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.

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease


Biomarkers are becoming increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered 'synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: they target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that these agents can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and substantially improve early detection of cancer compared with current clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and point-of-care diagnostics.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Schematic of the approach.
Figure 2: Urinary detection of in vivo protease activity with peptide-nanoworms.
Figure 3: Photocaged iCORE libraries for multiplexed profiling of protease activities by LC-MS/MS.
Figure 4: Urinary biomarkers of hepatic fibrosis and resolution in DDC-treated mice.
Figure 5: Synthetic urinary biomarkers outperform serum CEA in early cancer detection.


  1. 1

    Sawyers, C.L. The cancer biomarker problem. Nature 452, 548–552 (2008).

    CAS  Article  Google Scholar 

  2. 2

    Hanash, S.M., Pitteri, S.J. & Faca, V.M. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).

    CAS  Article  Google Scholar 

  3. 3

    Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009).

    CAS  Article  Google Scholar 

  4. 4

    Findeisen, P. & Neumaier, M. Functional protease profiling for diagnosis of malignant disease. Proteomics Clin. Appl. 6, 60–78 (2012).

    CAS  Article  Google Scholar 

  5. 5

    Surinova, S. et al. On the development of plasma protein biomarkers. J. Proteome Res. 10, 5–16 (2011).

    CAS  Article  Google Scholar 

  6. 6

    Schwarzenbach, H., Hoon, D.S.B. & Pantel, K. Cell-free nucleic acids as biomarkers in cancer patients. Nat. Rev. Cancer 11, 426–437 (2011).

    CAS  Article  Google Scholar 

  7. 7

    Moon, P.-G., You, S., Lee, J.-E., Hwang, D. & Baek, M.-C. Urinary exosomes and proteomics. Mass Spectrom. Rev. 30, 1185–1202 (2011).

    CAS  Article  Google Scholar 

  8. 8

    Nagrath, S. et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450, 1235–1239 (2007).

    CAS  Article  Google Scholar 

  9. 9

    Lutz, A.M., Willmann, J.K., Cochran, F.V., Ray, P. & Gambhir, S.S. Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes. PLoS Med. 5, e170 (2008).

    Article  Google Scholar 

  10. 10

    Haun, J.B. et al. Micro-NMR for rapid molecular analysis of human tumor samples. Sci. Transl. Med. 3, 71ra16 (2011).

    Article  Google Scholar 

  11. 11

    Edgington, L.E., Verdoes, M. & Bogyo, M. Functional imaging of proteases: recent advances in the design and application of substrate-based and activity-based probes. Curr. Opin. Chem. Biol. 15, 798–805 (2011).

    CAS  Article  Google Scholar 

  12. 12

    Nomura, D.K., Dix, M.M. & Cravatt, B.F. Activity-based protein profiling for biochemical pathway discovery in cancer. Nat. Rev. Cancer 10, 630–638 (2010).

    CAS  Article  Google Scholar 

  13. 13

    Hilderbrand, S.A. & Weissleder, R. Near-infrared fluorescence: application to in vivo molecular imaging. Curr. Opin. Chem. Biol. 14, 71–79 (2010).

    CAS  Article  Google Scholar 

  14. 14

    Braet, F. & Wisse, E. Structural and functional aspects of liver sinusoidal endothelial cell fenestrae: a review. Comp. Hepatol. 1, 1 (2002).

    Article  Google Scholar 

  15. 15

    Jain, R.K. & Stylianopoulos, T. Delivering nanomedicine to solid tumors. Nat. Rev. Clin. Oncol. 7, 653–664 (2010).

    CAS  Article  Google Scholar 

  16. 16

    López-Otín, C. & Bond, J.S. Proteases: multifunctional enzymes in life and disease. J. Biol. Chem. 283, 30433–30437 (2008).

    Article  Google Scholar 

  17. 17

    Schuppan, D. & Afdhal, N.H. Liver cirrhosis. Lancet 371, 838–851 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Hori, S.S. & Gambhir, S.S. Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations. Sci. Transl. Med. 3, 109ra116 (2011).

    Article  Google Scholar 

  19. 19

    Bremer, C., Tung, C.H. & Weissleder, R. In vivo molecular target assessment of matrix metalloproteinase inhibition. Nat. Med. 7, 743–748 (2001).

    CAS  Article  Google Scholar 

  20. 20

    Kridel, S.J. et al. A unique substrate binding mode discriminates membrane type-1 matrix metalloproteinase from other matrix metalloproteinases. J. Biol. Chem. 277, 23788–23793 (2002).

    CAS  Article  Google Scholar 

  21. 21

    Lutolf, M.P. et al. Repair of bone defects using synthetic mimetics of collagenous extracellular matrices. Nat. Biotechnol. 21, 513–518 (2003).

    CAS  Article  Google Scholar 

  22. 22

    Mahmood, U. & Weissleder, R. Near-infrared optical imaging of proteases in cancer. Mol. Cancer Ther. 2, 489–496 (2003).

    CAS  PubMed  Google Scholar 

  23. 23

    Turk, B.E., Huang, L.L., Piro, E.T. & Cantley, L.C. Determination of protease cleavage site motifs using mixture-based oriented peptide libraries. Nat. Biotechnol. 19, 661–667 (2001).

    CAS  Article  Google Scholar 

  24. 24

    Park, J.-H. et al. Systematic surface engineering of magnetic nanoworms for in vivo tumor targeting. Small 5, 694–700 (2009).

    CAS  Article  Google Scholar 

  25. 25

    Fickert, P. et al. A new xenobiotic-induced mouse model of sclerosing cholangitis and biliary fibrosis. Am. J. Pathol. 171, 525–536 (2007).

    CAS  Article  Google Scholar 

  26. 26

    Morris, T.A. et al. Urine and plasma levels of fibrinopeptide b in patients with deep vein thrombosis and pulmonary embolism. Thromb. Res. 110, 159–165 (2003).

    CAS  Article  Google Scholar 

  27. 27

    Choi, H.S. et al. Renal clearance of quantum dots. Nat. Biotechnol. 25, 1165–1170 (2007).

    CAS  Article  Google Scholar 

  28. 28

    Park, J.-H. et al. Magnetic iron oxide nanoworms for tumor targeting and imaging. Adv. Mater. 20, 1630–1635 (2008).

    CAS  Article  Google Scholar 

  29. 29

    Villanueva, J. et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J. Clin. Invest. 116, 271–284 (2006).

    CAS  Article  Google Scholar 

  30. 30

    Villanueva, J. et al. A sequence-specific exopeptidase activity test (sseat) for “functional” biomarker discovery. Mol. Cell. Proteomics 7, 509–518 (2008).

    CAS  Article  Google Scholar 

  31. 31

    Brown, B.B., Wagner, D.S. & Geysen, H.M. A single-bead decode strategy using electrospray ionization mass spectrometry and a new photolabile linker: 3-amino-3-(2-nitrophenyl)propionic acid. Mol. Divers. 1, 4–12 (1995).

    CAS  Article  Google Scholar 

  32. 32

    Ross, P.L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.ralph. Mol. Cell. Proteomics 3, 1154–1169 (2004).

    CAS  Article  Google Scholar 

  33. 33

    Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003); erratum 75, 4942 (2003); erratum 78, 4235 (2006).

    CAS  Article  Google Scholar 

  34. 34

    Rockey, D.C. et al. Liver biopsy. Hepatology 49, 1017–1044 (2009).

    Article  Google Scholar 

  35. 35

    Popov, Y. & Schuppan, D. Targeting liver fibrosis: strategies for development and validation of antifibrotic therapies. Hepatology 50, 1294–1306 (2009).

    CAS  Article  Google Scholar 

  36. 36

    Bedossa, P., Dargère, D. & Paradis, V. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38, 1449–1457 (2003).

    Article  Google Scholar 

  37. 37

    Mischak, H. et al. Recommendations for biomarker identification and qualification in clinical proteomics. Sci. Transl. Med. 2, 46ps42 (2010).

    Article  Google Scholar 

  38. 38

    Popov, Y., Patsenker, E., Fickert, P., Trauner, M. & Schuppan, D. Mdr2 abcb4−/− mice spontaneously develop severe biliary fibrosis via massive dysregulation of pro- and antifibrogenic genes. J. Hepatol. 43, 1045–1054 (2005).

    CAS  Article  Google Scholar 

  39. 39

    Etzioni, R. et al. The case for early detection. Nat. Rev. Cancer 3, 243–252 (2003).

    CAS  Article  Google Scholar 

  40. 40

    D'Souza, A.L. et al. A strategy for blood biomarker amplification and localization using ultrasound. Proc. Natl. Acad. Sci. USA 106, 17152–17157 (2009).

    CAS  Article  Google Scholar 

  41. 41

    Dekker, L.J.M. et al. Differential expression of protease activity in serum samples of prostate carcinoma patients with metastases. Proteomics 10, 2348–2358 (2010).

    CAS  Article  Google Scholar 

  42. 42

    Ruoslahti, E., Bhatia, S.N. & Sailor, M.J. Targeting of drugs and nanoparticles to tumors. J. Cell Biol. 188, 759–768 (2010).

    CAS  Article  Google Scholar 

  43. 43

    Sugahara, K.N. et al. Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. Science 328, 1031–1035 (2010).

    CAS  Article  Google Scholar 

  44. 44

    Kulasingam, V., Pavlou, M.P. & Diamandis, E.P. Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer. Nat. Rev. Cancer 10, 371–378 (2010).

    CAS  Article  Google Scholar 

Download references


We thank R. Cook, N. Schiller and M. Brown (Swanson Biotechnology Center (SBC), Massachusetts Institute of Technology (MIT)) for peptide synthesis and tissue sectioning, C. Whittaker (SBC, MIT) for bioinformatic insight, R. Tomaino (Harvard Taplin mass spectrometry facility) for mass spectrometry analysis, F. Giammo and D. Kim (MIT) for initial probe work and urine purification, S. Carr (Broad Institute of MIT/Harvard) for mass spectrometry expertise and M. Sailor (University of California San Diego (UCSD)), J. Park (UCSD) and S. Friedman (Mount Sinai School of Medicine) for insightful discussions. This work was funded by the US National Institutes of Health (Bioengineering Research Partnership: R01 CA124427 to S.N.B.), the Kathy and Curt Marble Cancer Research Fund to S.N.B. and US National Institutes of Health grants U19 AI066313 and 1R21 DK075857 to D.S. G.A.K. is supported by the Ruth L. Kirschstein National Research Service Award (F32CA159496-01). S.N.B. is a Howard Hughes Institute Investigator.

Author information




G.A.K., G.v.M. and S.N.B. conceived the study and designed the experiments. G.v.M. and S.M. performed the in vitro substrate screen and initial pharmacokinetic studies. G.A.K. developed the mass-encoding scheme. G.A.K., G.M. and O.A. performed the fibrosis experiments. Y.P., D.Y.S., S.B.L. and D.S. developed and provided expertise for the fibrosis progression and reversal protocols, and performed fibrosis quantification assays. G.A.K. and A.D.W. performed cancer experiments. G.A.K. and I.A.P. collected in vivo mass spectrometry data. G.A.K., G.v.M. and S.N.B. wrote the manuscript.

Corresponding author

Correspondence to Sangeeta N Bhatia.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19 (PDF 1334 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kwong, G., von Maltzahn, G., Murugappan, G. et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat Biotechnol 31, 63–70 (2013).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing