Validation of a high-throughput methodology to assess the effects of biomaterials on dendritic cell phenotype

Abstract

A variety of combination products composed of biomaterials and biologics have been developed for tissue regeneration or vaccine delivery. The host immune response to the immunogenic biological components in such products may be modulated by the biomaterial component. Distinct biomaterials have been shown to differentially affect the maturation of dendritic cells (DCs). DCs are professional antigen-presenting cells (APCs) that bridge innate and adaptive immunity and play a central role in inducing immunity or initiating immune tolerance. However, the biomaterials systems used to study DC response thus far have been insufficient to draw a clear conclusion as to which biomaterial properties are the key to controlling DC phenotype. In this study, we developed a 96-well filter plate-based high-throughput (HTP) methodology to assess DC maturation upon biomaterial treatment. Equivalent biomaterial effects on DC phenotype were measured using the conventional flow cytometric and filter-plate method, which validated the HTP methodology. This methodology will be used to screen a large number of biomaterials simultaneously and to draw correlations between material properties and DC phenotype, thereby providing biomaterial design criteria and immunomodulatory strategies for both tissue engineering and vaccine delivery applications.

Figure 1

A schematic of the conventional method and the HTP method for analyzing DC response to biomaterials. For both of the analysis methods, DCs were derived from human peripheral blood mononuclear cells (PBMCs) using the same procedures until day 5. On day 5, for the conventional method, DCs were treated with biomaterials in a 6-well plate for 24 hours. The cells after treatment are then collected and stained, and flow cytometry is performed to analyze the cell surface marker expression. In contrast, for the HTP method, DCs are treated with biomaterials in a 96-well plate for 24 hours. On day 6, DCs are transferred to a 96-well filtration plate, fixed and then stained with anti-CD86-PE and anti-DC-SIGN-FITC antibodies for 1 hour and washed. The relative fluorescence intensity is subsequently measured by a Tecan Infinite F500 microplate reader. Simultaneously, the cell culture supernatants from each well can be aspirated into a collection plate and tested for cytotoxicity and stored for cytokine profiling using Multiplex technology.

Figure 2

B-cell percentage in the iDC and mDC cultures by flow cytometric analysis. The B cell percentages in the DC culture are shown with mean ± SEM, n=6 different donors. *: p<0.05 and represents statistical difference between iDCs and mDCs.

Figure 3

Dendritic cells purified by magnetic sorting were less responsive to LPS stimulation in comparison to unpurified counterparts. On day 5 of DC culture, DCs were magnetically isolated by removing CD19⁺ B cells and then positively selecting CD1c⁺ DCs, treated with LPS for mDCs or left untreated for iDCs. The geometric mean fluorescence intensity (gMFI) of these purified DCs was analyzed by flow cytometry for surface marker expression after 24 hrs and compared to the unpurified counterparts with mean ± range, n=2 donors.

Figure 4

CD86 expression on DCs and lymphocytes in the culture system. The antibody binding capacity of CD86 on DCs and B cells was measured by comparing the gMFIs of CD86 expression to a standard curve created by beads that bound known numbers of antibodies using the BD FACSDiva software with mean ± SEM, n=6 donors. The inset shows the gating of DCs and lymphocytes (Lym) based on the forward (FSC) and side scatters (SSC) of the two distinct cell populations for which CD86 expression levels were determined. *: p<0.05, lower than mDCs and higher than lymphocytes; #: p<0.05, higher than iDCs and lymphocytes.

Figure 5

Dendritic cell expression of CD1c and DC-SIGN by flow cytometric analysis. The fold change of gMFI for mDCs was compared to that for iDCs among donors with mean ± SEM, n=6 donors. #: p<0.05, compared to iDCs and higher than iDCs; +: p<0.05, compared to iDCs and lower than iDCs.

Figure 6

Validation of the HTP methodology for assessing DC responses to biomaterials. A) Treatment/control ratios of ‘maturation factor’ (defined as CD86/DC-SIGN) for DCs treated with biomaterials or controls in the 6-well format and analyzed by flow cytometry (set of black bars), in the 6-well format and analyzed by fluorescent plate reader (set of grey bars), and in the 96-well format and analyzed by fluorescent plate reader (set of white bars). B) Treatment/control ratios of CD86 expression for DCs treated and analyzed using the conventional format of 6-well plates and flow cytometry for DCs treated with biomaterials or controls. Mean ± SEM; n=8 (6 donors). *: p<0.05, statistically different from iDCs and higher than iDCs. Brackets: p<0.05, statistically different between two biomaterial treatments or between biomaterial treatment and mDCs.

Figure 7

Effect of PLGA and agarose on DC glucose-6-phosphate dehydrogenase (G6PD) release. Dendritic cells were cultured with or without biomaterials PLGA or agarose films in a 96-well format for 24 h. The supernatants were collected into a 96-well plate by centrifugation at 250 × g for 2 min and then measured for G6PD release using theVybrant Cytotoxicity Assay at 37°C. The fluorescence was measured at 30 min using a Tecan Infinite 500 microplate reader. Mean ± SEM; n =3. *: p<0.05 higher than all the treatment groups; #: p<0.05 higher than iDC and mDC.