Elucidation of dendritic cell response-material property relationships using high-throughput methodologies
Kou, Peng Meng
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Ongoing advances in tissue engineering with the goal to address the clinical shortage of donor organs have encouraged the design and development of biomaterials to be used in tissue-engineered scaffolds. Furthermore, biomaterials have been used as delivery vehicles for vaccines that aim to enhance the protective immunity against pathogenic agents. These tissue-engineered constructs or vaccines are usually combination products that combine biomaterial and biological (e.g. cells, proteins, and/or DNA) components. Upon introduction into the body, the host response towards these products will be a combination of both a non-specific inflammatory response towards the biomaterial and an antigen-specific immune response towards the biological component(s). Recently, the biomaterial component was shown to influence the immune response towards a co-delivered antigen. Specifically, poly(lactic-co-glycolic acid) (PLGA), but not agarose, scaffolds or microparticles (MPs) enhanced the humoral response to a model antigen, ovalbumin. This in vivo result echoed with the in vitro study that PLGA, but not agarose, supported a mature phenotype of dendritic cells (DCs), the most potent antigen-presenting cells. Therefore, it is hypothesized that the effect of biomaterials on DC phenotype may influence the adaptive immunity against a co-delivered antigen. Understanding how biomaterials affect DC response will facilitate the selection and design of biomaterials that direct a desired immune response for tissue engineering or vaccine delivery applications. The objectives of this research were to elucidate the correlations between material properties and DC phenotype, develop predictive models for DC response based on material properties, and uncover the molecular basis for DC response to biomaterials. Well-defined biomaterial systems, including clinical titanium (Ti) substrates and two polymer libraries, were chosen to study induced DC phenotype. Due to the time-consuming nature of conventional methods for assessing DC phenotype, a high-throughput (HTP) method was first developed to screen for DC maturation based on surface marker expression (CHAPTER 4). A 96-well filter plate-based HTP methodology was developed and validated for the assessment of DC response to biomaterials. A "maturation factor", defined as CD86/DC-SIGN and measured by immunostaining, was found to be a cell number-independent metric for DC maturation and could be adapted to screen for DC maturation in a microplate format. This methodology was shown to reproducibly yield similar results of DC maturation in response to biomaterial treatment as compared to the conventional flow cytometric method upon DC treatment in 6-well plates. In addition, the supernatants from each treatment could easily be collected for cytotoxicity assessment using glucose-6-phosphate dehydrogenase (G6PD)-based assay and cytokine profiling using multiplex technology. In other words, the 96-well filter plate-based methodology can generate three outcomes from one single cell culture: 1) maturation marker expression, 2) cytotoxicity, and 3) cytokine profile. To examine which material properties were critical in determining DC phenotype, a set of three clinical titanium (Ti) substrates with well-defined surfaces was used to treat DCs (CHAPTER 5). These Ti substrates included pretreatment (PT), sand-blasted and acid-etched (SLA), and modified SLA (modSLA), with different roughness and surface energy. DCs responded differentially to these substrates. Specifically, PT and SLA induced a mature DC (mDC) phenotype, while modSLA-treated DCs remained immature based on surface marker expression, cytokine production profiles and cell morphology. Both PT and SLA induced higher CD86 expression as compared to iDC control, while modSLA maintained CD86 expression at a level similar to iDC. PT- or SLA-treated DCs exhibited dendritic processes associated with a mDC phenotype, while modSLA-treated DCs were rounded, a morphology associated with an iDC phenotype. Furthermore, PT induced increased secretion of MCP-1 by DCs compared to iDCs, indicating that PT promoted a pro-inflammatory environment. SLA induced higher IL-16 production, which is a pleiotropic cytokine, by DCs, most likely as a pro-inflammatory response due to the enhanced maturation of DCs induced by SLA. In contrast, modSLA did not induced enhanced production of any cytokines examined. Principal component analysis (PCA) were used to reduce the multi-dimensional data space and confirmed these experimental results, and it also indicated that the non-stimulating property of modSLA co-varied with certain surface properties, such as high surface hydrophilicity, % oxygen and % titanium of the substrates. In contrast, high surface % carbon and % nitrogen were more associated with a mDC phenotype. Furthermore, PCA also suggested that surface line roughness (Ra) did not contribute to the expression of CD86, an important maturation marker, suggesting that roughness had little impact on DC response (CHAPTER 5). DC response-material property relationships were also derived using more complex materials from a combinatorial library of polymethacrylates (pMAs) (CHAPTER 6). Twelve pMAs were selected and were found to induce differential DC response using the HTP method described in CHAPTER 4. These pMAs resulted in a trend of increasing DC maturation represented by the metric CD86/DC-SIGN, which was consistent with the trends of the production of pro-inflammatory cytokine, TNF-α, and chemokine, IL-8. Interestingly, this set of pMAs induced an opposite trend of IL-16 production, which is most likely released as an anti-inflammatory cytokine in this situation. These polymers were characterized extensively for a number of material properties, including surface chemical composition, glass transition temperature (Tg), air-water contact angle, line roughness (Ra), surface roughness (Sa), and surface area. Similar to the results from the Ti study, PCA determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an iDC phenotype. In addition, Tg, Ra, and surface area were unimportant in determining DC response. Partial square linear regression (PLSR), a multivariate modeling approach, was implemented using the pMAs as the training set and a separate polymer library, which contained methacrylate- and acrylate-based terpolymers, as the prediction set. This model successfully predicted DC phenotype in terms of surface marker expression with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80 (CHAPTER 6). Nonetheless, one should note that a predictive model can be only as good as what it is trained on and cannot be used to predict the DC response induced by a type of materials different from the training set. Also, this model might not contain all the important material properties such as polymer swelling and cannot predict specific types of immune responses. However, these results demonstrated that a generalized immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection (CHAPTER 6). From the pMA library, pMAs that induced the two extremes of DC phenotype (mature or immature) were identified for elucidating the mechanistic basis of biomaterial-induced DC responses (CHAPTER 7). Two pMAs, polyhydroxyethylmethacrylate (pHEMA) and poly(isobutyl-co-benzyl-co-terahydrofurfuryl)methacrylate (pIBTMA), were selected because they induced the least and the most mature DC phenotype, respectively. These pMAs were used to elucidate the activation profiles of transcription factors in DCs after biomaterial treatment and were compared to the iDC and mDC controls. In addition, a combined treatment of pHEMA and LPS was also included to determine if pHEMA could maintain an iDC phenotype in the presence of LPS. Interestingly, pIBTMA induced DC maturation primarily through the activation of NF-κB, while pHEMA mediated suppression of DC maturation through multiple TFs, including the activation of ISRE, E2F-1, GR-PR, NFAT, and HSF. GR-PR and E2F-1 have been shown to be associated with the suppression of DC maturation; ISRE, E2F-1, and NFAT are linked to apoptosis induction; HSF regulates the production of heat shock proteins (HSPs) that induce DC maturation and inhibit apoptosis. The activation of HSF by pHEMA was most likely a natural defensive mechanism against the other apoptotic signals. Therefore, pHEMA suppressed DC maturation through the induction of apoptosis. Surprisingly, in the presence of pHEMA, the effect of LPS was completely eliminated, suggesting that biomaterials can override the effect of soluble factors. The morphology and surface marker expression of DCs treated with these different biomaterials or controls were consistent with TF activation profiles (CHAPTER 7). Overall, this research illustrates that biomaterial properties, within the chosen biomaterial space, can be correlated to DC phenotype and more importantly, can be used as predictors for relative levels of DC phenotype. Furthermore, the differential responses induced by different biomaterials were mediated through the distinct activation profiles of transcription factors. Together, these findings are expected to facilitate the design and selection of biomaterials that direct desired immune responses.