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Research Article

Particle elasticity influences polymeric artificial antigen presenting cell effectiveness in vivo via CD8+ T cell activation, macrophage uptake, and the protein corona

Savannah E. Est-Witte1, Sydney R. Shannon1, Dennis H. Gong1, Kaitlyn G. Calabresi1, Jawaun J. Harris1, Kaitlyn Storm1, Edwin J. Yoo1,3, Ariel Isser2, Vivek P. Jani3, Natalie K. Livingston2, Mary O. Omotoso2, Kelly Rhodes1, Elana Ben-Akiva1, Randall A. Meyer1, Zoe T. Hsieh1, Simone Sidoli3, Stephany Y. Tzeng1, Jonathan P. Schneck2, Jordan J. Green1( )
Translational Tissue Engineering Center, Institute for Nanobiotechnology, Department of Biomedical Engineering, Johns Hopkins School of Medicine, 400 N Broadway, Baltimore, MD 21231, USA
Institute for Cell Engineering, Department of Pathology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore, MD 21205, USA
Department of Biochemistry, Albert Einstein College of Medicine, The Bronx, New York City, NY 10461, USA
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Graphical Abstract

In this study, we generated a particulate artificial antigen presenting cell (aAPC) platform with tunable elasticity to study the effects of particle elasticity on aAPC effectiveness in vivo. We found elasticity significantly impacted T cell activation, particle clearance, and organ biodistribution, and we investigated possible mechanisms to explain these findings.

Abstract

Adoptive cell therapy (ACT) is an immunotherapy strategy for cancer that has seen widespread clinical success. During ACT, patient-derived lymphocytes are stimulated with the antigen of interest ex vivo, proliferated, then returned to the patient to initiate an antigen-specific antitumor response. While effective, this process is resource-intensive and logistically impossible for many patients. Particulate artificial antigen presenting cells (aAPCs) offer a potential “off-the-shelf” alternative to ex vivo ACT. While particulate aAPCs perform well in vitro, they have had limited success in vivo due to poor bioavailability after injection. Barriers to bioavailability include rapid clearance, unfavorable biodistribution, and inadequate interactions with CD8+ T cells at sites of interest. Biomaterial properties such as elasticity have been shown to vastly impact the bioavailability and particle-cell interactions, but this has yet to be investigated in the context of aAPCs for in vivo T-cell stimulation. Previous literature likewise indicates that biomaterial properties, especially elasticity, can modulate T-cell activation in vitro. With the goal of creating a more biomimetic, next-generation particulate aAPC, we developed a poly(ethylene) glycol hydrogel particle platform with tunable elasticity to investigate the impact of elasticity on antigen-specific T cell activation for in vivo adoptive transfer. Using this knowledge, we were able to gain more precise control over in vivo T cell activation and investigate possible mechanisms including the effects of aAPC elasticity on T cell binding, macrophage uptake, and the protein corona.

Keywords

adoptive cell therapyartificial antigen presenting cellsparticle drug deliveryelasticityprotein coronapoly(ethylene glycol)

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References

[1]

Raskov, H.; Orhan, A.; Christensen, J. P.; Gögenur, I. Cytotoxic CD8+ T cells in cancer and cancer immunotherapy. Br. J. Cancer 2021, 124, 359–367.
Crossref Google Scholar
[2]

Bailey, S. R.; Berger, T. R.; Graham, C.; Larson, R. C.; Maus, M. V. Four challenges to CAR T cells breaking the glass ceiling. Eur. J. Immunol. 2023, 53, 2250039.
Crossref Google Scholar
[3]

Wang, C.; Sun, W. J.; Ye, Y. Q.; Bomba, H. N.; Gu, Z. Bioengineering of artificial antigen presenting cells and lymphoid organs. Theranostics 2017, 7, 3504–3516.
Crossref Google Scholar
[4]

Est-Witte, S. E.; Livingston, N. K.; Omotoso, M. O.; Green, J. J.; Schneck, J. P. Nanoparticles for generating antigen-specific T cells for immunotherapy. Semin. Immunol. 2021, 56, 101541.
Crossref Google Scholar
[5]

Bandola-Simon, J.; Roche, P. A. Dysfunction of antigen processing and presentation by dendritic cells in cancer. Mol. Immunol. 2019, 113, 31–37.
Crossref Google Scholar
[6]

Zhang, Z.; Liu, S. S.; Zhang, B.; Qiao, L.; Zhang, Y.; Zhang, Y. T cell dysfunction and exhaustion in cancer. Front. Cell Dev. Biol. 2020, 8, 17.
Crossref Google Scholar
[7]

Ben-Akiva, E.; Witte, S. E.; Meyer, R. A.; Rhodes, K. R.; Green, J. J. Polymeric micro-and nanoparticles for immune modulation. Biomater. Sci. 2019, 7, 14–30.
Crossref Google Scholar
[8]

Meyer, R. A.; Sunshine, J. C.; Perica, K.; Kosmides, A. K.; Aje, K.; Schneck, J. P.; Green, J. J. Biodegradable nanoellipsoidal artificial antigen presenting cells for antigen specific T-cell activation. Small 2016, 11, 1519–1525.
Google Scholar
[9]

Meyer, R.; Sunshine, J.; Green, J. J. Biomimetic particles as therapeutics. Trends. Biotechnol. 2015, 33, 514–524
Crossref Google Scholar
[10]

Ben-Akiva, E.; Meyer, R. A.; Wilson, D. R.; Green, J. J. Surface engineering for lymphocyte programming. Adv. Drug Deliv. Rev. 2017, 114, 102–115.
Crossref Google Scholar
[11]

Rhodes, K. R.; Isser, A.; Hickey, J. W.; Ben-Akiva, E.; Meyer, R. A.; Kosmides, A. K.; Livingston, N. K.; Tzeng, S. Y.; Schneck, J. P.; Green, J. J. Biodegradable cationic polymer blends for fabrication of enhanced artificial antigen presenting cells to treat melanoma. ACS Appl. Mater. Interfaces 2021, 13, 7913–7923.
Crossref Google Scholar
[12]

Harrison, D. L.; Fang, Y.; Huang, J. T-cell mechanobiology: Force sensation, potentiation, and translation. Front. Phys. 2019, 7, 45.
Crossref Google Scholar
[13]

Du, H. X.; Bartleson, J. M.; Butenko, S.; Alonso, V.; Liu, W. F.; Winer, D. A.; Butte, M. J. Tuning immunity through tissue mechanotransduction. Nat. Rev. Immunol. 2023, 23, 174–188.
Crossref Google Scholar
[14]

Meng, K. P.; Majedi, F. S.; Thauland, T. J.; Butte, M. J. Mechanosensing through YAP controls T cell activation and metabolism. J. Exp. Med. 2020, 217, e20200053.
Crossref Google Scholar
[15]
Platzman, I.; Kannenberg, G.; Janiesch, J. W.; Matić, J.; Spatz, J. PEG-based antigen-presenting cell surrogates for immunological applications. In Soft Matter Nanotechnology: From Structure to Function. Chen, X.; Fuchs, H., Eds.; Wiley-VCH: Weinheim, 2015; pp 187–215.
[16]

Hickey, J. W.; Dong, Y.; Chung, J. W.; Salathe, S. F.; Pruitt, H. C.; Li, X. W.; Chang, C.; Fraser, A. K.; Bessell, C. A.; Ewald, A. J. et al. Engineering an artificial T-cell stimulating matrix for immunotherapy. Adv. Mater. 2019, 31, 1807359.
Crossref Google Scholar
[17]

Saitakis, M.; Dogniaux, S.; Goudot, C.; Bufi, N.; Asnacios, S.; Maurin, M.; Randriamampita, C.; Asnacios, A.; Hivroz, C. Different TCR-induced T lymphocyte responses are potentiated by stiffness with variable sensitivity. eLife 2017, 6, e23190.
Crossref Google Scholar
[18]

Guo, P.; Liu, D. X.; Subramanyam, K.; Wang, B. R.; Yang, J.; Huang, J.; Auguste, D. T.; Moses, M. A. Nanoparticle elasticity directs tumor uptake. Nat. Commun. 2018, 9, 130.
Crossref Google Scholar
[19]

Anselmo, A. C.; Mitragotri, S. Impact of particle elasticity on particle-based drug delivery systems. Adv. Drug Deliv. Rev. 2017, 108, 51–67.
Crossref Google Scholar
[20]

Anselmo, A. C.; Zhang, M. W.; Kumar, S.; Vogus, D. R.; Menegatti, S.; Helgeson, M. E.; Mitragotri, S. Elasticity of nanoparticles influences their blood circulation, phagocytosis, endocytosis, and targeting. ACS Nano 2015, 9, 3169–3177.
Crossref Google Scholar
[21]

Hui, Y.; Yi, X.; Hou, F.; Wibowo, D.; Zhang, F.; Zhao, D. Y.; Gao, H. J.; Zhao, C. X. Role of nanoparticle mechanical properties in cancer drug delivery. ACS Nano 2019, 13, 7410–7424.
Crossref Google Scholar
[22]

Cui, J. W.; De Rose, R.; Alt, K.; Alcantara, S.; Paterson, B. M.; Liang, K.; Hu, M.; Richardson, J. J.; Yan, Y.; Jeffery, C. M. et al. Engineering poly(ethylene glycol) particles for improved biodistribution. ACS Nano 2015, 9, 1571–1580.
Crossref Google Scholar
[23]

Van Thienen, T. G.; Demeester, J.; De Smedt, S. C. Screening poly(ethyleneglycol) micro- and nanogels for drug delivery purposes. Int. J. Pharm. 2008, 351, 174–185.
Crossref Google Scholar
[24]

Sunshine, J. C.; Perica, K.; Schneck, J. P.; Green, J. J. Particle shape dependence of CD8+ T cell activation by artificial antigen presenting cells. Biomaterials 2014, 35, 269–277.
Crossref Google Scholar
[25]

Akhtar, R.; Sherratt, M. J.; Cruickshank, J. K.; Derby, B. Characterizing the elastic properties of tissues. Mater. Today 2011, 14, 96–105.
Crossref Google Scholar
[26]

Doshi, N.; Zahr, A. S.; Bhaskar, S.; Lahann, J.; Mitragotri, S. Red blood cell-mimicking synthetic biomaterial particles. Proc. Natl. Acad. Sci. USA 2009, 106, 21495–21499.
Crossref Google Scholar
[27]

Overwijk, W. W.; Tsung, A.; Irvine, K. R.; Parkhurst, M. R.; Goletz, T. J.; Tsung, K.; Carroll, M. W.; Liu, C. L.; Moss, B.; Rosenberg, S. A.et al. gp100/pmel 17 is a murine tumor rejection antigen: Induction of "Self "-reactive, tumoricidal T cells using high-affinity, altered peptide ligand. J. Exp. Med. 1998, 188, 277–286.
Crossref Google Scholar
[28]

Waldman, A. D.; Fritz, J. M.; Lenardo, M. J. A guide to cancer immunotherapy: From T cell basic science to clinical practice. Nat. Rev. Immunol. 2020, 20, 651–668.
Crossref Google Scholar
[29]

Wauters, A. C.; Scheerstra, J. F.; Vermeijlen, I. G.; Hammink, R.; Schluck, M.; Woythe, L.; Wu, H. L.; Albertazzi, L.; Figdor, C. G.; Tel, J. et al. Artificial antigen-presenting cell topology dictates T Cell activation. ACS Nano 2022, 16, 15072–15085.
Crossref Google Scholar
[30]

Majedi, F. S.; Hasani-Sadrabadi, M. M.; Thauland, T. J.; Li, S.; Bouchard, L. S.; Butte, M. J. Augmentation of T-cell activation by oscillatory forces and engineered antigen-presenting cells. Nano Lett. 2019, 19, 6945–6954.
Crossref Google Scholar
[31]

Ye, L. L.; Wei, X. S.; Zhang, M.; Niu, Y. R.; Zhou, Q. The significance of tumor necrosis factor receptor type II in CD8+ regulatory T cells and CD8+ effector T cells. Front. Immunol. 2018, 9, 583.
Crossref Google Scholar
[32]

Majedi, F. S.; Hasani-Sadrabadi, M. M.; Thauland, T. J.; Li, S.; Bouchard, L. S.; Butte, M. J. T-cell activation is modulated by the 3D mechanical microenvironment. Biomaterials 2020, 252, 120058.
Crossref Google Scholar
[33]

Mishra, R. K.; Ahmad, A.; Vyawahare, A.; Alam, P.; Khan, T. H.; Khan, R. Biological effects of formation of protein corona onto nanoparticles. Int. J. Biol. Macromol. 2021, 175, 1–18.
Crossref Google Scholar
[34]

Li, H. M.; Wang, Y.; Tang, Q.; Yin, D.; Tang, C.; He, E.; Zou, L.; Peng, Q. The protein corona and its effects on nanoparticle-based drug delivery systems. Acta Biomater. 2021, 129, 57–72.
Crossref Google Scholar
[35]

Tomak, A.; Cesmeli, S.; Hanoglu, B. D.; Winkler, D.; Karakus, C. O. Nanoparticle-protein corona complex: Understanding multiple interactions between environmental factors, corona formation, and biological activity. Nanotoxicology 2021, 15, 1331–1357.
Crossref Google Scholar
[36]

Panico, S.; Capolla, S.; Bozzer, S.; Toffoli, G.; Dal Bo, M.; Macor, P. Biological features of nanoparticles: Protein corona formation and interaction with the immune system. Pharmaceutics 2022, 14, 2605.
Crossref Google Scholar
[37]

Tengjisi; Hui, Y.; Fan, Y. Y.; Zou, D.; Talbo, G. H.; Yang, G. Z.; Zhao, C. X. Influence of nanoparticle mechanical property on protein corona formation. J. Colloid Interface Sci. 2022, 606, 1737–1744.
Crossref Google Scholar
[38]

Partikel, K.; Korte, R.; Stein, N. C.; Mulac, D.; Herrmann, F. C.; Humpf, H. U.; Langer, K. Effect of nanoparticle size and PEGylation on the protein corona of PLGA nanoparticles. Eur. J. Pharm. Biopharm. 2019, 141, 70–80.
Crossref Google Scholar
[39]

Yu, F. C.; Teo, G. C.; Kong, A. T.; Haynes, S. E.; Avtonomov, D. M.; Geiszler, D. J.; Nesvizhskii, A. I. Identification of modified peptides using localization-aware open search. Nat. Commun. 2020, 11, 4065.
Crossref Google Scholar
[40]

Polasky, D. A.; Yu, F. C.; Teo, G. C.; Nesvizhskii, A. I. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat. Methods 2020, 17, 1125–1132.
Crossref Google Scholar
[41]

Teo, G. C.; Polasky, D. A.; Yu, F. C.; Nesvizhskii, A. I. Fast deisotoping algorithm and its implementation in the MSFragger search engine. J. Proteome Res. 2021, 20, 498–505.
Crossref Google Scholar
[42]

Yu, F. C.; Haynes, S. E.; Teo, G. C.; Avtonomov, D. M.; Polasky, D. A.; Nesvizhskii, A. I. Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Mol. Cell Proteomics 2020, 19, 1575–1585.
Crossref Google Scholar
[43]

Chang, H. Y.; Kong, A. T.; Da Veiga Leprevost, F.; Avtonomov, D. M.; Haynes, S. E.; Nesvizhskii, A. I. Crystal-C: A computational tool for refinement of open search results. J. Proteome Res. 2020, 19, 2511–2515.
Crossref Google Scholar
[44]

Geiszler, D. J.; Kong, A. T.; Avtonomov, D. M.; Yu, F. C.; Da Veiga Leprevost, V.; Nesvizhskii, A. I. PTM-Shepherd: Analysis and summarization of post-translational and chemical modifications from open search results. Mol. Cell Proteomics 2021, 20, 100018.
Crossref Google Scholar
[45]

Tsou, C. C.; Avtonomov, D.; Larsen, B.; Tucholska, M.; Choi, H.; Gingras, A. C.; Nesvizhskii, A. I. DIA-Umpire: Comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 2015, 12, 258–264.
Crossref Google Scholar
[46]

Gao, B. B.; Zhu, J.; Negi, S.; Zhang, X. M.; Gyoneva, S.; Casey, F.; Wei, R.; Zhang, B. H. Quickomics: Exploring omics data in an intuitive, interactive and informative manner. Bioinformatics 2021, 37, 3670–3672.
Crossref Google Scholar
[47]

Parhami-Seren, B.; Viswanathan, M.; Strong, R. K.; Margolies, M. N. Structural analysis of mutants of high-affinity and low-affinity p-azophenylarsonate-specific antibodies generated by alanine scanning of heavy chain complementarity-determining region 2. J. Immunol. 2001, 167, 5129–5135.
Crossref Google Scholar
[48]

Siegelman, M.; Capra, J. D. Complete amino acid sequence of light chain variable regions derived from five monoclonal anti-p-azophenylarsonate antibodies differing with respect to a crossreactive idiotype. Proc. Natl. Acad. Sci. USA 1981, 78, 7679–7683.
Crossref Google Scholar
[49]

Chinen, A. B.; Guan, C. M.; Ko, C. H.; Mirkin, C. A. The impact of protein corona formation on the macrophage cellular uptake and biodistribution of spherical nucleic acids. Small 2017, 13, 1603847.
Crossref Google Scholar
[50]

Santiago-Sánchez, G. S.; Pita-Grisanti, V.; Quiñones-Díaz, B.; Gumpper, K.; Cruz-Monserrate, Z.; Vivas-Mejía, P. E. Biological functions and therapeutic potential of Lipocalin 2 in cancer. Int. J. Mol. Sci. 2020, 21, 4365.
Crossref Google Scholar
[51]

Warszawska, J. M.; Gawish, R.; Sharif, O.; Sigel, S.; Doninger, B.; Lakovits, K.; Mesteri, I.; Nairz, M.; Boon, L.; Spiel, A. et al. Lipocalin 2 deactivates macrophages and worsens pneumococcal pneumonia outcomes. J. Clin. Invest. 2013, 123, 3363–3372.
Crossref Google Scholar
[52]

Zumwalde, N. A.; Domae, E.; Mescher, M. F.; Shimizu, Y. ICAM-1-dependent homotypic aggregates regulate CD8 T cell effector function and differentiation during T cell activation. J. Immunol. 2013, 191, 3681–3693.
Crossref Google Scholar
[53]

Cox, M. A.; Barnum, S. R.; Bullard, D. C.; Zajac, A. J. ICAM-1-dependent tuning of memory CD8 T-cell responses following acute infection. Proc. Natl. Acad. Sci. USA 2013, 110, 1416–1421.
Crossref Google Scholar
[54]

Zhong, H. H.; Lin, H. T.; Pang, Q. N.; Zhuang, J. L.; Liu, X. L.; Li, X. L.; Liu, J. H.; Tang, J. Macrophage ICAM-1 functions as a regulator of phagocytosis in LPS induced endotoxemia. Inflamm. Res. 2021, 70, 193–203.
Crossref Google Scholar
[55]

Crompton, J. G.; Narayanan, M.; Cuddapah, S.; Roychoudhuri, R.; Ji, Y.; Yang, W.; Patel, S. J.; Sukumar, M.; Palmer, D. C.; Peng, W. et al. Lineage relationship of CD8+ T cell subsets is revealed by progressive changes in the epigenetic landscape. Cell. Mol. Immunol. 2015, 13, 502–513.
Crossref Google Scholar
[56]

Cho, J.; Seo, J.; Lim, C. H.; Yang, L.; Shiratsuchi, T.; Lee, M. H.; Chowdhury, R. R.; Kasahara, H.; Kim, J. S.; Oh, S. P. Mitochondrial ATP transporter Ant2 depletion impairs erythropoiesis and B lymphopoiesis. Cell Death Differ. 2015, 22, 1437–1450.
Crossref Google Scholar
[57]

Saragovi, A.; Abramovich, I.; Omar, I.; Arbib, E.; Toker, O.; Gottlieb, E.; Berger, M. Systemic hypoxia inhibits T cell response by limiting mitobiogenesis via matrix substrate-level phosphorylation arrest. eLife 2020, 9, e56612.
Crossref Google Scholar
[58]

Moon, J. S.; Da Cunha, F. F.; Huh, J. Y.; Andreyev, A. Y.; Lee, J.; Mahata, S. K.; Reis, F. C. G.; Nasamran, C. A.; Lee, Y. S. ANT2 drives proinflammatory macrophage activation in obesity. JCI Insight 2021, 22, e147033
Crossref Google Scholar
[59]

Beck, I. M.; Rückert, R.; Brandt, K.; Mueller, M. S.; Sadowski, T.; Brauer, R.; Schirmacher, P.; Mentlein, R.; Sedlacek, R. MMP19 is essential for T cell development and T cell-mediated cutaneous immune responses. PLoS One 2008, 3, e2343
Crossref Google Scholar
[60]

Matysiak-Kucharek, M.; Czajka, M.; Sawicki, K.; Kruszewski, M.; Kapka-Skrzypczak, L. Effect of nanoparticles on the expression and activity of matrix metalloproteinases. Nanotechnol. Rev. 2018, 7, 541–553.
Crossref Google Scholar
[61]

Huang, W. C.; Sala-Newby, G. B.; Susana, A.; Johnson, J. L.; Newby, A. C. Classical macrophage activation up-regulates several matrix metalloproteinases through mitogen activated protein kinases and nuclear factor-κB. PLoS One 2012, 7, e42507.
Crossref Google Scholar
[62]

Park, E. J.; Yi, J.; Kim, Y.; Choi, K.; Park, K. Silver nanoparticles induce cytotoxicity by a Trojan-horse type mechanism. Toxicol. Vitro 2010, 24, 872–878.
Crossref Google Scholar
[63]

Zuidema, J. M.; Rivet, C. J.; Gilbert, R. J.; Morrison, F. A. A protocol for rheological characterization of hydrogels for tissue engineering strategies. J. Biomed. Mater. Res. B Appl. Biomater. 2014, 102, 1063–1073.
Crossref Google Scholar
[64]

Abramoff, M. D.; Magalhaes, P. J.; Ram, S. J. Image processing with ImageJ. Biophoton. Int. 2004, 11, 36–42.
Crossref Google Scholar
Nano Research
Volume 17 Issue 10,
October 2024
Pages 9052-9064
DOI: 10.1007/s12274-024-6589-2
Cite this article:
Est-Witte SE, Shannon SR, Gong DH, et al. Particle elasticity influences polymeric artificial antigen presenting cell effectiveness in vivo via CD8+ T cell activation, macrophage uptake, and the protein corona. Nano Research, 2024, 17(10): 9052-9064. https://doi.org/10.1007/s12274-024-6589-2
Topics:
Biomaterials BiopolymersBiotechnologyDrug deliveryNanobiotechnology
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Received: 24 September 2023
Revised: 23 February 2024
Accepted: 26 February 2024
Published: 04 April 2024
© Tsinghua University Press 2024
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