guidance pertaining to statistical analysis, Dr. Bhaskar Mitra for support in developing the fabrication protocols, and modeFrontier customer support for extensive troubleshooting of the optimization platform.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, 08854, USA

    Mehdi Ghodbane, Anthony Kulesa, Henry H. Yu, Tim J. Maguire, Rene S. Schloss, Jeffrey D. Zahn & Martin L. Yarmush

  2. Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA

    Rohit Ramachandran

  3. Center for Engineering in Medicine/Surgical Services, Massachusetts General Hospital, 51 Blossom Street, Boston, MA, 02114, USA

    Martin L. Yarmush

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  1. Mehdi Ghodbane
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Correspondence to Martin L. Yarmush.

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Ghodbane, M., Kulesa, A., Yu, H.H. et al. Development of a low-volume, highly sensitive microimmunoassay using computational fluid dynamics-driven multiobjective optimization. Microfluid Nanofluid 18, 199–214 (2015). https://doi.org/10.1007/s10404-014-1416-9

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