Selective uptake of single-walled carbon nanotubes by circulating monocytes for enhanced tumour delivery

Journal name:
Nature Nanotechnology
Year published:
Published online


In cancer imaging, nanoparticle biodistribution is typically visualized in living subjects using ‘bulk’ imaging modalities such as magnetic resonance imaging, computerized tomography and whole-body fluorescence. Accordingly, nanoparticle influx is observed only macroscopically, and the mechanisms by which they target cancer remain elusive. Nanoparticles are assumed to accumulate via several targeting mechanisms, particularly extravasation (leakage into tumour). Here, we show that, in addition to conventional nanoparticle-uptake mechanisms, single-walled carbon nanotubes are almost exclusively taken up by a single immune cell subset, Ly-6Chi monocytes (almost 100% uptake in Ly-6Chi monocytes, below 3% in all other circulating cells), and delivered to the tumour in mice. We also demonstrate that a targeting ligand (RGD) conjugated to nanotubes significantly enhances the number of single-walled carbon nanotube-loaded monocytes reaching the tumour (P < 0.001, day 7 post-injection). The remarkable selectivity of this tumour-targeting mechanism demonstrates an advanced immune-based delivery strategy for enhancing specific tumour delivery with substantial penetration.

At a glance


  1. SWNT uptake into circulating cells.
    Figure 1: SWNT uptake into circulating cells.

    a, Representative intravital fluorescence image of SWNT-laden circulating cells in tumour vasculature (an example cell is circled). Greyscale, SWNT-Cy5.5; red, long-circulating dye highlighting tumour blood vessels; green, enhanced green fluorescent protein (EGFP)-transfected tumour cells. Scale bar, 40 µm. b, Schematic of SWNTs non-covalently coated with a block amphiphile phospholipid PEG on the surface, with the hydrophobic segment (light blue) associated with the SWNT surface and the hydrophilic segment (green) attached to Cy5.5 fluorescent dye (red sphere) and/or RGD (or RAD) peptide (blue cyclic ball structure). SWNTs were injected into SCID mice with implanted window chambers and imaged dynamically over weeks. c, Dark-field image of SWNTs in a live circulating blood cell extracted from a mouse and FACS-sorted for positive SWNT–Cy5.5 signal (SWNTs pseudocoloured in red, analysed by hyperspectral imaging). Scale bar, 1 µm. d, Hyperspectral spectra taken from a plain SWNT–Cy5.5 solution in PBS. Each curve represents scattered photons from a pixel in the SWNT solution. The y axis is in dimensionless units, a digital number representing a conversion from the camera voltage to a digital quantity. SWNTs are pseudocoloured red in c based on the spatial localization of this reference SWNT hyperspectral spectrum overlaid on the dark-field image. This analysis confirms the presence of SWNTs in circulating blood cells.

  2. Flow cytometry plots showing selective uptake of SWNTs into a blood monocyte subset.
    Figure 2: Flow cytometry plots showing selective uptake of SWNTs into a blood monocyte subset.

    a, Blood was harvested 2 h and 6 h after injection of plain SWNT–Cy5.5 and stained with specific antibodies for flow cytometry analysis. Contour plots are used to represent cell numbers graphically. The upper left plot represents total single live white blood cells analysed for their surface expression levels of CD11b and Gr-1 (a marker that identifies granulocytes and is highly expressed on neutrophils). Cells expressing higher levels (fluorescence intensity) of surface CD11b and Gr-1 are known to be neutrophils, which represent 52% of total cells in blood (upper right cluster of cells gated as neutrophils; the proportion of neutrophils taking up SWNTs is displayed in plots along the top right row). Non-neutrophils are gated in the lower left of the plot. The cells expressing higher levels of Ly-6C and CD11b (among the non-neutrophil cells), gated in the upper right corner of the left-most plot of the second row, represent Ly-6Chi monocytes (inflammatory monocytes). The three plots on the right in the second row demonstrate the selectivity of SWNT uptake into Ly-6Chi monocytes (nearly 100% of blood monocytes take up SWNTs at 2 h, middle right plot). Less than 3% of cells in other subpopulations in blood (including neutrophils) take up SWNTs. b, At 6 h p.i., other cell types in the blood still do not take up SWNTs. The cells contained in each plot are derived from gates drawn in a and labelled 1, 2 and 3. These plots consist of the remaining blood cells after neutrophils and Ly-6Chi monocytes were removed from the analysis. These cell populations include Natural Killer cells, Ly-6Clow monocytes and dendritic cells (CD11c+ populations). Flow cytometry plot data are representative of n = 5 mice per group. Numbers near the gates represent the percentage of cells in the plot that are present within the drawn gate. Gates are drawn based on fluorescence minus one (FMO) negative controls44.

  3. SWNT-laden monocytes enter the tumour interstitium in a peptide-dependent manner.
    Figure 3: SWNT-laden monocytes enter the tumour interstitium in a peptide-dependent manner.

    a, Representative intravital micrograph of a tumour region (tumour cells, green; blood vessels, red; SWNTs, greyscale). Yellow arrows point to several SWNT-laden monocytes within the tumour interstitium. Scale bar, 50 µm. b, Bar graph showing that significantly more monocytes carrying RGD–SWNTs accumulate in tumour interstitium than monocytes carrying RAD–SWNTs and plain SWNTs on days 1 and 7 p.i. of SWNTs. Moreover, significantly more RGD–SWNT-laden monocytes are in the interstitium per field of view (FOV) on day 7 than on day 1 (P < 0.001). *P < 0.05; **P < 0.0005. Error bars represent s.e.m. c, SWNTs can enter the tumour via a variety of mechanisms, such as leakage through blood vessel pores. This graph shows the relative amounts of SWNTs in the tumour interstitium that were ferried in via the Trojan horse monocytes compared with all SWNTs within the tumour interstitium as a function of peptide on day 1 p.i. More than 20% of SWNTs in the tumour interstitium in the RGD–SWNT condition are carried in via monocytes. *P < 0.0001; **P < 0.05. Error bars represent s.d.


  1. Maeda, H. The link between infection and cancer: tumor vasculature, free radicals, and drug delivery to tumors via the EPR effect. Cancer Sci. 7, 779789 (2013).
  2. Hirsjarvi, S., Passirani, C. & Benoit, J. P. Passive and active tumour targeting with nanocarriers. Curr. Drug Disc. Technol. 8, 188196 (2011).
  3. Holgado, M. A., Martin-Banderas, L., Alvarez-Fuentes, J., Fernandez-Arevalo, M. & Arias, J. L. Drug targeting to cancer by nanoparticles surface functionalized with special biomolecules. Curr. Med. Chem. 19, 31883195 (2012).
  4. Yu, M. K., Park, J. & Jon, S. Targeting strategies for multifunctional nanoparticles in cancer imaging and therapy. Theranostics 2, 344 (2012).
  5. Liu, Z. et al. In vivo biodistribution and highly efficient tumour targeting of carbon nanotubes in mice. Nature Nanotech. 2, 4752 (2007).
  6. Hahn, M. A., Singh, A. K., Sharma, P., Brown, S. C. & Moudgil, B. M. Nanoparticles as contrast agents for in-vivo bioimaging: current status and future perspectives. Anal. Bioanal. Chem. 399, 327 (2011).
  7. Smith, B. R. et al. High-resolution, serial intravital microscopic imaging of nanoparticle delivery and targeting in a small animal tumor model. Nano Today 8, 126137 (2013).
  8. Smith, B. R. et al. Shape matters: intravital microscopy reveals surprising geometrical dependence for nanoparticles in tumor models of extravasation. Nano Lett. 12, 33693377 (2012).
  9. Kam, N. W., Liu, Z. & Dai, H. Carbon nanotubes as intracellular transporters for proteins and DNA: an investigation of the uptake mechanism and pathway. Angew. Chem. Int Ed. 45, 577581 (2006).
  10. Lacerda, L. et al. Translocation mechanisms of chemically functionalised carbon nanotubes across plasma membranes. Biomaterials 33, 33343343 (2012).
  11. Al-Jamal, K. T. et al. Cellular uptake mechanisms of functionalised multi-walled carbon nanotubes by 3D electron tomography imaging. Nanoscale 3, 26272635 (2011).
  12. Moghimi, S. M. et al. Particulate systems for targeting of macrophages: basic and therapeutic concepts. J. Innate Immun. 4, 509528 (2012).
  13. Porter, A. E. et al. Direct imaging of single-walled carbon nanotubes in cells. Nature Nanotech. 2, 713717 (2007).
  14. Smith, B. R. et al. Real-time intravital imaging of RGD–quantum dot binding to luminal endothelium in mouse tumor neovasculature. Nano Lett. 8, 25992606 (2008).
  15. Smith, B. R., Cheng, Z., De, A., Rosenberg, J. & Gambhir, S. S. Dynamic visualization of RGD–quantum dot binding to tumor neovasculature and extravasation in multiple living mouse models using intravital microscopy. Small 6, 22222229 (2010).
  16. Leimgruber, A. et al. Behavior of endogenous tumor-associated macrophages assessed in vivo using a functionalized nanoparticle. Neoplasia 11, 459468 (2009).
  17. Prabhakar, U. et al. Challenges and key considerations of the enhanced permeability and retention effect for nanomedicine drug delivery in oncology. Cancer Res. 73, 24122417 (2013).
  18. Choi, M. R. et al. A cellular trojan horse for delivery of therapeutic nanoparticles into tumors. Nano Lett. 7, 37593765 (2007).
  19. Owen, M. R. et al. Mathematical modeling predicts synergistic antitumor effects of combining a macrophage-based, hypoxia-targeted gene therapy with chemotherapy. Cancer Res. 71, 28262837 (2011).
  20. Murdoch, C., Giannoudis, A. & Lewis, C. E. Mechanisms regulating the recruitment of macrophages into hypoxic areas of tumors and other ischemic tissues. Blood 104, 22242234 (2004).
  21. Smith, B. R. et al. Localization to atherosclerotic plaque and biodistribution of biochemically derivatized superparamagnetic iron oxide nanoparticles (SPIONs) contrast particles for magnetic resonance imaging (MRI). Biomed. Microdev. 9, 719727 (2007).
  22. Lacerda, L., Raffa, S., Prato, M., Bianco, A. & Kostarelos, K. Cell-penetrating CNTs for delivery of therapeutics. Nano Today 2, 3843 (2007).
  23. Yeste, A., Nadeau, M., Burns, E. J., Weiner, H. L. & Quintana, F. J. Nanoparticle-mediated codelivery of myelin antigen and a tolerogenic small molecule suppresses experimental autoimmune encephalomyelitis. Proc. Natl Acad. Sci. USA 109, 1127011275 (2012).
  24. Cubillos-Ruiz, J. R. et al. Polyethylenimine-based siRNA nanocomplexes reprogram tumor-associated dendritic cells via TLR5 to elicit therapeutic antitumor immunity. J. Clin. Invest. 119, 22312244 (2009).
  25. Leuschner, F. et al. Therapeutic siRNA silencing in inflammatory monocytes in mice. Nature Biotechnol. 29, 10051010 (2011).
  26. Roy, A., Singh, M. S., Upadhyay, P. & Bhaskar, S. Combined chemo-immunotherapy as a prospective strategy to combat cancer: a nanoparticle based approach. Mol. Pharmacol. 7, 177888 (2010).
  27. Beatty, G. L. et al. CD40 agonists alter tumor stroma and show efficacy against pancreatic carcinoma in mice and humans. Science 331, 16121616 (2011).
  28. Movahedi, K. et al. Different tumor microenvironments contain functionally distinct subsets of macrophages derived from Ly6C(high) monocytes. Cancer Res. 70, 57285739 (2010).
  29. Gu, L. et al. Multivalent porous silicon nanoparticles enhance the immune activation potency of agonistic CD40 antibody. Adv. Mater. 24, 39813987 (2012).
  30. Elamanchili, P., Diwan, M., Cao, M. & Samuel, J. Characterization of poly(D,L-lactic-co-glycolic acid) based nanoparticulate system for enhanced delivery of antigens to dendritic cells. Vaccine 22, 24062412 (2004).
  31. Cruz, L. J. et al. Targeting nanosystems to human DCs via Fc receptor as an effective strategy to deliver antigen for immunotherapy. Mol. Pharmacol. 8, 104116 (2011).
  32. Cruz, L. J. et al. Multimodal imaging of nanovaccine carriers targeted to human dendritic cells. Mol. Pharmacol. 8, 520531 (2011).
  33. Gunn, J. et al. A multimodal targeting nanoparticle for selectively labeling T cells. Small 4, 712715 (2008).
  34. Saha, P. & Geissmann, F. Toward a functional characterization of blood monocytes. Immunol. Cell Biol. 89, 24 (2011).
  35. Yona, S. & Jung, S. Monocytes: subsets, origins, fates and functions. Curr. Opin. Hematol. 17, 5359 (2010).
  36. Shi, C. & Pamer, E. G. Monocyte recruitment during infection and inflammation. Nature Rev. Immunol. 11, 762774 (2011).
  37. Primeau, A. J., Rendon, A., Hedley, D., Lilge, L. & Tannock, I. F. The distribution of the anticancer drug Doxorubicin in relation to blood vessels in solid tumors. Clin. Cancer Res. 11, 87828788 (2005).
  38. Ingersoll, M. A. et al. Comparison of gene expression profiles between human and mouse monocyte subsets. Blood 115, e10e19 (2010).
  39. De Nicola, M. et al. Effects of carbon nanotubes on human monocytes. Ann. NY Acad. Sci. 1171, 600605 (2009).
  40. Schipper, M. L. et al. A pilot toxicology study of single-walled carbon nanotubes in a small sample of mice. Nature Nanotech. 3, 216221 (2008).
  41. Lee, H. J. et al. Amine-modified single-walled carbon nanotubes protect neurons from injury in a rat stroke model. Nature Nanotech. 6, 121125 (2011).
  42. Liu, Z., Tabakman, S. M., Chen, Z. & Dai, H. Preparation of carbon nanotube bioconjugates for biomedical applications. Nature Protoc. 4, 13721382 (2009).
  43. Herzenberg, L. A., Tung, J., Moore, W. A. & Parks, D. R. Interpreting flow cytometry data: a guide for the perplexed. Nature Immunol. 7, 681685 (2006).
  44. Ghosn, E. E. et al. Two physically, functionally, and developmentally distinct peritoneal macrophage subsets. Proc. Natl Acad. Sci. USA 107, 25682573 (2010).

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Author information


  1. Molecular Imaging Program at Stanford, The James H Clark Center, Stanford University, Stanford, California 94305, USA

    • Bryan Ronain Smith,
    • Harikrishna Rallapalli,
    • Jennifer A. Prescher,
    • Timothy Larson &
    • Sanjiv Sam Gambhir
  2. Department of Radiology, Stanford University School of Medicine, Stanford, California 94305, USA

    • Bryan Ronain Smith,
    • Timothy Larson &
    • Sanjiv Sam Gambhir
  3. Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA

    • Eliver Eid Bou Ghosn &
    • Leonore A. Herzenberg
  4. Department of Bioengineering and Department of Materials Science & Engineering, Stanford University, Stanford, California 94305, USA.

    • Sanjiv Sam Gambhir
  5. Present address: Department of Chemistry, University of California, Irvine, Irvine, California 92697, USA

    • Jennifer A. Prescher


B.R.S. and S.S.G. conceived and designed the experiments. B.R.S. performed the experiments and contributed materials and analysis tools. B.R.S. and S.S.G. analysed the data and wrote the manuscript. E.E.B.G. designed, performed and analysed 12-colour, 14-parameter FACS for immune cell analysis of blood samples, discussed results, contributed reagents, and commented on the manuscript. L.A.H. provided guidance and assisted with analysis regarding high-dimensional flow cytometry and contributed materials and analysis tools. H.R. assisted with analyses of intravital imaging experiments. B.R.S. and J.A.P. designed, performed and analysed initial flow cytometry assays. T.L. assisted with characterization of nanotubes.

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The authors declare no competing financial interests.

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