A plasmonic chip for biomarker discovery and diagnosis of type 1 diabetes

Journal name:
Nature Medicine
Volume:
20,
Pages:
948–953
Year published:
DOI:
doi:10.1038/nm.3619
Received
Accepted
Published online

Abstract

Type 1 diabetes (T1D) is an autoimmune disease, whereas type 2 diabetes (T2D) results from insulin resistance and beta cell dysfunction. Previously, the onset of these two separate diseases was easily distinguished, with children being most at risk for T1D and T2D occurring in overweight adults. However, the dramatic rise in obesity, coupled with the notable increase in T1D, has created a large overlap in these previously discrete patient populations. Delayed diagnosis of T1D can result in severe illness or death, and rapid diagnosis of T1D is critical for the efficacy of emerging therapies. However, attempts to apply next-generation platforms have been unsuccessful for detecting diabetes biomarkers. Here we describe the development of a plasmonic gold chip for near-infrared fluorescence–enhanced (NIR-FE) detection of islet cell–targeting autoantibodies. We demonstrate that this platform has high sensitivity and specificity for the diagnosis of T1D and can be used to discover previously unknown biomarkers of T1D.

At a glance

Figures

  1. Greater signal detection on plasmonic gold surface compared with standard surfaces.
    Figure 1: Greater signal detection on plasmonic gold surface compared with standard surfaces.

    (a) Electron micrograph demonstrating gold islands and abundant nanogaps in the nanostructured gold plasmonic film. (b) Schematic depicting the spatial relationship of the platform's PEG layer, the islet-specific antigens, the primary autoantibodies (Abs) from diluted human serum or blood and the detection antibodies conjugated with a fluorophore signal. (c) Calibration curves comparing detection limit and dynamic range of islet antigen autoantibody quantification on plasmonic gold, glass and evaporated gold substrates. Samples used for the calibration curves were standards containing known concentrations of autoantibodies in serum provided by the vendor (Kronus). Concentrations of standard samples for auto-insulin are in vendor's unit (KU/ml), and concentrations of standard samples for autoantibodies against GAD65 and IA2 are in the international unit of U/ml. We performed three independent experiments. Data shown as the mean ± s.d. (n = 3). (d) Comparison of detection limits and dynamic ranges of insulin-specific autoantibody quantification between plasmonic gold and RIA. RIA data is from vendor-provided RIA kits. Data shown as the mean ± s.d. (n = 3).

  2. The plasmonic chip readily differentiates T1D and T2D in ultralow serum or blood samples.
    Figure 2: The plasmonic chip readily differentiates T1D and T2D in ultralow serum or blood samples.

    (a) Schematic layout of triplicate islet cell antigens, human IgG (positive control for the detection antibody), PBS (negative control) and tetanus toxoid (positive control for antibody detection in human serum) on the plasmonic gold chip. (b) Fluorescence mapping result (left) and signal quantification (right) on plasmonic gold chips for islet antigen–specific autoantibody detection of typical patients with T1D or T2D and nondiabetic controls. Error bars represent error between signals of the triplicate spots for each antigen shown in (b, left); we conducted the array experiments three times, as reflected in Supplementary Figure 6. (c) Comparison of signal on chips tested with whole blood or serum from a typical subject (left) and quantification of signals comparing whole blood and serum in four independent patients (right). We performed three independent experiments. Data shown as the mean ± s.d. (d) Ultralow sample volumes can be used to profile diabetes autoantibodies in human serum or blood.

  3. Scatter plot for diabetes autoantibodies.
    Figure 3: Scatter plot for diabetes autoantibodies.

    Analysis of our subject pool of 26 children with new-onset T1D, 13 children with new-onset T2D and 5 nondiabetic children demonstrates a specific (i.e., non-normal) distribution of MFI values for the patients with T1D. Each point represents an individual patient. The titer for each of the three autoantibodies for the individual patient is plotted on the three-dimensional axes. Points that fall within the blue box are negative for T1D by plasmonic gold platform testing. Ab, antibody.

  4. The plasmonic chip permits differentiation of immunoglobulin isotypes from a single ultralow volume sample.
    Figure 4: The plasmonic chip permits differentiation of immunoglobulin isotypes from a single ultralow volume sample.

    (a) Absorbance and fluorescence emission of fluorophore Cy3, Cy5 and IRDye800. The emission spectra of the three fluorophores do not overlap with the emission spectrum at the trough of the absorbance spectrum (black), preventing absorption by fluorophores with adjacent emission spectra. (b) Simultaneous, specific detection of IgG, IgM and IgA isotypes on a multiplexed plasmonic gold chip using secondary antibodies with narrow, nonoverlapping emission spectra. (c) Multiplexed detection of islet cell–targeting autoantibody isotypes in a child with new-onset T1D. The multiplexed plasmonic gold chip simultaneously detects specific IgG autoantibodies against GAD65 and IA2, as well as IgM autoantibodies against insulin and GAD65, from a single sample from the example patient.

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Author information

  1. These authors contributed equally to this work.

    • Bo Zhang &
    • Rajiv B Kumar

Affiliations

  1. Stanford University, Stanford, California, USA.

    • Bo Zhang,
    • Rajiv B Kumar,
    • Hongjie Dai &
    • Brian J Feldman
  2. Department of Chemistry, Stanford University School of Humanities and Sciences, Stanford, California, USA.

    • Bo Zhang &
    • Hongjie Dai
  3. Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.

    • Rajiv B Kumar &
    • Brian J Feldman
  4. Program in Regenerative Medicine, Stanford University, Stanford, California, USA.

    • Brian J Feldman

Contributions

B.J.F. conceived of the study. B.Z., R.B.K., H.D. and B.J.F. designed the experiments, analyzed the data and wrote the manuscript. B.Z. and R.B.K. conducted the experiments.

Competing financial interests

Stanford University and the authors have filed patents for both the technology and the use of the technology to detect islet cell–targeting autoantibodies with the US Patent and Trademark Office and via the Patent Cooperation Treaty.

Corresponding authors

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Supplementary information

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  1. Supplementary Text and Figures (1,251 KB)

    Supplementary Figures 1–7, Supplementary Tables 1–3, and Supplementary Methods

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