Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Greater signal detection on plasmonic gold surface compared with standard surfaces.
Figure 2: The plasmonic chip readily differentiates T1D and T2D in ultralow serum or blood samples.
Figure 3: Scatter plot for diabetes autoantibodies.
Figure 4: The plasmonic chip permits differentiation of immunoglobulin isotypes from a single ultralow volume sample.

Similar content being viewed by others


  1. Smyth, S. & Heron, A. Diabetes and obesity: the twin epidemics. Nat. Med. 12, 75–80 (2006).

    Article  CAS  Google Scholar 

  2. Karvonen, M. et al. Incidence of childhood type 1 diabetes worldwide. Diabetes Mondiale (DiaMond) Project Group. Diabetes Care 23, 1516–1526 (2000).

    Article  CAS  Google Scholar 

  3. EURODIAB ACE Study Group. Variation and trends in incidence of childhood diabetes in Europe. Lancet 355, 873–876 (2000).

  4. International Diabetes Federation (IDF). Diabetes in children: epidemiology. Pediatr. Diabetes 8 (S8), 10–18 (2007).

  5. Patterson, C.C. et al. Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and predicted new cases 2005–20: a multicentre prospective registration study. Lancet 373, 2027–2033 (2009).

    Article  Google Scholar 

  6. Maahs, D.M., West, N.A., Lawrence, J.M. & Mayer-Davis, E.J. Epidemiology of type 1 diabetes. Endocrinol. Metab. Clin. North Am. 39, 481–497 (2010).

    Article  Google Scholar 

  7. Liese, A.D. et al. The burden of diabetes mellitus among US youth: prevalence estimates from the SEARCH for Diabetes in Youth Study. Pediatrics 118, 1510–1518 (2006).

    Article  Google Scholar 

  8. Imperatore, G. et al. Projections of type 1 and type 2 diabetes burden in the U.S. population aged <20 years through 2050: dynamic modeling of incidence, mortality, and population growth. Diabetes Care 35, 2515–2520 (2012).

    Article  Google Scholar 

  9. Jones, K.L. Role of obesity in complicating and confusing the diagnosis and treatment of diabetes in children. Pediatrics 121, 361–368 (2008).

    Article  Google Scholar 

  10. Zeitler, P. Approach to the obese adolescent with new-onset diabetes. J. Clin. Endocrinol. Metab. 95, 5163–5170 (2010).

    Article  CAS  Google Scholar 

  11. Michels, A.W. & Eisenbarth, G.S. Immune intervention in type 1 diabetes. Semin. Immunol. 23, 214–219 (2011).

    Article  CAS  Google Scholar 

  12. Greenbaum, C.J., Schatz, D.A., Haller, M.J. & Sanda, S. Through the fog: recent clinical trials to preserve beta-cell function in type 1 diabetes. Diabetes 61, 1323–1330 (2012).

    Article  CAS  Google Scholar 

  13. Orban, T. et al. Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial. Lancet 378, 412–419 (2011).

    Article  CAS  Google Scholar 

  14. Greenbaum, C.J., Palmer, J.P., Kuglin, B. & Kolb, H. Insulin autoantibodies measured by radioimmunoassay methodology are more related to insulin-dependent diabetes mellitus than those measured by enzyme-linked immunosorbent assay: results of the Fourth International Workshop on the Standardization of Insulin Autoantibody Measurement. J. Clin. Endocrinol. Metab. 74, 1040–1044 (1992).

    CAS  PubMed  Google Scholar 

  15. Liu, E. & Eisenbarth, G.S. Accepting clocks that tell time poorly: fluid-phase versus standard ELISA autoantibody assays. Clin. Immunol. 125, 120–126 (2007).

    Article  CAS  Google Scholar 

  16. Valdez, S.N. & Poskus, E. Autoimmune diabetes mellitus: the importance of autoantibodies for disease prediction and diagnostic support. Curr. Immunol. Rev. 6, 299–313 (2010).

    Article  CAS  Google Scholar 

  17. Bottazzo, G.F., Florin-Christensen, A. & Doniach, D. Islet-cell antibodies in diabetes mellitus with autoimmune polyendocrine deficiencies. Lancet 304, 1279–1283 (1974).

    Article  Google Scholar 

  18. Bingley, P.J., Bonifacio, E. & Mueller, P.W. Diabetes Antibody Standardization Program: first assay proficiency evaluation. Diabetes 52, 1128–1136 (2003).

    Article  CAS  Google Scholar 

  19. Bingley, P.J. et al. Measurement of islet cell antibodies in the Type 1 Diabetes Genetics Consortium: efforts to harmonize procedures among the laboratories. Clin. Trials 7, S56–S64 (2010).

    Article  Google Scholar 

  20. Schlosser, M., Mueller, P.W., Torn, C., Bonifacio, E. & Bingley, P.J. Diabetes Antibody Standardization Program: evaluation of assays for insulin autoantibodies. Diabetologia 53, 2611–2620 (2010).

    Article  CAS  Google Scholar 

  21. Yu, L. et al. Distinguishing persistent insulin autoantibodies with differential risk: nonradioactive bivalent proinsulin/insulin autoantibody assay. Diabetes 61, 179–186 (2012).

    Article  CAS  Google Scholar 

  22. Törn, C. et al. Diabetes Antibody Standardization Program: evaluation of assays for autoantibodies to glutamic acid decarboxylase and islet antigen-2. Diabetologia 51, 846–852 (2008).

    Article  Google Scholar 

  23. Tabakman, S.M. et al. Plasmonic substrates for multiplexed protein microarrays with femtomolar sensitivity and broad dynamic range. Nat. Commun. 2, 466 (2011).

    Article  Google Scholar 

  24. Zhang, B. et al. Multiplexed cytokine detection on plasmonic gold substrates with enhanced near-infrared fluorescence. Nano Research 6, 113–120 (2013).

    Article  CAS  Google Scholar 

  25. Achenbach, P. et al. Combined testing of antibody titer and affinity improves insulin autoantibody measurement: Diabetes Antibody Standardization Program. Clin. Immunol. 122, 85–90 (2007).

    Article  CAS  Google Scholar 

  26. Roberts, M.J., Bentlye, M.D. & Harris, J.M. Chemistry for peptide and protein PEGylation. Adv. Drug Deliv. Rev. 54, 459–476 (2002).

    Article  CAS  Google Scholar 

  27. Oak, S., Phan, T.T., Gilliam, L.K., Hirsch, I.B. & Hampe, C.S. Animal insulin therapy induces a biased insulin antibody response that persists for years after introduction of human insulin. Acta Diabetol. 47, 131–135 (2010).

    Article  CAS  Google Scholar 

  28. Naserke, H.E., Dozio, N., Ziegler, A.G. & Bonifacio, E. Comparison of a novel micro-assay for insulin autoantibodies with the conventional radiobinding assay. Diabetologia 41, 681–683 (1998).

    Article  CAS  Google Scholar 

  29. Ljungberg, U.K. et al. The interaction between different domains of staphylococcal protein A and human polyclonal IgG, IgA, IgM and F(ab′)2: separation of affinity from specificity. Mol. Immunol. 30, 1279–1285 (1993).

    Article  CAS  Google Scholar 

  30. Wabl, M., Cascalho, M. & Steinberg, C. Hypermutation in antibody affinity maturation. Curr. Opin. Immunol. 11, 186–189 (1999).

    Article  CAS  Google Scholar 

  31. Lakowicz, J.R. Radiative decay engineering 5: metal-enhanced fluorescence and plasmon emission. Anal. Biochem. 337, 171–194 (2005).

    Article  CAS  Google Scholar 

Download references


We thank the patient volunteers. We thank P.J. Utz and D. Wilson for helpful discussions. We thank M. Gong, J. Wu and L. Zhang for help with experiments; we thank C. Yan for help with the scatter plot for diabetes autoantibodies. This work was supported by grants from the Stanford C-IDEA program (US National Institutes of Health grant 1 RC4 TW008781-01), the US National Institutes of Health DP2OD006740 (to B.J.F.) and the Juvenile Diabetes Research Foundation 17-2013-528 (to B.J.F.) and the National Cancer Institute of the US National Institutes of Health (5R01CA135109-02) and the Stanford SPARK program (to H.D.). B.Z. acknowledges support from the Stanford Bio-X SIGF fellowship. R.B.K. received unrestricted fellowship support from Genentech and the Child Health Research Institute at Stanford. B.J.F. is a Bechtel Endowed Faculty Scholar.

Author information

Authors and Affiliations



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.

Corresponding authors

Correspondence to Hongjie Dai or Brian J Feldman.

Ethics declarations

Competing 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.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 1–3, and Supplementary Methods (PDF 1222 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, B., Kumar, R., Dai, H. et al. A plasmonic chip for biomarker discovery and diagnosis of type 1 diabetes. Nat Med 20, 948–953 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research