Only a tiny fraction of the nanomedicine-design space has been explored, owing to the structural complexity of nanomedicines and the lack of relevant high-throughput synthesis and analysis methods. Here, we report a methodology for determining structure–activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates. First, we identified ~1,000 candidate SNAs on the basis of reasonable ranges for 11 design parameters that can be systematically and independently varied to optimize SNA performance. Second, we developed a high-throughput method for making SNAs at the picomolar scale in a 384-well format, and used a mass spectrometry assay to rapidly measure SNA immune activation. Third, we used machine learning to quantitatively model SNA immune activation and identify the minimum number of SNAs needed to capture optimum structure–activity relationships for a given SNA library. Our methodology is general, can reduce the number of nanoparticles that need to be tested by an order of magnitude, and could serve as a screening tool for the development of nanoparticle therapeutics.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Code availability

The custom codes used to generate the results reported in this manuscript are available from the corresponding authors upon reasonable request.

Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information. All data generated in this study are available from the corresponding authors upon reasonable request.

Additional information

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


  1. 1.

    Bobo, D., Robinson, K. J., Islam, J., Thurecht, K. J. & Corrie, S. R. Nanoparticle-based medicines: a review of FDA-approved materials and clinical trials to date. Pharm. Res. 33, 2373–2387 (2016).

  2. 2.

    Mirkin, C. A., Letsinger, R. L., Mucic, R. C. & Storhoff, J. J. A DNA-based method for rationally assembling nanoparticles into macroscopic materials. Nature 382, 607–609 (1996).

  3. 3.

    Cutler, J. I., Auyeung, E. & Mirkin, C. A. Spherical nucleic acids. J. Am. Chem. Soc. 134, 1376–1391 (2012).

  4. 4.

    Choi, C. H. J., Hao, L., Narayan, S. P., Auyeung, E. & Mirkin, C. A. Mechanism for the endocytosis of spherical nucleic acid nanoparticle conjugates. Proc. Natl Acad. Sci. USA 110, 7625–7630 (2013).

  5. 5.

    Radovic-Moreno, A. F. et al. Immunomodulatory spherical nucleic acids. Proc. Natl Acad. Sci. USA 112, 3892–3897 (2015).

  6. 6.

    Rosi, N. L. et al. Oligonucleotide-modified gold nanoparticles for intracellular gene regulation. Science 312, 1027–1030 (2006).

  7. 7.

    Seferos, D. S., Prigodich, A. E., Giljohann, D. A., Patel, P. C. & Mirkin, C. A. Polyvalent DNA nanoparticle conjugates stabilize nucleic acids. Nano Lett. 9, 308–311 (2009).

  8. 8.

    Li, J. et al. A review on phospholipids and their main applications in drug delivery systems. Asian J. Pharm. 10, 81–98 (2015).

  9. 9.

    Schroit, A. J., Madsen, J. & Nayar, R. Liposome–cell interactions: in vitro discrimination of uptake mechanism and in vivo targeting strategies to mononuclear phagocytes. Chem. Phys. Lipids 40, 373–393 (1986).

  10. 10.

    Simoes, S., Slepushkin, V., Duzgunes, N. & Pedroso de Lima, M. C. On the mechanisms of internalization and intracellular delivery mediated by pH-sensitive liposomes. Biochim. Biophys. Acta 1515, 23–37 (2001).

  11. 11.

    McCluskie, M. J. & Davis, H. L. CpG DNA as mucosal adjuvant. Vaccine 18, 231–237 (1999).

  12. 12.

    Krieg, A. M. et al. CpG motifs in bacterial DNA trigger direct B-cell activation. Nature 374, 546–549 (1995).

  13. 13.

    Hemmi, H. et al. A Toll-like receptor recognizes bacterial DNA. Nature 408, 740–745 (2000).

  14. 14.

    Zhao, Q., Temsamani, J., Iadarola, P. L., Jiang, Z. & Agrawal, S. Effect of different chemically modified oligodeoxynucleotides on immune stimulation. Biochem. Pharmacol. 51, 173–182 (1996).

  15. 15.

    Giljohann, D. A. et al. Oligonucleotide loading determines cellular uptake of DNA-modified gold nanoparticles. Nano Lett. 7, 3818–3821 (2007).

  16. 16.

    Prigodich, A. E., Alhasan, A. H. & Mirkin, C. A. Selective enhancement of nucleases by polyvalent DNA-functionalized gold nanoparticles. J. Am. Chem. Soc. 133, 2120–2123 (2011).

  17. 17.

    Gendron, K. B., Rodriguez, A. & Sewell, D. A. Vaccination with human papillomavirus type 16 E7 peptide with CpG oligonucleotides for prevention of tumor growth in mice. Arch. Otolaryngol. Head Neck Surg. 132, 327–332 (2006).

  18. 18.

    Berns, E. J., Cabezas, M. D. & Mrksich, M. Cellular assays with a molecular endpoint measured by SAMDI mass spectrometry. Small. 12, 3811–3818 (2016).

  19. 19.

    Min, D. H., Tang, W. J. & Mrksich, M. Chemical screening by mass spectrometry to identify inhibitors of anthrax lethal factor. Nat. Biotechnol. 22, 717–723 (2004).

  20. 20.

    Mrksich, M. Mass spectrometry of self-assembled monolayers: a new tool for molecular surface science. ACS Nano 2, 7–18 (2008).

  21. 21.

    Su, J. & Mrksich, M. Using mass spectrometry to characterize self-assembled monolayers presenting peptides, proteins, and carbohydrates. Angew. Chem. Int. Ed. Engl. 41, 4715–4718 (2002).

  22. 22.

    Su, J., Rajapaksha, T. W., Peter, M. E. & Mrksich, M. Assays of endogenous caspase activities: a comparison of mass spectrometry and fluorescence formats. Anal. Chem. 78, 4945–4951 (2006).

  23. 23.

    Humerickhouse, R., Lohrbach, K., Li, L., Bosron, W. F. & Dolan, M. E. Characterization of CPT-11 hydrolysis by human liver carboxylesterase isoforms hCE-1 and hCE-2. Cancer Res. 60, 1189–1192 (2000).

  24. 24.

    Li, Y. et al. Free cholesterol-loaded macrophages are an abundant source of tumor necrosis factor-α and interleukin-6: model of NF-κB and MAP kinase-dependent inflammation in advanced atherosclerosis. J. Biol. Chem. 280, 21763–21772 (2005).

  25. 25.

    Yu, D., Zhao, Q., Kandimalla, E. R. & Agrawal, S. Accessible 5′-end of CpG-containing phosphorothioate oligodeoxynucleotides is essential for immunostimulatory activity. Bioorg. Med. Chem. Lett. 10, 2585–2588 (2000).

  26. 26.

    Kandimalla, E. R. et al. Conjugation of ligands at the 5′-end of CpG DNA affects immunostimulatory activity. Bioconjug. Chem. 13, 966–974 (2002).

  27. 27.

    De Clercq, E., Eckstein, E. & Merigan, T. C. Interferon induction increased through chemical modification of a synthetic polyribonucleotide. Science 165, 1137–1139 (1969).

  28. 28.

    Roberts, T. L., Sweet, M. J., Hume, D. A. & Stacey, K. J. Cutting edge: species-specific TLR9-mediated recognition of CpG and non-CpG phosphorothioate-modified oligonucleotides. J. Immunol. 174, 605–608 (2005).

  29. 29.

    Flierl, U. et al. Phosphorothioate backbone modifications of nucleotide-based drugs are potent platelet activators. J. Exp. Med. 212, 129–137 (2015).

  30. 30.

    Henry, S. P. et al. Complement activation is responsible for acute toxicities in rhesus monkeys treated with a phosphorothioate oligodeoxynucleotide. Int. Immunopharmacol. 2, 1657–1666 (2002).

  31. 31.

    Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).

  32. 32.

    Menard, S. Applied Logistic Regression Analysis Vol. 106 (Sage, Thousand Oaks, 2002).

  33. 33.

    Schuurmann, G., Ebert, R. U., Chen, J., Wang, B. & Kuhne, R. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J. Chem. Inf. Model. 48, 2140–2145 (2008).

  34. 34.

    Golbraikh, A. & Tropsha, A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J. Comput. Aided. Mol. Des. 16, 357–369 (2002).

  35. 35.

    Akinc, A. et al. A combinatorial library of lipid-like materials for delivery of RNAi therapeutics. Nat. Biotechnol. 26, 561–569 (2008).

  36. 36.

    Anderson, D. G., Lynn, D. M. & Langer, R. Semi-automated synthesis and screening of a large library of degradable cationic polymers for gene delivery. Angew. Chem. Int. Ed. Engl. 42, 3153–3158 (2003).

  37. 37.

    Banga, R. J., Chernyak, N., Narayan, S. P., Nguyen, S. T. & Mirkin, C. A. Liposomal spherical nucleic acids. J. Am. Chem. Soc. 136, 9866–9869 (2014).

Download references


Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number U54CA199091. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work made use of the IMSERC at Northwestern University, which has received support from Northwestern University and the State of Illinois.

Author information

Author notes

  1. These authors contributed equally: Gokay Yamankurt, Eric J. Berns.


  1. Interdisciplinary Biological Sciences Graduate Program, Northwestern University, Evanston, IL, USA

    • Gokay Yamankurt
  2. International Institute for Nanotechnology, Northwestern University, Evanston, IL, USA

    • Gokay Yamankurt
    •  & Chad A. Mirkin
  3. Department of Chemistry, Northwestern University, Evanston, IL, USA

    • Gokay Yamankurt
    • , Milan Mrksich
    •  & Chad A. Mirkin
  4. Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA

    • Eric J. Berns
    •  & Milan Mrksich
  5. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

    • Albert Xue
    • , Andrew Lee
    •  & Neda Bagheri
  6. Department of Cell and Molecular Biology, Northwestern University, Chicago, IL, USA

    • Milan Mrksich


  1. Search for Gokay Yamankurt in:

  2. Search for Eric J. Berns in:

  3. Search for Albert Xue in:

  4. Search for Andrew Lee in:

  5. Search for Neda Bagheri in:

  6. Search for Milan Mrksich in:

  7. Search for Chad A. Mirkin in:


G.Y., E.J.B., A.L., M.M. and C.A.M. designed the experiments. G.Y. and E.J.B. performed the experiments. E.J.B. and A.X. wrote the code for the data analysis. All authors analysed the data and wrote the manuscript.

Competing interests

C.A.M. and M.M. own stock from Exicure, which has licensed the SNA technology. M.M. owns stock in SAMDI Tech—the company that has licensed the SAMDI technology.

Corresponding authors

Correspondence to Andrew Lee or Neda Bagheri or Milan Mrksich or Chad A. Mirkin.

Supplementary information

  1. Supplementary information

    Supplementary figures and tables.

  2. Reporting Summary

About this article

Publication history