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 fluorescence sandwich immunoassay for the real-time continuous detection of glucose and insulin in live animals


Biosensors that continuously measure circulating biomolecules in real time could provide insights into the health status of patients and their response to therapeutics. But biosensors for the continuous real-time monitoring of analytes in vivo have only reached nanomolar sensitivity and can measure only a handful of molecules, such as glucose and blood oxygen. Here we show that multiple analytes can be continuously and simultaneously measured with picomolar sensitivity and sub-second resolution via the integration of aptamers and antibodies into a bead-based fluorescence sandwich immunoassay implemented in a custom microfluidic chip. After an incubation time of 30 s, bead fluorescence is measured using a high-speed camera under spatially multiplexed two-colour laser illumination. We used the assay for continuous quantification of glucose and insulin concentrations in the blood of live diabetic rats to resolve inter-animal differences in the pharmacokinetic response to insulin as well as discriminate pharmacokinetic profiles from different insulin formulations. The assay can be readily modified to continuously and simultaneously measure other blood analytes in vivo.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


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

Fig. 1: Overview and validation of the RT-ELISA assay strategy.
Fig. 2: Overview of the RT-ELISA technology.
Fig. 3: Testing and optimization of the core components of RT-ELISA.
Fig. 4: Continuous in vitro monitoring of glucose and insulin in whole blood.
Fig. 5: Continuous real-time measurements of glucose and insulin in diabetic rats.
Fig. 6: Glucose and insulin measurements in an awake diabetic rat.
Fig. 7: Comparison of different insulin formulation pharmacokinetics.

Data availability

The data supporting the results in this study are available within the paper and its Supplementary Information. All raw and annotated data generated in this study are available from figshare with the identifier

Code availability

The Python code used to analyse bead images is provided at


  1. Hamburg, M. A. & Collins, F. S. The path to personalized medicine. N. Engl. J. Med. 363, 301–304 (2010).

    CAS  PubMed  Google Scholar 

  2. Puhr, S., Calhoun, P., Welsh, J. & Walker, T. The effect of reduced self-monitored blood glucose testing after adoption of continuous glucose monitoring on hemoglobin A1c and time in range. Diabetes Technol. Ther. 20, 557–560 (2018).

    CAS  PubMed  Google Scholar 

  3. Meuwese, C., Stenvinkel, P., Dekker, F. & Carrero, J. Monitoring of inflammation in patients on dialysis: forewarned is forearmed. Nat. Rev. Nephrol. 7, 166–176 (2011).

    CAS  PubMed  Google Scholar 

  4. Della Ciana, L. & Caputo, G. Robust, reliable biosensor for continuous monitoring of urea during dialysis. Clin. Chem. 42, 1079–1085 (1996).

    PubMed  Google Scholar 

  5. Nagler, R. M. Saliva analysis for monitoring dialysis and renal function. Clin. Chem. 54, 1415–1417 (2008).

    CAS  PubMed  Google Scholar 

  6. Pickering, J. W. et al. Rapid rule-out of acute myocardial infarction with a single high-sensitivity cardiac troponin T measurement below the limit of detection: a collaborative meta-analysis. Ann. Intern. Med. 166, 715–724 (2017).

    PubMed  Google Scholar 

  7. Hovorka, R. Continuous glucose monitoring and closed-loop systems. Diabet. Med. 23, 1–12 (2006).

    CAS  PubMed  Google Scholar 

  8. Baker, D. A. & Gough, D. A. A continuous, implantable lactate sensor. Anal. Chem. 67, 1536–1540 (1995).

    CAS  Google Scholar 

  9. Khan, Y. et al. A flexible organic reflectance oximeter array. Proc. Natl Acad. Sci. USA 115, E11015–E11024 (2018).

    CAS  PubMed  Google Scholar 

  10. Ferguson, B. S. et al. Real-time, aptamer-based tracking of circulating therapeutic agents in living animals. Sci. Transl. Med. 5, 213ra165 (2013).

  11. Arroyo-currás, N. et al. Real-time measurement of small molecules directly in awake, ambulatory animals. Proc. Natl Acad. Sci. USA 114, 645–650 (2017).

    PubMed  Google Scholar 

  12. Navaneelan, T., Alam, S., Peters, P. A. & Phillips, O. Deaths involving sepsis in Canada. Health at a Glance (21 July 2016).

  13. Garg, S. K., Rewers, A. H. & Akturk, H. K. Ever-increasing insulin-requiring patients globally. Diabetes Technol. Ther. 20, S21–S24 (2018).

    PubMed  Google Scholar 

  14. Diabetes: Key Facts (World Health Organization, 2018);

  15. Basu, S. et al. Estimation of global insulin use for type 2 diabetes, 2018–30: a microsimulation analysis. Lancet Diabetes Endocrinol. 7, 25–33 (2019).

    PubMed  Google Scholar 

  16. Casparie, A. F. & Elving, L. D. Severe hypoglycemia in diabetic patients: frequency, causes, prevention. Diabetes Care 8, 141–145 (1985).

    CAS  PubMed  Google Scholar 

  17. Cryer, P., Davis, S. & Shamoon, H. Hypoglycemia in diabetes. Diabetes Care 26, 1902–1912 (2003).

    CAS  PubMed  Google Scholar 

  18. Geller, A. et al. National estimates of insulin-related hypoglycemia and errors leading to emergency department visits and hospitalizations. JAMA Intern. Med. 174, 678–686 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Guerci, B. & Sauvanet, J. P. Subcutaneous insulin: pharmacokinetic variability and glycemic variability. Diabetes Metab. 31, 7–24 (2005).

    Google Scholar 

  20. Gin, H. & Hanaire-Broutin, H. Reproducibility and variability in the action of injected insulin. Diabetes Metab. 31, 7–13 (2005).

    CAS  PubMed  Google Scholar 

  21. Heinemann, L. Variability of insulin absorption and insulin action. Diabetes Technol. Ther. 4, 673–682 (2002).

    PubMed  Google Scholar 

  22. Wilson, B. D., Hariri, A. A., Thompson, I. A. P. & Eisenstein, M. Independent Control of the thermodynamic and kinetic properties of aptamer switches. Nat. Commun. 10, 5079 (2019).

  23. Tang, Z. et al. Aptamer switch probe based on intramolecular displacement. J. Am. Chem. Soc. 130, 11268–11269 (2008).

    CAS  PubMed  Google Scholar 

  24. Munzar, J. D., Ng, A. & Juncker, D. Duplexed aptamers: history, design, theory, and application to biosensing. Chem. Soc. Rev. 48, 1390–1419 (2019).

    CAS  PubMed  Google Scholar 

  25. Nakatsuka, N. et al. Aptamer-field-effect transistors overcome debye length limitations for small-molecule sensing. Science 362, 319–324 (2019).

    Google Scholar 

  26. Li, J., Janle, E. & Campbell, W. W. Postprandial glycemic and insulinemic responses to common breakfast beverages consumed with a standard meal in adults who are overweight and obese. Nutrients 9, 32 (2017).

    PubMed Central  Google Scholar 

  27. Bantle, J. P. et al. Postprandial glucose and insulin responses to meals containing different carbohydrates in normal and diabetic subjects. N. Engl. J. Med. 309, 7–12 (1983).

    CAS  PubMed  Google Scholar 

  28. ter Braak, E. W. et al. Injection site effects on the pharmacokinetics and glucodynamics of insulin lispro and regular insulin. Diabetes Care 19, 1437–1440 (1996).

    PubMed  Google Scholar 

  29. Home, P. D. The pharmacokinetics and pharmacodynamics of rapid-acting insulin analogues and their clinical consequences. Diabetes Obes. Metab. 14, 780–788 (2012).

    CAS  PubMed  Google Scholar 

  30. Freckmann, G. et al. Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals. J. Diabetes Sci. Technol. 1, 695–703 (2007).

    PubMed  PubMed Central  Google Scholar 

  31. Meijer, H. E. H., Singh, M. K., Kang, T. G., Den Toonder, J. M. J. & Anderson, P. D. Passive and active mixing in microfluidic devices. Macromol. Symp. 279, 201–209 (2009).

    CAS  Google Scholar 

  32. Marschewski, J. et al. Mixing with herringbone-inspired microstructures: overcoming the diffusion limit in co-laminar microfluidic devices. Lab Chip 15, 1923–1933 (2015).

    CAS  PubMed  Google Scholar 

  33. Paek, S. H., Lee, S. H., Cho, J. H. & Kim, Y. S. Development of rapid one-step immunochromatographic assay. Methods 22, 53–60 (2000).

    CAS  PubMed  Google Scholar 

  34. Pollema, C. H., Ruzicka, J., Christian, G. D., Lernmark, A. & Lernmark, A. Sequential injection immunoassay utilizing immunomagnetic beads. Anal. Chem. 64, 1356–1361 (1992).

    CAS  PubMed  Google Scholar 

  35. Chang, L. et al. Single molecule enzyme-linked immunosorbent assays: theoretical considerations. J. Immunol. Methods 378, 102–115 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. McGrath, J., Jimenez, M. & Bridle, H. Deterministic lateral displacement for particle separation: a review. Lab Chip 14, 4139–4158 (2014).

    CAS  PubMed  Google Scholar 

  37. Gomis, S. et al. Single-cell tumbling enables high-resolution size profiling of retinal stem cells. ACS Appl. Mater. Interfaces 10, 34811–34816 (2018).

    CAS  PubMed  Google Scholar 

  38. Peiris, H. et al. Discovering human diabetes-risk gene function with genetics and physiological assays. Nat. Commun. 9, 3855 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Adolfsson, P., Parkin, C. G., Thomas, A. & Krinelke, L. G. Selecting the appropriate continuous glucose monitoring system—a practical approach. Eur. Endocrinol. 14, 24–29 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. Wu, K. & Huan, Y. Streptozotocin-induced diabetic models in mice and rats. Curr. Protoc. Pharm. 5, 5.47.1–5.47.20 (2008).

    Google Scholar 

  41. Humulin R (Eli Lilly and Company, 2018);

  42. Humulin N (Eli Lilly and Company, 2018);

  43. Woodworth, J. R., Howey, D. C. & Bowsher, R. R. Establishment of time-action profiles for regular and NPH insulin using pharmacodynamic modeling. Diabetes Care 17, 64–68 (1994).

    CAS  PubMed  Google Scholar 

  44. Kim, J., Campbell, A. S., De Ávila, B. E. F. & Wang, J. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37, 389–406 (2019).

    CAS  PubMed  Google Scholar 

  45. Munje, R. D., Muthukumar, S., Jagannath, B. & Prasad, S. A new paradigm in sweat based wearable diagnostics biosensors using room temperature ionic liquids (RTILs). Sci. Rep. 7, 1950 (2017).

    PubMed  PubMed Central  Google Scholar 

  46. Hao, Z. et al. Measurement of cytokine biomarkers using an aptamer-based affinity graphene nanosensor on a flexible substrate toward wearable applications. Nanoscale 10, 21681–21688 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Visser, E. W. A., Yan, J., Van IJzendoorn, L. J. & Prins, M. W. J. Continuous biomarker monitoring by particle mobility sensing with single molecule resolution. Nat. Commun. 9, 2541 (2018).

    PubMed  PubMed Central  Google Scholar 

  48. Goodwin, M. L., Harris, J. E., Hernández, A. & Gladden, L. B. Blood lactate measurements and analysis during exercise: A guide for clinicians. J. Diabetes Sci. Technol. 1, 558–569 (2007).

    PubMed  PubMed Central  Google Scholar 

  49. Graf, A. et al. Moving toward a unified platform for insulin delivery and sensing of inputs relevant to an artificial pancreas. J. Diabetes Sci. Technol. 11, 308–314 (2017).

    PubMed  Google Scholar 

  50. Wiesli, P. et al. Acute psychological stress affects glucose concentrations in patients with type 1 diabetes following food intake but not in the fasting state. Diabetes Care 28, 1910–1915 (2005).

    PubMed  Google Scholar 

Download references


This research was supported by the Chan Zuckerberg Biohub, a Stanford Diabetes Research Center (SDRC) Pilot grant and the Transdisciplinary Initiative Program (TIP) from the Stanford Maternal & Child Health Research Institute (MCHRI). C.L.M. was supported by a NSERC Postgraduate Scholarship and Stanford Bio-X Bowes Graduate Student Fellowship. We thank B. Buckingham and R. Lal for their helpful discussions as well as N. Maganzini and I. Thompson for their review and edits of the manuscript. We thank the Stanford Nanofabrication Facility (NSF) for their cleanroom facilities and the Canary Center at Stanford for Cancer Early Detection for their biolayer interferometry instrument. We thank the Stanford Veterinary Service Center staff for their assistance with animal care and procedures.

Author information

Authors and Affiliations



M.P., C.L.M. and H.T.S. conceived the initial concept. M.P. and C.L.M. designed experiments. M.P., C.L.M., E.Y.M., J.P., D.M., Y.H., A.Y. and S.W.B. executed experiments. A.B. developed the imaging analysis algorithm. M.P. and C.L.M. analysed the data. M.P., C.L.M., M.E. and H.T.S. wrote the manuscript. All authors edited, discussed and approved the whole paper.

Corresponding authors

Correspondence to Eric A. Appel or H. Tom Soh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, figures, video captions and references.

Reporting Summary

Supplementary Video 1

Separation, by DLD, of fluorescently labelled microbeads from blood cells and free fluorescently tagged antibodies.

Supplementary Video 2

Glucose and insulin beads passing through the detection window.

Supplementary Video 3

Control experiment showing that glucose beads only fluoresce in their specific region in the upper part of the detection window.

Supplementary Video 4

Control experiment showing that insulin beads only fluoresce in their specific region in the bottom part of the detection window.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Poudineh, M., Maikawa, C.L., Ma, E.Y. et al. A fluorescence sandwich immunoassay for the real-time continuous detection of glucose and insulin in live animals. Nat Biomed Eng 5, 53–63 (2021).

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