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Models for evaluating the immune response to naturally derived biomaterials
DDMOD-482; No of Pages 7
Drug Discovery Today: Disease Models
DRUG DISCOVERY
TODAY
Vol. xxx, No. xx 2018
Editors-in-Chief Jan Tornell – AstraZeneca, Sweden Andrew McCulloch – University of California, SanDiego, USA
DISEASE
MODELS
Models for evaluating the immune response to naturally derived biomaterials Jenna L. Dziki1,2, Stephen F. Badylak1,3,* 1
McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States 3 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States 2
The immune response to biomaterials has emerged as a critical determinant of tissue repair outcomes and is complex, involving multiple cell types, distinct spatiotemporal phenotypes, and is influenced by variables including processing of the material and host-related factors. This
Section editors: Karen L. Christman – Department of Bioengineering, Sanford Consortium for Regenerative Medicine, University of California, San Diego. Roberto Gaetani – Department of Bioengineering, Sanford Consortium for Regenerative Medicine, University of California, San Diego.
interaction between implanted material and the host immune cells has stimulated interest in analytical methods to characterize the immune response. The present review discusses these methods including in vitro, in vivo, ex vivo, in silico, and combination models utilized to evaluate the immune response to biomaterials and their applicability to clinical scenarios. Recent developments in modeling the immune response to emerging technologies that may provide better predictors of the immune response to implanted materials and ultimately lead to improved clinical outcomes are reviewed.
Introduction All implanted biomaterials prompt an immune response by the recipient. As our depth and breadth of understanding the adaptability, plasticity, paracrine capabilities, and molecular *Corresponding author.: S.F. Badylak ([email protected])
mechanisms of the various cell types that comprise the immune system continues to expand, it has become obvious that clinical outcomes that involve the use of biomaterials are largely dictated by the acute and chronic local immune response. If in vitro models could faithfully predict a patients’ response to a particular biomaterial, it is certain that complications and failures associated with biomaterial implantation would be markedly reduced and successful outcomes could be enhanced. Historically, the immune system has been believed to exist for the purpose of protecting the host from pathogens and assisting in tissue repair following injury. It is now recognized however, that the immune system plays essential roles in development [1], tissue and organ homeostasis [2–4], response to injury [3], response to pathogens [5], and in response to implanted biomaterials [6]. Therefore, attempts to regulate the local tissue immune response to biomaterials has become an important design consideration.
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Biomaterials can be categorized broadly as synthetic or biologic, (i.e. materials composed of naturally occurring components), or as hybrids of synthetic and biologic materials. Each of these categories can be further divided into degradable, non-degradable, or partially-degradable materials, properties which markedly affect the host response [7]. Biomaterials composed of naturally occurring components such as the extracellular matrix (ECM) typically elicit a different immune response than their synthetic counterparts [7]. The immune response to ECM biomaterials is complex, involving multiple cell types, a unique spatiotemporal component, and is modulated by many hostrelated variables including age, weight, co-morbidities, and environmental factors such as mechanical loading. The complexity of the immune response in normal physiology combined with the number of variables that can affect the response to an implanted biomaterial emphasizes the need for, and value of, in-vitro and in-vivo models that can predict such a response. The present review focuses upon one class of biomaterials, specifically biomaterials composed of ECM and the host immune response to ECM biomaterials following implantation. Both in vitro and in vivo models are discussed with reference to their ability to ability to direct biomaterial design and predict downstream outcomes.
What is the immune response to naturally derived biomaterials? Any model of the immune response to naturally derived biomaterials must be based upon prerequisite facts. A significant amount of work has been conducted to characterize the immune response to ECM-based biomaterials and therefore at least some of these known facts are available for consideration when developing in-vitro models. It is worth noting that the host response to the ECM-based materials is dependent upon several biomaterial processing variables, one of which includes the efficacy of the decellularization process. The ultimate objective of the decellularization process is to remove antigenic epitopes which may prompt an adverse immune response (similar to xenogeneic transplant rejection) while simultaneously preserving the beneficial biomolecular composition and ultrastructure of the native matrix. Development of decellularization protocols and numerous preclinical and clinical studies have shown that fears of immune-mediated rejection of xenogeneic ECM-based biomaterials due to the presence of alpha galactosyl-3-galactose (alpha-Gal) epitope are unfounded [8]. The amount of the gal-epitope in these bioscaffolds is exceedingly small, and although in-vivo exposure to these materials elicits an IgG response, this response fails to cause complement activation and the amount of epitope present fails to cause measurable IgM binding [8–10]. Notably, repeated implan2
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tation of ECM bioscaffolds has not been associated with an adverse sensitization response [11]. When adequately decellularized, ECM-based materials elicit a robust localized cell response that transitions from a mixture of neutrophils and mononuclear cells to entirely mononuclear cells within 48–72 h [12]. These mononuclear cells are almost all macrophages which represent a major component of the innate immune response to any implanted biomaterial [13,14]. The downstream remodeling response after ECM bioscaffold implantation is different than the default response to either tissue injury or implantation of synthetic biomaterials, both of which eventually lead to fibrosis and scar tissue formation. A distinguishing factor between ECM-based materials and their synthetic counterparts is the responding phenotype (i.e. activation state) of macrophages (and other immune cells) and the spatiotemporal macrophage activation response. Specifically, ECM bioscaffolds promote an early transition of macrophages from an M1-like pro-inflammatory phenotype towards an M2-like pro-remodeling phenotype, and an adaptive immune response that is characterized by a predominantly Th2 phenotype [15–17]. In contrast, synthetic materials have been largely associated with a persistent M1like or Th1 response following implantation [18]. While the signaling molecules that mediate the immunomodulatory effects of ECM bioscaffolds are not entirely understood, a growing body of evidence suggests that matrix components including matrix-bound nanovesicles (MBV) and their bioactive cargo play an important role [19]. These facts and variables can and should be considered during the development of models of the immune response to naturally derived biomaterials.
Factors which affect the immune response to ECM bioscaffolds Processing factors among other controllable components of ECM bioscaffold production can have a profound impact upon the host response. Some of the more recognized processing variables include remnant cell surface epitopes and residual cytoplasmic and nuclear material including DNA [20] and mitochondria [21], the source tissue from which the ECM material is derived [22], the age of the animal from which the source tissue is harvested [23], residual detergents after decellularization [24], the form in which ECM is utilized (i.e. sheet, powder, hydrogel, single component, etc.) [25], the method of terminal sterilization utilized [26], and the use of chemical crosslinking agents [15]. Post-implantation variables also can affect the immune response including the provision of targeted mechanical loading via physical therapy and/or weight bearing [27]. Not only do processing and other external factors affect the host response to ECM biomaterials, but host-related factors including comorbidities like obesity, age, anatomic factors, chemotherapeutic and radiation ther-
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apy, among others, affect the immune response to ECM based materials [28]. Each of these factors provide insight into the crosstalk between ECM and the host immune cells. Therefore, there is a clear need for reliable in vitro, in vivo, and in silico models that consider these factors to describe and evaluate the host response.
In vitro models to evaluate the host response The in vitro evaluation of the host response to biomaterials by definition involves the use of an artificial environment. Some of the earliest events that occur in-vivo following implantation of a biomaterial include the deposition of plasma proteins on the surface (i.e., Vroman effect), followed by attraction of local and extravasated innate immune cells (e.g., neutrophils, tissue macrophages), participation of platelets and the paracrine effects of released cytokines and chemokines. Because in vitro models lack a vascular basement membrane, the entire extravasation process and the ability of the implanted ECM bioscaffold to influence that process as one of the first steps of the host response is nonexistent. Similarly, a lack of vasculature including the perivascular cuff and the associated perivascular cells, limits the in vitro modeling of a true host response [29]. The absence of a complete cytokine network and protease milieu from multiple cell types, even those not considered immune cells, will likewise oversimplify the applicability of in vitro models and the results obtained using such models. The generation of bioactive molecular fragments from the ECM bioscaffold and the host response to individual components or degradation products of the matrix has been evaluated extensively in-vitro, but the same limitations mentioned above still exist [30–32]. Although much attention has been placed upon the macrophage response to ECM-based biomaterials, and rightly so due to their abundant and immediate response upon implantation, the cross talk with other cell types including other immune cells and stem/progenitor cells and spatiotemporal response of macrophages has been shown to be important in dictating remodeling outcomes. This crosstalk does not have the opportunity to occur in most in-vitro models. The physiological forces present during tissue remodeling are absent in almost all in vitro models and must be acknowledged. The use of simple uniaxial or equibiaxial strain in vitro has been utilized and results of these studies highlight the important consideration of mechanotransduction upon host cell phenotype [27], but any attempt to relate in vitro strain protocols to physiologic forces is futile. Overall, while individual immune cell analyses in combination with data-driven analyses in vitro must continue because of the recognized value of isolating specific components of the host response, the limitations of the in vitro test system must be recognized in any interpretation of results. Variables such as the culture media utilized, oxygen concentration, cell culture substrate
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composition, spatial arrangement (i.e. 2D vs 3D culture), and inclusion of dynamic conditions such as mechanical strain, co-culture conditions, among many other variables all impact the phenotype of cultured cells. Although the limitations of in-vitro test systems are significant, there are clear advantages of in vitro models that should be recognized. For example, the ability to isolate the interaction between any given environmental factor and a particular immune cell type is possible. In vitro models can provide useful data by controlling the mode of cell-ECM contact. For example, one can limit the cell-matrix interface to a particular surface of an ECM sheet, or one can investigate the effect of scaffold degradation products without the associated influence of host-derived signaling molecules upon cell behavior. This consideration of ECM biomaterial degradation and the effect of released degradation products is especially relevant since most ECM bioscaffolds degrade rapidly upon implantation [13]. Furthermore, much of the bioactivity of ECM bioscaffolds relies upon degradation of the ECM which is an important factor to note when analyzing immune cell response to ECM in vitro. Enzymatic degradation of the ECM in vitro has been shown to result in low molecular weight peptides, among other bioactive constituents, that can act as chemoattractants and also affect macrophage activation state [33]. Variation of degradation method and the time course of degradation can have a significant impact upon the in vitro cell response and should be taken into consideration.
Single cell analysis Though the host response to any implanted biomaterial is clearly multi-cellular, isolating single cell types and characterizing their response to ECM-based biomaterials can provide useful information regarding the mechanism(s) and effector molecules that mediate the host response. The macrophage response to implanted biomaterials in particular has emerged as a focal area of interest not only within the biomaterials field but also in the field of immunology. Though traditionally considered as an antigen presenting phagocyte, the now recognized diverse and essential role of macrophages in disease pathogenesis, tissue homeostasis, inflammation resolution, tissue development, and regeneration has increased interest in these cells as predictors of clinical outcome following biomaterial implantation. The term macrophage ‘‘activation’’ has been utilized to describe the plasticity of these cell types, namely their ability to assume a microenvironment-specific phenotype, including phenotypes that promote either a pro-inflammatory or resolution response. For a variety of reasons, reports published in recent years that investigate the macrophage activation state have contributed to confusion and inconsistencies with respect to both in vitro and in vivo models and metrics used to evaluate cell phenotype. A common activator-based nomenwww.drugdiscoverytoday.com
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clature has been proposed for describing macrophage phenotype [34]. Recent studies suggest that even the source of macrophage utilized in in-vitro studies (i.e. primary, cell line, species, culture conditions) can have profound effects upon experimental results and their interpretation [35]. For example, human cell lines compared to primary murine macrophages can be associated with markedly reduced in vitro function (i.e. phagocytosis and nitric oxide production), and have been associated with anergy due to smaller magnitude changes in gene expression and protein expression after stimulus [35]. The source of macrophages, definition of activators, and a consensus for markers utilized to describe distinct macrophage phenotype are necessary to unify the interpretation of experimental results across the scientific community. The number of potential markers that can be used to characterize macrophage phenotype is expansive and includes surface epitopes, cell morphology, transcriptome profile, mRNA and miRNA profile, proteomics and metabolomics, and functional assays. For example, a study by Wolf et al. showed that a combination of in silico and in vitro analyses can be used to characterize and predict the in vivo host response to biomaterials that include ECM-based and synthetic biomaterials [36]. This study used a combination of data-driven approaches including principle component analysis and dynamic network analysis to interpret the in vitro macrophage response. Both changes in cell phenotype and preliminary mechanistic analyses and yielded acceptable and repeatable results that were predictive of the in vivo remodeling and macrophage response in a rodent skeletal muscle injury model. A study by Xue et al. showed that human macrophages stimulated with diverse activation signals resulted in acquisition of 299 macrophage transcriptomes. Using network analyses applied and integrated with human and murine derived macrophages that simulated disease processes in vitro, key mechanistic insights into the differences between human and murine cell sources were identified [37] that formed the basis for future studies. These and other studies allow for a prioritization of models and tools at the single-cell level to ultimately predict the host response to biomaterials, among other stimuli, by using in vitro setups.
In vivo and ex vivo methods to evaluate the host response As described above, the limitations of in vitro models include an inability to faithfully mimic the microenvironment, the heterogeneity of both normal and diseased tissue, genetic and epigenetic factors that contribute to the ability of the host to response to an implanted biomaterial, and the effect of any concomitant pharmacologic therapy (e.g., non-steroidal antiinflammatory drug usage), among others. A combination of in vivo and ex vivo methods to evaluate the host response may 4
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be the next logical step in model development, although even these approaches are associated with limitations. Traditionally, the in vivo host response to biomaterials has considered histologic findings as one of the gold-standards of analysis, including the use of hematoxylin and eosin (H&E) staining, Masson’s trichrome, and many other common histologic stains. Recently, the use of immunolabeling to detect phenotype specific antigens has allowed for a more accurate analysis of macrophage and other immune cell behavior. Semi-quantitative scoring criteria and quantification of immunofluorescent staining have allowed for a measure of the spatiotemporal host response to ECM bioscaffold materials [18,38]. However, it is recognized that single-marker analysis may fall short of capturing the entire picture of the host response due to the plasticity of macrophages, their ability to express multiple markers, and the lack of consensus in the field regarding which markers indicate which phenotype and functional capability. Moreover, the process of histochemical staining or immunolabeling is arduous and limits precise quantitative assessments, though the advantage of visualizing the spatiotemporal host response with such methods should be noted. The use of enzyme-linked immunosorbent assays (ELISA) on homogenized/solubilized tissues such as circulating cytokine levels in the blood in combination with these approaches may help to clarify the systemic host response. However, the added steps required for such methods and the lack of spatial resolution limits this approach.
In vivo disease models and their applicability to the human response The use of in vivo animal models allows for a true recapitulation of a normal mammalian physiologic microenvironment to evaluate the host response, and further allows for the use of other analysis metrics including monitoring systemic affects and measurement of temporal changes in pH [39], visualization of immediate protein adsorption upon the material surface [40], and detection of reactive oxygen species (ROS), among other biomarkers associated with the macrophage response [41]. However, the accuracy of such host response analysis methods and others when using in vivo models starts with the applicability of the animal (both the animal physiology itself and the site/injury/pathology modeled) to true human physiology. Many markers of animal macrophages (mostly murine) have not translated to human macrophages [42]. Though side-by-side analyses have been conducted to compare murine vs human macrophages [35], variables such as cell sourcing (primary, cell line, etc.) have been considered notable effectors of the results. Disease modeling in rodents may also fail to consider the sequential events in the pathogenesis of human disease, thereby limiting the relevance and potential therapeutic implications of results. These concerns along with the ethical and expense
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considerations of using animal models should further stimulate the development of novel in-vitro models to evaluate the immune response including organ-on-a-chip technologies and patient-derived cell models. For example, ex vivo tissue culture assays have been shown to have high physiological relevance and can incorporate the use of human tissue samples [43]. The limited supply of human tissues, the short halflife of viable tissue ex vivo, and the lack of scalability are problems that need to be overcome for such methods to become a gold-standard. Organ-on-a-chip and microfluidic assays have similar physiologic relevance and allow for analysis of multiple cell types, tissues, and other parameters including mechanical stimuli and blood flow. However, such approaches represent a low-throughput modality to date which has limited their widespread adoption for evaluation of the host response.
Next generation and combination approaches to evaluate the immune response Identification of the key ingredients and essential processes required for accurate models A key step in developing next generation and combination approaches to evaluate the host immune response to implanted biomaterials includes establishing a benchmark by which success and/or failure can be measured. Specifically, for regenerative medicine and tissue engineering applications the ultimate goal is to achieve perfect regeneration of tissues. Though technology has not allowed acceptable regeneration of human tissues organs to date, advances in understanding of the immune response in regenerative species may rapidly advance the field. The recent recognition of the essential role of the immune system for normal tissue and organ development, and regeneration following injury, fully regenerative species like the axolotl, partially regenerative species like lizards, and even relatively non-regenerative species like humans during fetal development should stimulate the development of novel test systems. Godwin et al. thoroughly reviewed the process of identifying the recipe for a proregenerative immune system including the heterogeneity among both the innate and adaptive immune system, their development and origins, and the crosstalk of immune cells with stem cells, among other attributes [44]. The similarities and dissimilarities between amphibian, fish, and mammalian tissue injury models and have identified that the winning recipe for tissue regeneration in all species is most likely to be elucidated when the genetic and epigenetic controls that regulate such processes are identified [44].
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for the capture of a complete picture of the role of the immune system in the regenerative process. Big data concepts are now actively being pursued to model immunologic responses. ‘‘Omics’’ technologies are now driving data collection and analysis. For example, a study by Xue et al. showed that transcriptome-based network analysis allows for a more accurate picture of the macrophage phenotypic spectrum than previously possible. With this information, the central transcriptional regulators associated with each macrophage phenotype can be identified and applied to treat diseased macrophages [37]. The advancements in next-generation sequencing data, sequencing capacity, and approaches to assess DNA and RNA structure, interactions, and species differences is evolving at an exponential rate, and the immune response will greatly benefit from using this big data due to its inherent complexity. Time-resolved molecular maps that identify temporal transcriptomics, proteomics, and phosphoproteomics are actively being pursued to evaluate the immune response to inform functional experiments [45]. Therefore, the task to train next generation immunologists in utilizing big data analysis methods will be an important area for investment in academia [46]. Cell-based strategies are likewise evolving to allow for generation of macrophages and other immune cells from iPS cell lines and implementation of 3D cell culture and co-culture models to more accurately depict physiologic processes while maintaining high-throughput data collection capabilities [43,47,48]. The use of data-driven approaches including principle component analysis (PCA) or dynamic network analysis (DyNA) can likewise allow for the discovery of biomaterial-specific immune responses and also allow for determining which assays and experiments are the best predictors of outcomes [36,49].
Conclusion The emerging importance of the immune system in multiple processes of tissue and organ development and in the response to injury has driven and will continue to drive the development of new biomaterials that incorporate the beneficial immunomodulatory properties of naturally occurring biomaterials derived from the ECM. Development of new models to evaluate the host response to such materials is crucial to progress and consensus in the field. The growth of big-data-based technologies, improved ex vivo testing, and high throughput methods will be at the cornerstone of evaluating biomaterials and especially their impact upon the immune response.
Emerging technologies and future directions It is now clear that an oversimplification of characterizing the immune response to biomaterials causes more harm than good when interpreting experimental results. However, emerging tools for modeling the immune response may allow
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integration to predict neuro-pharmacokinetics and pharmacodynamics. Expert Opin Drug Discov 2017;12(12):1207–18. [49] Vishwakarma A, Bhise NS, Evangelista MB, Rouwkema J, Dokmeci MR, Ghaemmaghami AM, et al. Engineering immunomodulatory biomaterials to tune the inflammatory response. Trends Biotechnol 2016;34(6):470–82.
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DRUG DISCOVERY
TODAY
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Editors-in-Chief Jan Tornell – AstraZeneca, Sweden Andrew McCulloch – University of California, SanDiego, USA
DISEASE
MODELS
Models for evaluating the immune response to naturally derived biomaterials Jenna L. Dziki1,2, Stephen F. Badylak1,3,* 1
McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States 3 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States 2
The immune response to biomaterials has emerged as a critical determinant of tissue repair outcomes and is complex, involving multiple cell types, distinct spatiotemporal phenotypes, and is influenced by variables including processing of the material and host-related factors. This
Section editors: Karen L. Christman – Department of Bioengineering, Sanford Consortium for Regenerative Medicine, University of California, San Diego. Roberto Gaetani – Department of Bioengineering, Sanford Consortium for Regenerative Medicine, University of California, San Diego.
interaction between implanted material and the host immune cells has stimulated interest in analytical methods to characterize the immune response. The present review discusses these methods including in vitro, in vivo, ex vivo, in silico, and combination models utilized to evaluate the immune response to biomaterials and their applicability to clinical scenarios. Recent developments in modeling the immune response to emerging technologies that may provide better predictors of the immune response to implanted materials and ultimately lead to improved clinical outcomes are reviewed.
Introduction All implanted biomaterials prompt an immune response by the recipient. As our depth and breadth of understanding the adaptability, plasticity, paracrine capabilities, and molecular *Corresponding author.: S.F. Badylak ([email protected])
mechanisms of the various cell types that comprise the immune system continues to expand, it has become obvious that clinical outcomes that involve the use of biomaterials are largely dictated by the acute and chronic local immune response. If in vitro models could faithfully predict a patients’ response to a particular biomaterial, it is certain that complications and failures associated with biomaterial implantation would be markedly reduced and successful outcomes could be enhanced. Historically, the immune system has been believed to exist for the purpose of protecting the host from pathogens and assisting in tissue repair following injury. It is now recognized however, that the immune system plays essential roles in development [1], tissue and organ homeostasis [2–4], response to injury [3], response to pathogens [5], and in response to implanted biomaterials [6]. Therefore, attempts to regulate the local tissue immune response to biomaterials has become an important design consideration.
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Biomaterials can be categorized broadly as synthetic or biologic, (i.e. materials composed of naturally occurring components), or as hybrids of synthetic and biologic materials. Each of these categories can be further divided into degradable, non-degradable, or partially-degradable materials, properties which markedly affect the host response [7]. Biomaterials composed of naturally occurring components such as the extracellular matrix (ECM) typically elicit a different immune response than their synthetic counterparts [7]. The immune response to ECM biomaterials is complex, involving multiple cell types, a unique spatiotemporal component, and is modulated by many hostrelated variables including age, weight, co-morbidities, and environmental factors such as mechanical loading. The complexity of the immune response in normal physiology combined with the number of variables that can affect the response to an implanted biomaterial emphasizes the need for, and value of, in-vitro and in-vivo models that can predict such a response. The present review focuses upon one class of biomaterials, specifically biomaterials composed of ECM and the host immune response to ECM biomaterials following implantation. Both in vitro and in vivo models are discussed with reference to their ability to ability to direct biomaterial design and predict downstream outcomes.
What is the immune response to naturally derived biomaterials? Any model of the immune response to naturally derived biomaterials must be based upon prerequisite facts. A significant amount of work has been conducted to characterize the immune response to ECM-based biomaterials and therefore at least some of these known facts are available for consideration when developing in-vitro models. It is worth noting that the host response to the ECM-based materials is dependent upon several biomaterial processing variables, one of which includes the efficacy of the decellularization process. The ultimate objective of the decellularization process is to remove antigenic epitopes which may prompt an adverse immune response (similar to xenogeneic transplant rejection) while simultaneously preserving the beneficial biomolecular composition and ultrastructure of the native matrix. Development of decellularization protocols and numerous preclinical and clinical studies have shown that fears of immune-mediated rejection of xenogeneic ECM-based biomaterials due to the presence of alpha galactosyl-3-galactose (alpha-Gal) epitope are unfounded [8]. The amount of the gal-epitope in these bioscaffolds is exceedingly small, and although in-vivo exposure to these materials elicits an IgG response, this response fails to cause complement activation and the amount of epitope present fails to cause measurable IgM binding [8–10]. Notably, repeated implan2
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tation of ECM bioscaffolds has not been associated with an adverse sensitization response [11]. When adequately decellularized, ECM-based materials elicit a robust localized cell response that transitions from a mixture of neutrophils and mononuclear cells to entirely mononuclear cells within 48–72 h [12]. These mononuclear cells are almost all macrophages which represent a major component of the innate immune response to any implanted biomaterial [13,14]. The downstream remodeling response after ECM bioscaffold implantation is different than the default response to either tissue injury or implantation of synthetic biomaterials, both of which eventually lead to fibrosis and scar tissue formation. A distinguishing factor between ECM-based materials and their synthetic counterparts is the responding phenotype (i.e. activation state) of macrophages (and other immune cells) and the spatiotemporal macrophage activation response. Specifically, ECM bioscaffolds promote an early transition of macrophages from an M1-like pro-inflammatory phenotype towards an M2-like pro-remodeling phenotype, and an adaptive immune response that is characterized by a predominantly Th2 phenotype [15–17]. In contrast, synthetic materials have been largely associated with a persistent M1like or Th1 response following implantation [18]. While the signaling molecules that mediate the immunomodulatory effects of ECM bioscaffolds are not entirely understood, a growing body of evidence suggests that matrix components including matrix-bound nanovesicles (MBV) and their bioactive cargo play an important role [19]. These facts and variables can and should be considered during the development of models of the immune response to naturally derived biomaterials.
Factors which affect the immune response to ECM bioscaffolds Processing factors among other controllable components of ECM bioscaffold production can have a profound impact upon the host response. Some of the more recognized processing variables include remnant cell surface epitopes and residual cytoplasmic and nuclear material including DNA [20] and mitochondria [21], the source tissue from which the ECM material is derived [22], the age of the animal from which the source tissue is harvested [23], residual detergents after decellularization [24], the form in which ECM is utilized (i.e. sheet, powder, hydrogel, single component, etc.) [25], the method of terminal sterilization utilized [26], and the use of chemical crosslinking agents [15]. Post-implantation variables also can affect the immune response including the provision of targeted mechanical loading via physical therapy and/or weight bearing [27]. Not only do processing and other external factors affect the host response to ECM biomaterials, but host-related factors including comorbidities like obesity, age, anatomic factors, chemotherapeutic and radiation ther-
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apy, among others, affect the immune response to ECM based materials [28]. Each of these factors provide insight into the crosstalk between ECM and the host immune cells. Therefore, there is a clear need for reliable in vitro, in vivo, and in silico models that consider these factors to describe and evaluate the host response.
In vitro models to evaluate the host response The in vitro evaluation of the host response to biomaterials by definition involves the use of an artificial environment. Some of the earliest events that occur in-vivo following implantation of a biomaterial include the deposition of plasma proteins on the surface (i.e., Vroman effect), followed by attraction of local and extravasated innate immune cells (e.g., neutrophils, tissue macrophages), participation of platelets and the paracrine effects of released cytokines and chemokines. Because in vitro models lack a vascular basement membrane, the entire extravasation process and the ability of the implanted ECM bioscaffold to influence that process as one of the first steps of the host response is nonexistent. Similarly, a lack of vasculature including the perivascular cuff and the associated perivascular cells, limits the in vitro modeling of a true host response [29]. The absence of a complete cytokine network and protease milieu from multiple cell types, even those not considered immune cells, will likewise oversimplify the applicability of in vitro models and the results obtained using such models. The generation of bioactive molecular fragments from the ECM bioscaffold and the host response to individual components or degradation products of the matrix has been evaluated extensively in-vitro, but the same limitations mentioned above still exist [30–32]. Although much attention has been placed upon the macrophage response to ECM-based biomaterials, and rightly so due to their abundant and immediate response upon implantation, the cross talk with other cell types including other immune cells and stem/progenitor cells and spatiotemporal response of macrophages has been shown to be important in dictating remodeling outcomes. This crosstalk does not have the opportunity to occur in most in-vitro models. The physiological forces present during tissue remodeling are absent in almost all in vitro models and must be acknowledged. The use of simple uniaxial or equibiaxial strain in vitro has been utilized and results of these studies highlight the important consideration of mechanotransduction upon host cell phenotype [27], but any attempt to relate in vitro strain protocols to physiologic forces is futile. Overall, while individual immune cell analyses in combination with data-driven analyses in vitro must continue because of the recognized value of isolating specific components of the host response, the limitations of the in vitro test system must be recognized in any interpretation of results. Variables such as the culture media utilized, oxygen concentration, cell culture substrate
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composition, spatial arrangement (i.e. 2D vs 3D culture), and inclusion of dynamic conditions such as mechanical strain, co-culture conditions, among many other variables all impact the phenotype of cultured cells. Although the limitations of in-vitro test systems are significant, there are clear advantages of in vitro models that should be recognized. For example, the ability to isolate the interaction between any given environmental factor and a particular immune cell type is possible. In vitro models can provide useful data by controlling the mode of cell-ECM contact. For example, one can limit the cell-matrix interface to a particular surface of an ECM sheet, or one can investigate the effect of scaffold degradation products without the associated influence of host-derived signaling molecules upon cell behavior. This consideration of ECM biomaterial degradation and the effect of released degradation products is especially relevant since most ECM bioscaffolds degrade rapidly upon implantation [13]. Furthermore, much of the bioactivity of ECM bioscaffolds relies upon degradation of the ECM which is an important factor to note when analyzing immune cell response to ECM in vitro. Enzymatic degradation of the ECM in vitro has been shown to result in low molecular weight peptides, among other bioactive constituents, that can act as chemoattractants and also affect macrophage activation state [33]. Variation of degradation method and the time course of degradation can have a significant impact upon the in vitro cell response and should be taken into consideration.
Single cell analysis Though the host response to any implanted biomaterial is clearly multi-cellular, isolating single cell types and characterizing their response to ECM-based biomaterials can provide useful information regarding the mechanism(s) and effector molecules that mediate the host response. The macrophage response to implanted biomaterials in particular has emerged as a focal area of interest not only within the biomaterials field but also in the field of immunology. Though traditionally considered as an antigen presenting phagocyte, the now recognized diverse and essential role of macrophages in disease pathogenesis, tissue homeostasis, inflammation resolution, tissue development, and regeneration has increased interest in these cells as predictors of clinical outcome following biomaterial implantation. The term macrophage ‘‘activation’’ has been utilized to describe the plasticity of these cell types, namely their ability to assume a microenvironment-specific phenotype, including phenotypes that promote either a pro-inflammatory or resolution response. For a variety of reasons, reports published in recent years that investigate the macrophage activation state have contributed to confusion and inconsistencies with respect to both in vitro and in vivo models and metrics used to evaluate cell phenotype. A common activator-based nomenwww.drugdiscoverytoday.com
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clature has been proposed for describing macrophage phenotype [34]. Recent studies suggest that even the source of macrophage utilized in in-vitro studies (i.e. primary, cell line, species, culture conditions) can have profound effects upon experimental results and their interpretation [35]. For example, human cell lines compared to primary murine macrophages can be associated with markedly reduced in vitro function (i.e. phagocytosis and nitric oxide production), and have been associated with anergy due to smaller magnitude changes in gene expression and protein expression after stimulus [35]. The source of macrophages, definition of activators, and a consensus for markers utilized to describe distinct macrophage phenotype are necessary to unify the interpretation of experimental results across the scientific community. The number of potential markers that can be used to characterize macrophage phenotype is expansive and includes surface epitopes, cell morphology, transcriptome profile, mRNA and miRNA profile, proteomics and metabolomics, and functional assays. For example, a study by Wolf et al. showed that a combination of in silico and in vitro analyses can be used to characterize and predict the in vivo host response to biomaterials that include ECM-based and synthetic biomaterials [36]. This study used a combination of data-driven approaches including principle component analysis and dynamic network analysis to interpret the in vitro macrophage response. Both changes in cell phenotype and preliminary mechanistic analyses and yielded acceptable and repeatable results that were predictive of the in vivo remodeling and macrophage response in a rodent skeletal muscle injury model. A study by Xue et al. showed that human macrophages stimulated with diverse activation signals resulted in acquisition of 299 macrophage transcriptomes. Using network analyses applied and integrated with human and murine derived macrophages that simulated disease processes in vitro, key mechanistic insights into the differences between human and murine cell sources were identified [37] that formed the basis for future studies. These and other studies allow for a prioritization of models and tools at the single-cell level to ultimately predict the host response to biomaterials, among other stimuli, by using in vitro setups.
In vivo and ex vivo methods to evaluate the host response As described above, the limitations of in vitro models include an inability to faithfully mimic the microenvironment, the heterogeneity of both normal and diseased tissue, genetic and epigenetic factors that contribute to the ability of the host to response to an implanted biomaterial, and the effect of any concomitant pharmacologic therapy (e.g., non-steroidal antiinflammatory drug usage), among others. A combination of in vivo and ex vivo methods to evaluate the host response may 4
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be the next logical step in model development, although even these approaches are associated with limitations. Traditionally, the in vivo host response to biomaterials has considered histologic findings as one of the gold-standards of analysis, including the use of hematoxylin and eosin (H&E) staining, Masson’s trichrome, and many other common histologic stains. Recently, the use of immunolabeling to detect phenotype specific antigens has allowed for a more accurate analysis of macrophage and other immune cell behavior. Semi-quantitative scoring criteria and quantification of immunofluorescent staining have allowed for a measure of the spatiotemporal host response to ECM bioscaffold materials [18,38]. However, it is recognized that single-marker analysis may fall short of capturing the entire picture of the host response due to the plasticity of macrophages, their ability to express multiple markers, and the lack of consensus in the field regarding which markers indicate which phenotype and functional capability. Moreover, the process of histochemical staining or immunolabeling is arduous and limits precise quantitative assessments, though the advantage of visualizing the spatiotemporal host response with such methods should be noted. The use of enzyme-linked immunosorbent assays (ELISA) on homogenized/solubilized tissues such as circulating cytokine levels in the blood in combination with these approaches may help to clarify the systemic host response. However, the added steps required for such methods and the lack of spatial resolution limits this approach.
In vivo disease models and their applicability to the human response The use of in vivo animal models allows for a true recapitulation of a normal mammalian physiologic microenvironment to evaluate the host response, and further allows for the use of other analysis metrics including monitoring systemic affects and measurement of temporal changes in pH [39], visualization of immediate protein adsorption upon the material surface [40], and detection of reactive oxygen species (ROS), among other biomarkers associated with the macrophage response [41]. However, the accuracy of such host response analysis methods and others when using in vivo models starts with the applicability of the animal (both the animal physiology itself and the site/injury/pathology modeled) to true human physiology. Many markers of animal macrophages (mostly murine) have not translated to human macrophages [42]. Though side-by-side analyses have been conducted to compare murine vs human macrophages [35], variables such as cell sourcing (primary, cell line, etc.) have been considered notable effectors of the results. Disease modeling in rodents may also fail to consider the sequential events in the pathogenesis of human disease, thereby limiting the relevance and potential therapeutic implications of results. These concerns along with the ethical and expense
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considerations of using animal models should further stimulate the development of novel in-vitro models to evaluate the immune response including organ-on-a-chip technologies and patient-derived cell models. For example, ex vivo tissue culture assays have been shown to have high physiological relevance and can incorporate the use of human tissue samples [43]. The limited supply of human tissues, the short halflife of viable tissue ex vivo, and the lack of scalability are problems that need to be overcome for such methods to become a gold-standard. Organ-on-a-chip and microfluidic assays have similar physiologic relevance and allow for analysis of multiple cell types, tissues, and other parameters including mechanical stimuli and blood flow. However, such approaches represent a low-throughput modality to date which has limited their widespread adoption for evaluation of the host response.
Next generation and combination approaches to evaluate the immune response Identification of the key ingredients and essential processes required for accurate models A key step in developing next generation and combination approaches to evaluate the host immune response to implanted biomaterials includes establishing a benchmark by which success and/or failure can be measured. Specifically, for regenerative medicine and tissue engineering applications the ultimate goal is to achieve perfect regeneration of tissues. Though technology has not allowed acceptable regeneration of human tissues organs to date, advances in understanding of the immune response in regenerative species may rapidly advance the field. The recent recognition of the essential role of the immune system for normal tissue and organ development, and regeneration following injury, fully regenerative species like the axolotl, partially regenerative species like lizards, and even relatively non-regenerative species like humans during fetal development should stimulate the development of novel test systems. Godwin et al. thoroughly reviewed the process of identifying the recipe for a proregenerative immune system including the heterogeneity among both the innate and adaptive immune system, their development and origins, and the crosstalk of immune cells with stem cells, among other attributes [44]. The similarities and dissimilarities between amphibian, fish, and mammalian tissue injury models and have identified that the winning recipe for tissue regeneration in all species is most likely to be elucidated when the genetic and epigenetic controls that regulate such processes are identified [44].
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for the capture of a complete picture of the role of the immune system in the regenerative process. Big data concepts are now actively being pursued to model immunologic responses. ‘‘Omics’’ technologies are now driving data collection and analysis. For example, a study by Xue et al. showed that transcriptome-based network analysis allows for a more accurate picture of the macrophage phenotypic spectrum than previously possible. With this information, the central transcriptional regulators associated with each macrophage phenotype can be identified and applied to treat diseased macrophages [37]. The advancements in next-generation sequencing data, sequencing capacity, and approaches to assess DNA and RNA structure, interactions, and species differences is evolving at an exponential rate, and the immune response will greatly benefit from using this big data due to its inherent complexity. Time-resolved molecular maps that identify temporal transcriptomics, proteomics, and phosphoproteomics are actively being pursued to evaluate the immune response to inform functional experiments [45]. Therefore, the task to train next generation immunologists in utilizing big data analysis methods will be an important area for investment in academia [46]. Cell-based strategies are likewise evolving to allow for generation of macrophages and other immune cells from iPS cell lines and implementation of 3D cell culture and co-culture models to more accurately depict physiologic processes while maintaining high-throughput data collection capabilities [43,47,48]. The use of data-driven approaches including principle component analysis (PCA) or dynamic network analysis (DyNA) can likewise allow for the discovery of biomaterial-specific immune responses and also allow for determining which assays and experiments are the best predictors of outcomes [36,49].
Conclusion The emerging importance of the immune system in multiple processes of tissue and organ development and in the response to injury has driven and will continue to drive the development of new biomaterials that incorporate the beneficial immunomodulatory properties of naturally occurring biomaterials derived from the ECM. Development of new models to evaluate the host response to such materials is crucial to progress and consensus in the field. The growth of big-data-based technologies, improved ex vivo testing, and high throughput methods will be at the cornerstone of evaluating biomaterials and especially their impact upon the immune response.
Emerging technologies and future directions It is now clear that an oversimplification of characterizing the immune response to biomaterials causes more harm than good when interpreting experimental results. However, emerging tools for modeling the immune response may allow
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