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Microfluidics and Nanofluidics

, Volume 18, Issue 2, pp 199–214 | Cite as

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

  • Mehdi Ghodbane
  • Anthony Kulesa
  • Henry H. Yu
  • Tim J. Maguire
  • Rene S. Schloss
  • Rohit Ramachandran
  • Jeffrey D. Zahn
  • Martin L. YarmushEmail author
Research Paper

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.

Keywords

Immunoassay Microfluidic Computational fluid dynamics Multiobjective optimization 

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mehdi Ghodbane
    • 1
  • Anthony Kulesa
    • 1
  • Henry H. Yu
    • 1
  • Tim J. Maguire
    • 1
  • Rene S. Schloss
    • 1
  • Rohit Ramachandran
    • 2
  • Jeffrey D. Zahn
    • 1
  • Martin L. Yarmush
    • 1
    • 3
    Email author
  1. 1.Department of Biomedical EngineeringRutgers, The State University of New JerseyPiscatawayUSA
  2. 2.Department of Chemical and Biochemical EngineeringRutgers, The State University of New JerseyPiscatawayUSA
  3. 3.Center for Engineering in Medicine/Surgical ServicesMassachusetts General HospitalBostonUSA

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