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Threshold-Based Quantification in a Multiline Lateral Flow Assay via Computationally Designed Capture Efficiency

  • David J. Gasperino*
    David J. Gasperino
    Intellectual Ventures, Bellevue, Washington 98005, United States
    *E-mail: [email protected] (D.J.G.)
  • Daniel Leon
    Daniel Leon
    University of Washington, Seattle, Washington 98195, United States
    More by Daniel Leon
  • Barry Lutz
    Barry Lutz
    University of Washington, Seattle, Washington 98195, United States
    More by Barry Lutz
  • David M. Cate
    David M. Cate
    Intellectual Ventures, Bellevue, Washington 98005, United States
  • Kevin P. Nichols
    Kevin P. Nichols
    Intellectual Ventures, Bellevue, Washington 98005, United States
  • David Bell
    David Bell
    Intellectual Ventures, Bellevue, Washington 98005, United States
    More by David Bell
  • , and 
  • Bernhard H. Weigl
    Bernhard H. Weigl
    Intellectual Ventures, Bellevue, Washington 98005, United States
    University of Washington, Seattle, Washington 98195, United States
Cite this: Anal. Chem. 2018, 90, 11, 6643–6650
Publication Date (Web):April 23, 2018
https://doi.org/10.1021/acs.analchem.8b00440
Copyright © 2018 American Chemical Society
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Supporting Info (1)»

Abstract

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Lateral flow assays (LFAs) are widely used for yes/no detection of analytes, but they are not well-suited for quantification. We show that the sensitivity of the test line in a lateral flow assay can be tuned to appear at a specific sample concentration by varying the density of capture molecules at the test line and that when test lines tuned for different responses are combined into a single test strip, lines appear at specific thresholds of sample concentration. We also developed a model based on mass-action kinetics that accurately described test line signal and shape over a wide matrix of capture molecules and sample concentrations in single-line strips. The model was used to design a three-line test strip with lines designed to appear at logarithmically spaced sample concentrations, and the experiments showed a remarkable match to predictions. The response of this “graded ladder bar” format is due to the effect of test line concentration on capture efficiency at each test line, not on sample depletion effects, and the effect is maintained whether a system is under kinetic or equilibrium control. These features enable design of nonlinear responses (logarithmic here) and suggest robustness for different systems. Thus, the graded ladder bar format could be a useful tool for applications requiring quantification of sample concentrations over a wide dynamic range.

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.8b00440.

  • Mathematical background of the LFA model, octet kinetic experiments and data analysis, LFA model parameters, kinetic binding rates for ferritin–anti-ferritin LFA, sequential reagent delivery steps for single-line α-ferritin LFAs, images of a single-line LFA, description of the LFA model validation process, maximum test line signal for a ferritin system with model predictions, images of graded ladder bar LFA, percent of ferritin capture, model prediction for different test line layout, experimental data for a four-line LFA, coefficient of variation for signal within linear response region (PDF)

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Cited By

This article is cited by 14 publications.

  1. Amadeo Sena-Torralba, Ruslan Álvarez-Diduk, Claudio Parolo, Andrew Piper, Arben Merkoçi. Toward Next Generation Lateral Flow Assays: Integration of Nanomaterials. Chemical Reviews 2022, Article ASAP.
  2. Justin M. Rosenbohm, Catherine M. Klapperich, Mario Cabodi. Tunable Duplex Semiquantitative Detection of Nucleic Acids with a Visual Lateral Flow Immunoassay Readout. Analytical Chemistry 2022, 94 (9) , 3956-3962. https://doi.org/10.1021/acs.analchem.1c05039
  3. Daquan Li, Mei Huang, Ziyu Shi, Liang Huang, Jiening Jin, Chenxing Jiang, Wenbo Yu, Zhiyong Guo, Jing Wang. Ultrasensitive Competitive Lateral Flow Immunoassay with Visual Semiquantitative Inspection and Flexible Quantification Capabilities. Analytical Chemistry 2022, 94 (6) , 2996-3004. https://doi.org/10.1021/acs.analchem.1c05364
  4. Kazushi Misawa, Tomohiro Yamamoto, Yuki Hiruta, Hiroki Yamazaki, Daniel Citterio. Text-Displaying Semiquantitative Competitive Lateral Flow Immunoassay Relying on Inkjet-Printed Patterns. ACS Sensors 2020, 5 (7) , 2076-2085. https://doi.org/10.1021/acssensors.0c00637
  5. Sadagopan Krishnan, Zia ul Quasim Syed. Colorimetric Visual Sensors for Point-of-needs Testing. Sensors and Actuators Reports 2022, 4 , 100078. https://doi.org/10.1016/j.snr.2022.100078
  6. Xinran Xiang, Qinghua Ye, Yuting Shang, Fan Li, Baoqing Zhou, Yanna Shao, Chufang Wang, Jumei Zhang, Liang Xue, Moutong Chen, Yu Ding, Qingping Wu. Quantitative detection of aflatoxin B1 using quantum dots-based immunoassay in a recyclable gravity-driven microfluidic chip. Biosensors and Bioelectronics 2021, 190 , 113394. https://doi.org/10.1016/j.bios.2021.113394
  7. Lalitha Pratyusha Bheemavarapu, Malay Ilesh Shah, Jayaraj Joseph, Mohanasankar Sivaprakasam. IQVision: An Image-Based Evaluation Tool for Quantitative Lateral Flow Immunoassay Kits. Biosensors 2021, 11 (7) , 211. https://doi.org/10.3390/bios11070211
  8. Guo Xia, Jiangtao Wang, Zhijian Liu, Lihao Bai, Long Ma. Effect of sample volume on the sensitivity of lateral flow assays through computational modeling. Analytical Biochemistry 2021, 619 , 114130. https://doi.org/10.1016/j.ab.2021.114130
  9. V. Sejian, M. V. Silpa, S. S. Chauhan, M. Bagath, C. Devaraj, G. Krishnan, M. R. Reshma Nair, J. P. Anisha, A. Manimaran, S. Koenig, R. Bhatta, F. R. Dunshea. Eco-Intensified Breeding Strategies for Improving Climate Resilience in Goats. 2021,,, 627-655. https://doi.org/10.1007/978-981-33-4203-3_18
  10. Dmitriy V. Sotnikov, Anatoly V. Zherdev, Boris B. Dzantiev. Lateral Flow Serodiagnosis in the Double-Antigen Sandwich Format: Theoretical Consideration and Confirmation of Advantages. Sensors 2021, 21 (1) , 39. https://doi.org/10.3390/s21010039
  11. Changrui Xing, Xue Dong, Tao Xu, Jian Yuan, Wenjing Yan, Xiaonan Sui, Xiaoxu Zhao. Analysis of multiple mycotoxins-contaminated wheat by a smart analysis platform. Analytical Biochemistry 2020, 610 , 113928. https://doi.org/10.1016/j.ab.2020.113928
  12. Dmitriy V. Sotnikov, Nadezhda A. Byzova, Elena A. Zvereva, Anastasia V. Bartosh, Anatoly V. Zherdev, Boris B. Dzantiev. Mathematical modeling of immunochromatographic test systems in a competitive format: Analytical and numerical approaches. Biochemical Engineering Journal 2020, 164 , 107763. https://doi.org/10.1016/j.bej.2020.107763
  13. N. Sathishkumar, Bhushan J. Toley. Development of an experimental method to overcome the hook effect in sandwich-type lateral flow immunoassays guided by computational modelling. Sensors and Actuators B: Chemical 2020, 324 , 128756. https://doi.org/10.1016/j.snb.2020.128756
  14. Bowei Li, Ji Qi, Longwen Fu, Jinglong Han, Jaebum Choo, Andrew J. deMello, Bingcheng Lin, Lingxin Chen. Integrated hand-powered centrifugation and paper-based diagnosis with blood-in/answer-out capabilities. Biosensors and Bioelectronics 2020, 165 , 112282. https://doi.org/10.1016/j.bios.2020.112282

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