Abstract

Mesoporous bioactive glass nanospheres (NanoMBGs) have high potential for clinical applications. However, the impact of these nanoparticles on the immune system needs to be addressed. In this study, the biocompatibility of SiO2-CaO NanoMBGs was evaluated on different mouse immune cells, including spleen cells subsets, bone marrow-derived dendritic cells (BMDCs), or cell lines like SR.D10 Th2 CD4+ lymphocytes and DC2.4 dendritic cells. Flow cytometry and confocal microscopy show that the nanoparticles were rapidly and efficiently taken up in vitro by T and B lymphocytes or by specialized antigen-presenting cells (APCs) like dendritic cells (DCs). Nanoparticles were not cytotoxic and had no effect on cell viability or proliferation under T-cell (anti-CD3) or B cell (LPS) stimuli. Besides, NanoMBGs did not affect the balance of spleen cell subsets, or the production of intracellular or secreted pro- and anti-inflammatory cytokines (TNF-α, IFN-γ, IL-2, IL-6, IL-10) by activated T, B, and dendritic cells (DC), as determined by flow cytometry and ELISA. T cell activation surface markers (CD25, CD69 and Induced Costimulator, ICOS) were not altered by NanoMBGs. Maturation of BMDCs or DC2.4 cells in vitro was not altered by NanoMBGs, as shown by expression of Major Histocompatibility Complex (MHC) and costimulatory molecules (CD40, CD80, CD86), or IL-6 secretion. The effect of wortmannin and chlorpromazine indicate a role for phosphoinositide 3-kinase (PI3K), actin and clathrin-dependent pathways in NanoMBG internalization. We thus demonstrate that these NanoMBGs are both non-toxic and non-inflammagenic for murine lymphoid cells and myeloid DCs despite their efficient intake by the cells.

Highlights

  • Among the growing number of nanomaterials with therapeutic potential, mesoporous silica nanoparticles (MSN) have gained the attention of the nanomedicine research community, especially for the potential treatment of different biomedical challenges, including cancer, infectious processes and others [1,2,3,4,5,6].Mesoporous silica nanoparticles have unique, advantageous physical-chemical properties that make them ideal platforms to design multifunctional nanosystems [7]

  • T cell activation via antigen presentation does not occur in contact with a biomaterial if the biomaterial is not degradable or if no bacteria transiently attach to its surface

  • It has been suggested that synthetic biomaterials present functional groups on their surfaces acting as mitogens which can polyclonally trigger lymphocytes by cell surface glycoprotein cross-linking [11,25]

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Summary

Introduction

Among the growing number of nanomaterials with therapeutic potential, mesoporous silica nanoparticles (MSN) have gained the attention of the nanomedicine research community, especially for the potential treatment of different biomedical challenges, including cancer, infectious processes and others [1,2,3,4,5,6]. Silica-based mesoporous nanoparticles present low toxicity and high biocompatibility, easy functionalization and great loading capacity of many different types of therapeutic agents within their pores, favoring the possibility of modifying the surface to target the particles to the malignant areas The advantage of these nanomaterial formulations over conventional systems is that they can increase treatment efficacy and decrease side effects through their precise targeting mode of action [7,8,9,10]. An exhaustive analysis of the specific markers and pro- or anti-inflammatory cytokines of each cell type has been carried out, as well as the molecular mechanisms of entry of these nanoparticles into these different cell types of the immune system

Incorporation of NanoMBGs by Murine Spleen Cells
Mechanisms of NanoMBG Incorporation into the Cells
Animals
Culture and Activation of Murine Cells
Flow Cytometry Assays
Analysis of Intracellular Cytokine Expression in Murine Spleen Cells
Analysis of Cell Proliferation by Dye Dilution
Detection of Spontaneous Apoptosis in Cell Cultures
Analysis of FITC-NanoMBG-Cell Interaction
3.10. Confocal Microscopy
3.11. Statistics
Conclusions
Full Text
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