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Identifying cell receptors for the nanoparticle protein corona using genome screens

Abstract

Nanotechnology provides platforms to deliver medical agents to specific cells. However, the nanoparticle’s surface becomes covered with serum proteins in the blood after administration despite engineering efforts to protect it with targeting or blocking molecules. Here, we developed a strategy to identify the main interactions between nanoparticle-adsorbed proteins and a cell by integrating mass spectrometry with pooled genome screens and Search Tool for the Retrieval of Interacting Genes analysis. We found that the low-density lipoprotein (LDL) receptor was responsible for approximately 75% of serum-coated gold nanoparticle uptake in U-87 MG cells. Apolipoprotein B and complement C8 proteins on the nanoparticle mediated uptake through the LDL receptor. In vivo, nanoparticle accumulation correlated with LDL receptor expression in the organs of mice. A detailed understanding of how adsorbed serum proteins bind to cell receptors will lay the groundwork for controlling the delivery of nanoparticles at the molecular level to diseased tissues for therapeutic and diagnostic applications.

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Fig. 1: Proposed integrated approach to identify receptor-adsorbed protein interactions that mediate nanoparticle uptake.
Fig. 2: Identifying proteins absorbed to the nanoparticle surface.
Fig. 3: Pooled genome-wide CRISPR screens identified genes involved in HS-GNP uptake.
Fig. 4: Identifying and validating interactions between proteins in the nanoparticle corona and cell receptors.
Fig. 5: LDL receptor expression correlates with nanoparticle organ accumulation in mice.

Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information files. The sequencing data are uploaded on Figshare (https://doi.org/10.6084/m9.figshare.19950494). STRING (https://string-db.org/), IMEx (https://www.imexconsortium.org/) and BioGRID (https://thebiogrid.org/) databases are available online. Any other data generated and analyzed during this study are available from the corresponding author upon reasonable request.

References

  1. Walkey, C. D. & Chan, W. C. W. Understanding and controlling the interaction of nanomaterials with proteins in a physiological environment. Chem. Soc. Rev. 41, 2780–2799 (2012).

    CAS  PubMed  Google Scholar 

  2. Aliyandi, A., Zuhorn, I. S. & Salvati, A. Disentangling biomolecular corona interactions with cell receptors and implications for targeting of nanomedicines. Front. Bioeng. Biotechnol. 8, 599454 (2020).

    PubMed  PubMed Central  Google Scholar 

  3. Illum, L. & Davis, S. S. The organ uptake of intravenously administered colloidal particles can be altered using a non-ionic surfactant (Poloxamer 338). FEBS Lett. 167, 79–82 (1984).

    CAS  PubMed  Google Scholar 

  4. Gabizon, A. & Papahadjopoulos, D. Liposome formulations with prolonged circulation time in blood and enhanced uptake by tumors. Proc. Natl Acad. Sci. USA 85, 6949–6953 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Chonn, A., Semple, S. C. & Cullis, P. R. Association of blood proteins with large unilamellar liposomes in vivo. Relation to circulation lifetimes. J. Biol. Chem. 267, 18759–18765 (1992).

    CAS  PubMed  Google Scholar 

  6. Chithrani, B. D., Ghazani, A. A. & Chan, W. C. W. Determining the size and shape dependence of gold nanoparticle uptake into mammalian cells. Nano Lett. 6, 662–668 (2006).

    CAS  PubMed  Google Scholar 

  7. Cedervall, T. et al. Understanding the nanoparticle-protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles. Proc. Natl Acad. Sci. USA 104, 2050–2055 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Lazarovits, J. et al. Supervised learning and mass spectrometry predicts the in vivo fate of nanomaterials. ACS Nano 13, 8023–8034 (2019).

    CAS  PubMed  Google Scholar 

  9. Walkey, C. D. et al. Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles. ACS Nano 8, 2439–2455 (2014).

    CAS  PubMed  Google Scholar 

  10. Lara, S. et al. Identification of receptor binding to the biomolecular corona of nanoparticles. ACS Nano 11, 1884–1893 (2017).

    CAS  PubMed  Google Scholar 

  11. Ritz, S. et al. Protein corona of nanoparticles: distinct proteins regulate the cellular uptake. Biomacromolecules 16, 1311–1321 (2015).

    CAS  PubMed  Google Scholar 

  12. Francia, V. et al. Corona composition can affect the mechanisms cells use to internalize nanoparticles. ACS Nano 13, 11107–11121 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang, Y., Wu, J. L. Y., Lazarovits, J. & Chan, W. C. W. An analysis of the binding function and structural organization of the protein corona. J. Am. Chem. Soc. 142, 8827–8836 (2020).

    PubMed  Google Scholar 

  14. Patel, P. C. et al. Scavenger receptors mediate cellular uptake of polyvalent oligonucleotide-functionalized gold nanoparticles. Bioconjug. Chem. 21, 2250–2256 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Walkey, C. D., Olsen, J. B., Guo, H., Emili, A. & Chan, W. C. W. Nanoparticle size and surface chemistry determine serum protein adsorption and macrophage uptake. J. Am. Chem. Soc. 134, 2139–2147 (2012).

    CAS  PubMed  Google Scholar 

  16. Cullis, P. R., Chonn, A. & Semple, S. C. Interactions of liposomes and lipid-based carrier systems with blood proteins: relation to clearance behaviour in vivo. Adv. Drug Deliv. Rev. 32, 3–17 (1998).

    CAS  PubMed  Google Scholar 

  17. Aliyandi, A., Reker-Smit, C., Bron, R., Zuhorn, I. S. & Salvati, A. Correlating corona composition and cell uptake to identify proteins affecting nanoparticle entry into endothelial cells. ACS Biomater. Sci. Eng. 7, 5573–5584 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3: Genes, Genomes, Genet. 7, 2719–2727 (2017).

    CAS  Google Scholar 

  19. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kinouchi, K. et al. The (pro)renin receptor/ATP6AP2 is essential for vacuolar H+-ATPase assembly in murine cardiomyocytes. Circ. Res. 107, 30–34 (2010).

    CAS  PubMed  Google Scholar 

  21. Colic, M. et al. Identifying chemogenetic interactions from CRISPR screens with drugZ. Genome Med. 11, 52 (2019).

    PubMed  PubMed Central  Google Scholar 

  22. Kaksonen, M. & Roux, A. Mechanisms of clathrin-mediated endocytosis. Nat. Rev. Mol. Cell Biol. 19, 313–326 (2018).

    CAS  PubMed  Google Scholar 

  23. Haney, M. S. et al. Identification of phagocytosis regulators using magnetic genome-wide CRISPR screens. Nat. Genet. 50, 1716–1727 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang, B. et al. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nat. Protoc. 14, 756–780 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Sentmanat, M. F., Peters, S. T., Florian, C. P., Connelly, J. P. & Pruett-Miller, S. M. A survey of validation strategies for CRISPR-Cas9 editing. Sci. Rep. 8, 888 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. Park, J. et al. Enhanced genome editing efficiency of CRISPR PLUS: Cas9 chimeric fusion proteins. Sci. Rep. 11, 16199 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhou, Q. et al. Enzyme-activatable polymer–drug conjugate augments tumour penetration and treatment efficacy. Nat. Nanotechnol. 14, 799–809 (2019).

    CAS  PubMed  Google Scholar 

  29. Ross-Thriepland, D. et al. Arrayed CRISPR screening identifies novel targets that enhance the productive delivery of mRNA by MC3-based lipid nanoparticles. SLAS Discov.: Adv. Sci. Drug Discov. 25, 605–617 (2020).

    CAS  Google Scholar 

  30. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2018).

    PubMed Central  Google Scholar 

  31. Nassar, T. et al. Platelet factor 4 enhances the binding of oxidized low-density lipoprotein to vascular wall cells. J. Biol. Chem. 278, 6187–6193 (2003).

    CAS  PubMed  Google Scholar 

  32. Akinc, A. et al. Targeted delivery of RNAi therapeutics with endogenous and exogenous ligand-based mechanisms. Mol. Ther. 18, 1357–1364 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Gordon, S. M. et al. A comparison of the mouse and human lipoproteome: suitability of the mouse model for studies of human lipoproteins. J. Proteome Res. 14, 2686–2695 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Kaabia, Z. et al. Plasma lipidomic analysis reveals strong similarities between lipid fingerprints in human, hamster and mouse compared to other animal species. Sci. Rep. 8, 15893 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. Yeo, E. L. L., Cheah, J. U.-J., Thong, P. S. P., Soo, K. C. & Kah, J. C. Y. Gold nanorods coated with apolipoprotein E protein corona for drug delivery. ACS Appl. Nano Mater. 2, 6220–6229 (2019).

    CAS  Google Scholar 

  36. Liu K, et al. An obesity model and corona multiomics analysis reveal high-density lipoprotein effects on lipid nanoparticle function. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-827883/v1 (2021).

  37. Asztalos, B. F. et al. Differential effects of HDL subpopulations on cellular ABCA1- and SR-BI-mediated cholesterol efflux. J. Lipid Res. 46, 2246–2253 (2005).

    CAS  PubMed  Google Scholar 

  38. Abuchowski, A., McCoy, J. R., Palczuk, N. C., van Es, T. & Davis, F. F. Effect of covalent attachment of polyethylene glycol on immunogenicity and circulating life of bovine liver catalase. J. Biol. Chem. 252, 3582–3586 (1977).

    CAS  PubMed  Google Scholar 

  39. Blume, G. & Cevc, G. Liposomes for the sustained drug release in vivo. Biochim. Biophys. Acta 1029, 91–97 (1990).

    CAS  PubMed  Google Scholar 

  40. Gabizon, A., Shmeeda, H. & Barenholz, Y. Pharmacokinetics of pegylated liposomal Doxorubicin: review of animal and human studies. Clin. Pharmacokinet. 42, 419–436 (2003).

    CAS  PubMed  Google Scholar 

  41. Perrault, S. D., Walkey, C., Jennings, T., Fischer, H. C. & Chan, W. C. W. Mediating tumor targeting efficiency of nanoparticles through design. Nano Lett. 9, 1909–1915 (2009).

    CAS  PubMed  Google Scholar 

  42. Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci. Adv. https://doi.org/10.1126/sciadv.abh2169 (2021).

  43. Uhlén, M. et al. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol. Cell Proteom. 4, 1920–1932 (2005).

    Google Scholar 

  44. Tavori, H. et al. Serum proprotein convertase subtilisin/kexin type 9 and cell surface low-density lipoprotein receptor: evidence for a reciprocal regulation. Circulation 127, 2403–2413 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Sulheim, E. et al. Multi-modal characterization of vasculature and nanoparticle accumulation in five tumor xenograft models. J. Control. Release 279, 292–305 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Wischnjow, A. et al. Renal targeting: peptide-based drug delivery to proximal tubule cells. Bioconjug Chem. 27, 1050–1057 (2016).

    CAS  PubMed  Google Scholar 

  47. Du, B., Yu, M. & Zheng, J. Transport and interactions of nanoparticles in the kidneys. Nat. Rev. Mater. 3, 358–374 (2018).

    Google Scholar 

  48. Frens, G. Particle size and sol stability in metal colloids. Kolloid-Z. Z. für. Polym. 250, 736–741 (1972).

    CAS  Google Scholar 

  49. Perrault, S. D. & Chan, W. C. W. Synthesis and surface modification of highly monodispersed, spherical gold nanoparticles of 50-200. J. Am. Chem. Soc. 131, 17042–17043 (2009).

    CAS  PubMed  Google Scholar 

  50. Chan, K., Tong, A. H. Y., Brown, K. R., Mero, P. & Moffat, J. Pooled CRISPR-based genetic screens in mammalian cells. J. Vis. Exp. https://doi.org/10.3791/59780 (2019).

  51. Ellis, E. L. & Delbrück, M. The growth of bacteriophage. J. Gen. Physiol. 22, 365–384 (1939).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. de Boer, C. G., Ray, J. P., Hacohen, N. & Regev, A. MAUDE: inferring expression changes in sorting-based CRISPR screens. Genome Biol. 21, 134 (2020).

    PubMed  PubMed Central  Google Scholar 

  53. Condon, K. J. et al. Genome-wide CRISPR screens reveal multitiered mechanisms through which mTORC1 senses mitochondrial dysfunction. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2022120118 (2021).

  54. Yeung, A. T. Y. et al. A genome-wide knockout screen in human macrophages identified host factors modulating salmonella infection. MBio 10, 1–17 (2019).

    Google Scholar 

  55. Mering, C. et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 31, 258–261 (2003).

    Google Scholar 

  56. Andersen, C. L., Jensen, J. L. & Ørntoft, T. F. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 64, 5245–5250 (2004).

    CAS  PubMed  Google Scholar 

  57. Vandesompele, J. et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3, research0034.1 (2002).

    Google Scholar 

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Acknowledgements

W.C.W.C. acknowledges Collaborative Health Research Program grant no. CPG-146468; Canadian Institute of Health Research grant nos. FDN159932 and MOP-1301431; Canadian Research Chairs Program grant no. 950-223824; and Nanomedicines Innovation Network, 2019-T3-01. J.M. acknowledges CIHR grant nos. CBT-438323 and GMX-463531 and is a Canada Research Chair in Functional Genetics. We thank NSERC (W.N., J.L.Y.W. and A.M.S.), Ontario Graduate Scholarships (J.Z.), the Cecil Yip Award (W.N., J.L.Y.W. and A.G.F.), Wildcat Foundation (W.N.), the Jennifer Dorrington Award (J.L.Y.W.), Faculty of Applied Science & Engineering Graduate Student Endowment Fund (B.B.) and the Barbara and Frank Milligan family (J.L.Y.W.) for student fellowships and scholarships. We thank S. Zhao and O. Subedar at the SickKids-UHN Flow Cytometry Facility for assistance with cell sorting; A. Androschuk from Sefton Laboratory for help with the RT–qPCR; the Nanoscale Biomedical Imaging Facility, The Hospital for Sick Children, Toronto, Canada, for assistance with TEM, M. Ganguly, Gregory Ossetchkine and V. Bradaschia at the The Centre for Phenogenomics, Toronto, Canada for assistance with histology studies.

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Authors and Affiliations

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Contributions

W.N., J.L.Y.W., A.M.S. and W.C.W.C. conceptualized the project. W.N., J.L.Y.W. and W.C.W.C. designed the research. W.N. and J.L.Y.W. performed the CRISPR screen and validation experiments. Z.P.L., J.L.Y.W. and W.N. performed flow cytometry experiments. B.B. and W.N. performed competition experiments, Y.Z. and W.N. performed mass spectrometry experiments. W.N., J.L.Y.W. and Z.P.L performed the animal experiments. A.H. prepped sequencing libraries. K.C. and J.M. advised on CRISPR screen experiments. W.N. and A.G.F. performed the double knockout experiments. W.N., J.L.Y.W., A.M.S. and W.C.W.C. analyzed the data. W.N., J.L.Y.W. and W.C.W.C. wrote the manuscript. All authors contributed to editing and revising the manuscript.

Corresponding author

Correspondence to Warren C. W. Chan.

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Competing interests

W.C.W.C. is a cofounder of Luna Nanotech. J.M. is a shareholder and consultant for Century Therapeutics.

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Nature Chemical Biology thanks Chung Hang Jonathan Choi, Emily Day and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flow cytometry and fluorescent activated cell sorting (FACS) gating strategies.

a, Representative gating strategy used to quantify nanoparticle uptake and LDL receptor expression. Negative control in blue and samples in orange. b, Representative fraction of cells collected by FACS for the genome-wide screens. We isolated the bottom 13% of the histogram.

Extended Data Fig. 2 Nanoparticle uptake in knockout cells quantified by flow cytometry.

a, Knockouts of genes (as indicated) depleted in the bottom population of nanoparticle uptake from the genome-wide screen. The data is normalized to the wild type cells. Mean ± std are reported from 3 independent replicates. Statistics are calculated using one-way ANOVA tests with Dunnett multiple comparisons, where “ns” indicates not significant. b, Knockouts of genes (as indicated) from literature. These genes were not identified in our screen. The data is normalized to the negative control cells. Mean ± std are reported from 3 independent replicates. Statistics were calculated using one-way ANOVA tests with Dunnett multiple comparisons, where “ns” indicates not significant.

Extended Data Fig. 3 Quantification of nanoparticle uptake in U-87 MG cells treated with proprotein convertase subtilisin/kexin type 9 (PCSK9).

Data is normalized to the uptake condition without PCSK9. Mean ± std are reported from 3 independent replicates.

Extended Data Fig. 4

Schematic of hits from the genome-wide knockout screen grouped according to the step in the uptake pathway they are potentially involved.

Extended Data Fig. 5 Apolipoprotein E (ApoE) binds the LDL receptor to mediate nanoparticle uptake in the absence of apolipoprotein B (ApoB).

a, Uptake of ApoE coated gold nanoparticles in unedited, LACZ knockout and LDL receptor knockout cells. Mean ± std are reported from 3 independent replicates. b, Quantification of ApoE coated gold nanoparticle uptake with ApoE at 100-fold molar excess of ligands to nanoparticles. Data is normalized to uptake without competitor ligands. Mean ± std are reported from 3 independent replicates. P-values are indicated and calculated using an unpaired two-tailed t-test. c, Abundance of ApoB and ApoE in the C57BL/6 J mouse protein corona compared to human protein corona determined by mass spectrometry.

Extended Data Fig. 6 Depleting apolipoprotein B-100 (ApoB) from human serum.

a, Quantifying the concentration of ApoB in human serum before and after depletion with an enzyme-linked immunosorbent assay. Mean ± std are reported from 3 independent replicates. b, Amount of ApoB on the nanoparticles after coating with normal and ApoB-depleted human serum using an enzyme-linked immunosorbent assay. Mean ± std are reported from 3 independent replicates.

Extended Data Fig. 7 Uptake of serum-coated gold nanoparticles (HS-GNP) in different cell types.

a, HS-GNP uptake in wild-type (WT) and LDL receptor knockout (KO) HAP1 cells. Uptake was quantified by flow cytometry. Mean ± std are reported from 3 independent replicates. Statistics were calculated using a two-sided t-test. b, LDL receptor expression of A-431 and HEK-293T cells was quantified using immunocytochemistry staining and flow cytometry. Expression is displayed as the geometric mean of the fluorescence. Mean ± std are reported from 3 independent replicates. c, The amount of HS-GNP uptake in A-431 and HEK-293T cells. HS-GNP uptake was quantified using inductively coupled plasma-mass spectrometry. Mean ± std are reported from 3 independent replicates.

Extended Data Fig. 8 Quantification of LDL receptor protein expression using immunohistochemistry staining.

a, Representative images of LDL receptor staining in the heart, kidney, spleen, and liver across three mice. Scale bar = 50 µm. b, Quantification of the mean fluorescence intensity (MFI) from LDL receptor staining for the heart, kidney, spleen, and liver in each mouse. Mean ± std are reported from 3 regions of interest within the same organ tissue.

Extended Data Fig. 9 Measuring amount of overlap between gold nanoparticle (GNP) signal and LDL receptor (LDLR) signal on immunohistochemistry images of heart, kidney, spleen, and liver.

a, The portion of pixels positive with GNP that were also positive of LDL receptor signal Mean ± SEM are reported from 3 mice. b, Organ images from each mouse for computing the degree of overlap between GNP and LDLR. Scale bar = 50 µm.

Extended Data Fig. 10 Immunohistochemistry images of LDL receptor staining controls.

LDL receptors were stained using fluorescently-labeled antibodies (red) and cell nuclei were stained using DAPI (blue). a, Representative mouse liver section stained with a rabbit IgG isotype control of one independent experiment. Scale bar = 20 µm. b, Representative mouse pancreas section and a mouse liver section stained with anti-LDL-receptor antibody of 2 independent experiments. Scale bar = 20 µm.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 1–7.

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Supplementary Data 1

Contains LC–MS/MS, drugz and mageck data.

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Ngo, W., Wu, J.L.Y., Lin, Z.P. et al. Identifying cell receptors for the nanoparticle protein corona using genome screens. Nat Chem Biol 18, 1023–1031 (2022). https://doi.org/10.1038/s41589-022-01093-5

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