Abstract
Nanoscale objects are processed by living organisms using highly evolved and sophisticated endogenous cellular networks, specifically designed to manage objects of this size. While these processes potentially allow nanostructures unique access to and control over key biological machineries, they are also highly protected by cell or host defence mechanisms at all levels. A thorough understanding of bionanoscale recognition events, including the molecules involved in the cell recognition machinery, the nature of information transferred during recognition processes and the coupled downstream cellular processing, would allow us to achieve a qualitatively novel form of biological control and advanced therapeutics. Here we discuss evolving fundamental microscopic and mechanistic understanding of biological nanoscale recognition. We consider the interface between a nanostructure and a target cell membrane, outlining the categories of nanostructure properties that are recognized, and the associated nanoscale signal transduction and cellular programming mechanisms that constitute biological recognition.
Similar content being viewed by others
References
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).
Lynch, I., Salvati, A. & Dawson, K. A. What does the cell see? Nat. Nanotechnol. 4, 546–547 (2009).
Monopoli, M. P., Åberg, C., Salvati, A. & Dawson, K. A. Biomolecular coronas provide the biological identity of nanosized materials. Nat. Nanotechnol. 7, 779–786 (2012).
Puri, P. L. et al. A myogenic differentiation checkpoint activated by genotoxic stress. Nat. Genet. 32, 585–593 (2002).
Old foes and new enemies. Nat. Immunol. 19, 1147 (2018).
Maeda, H. & Khatami, M. Analyses of repeated failures in cancer therapy for solid tumors: poor tumor-selective drug delivery, low therapeutic efficacy and unsustainable costs. Clin. Transl. Med. 7, e11 (2018).
Lin, A. et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci. Transl. Med. 11, eaaw8412 (2019).
Rosenblum, D., Joshi, N., Tao, W., Karp, J. M. & Peer, D. Progress and challenges towards targeted delivery of cancer therapeutics. Nat. Commun. 9, 1410 (2018).
Papoian, T. et al. Secondary pharmacology data to assess potential off-target activity of new drugs: a regulatory perspective. Nat. Rev. Drug Discov. 14, 294–294 (2015).
Cao, X.-Z., Merlitz, H., Wu, C.-X., Egorov, S. A. & Sommer, J.-U. Effective pair potentials between nanoparticles induced by single monomers and polymer chains. Soft Matter 9, 5916–5926 (2013).
Dawson, K. A. The glass paradigm for colloidal glasses, gels, and other arrested states driven by attractive interactions. Curr. Opin. Colloid Interface Sci. 7, 218–227 (2002).
Huang, K. & Szlufarska, I. Effect of interfaces on the nearby Brownian motion. Nat. Commun. 6, 8558 (2015).
Lara, S. et al. Differential recognition of nanoparticle protein corona and modified low-density lipoprotein by macrophage receptor with collagenous structure. ACS Nano 12, 4930–4937 (2018).
Sieben, C. et al. Influenza virus binds its host cell using multiple dynamic interactions. Proc. Natl Acad. Sci. USA 109, 13626–13631 (2012).
Ehrlich, M. et al. Endocytosis by random initiation and stabilization of clathrin-coated pits. Cell 118, 591–605 (2004).
Curk, T., Dobnikar, J. & Frenkel, D. Optimal multivalent targeting of membranes with many distinct receptors. Proc. Natl Acad. Sci. USA 114, 7210 (2017).
Blaszczyk, M., Harmer, N. J., Chirgadze, D. Y., Ascher, D. B. & Blundell, T. L. Achieving high signal-to-noise in cell regulatory systems: spatial organization of multiprotein transmembrane assemblies of FGFR and MET receptors. Prog. Biophys. Mol. Biol. 118, 103–111 (2015).
Bicknell, B. A., Dayan, P. & Goodhill, G. J. The limits of chemosensation vary across dimensions. Nat. Commun. 6, 7468 (2015).
Adami, C. What is information? Philos. Trans. A 374, 20150230 (2016).
Tsimring, L. S. Noise in biology. Rep. Prog. Phys. 77, 026601 (2014).
Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002).
Lestas, I., Vinnicombe, G. & Paulsson, J. Fundamental limits on the suppression of molecular fluctuations. Nature 467, 174–178 (2010).
Dustin, M. L. The immunological synapse. Cancer Immunol. Res. 2, 1023 (2014).
Fooksman, D. R. et al. Functional anatomy of T cell activation and synapse formation. Annu. Rev. Immunol. 28, 79–105 (2010).
Kuokkanen, E., Šuštar, V. & Mattila, P. K. Molecular control of B cell activation and immunological synapse formation. Traffic 16, 311–326 (2015).
Dustin, M. L. What counts in the immunological synapse? Mol. Cell 54, 255–262 (2014).
Igakura, T. et al. Spread of HTLV-I between lymphocytes by virus-induced polarization of the cytoskeleton. Science 299, 1713 (2003).
Salvati, A. et al. Transferrin-functionalized nanoparticles lose their targeting capabilities when a biomolecule corona adsorbs on the surface. Nat. Nanotechnol. 8, 137–143 (2013).
Weil, K. G. J. S., Rowlinson & Widom, B. Molecular Theory of Capillarity, Clarendon Press, Oxford 1982. 327 Seiten, Preis: £ 30,–. Ber. Bunsenges. Phys. Chem. 88, 586–586 (1984).
Milani, S. et al. Reversible versus irreversible binding of transferrin to polystyrene nanoparticles: soft and hard corona. ACS Nano 6, 2532–2541 (2012).
Monopoli, M. P. et al. Physical−chemical aspects of protein corona: relevance to in vitro and in vivo biological impacts of nanoparticles. J. Am. Chem. Soc. 133, 2525–2534 (2011).
Treuel, L., Docter, D., Maskos, M. & Stauber, R. H. Protein corona—from molecular adsorption to physiological complexity. Beilstein J. Nanotechnol. 6, 857–873 (2015).
Bertoli, F., Garry, D., Monopoli, M. P., Salvati, A. & Dawson, K. A. The intracellular destiny of the protein corona: a study on its cellular internalization and evolution. ACS Nano 10, 10471–10479 (2016).
Lara, S. et al. Identification of receptor binding to the biomolecular corona of nanoparticles. ACS Nano 11, 1884–1893 (2017).
Röcker, C., Pötzl, M., Zhang, F., Parak, W. J. & Nienhaus, G. U. A quantitative fluorescence study of protein monolayer formation on colloidal nanoparticles. Nat. Nanotechnol. 4, 577–580 (2009).
Martinez-Moro, M., Di Silvio, D. & Moya, S. E. Fluorescence correlation spectroscopy as a tool for the study of the intracellular dynamics and biological fate of protein corona. Biophys. Chem. 253, 106218 (2019).
Hargett, A. A. & Renfrow, B. R. Glycosylation of viral surface proteins probed by mass spectrometry. Curr. Opin. Virol. 35, 56–66 (2019).
Kundu, S. K. et al. Relaxation dynamics of liposomes in an aqueous solution. Phys. Chem. Chem. Phys. 17, 18449–18455 (2015).
Sandin, P., Fitzpatrick, L. W., Simpson, J. C. & Dawson, K. A. High-speed imaging of Rab family small GTPases reveals rare events in nanoparticle trafficking in living cells. ACS Nano 6, 1513–1521 (2012).
Boselli, L. et al. Classification and biological identity of complex nano shapes. Commun. Mater. 1, 35 (2020).
Blume, J. E. et al. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat. Commun. 11, 3662 (2020).
Kelly, P. M. et al. Mapping protein binding sites on the biomolecular corona of nanoparticles. Nat. Nanotechnol. 10, 472–479 (2015).
Herda, L. M. et al. Mapping of molecular structure of the nanoscale surface in bionanoparticles. J. Am. Chem. Soc. 139, 111–114 (2017).
Lo Giudice, M. C., Herda, L. M., Polo, E. & Dawson, K. A. In situ characterization of nanoparticle biomolecular interactions in complex biological media by flow cytometry. Nat. Commun. 7, 13475 (2016).
Mohammad-Beigi, H. et al. Mapping and identification of soft corona proteins at nanoparticles and their impact on cellular association. Nat. Commun. 11, 4535 (2020).
Lynch, I., Dawson, K. A. & Linse, S. Detecting cryptic epitopes created by nanoparticles. Sci. STKE 2006, pe14 (2006).
Akinc, A. et al. The Onpattro story and the clinical translation of nanomedicines containing nucleic acid-based drugs. Nat. Nanotechnol. 14, 1084–1087 (2019).
Bohnert, M. & Schuldiner, M. Stepping outside the comfort zone of membrane contact site research. Nat. Rev. Mol. Cell Biol. 19, 483–484 (2018).
Joshi, A. S., Zhang, H. & Prinz, W. A. Organelle biogenesis in the endoplasmic reticulum. Nat. Cell Biol. 19, 876–882 (2017).
Shpilka, T. & Haynes, C. M. The mitochondrial UPR: mechanisms, physiological functions and implications in ageing. Nat. Rev. Mol. Cell Biol. 19, 109–120 (2018).
Fang, E. F. et al. Nuclear DNA damage signalling to mitochondria in ageing. Nat. Rev. Mol. Cell Biol. 17, 308–321 (2016).
Fehervari, Z. Building an immune synapse. Nat. Immunol. 13, 816 (2012).
Casaletto, J. B. & McClatchey, A. I. Spatial regulation of receptor tyrosine kinases in development and cancer. Nat. Rev. Cancer 12, 387–400 (2012).
Friedl, P., den Boer, A. T. & Gunzer, M. Tuning immune responses: diversity and adaptation of the immunological synapse. Nat. Rev. Immunol. 5, 532–545 (2005).
Wales, D., Saykally, R., Zewail, A. & King, D. Energy Landscapes: Applications to Clusters, Biomolecules and Glasses (Cambridge University Press, 2003).
Amit, D. J. Modeling Brain Function: the World of Attractor Neural Networks (Cambridge University Press, 1989).
Baldassi, C., Pittorino, F. & Zecchina, R. Shaping the learning landscape in neural networks around wide flat minima. Proc. Natl Acad. Sci. USA 117, 161 (2020).
Silver, D. et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140 (2018).
Acknowledgements
K.A.D. and Y.Y. acknowledge that this publication has emanated from research supported in part by a grant from Science Foundation Ireland (17/NSFC/4898 (K.A.D.)), funding under Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumor Microenvironment (2019KSYS008 (K.A.D.)) and grants from Science Foundation Ireland (15/SIRG/3423 (Y.Y.), 17/ERCD/4962 (K.A.D.) and 16/ENM-ERA/3457 (Y.Y.)). We would like to thank Yijun Jiang, Guangzhou Hongjun Scientific Co., Ltd, for creating the images in Figs. 1, 2, 3a,e,g,i,k and 4.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature Nanotechnology thanks Jie Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Video 1
Live-cell imaging of nanoparticle–cell interactions. Cells in the exponential growth phase were seeded in a confocal dish 24 h before the imaging. After the cell membrane was stained with CellMask Orange, the cells were treated with fluorescein isothiocyanate-labelled polystyrene nanoparticles (100 nm in diameter). Subsequently, the cells were placed in a live-cell imaging chamber and imaged using spinning disc microscopy with a ×63 lens (oil immersion).
Rights and permissions
About this article
Cite this article
Dawson, K.A., Yan, Y. Current understanding of biological identity at the nanoscale and future prospects. Nat. Nanotechnol. 16, 229–242 (2021). https://doi.org/10.1038/s41565-021-00860-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41565-021-00860-0
- Springer Nature Limited
This article is cited by
-
Controlling the protein corona of polymeric nanocapsules: effect of polymer shell on protein adsorption
Drug Delivery and Translational Research (2024)
-
Protein corona and exosomes: new challenges and prospects
Cell Communication and Signaling (2023)
-
Cellular shortening and calcium dynamics are improved by noisy stimulus in a model of cardiomyopathy
Scientific Reports (2023)
-
The protein corona from nanomedicine to environmental science
Nature Reviews Materials (2023)
-
Controlling the biodistribution and clearance of nanomedicines
Nature Reviews Bioengineering (2023)