Development of biomimetic
environments for physiologically
relevant in-vitro models
Doctoral School of Engineering “Leonardo da Vinci” PhD Program on Automatic, Robotics and Bioengineering
XXVI cycle
SSD: ING-IND/34
Author: Serena Giusti
Tutors:
Prof. Arti Ahluwalia Eng. Daniele Mazzei
Automatic, Robotics and Bioengineering PhD Cycle XXVI (2011-2013)
Thesis title:
Development of biomimetic environments for physiologically relevant in-vitro models
Author:
Serena Giusti Supervisor:
Prof. Arti Ahluwalia Co-supervisor:
The aim of this thesis was to design and develop different systems and methods for in-vitro research, in order to create different dynamic envi-ronments for simulating different organs.
This work is organized in two parts: the first part describes the opti-mization of a control unit able to monitor and actively control all the main parameters for performing cell cultures (i.e. temperature, pH and pressure) and its use for creating physio-pathological models. The second part concerns the development of a stand-alone and sensorized bioreac-tor for mechanical stimulation of engineered constructs. This system was then used to develop an in-vitro model of cardiac tissue.
Firstly, an existing control unite (SUITE: Supervising Unit for In-vitro TEsting) was improved and optimized: real-time monitoring of the oxy-gen concentration was introduced, and the control algorithm modified in order to assure the generation of different hydrostatic pressures for long time. Moreover, additional tools were developed for allowing the con-nection of the control system to different bioreactors with fluid flow (i.e. single flow, double-flow with membrane interface, applying mechanical stimuli). Then, the system was used to simulate two different
physio-pathological conditions: a physio-pathological liver model for portal hyperten-sion, testing the ability of the control system to create a controlled hy-drostatic pressure in a single flow bioreactor, and a physiological model of the intestine, taking advantage of the pH control and regulation ap-plied by SUITE, and using a double flow bioreactor with membrane. In the latter model, the physical environment inside the bioreactor chamber will also be carefully characterized in term of fluid-induced forces on the intestinal epithelium, in order to evaluate the effect of such stimulus on the cell barrier.
In the second part, a Sensorized Squeeze PRessure bioreactor (S2PR) was designed, tested and finally validated on cardiac cells. The system is able to create a cyclic hydrodynamic pressure on the cell-seeded construct by the controlled movement of a piston inside a fluid-filled chamber. In order to assure high usability of the system, the bioreactor was developed to be stand-alone, automatically finding the starting position of the piston to apply the desired stimulus according to a user-defined cycle. The flex-ibility of the S2PR was evaluated using cardiac cells seeded on different constructs, as dynamic cardiac model with fluid-induced forces and pres-sures. Moreover, the device is connectable with the previous optimized control unit, to create complex patterns of physical stimuli with combi-nation of hydrostatic and hydrodynamic stimuli, pH control and oxygen monitoring.
The results of this study can be applied to several fields of biological sci-ences, like drug discovery and testing, toxicology, tissue engineering and regeneration, or development of disease models or personalized therapy. These areas critically need more relevant in-vitro models to better pre-dict human response to external stimuli such as chemical substances or physical environmental changes, in order to improve the results of in-vitro studies and reduce the animal use in research.
My PhD was a very great, hectic and satisfying period of my life, that put myself to the test and forced me to learn new things. All the work reported in this thesis would not have been possible without the support of many people around me, who helped me during the scientific activity as well as my lifetime.
At first, I would like to thank my supervisor, prof. Ahluwalia, for her constant support during these three years: thank you for guiding and en-couraging me when I was feeling lost, thank you for believing in me and in my work, thank you very much! I also would like to express my sin-cere thank to Daniele, my co-supervisor: despite some misunderstanding, I will never forget that all my scientific activity started with you. You taught me so much about electronics, informatics, mechanics, you intro-duced me in the “Arduino’s world”. Of course, we had some contrasts and discussions, but these things also helped me to grow up.
Besides my supervisors in Pisa, I would like to thank prof. Perpetua Pinto-do-O, for having given me the opportunity to join her research group at the INEB in Porto and improve my knowledge in cell culture techniques. I also would like to thank Diana, for supervising my work
at INEB and giving me important and useful suggestions, Ana Silva and Diogo, for the help during the activities in the lab, and for teaching me so many things.
Then, I would like to thank all my labmates: Annalisa, that loved me so much that we shared the apartment for some months; Giorgio, that also became my flatmate some months ago, and who also shared with me the stress for writing the doctoral thesis; Tommaso, our CEO, many thanks for supporting me when I was particularly down in the dumps; Danielino, the most smiling and precise guy in our lab; Nicole, who shared with me these last stressful days as well as all the jokes we were subjected to; and Chiara, Carmelo, Francesca M, Gianni, Abu as well as Margherita, Valentina, Federico, Francesca P, Dott. Domenici at CNR.
Finally, I want to thank all the people that supported me outside the lab. I cannot express well enough my gratitude to my family: my mother, my father, my uncle Paride, my aunt Teresa, and even my sister. I’m seeing them much less than before, but they are “happy for me being happy”. My friends in Pisa, who are like my little family: Stefano, Va-leria, Samantha, Alessandro, Alessio, Cristina, Elisa, Stefano, Sabbir, thank you all for let me smile even when i was sad for bad results in the lab. My very old friends and classmates at high-school: Anthony, Margherita, Francesco, i think that some friendships will never end. A special thank to the “best flatmate” ever, my favorite doctor, Gemy, I miss you so much sometimes!! I also want to thank all the new friends met in Porto: in particular Tamires, Wiebke, Martina, for all the trips and funny night spent there, Nuno and Andreia, for all the lunches and dinners at the cantine.
And, for any errors or inadequacies that may remain in this work, my apologies to the readers.
1 State of the art of in-vitro models 1
1.1 Overview . . . 1
1.2 Applications of in-vitro models . . . 4
1.2.1 Tissue engineering . . . 5
1.2.2 Drug studies . . . 7
1.2.3 Disease Models and Personalized Therapy . . . 8
1.2.4 Toxicity tests . . . 10
1.3 Advanced in-vitro models . . . 11
1.3.1 Organotypic models . . . 12
1.3.2 The organ-on-chip approach . . . 14
1.3.3 System-on-a-plate . . . 17
1.4 Workflow of the thesis . . . 18
I SUITE for physio-pathological in-vitro models 21
2 Optimization of the
2.1 Introduction . . . 24
2.2 Material and Methods . . . 27
2.2.1 Generation of Hydrostatic Pressures and pH control 27 2.2.2 Integration of the oxygen sensor . . . 30
2.2.3 Heating module . . . 33 2.3 Results . . . 36 2.4 Discussion . . . 40 3 In-vitro model of portal hypertension 43 3.1 Introduction . . . 44
3.2 Material and Methods . . . 46
3.2.1 Engineering of in-vitro environment . . . 46
3.2.2 Cell culture . . . 48 3.3 Results . . . 50 3.4 Discussion . . . 52 4 In-vitro model of intestine absorption 55 4.1 Introduction . . . 56
4.2 Material and Methods . . . 59
4.2.1 The Membrane Bioreactor . . . 59
4.2.2 CFD models and Pressure Measurements . . . 61
4.2.3 Cell culture and Immunostaining . . . 62
4.2.4 Solute Transport Tests . . . 64
4.3 Results . . . 66
4.3.1 Characterization of Fluid-Dynamic Environment . 66 4.3.2 Functionality of cell barrier and transport studies . 69 4.4 Discussion and Conclusion . . . 73
II S2PR Bioreactor 77
5 Design and Testing of S2PR 79
5.1 The SQueeze PRessure Bioreactor . . . 80
5.2 S2PR: Material and Methods . . . 84
5.2.1 Electronic Hardware . . . 85
5.2.2 Mechanical Design of S2PR . . . . 90
5.2.3 Control Software . . . 95
5.3 S2PR: Tests and Results . . . 99
5.3.1 Force Sensor . . . 99
5.4 Conclusion . . . 102
6 S2PR for in-vitro models of cardiac tissue 103 6.1 Introduction . . . 104
6.2 Preliminary experiments with HL-1 . . . 106
6.2.1 Material and Methods . . . 106
6.2.2 Results . . . 111
6.3 Phenotypic regulation of H9c2 cells . . . 114
6.3.1 Material and Methods . . . 116
6.3.2 Results and Discussion . . . 119
6.4 Conclusion . . . 122
Conclusions 127
List of Publications 134
Chapter
1
State of the art of in-vitro models
1.1
Overview
In-vitro research can be defined as the study of animal and human tissues, isolated cells, cell lines or cellular components outside the living system, maintained in appropriate supporting aqueous media. The main purpose of any in-vitro model is to simplify the study of complex phenomena of in-vivo environment, creating well-controlled and easily accessible con-ditions for quantitative and repeatable evaluation of cell response. How accurately these conditions must duplicate in-vivo conditions depends on the study design and desired outcomes.
On the contrary, the characterization and analysis of biomolecules and bi-ological systems in the context of intact organisms is known as in-vivo re-search. In-vivo experimental research became widespread with the use of microorganisms and animal models in genetic manipulation experiments as well as the use of animal models to study drug toxicity in pharmacol-ogy. However, given that most pathological and non processes are signif-icantly different in humans and animals, some species-specific metabolic
capacities or disease adaptation mechanisms are often ignored [1, 2]. For example, breathing and excretion rates change remarkably between small (rodents) and large mammals (monkeys and humans), causing discrepan-cies in drug distribution and tissue accumulation. Inter-spediscrepan-cies differences often mask toxicity signals of lead compounds in preclinical studies, re-sulting in costly withdrawals once moved into human clinical trials [3]. In fact, several reports showed that about the 92% of drugs that enter clin-ical trials after an extensive animal testing fail to achieve FDA approval. Moreover, half of the 8% approved drugs are withdrawn or relabeled due to adverse effects not detected during animal testing [4, 5]. In addition to this poor predictability of animal model, there are many ethical rea-sons for limiting the number of test animals to a necessary minimum. An important step for in-vitro research is the enunciation of the 3Rs prin-ciple by Russel and Burch in 1959 (Reduce, Refine and Replace animal experiments), which inspired the European Union to impose a total ban of animal use for screening of cosmetic ingredients since 2009, with an extension granted until 2013 for assessment of sensitizers and repeated dose toxicity. Since March 2013, cosmetics containing ingredients tested on animals can not be marketed (EU directive 655/2013). Animals may still be used for screening of therapeutics, but only with the implementa-tion of stringent reducimplementa-tion and refinement protocols.
The term “in-vitro” includes many different models widely used in bio-logical research, which can be divided in:
Monolayer cell-culture: it is the simpler and most common model used for cell biology and testing research. In many fields, this technique is synonymous with the term “in-vitro culture”. How-ever, it is becoming increasingly evident that this “flat biology” approach, in which key phenotypic and functional characteristics are often lost, is not predictive of in-vivo tissue responses.
3D cell cultures: given that tissues and organs are 3D structures, cells can be seeded in 3D scaffolds which provide structural support to the growing tissue and reproduce mechanical properties similar to the in-vivo environment. In recent studies, cell viability, pro-liferation, differentiation, morphology, gene and protein expression and function have been shown to exhibit significant differences in 3D compared with 2D, with 3D constructs more closely mirroring what is observed in-vivo.
Organotypic cell cultures: use of multiple different cell types to recapitulate in-vivo-like cell heterogeneity, using either primary or immortalized cell lines. Organotypic cultures can use supporting matrices to mimic organ cultures or use 3-D scaffolds to produce in-vivo-like tissue architectures and morphologies. These models have been shown to be more reflective of in-vivo cellular organization and function.
Tissue slices: also known as “natural tissue models”, they pre-serve part of tissue architecture and cellular interactions, maintain-ing tissue-specific functions for longer time than perfused organ, but cell viability and differentiated phenotype are still limited to few days. Moreover, these models are expensive and affected by donor specific issues, making them unsuitable for routine testing purposes.
In-vitro models present several advantages to the in-vivo experimenta-tion, because they are relative simple and fast to perform, they require small amount of substances to be tested, and they can render valuable scientific information thanks to their high repeatability and quantitative analysis. However, in-vitro models are a simplification of the more com-plex living animal, thus the interpretation of experimental results is often
difficult in term of human response.
As shown in Fig.1.1, in-vitro models have a wide range of applications in many different fields, from toxicology and drug testing to nutraceutics. In the next section, each application will be briefly described, focusing on the advantages that more relevant in-vitro models could give in each research field.
Figure 1.1: Application fields of the in-vitro models
1.2
Applications of in-vitro models
Ideally, a good in-vitro model should be an accurate representation of the physiological or pathological state in the human body. In the fol-lowing sections, the advantages of using more relevant in-vitro models with respect to traditional monolayer cell culture were described. Several fields of application of in-vitro systems, like new drug development, tox-icity tests, and finally the development of personalized therapies, will be discussed.
1.2.1 Tissue engineering
Tissue engineering was originally born to develop in-vitro biological sub-stitutes with the aim to restore, maintain or improve function of impaired tissues or whole organs [6]. Despite the initial enthusiasm for this new promising way to treat problems related to tissue or organs failure and transplantation, the need for safe and clinically effective autoloug tissue substitutes still remains unsatisfied [7]. For this reason, in the last ten years a more hopeful branch of tissue engineering had been developed, with the aim to provide new biological models for more realistic in-vitro studies.
The interest of scientists was initially focused on the understanding of molecular and cellular basis of tissue regulation, the role of extracellular matrix and the role of mechanical stress on the growth and develop-ment of natural tissue. The current evolution of this paradigm is the “biomimetic” approach (Fig.1.2) [8], which aims to recapitulate all the factors involved in the tissue development through the design of bioma-terial scaffolds (providing structural, mechanical and logistic templates for cell attachment and tissue formation) [9] and bioreactors (providing environmental control, exchange of nutrients and metabolites, and the molecular and physical regulatory signals) [10].
In this context, the external environment in which cells are cultured became important, particularly the presence of a three dimensional ar-chitecture and mechanical stimuli. For this reason, a new generation of tissue engineering systems is now being developed to recreate the complex set of stimuli felt by cells and tissues in their natural environment, which can be schematically divided in:
Cell-cell interaction: communications between cells, also known as cell signaling (direct contact, paracrine or endocrine signals). This mechanism is essential for cells to sense and respond to their
Figure 1.2: Schematic view of the “biomimetic approach” (modified from [8]).
surrounding environment, addressing critical processes in cell mi-gration, differentiation, healing and development.
Cell-matrix interaction: cells in-vivo are surrounded by their specific extra-cellular matrix (ECM), and its architecture, chemical composition and mechanical properties (like stiffness) can influence cell morphology and functionality [11]. For this reason, scaffolds for tissue engineering are realized with a wide range of mechanical properties (from Pa to GPa), architecture (fibrous, porous cryogels or hydrogels) and materials (natural or synthetics) [12, 13, 14]. Physical stimuli: this category include several different kind of
stimuli which compose the physical cellular microenvironment, such as the physic-chemical stimuli (oxygen concentration, pH, temper-ature) and physical forces (hydrodynamic, mechanical, electrical), and their spatial and temporal combinations [15]. Cells respond to these stimuli by remodeling their microenvironment, will a process
called mechanotransduction. In this context, bioreactors play an important role, because they are able to combine an efficient and uniform distribution of nutrients with the application of specific stimuli, depending on the functional requirements of the engineered tissue [10].
1.2.2 Drug studies
In-vitro models are widely used in many areas of drug discovery, for both the identification and validation of chemical compounds. Once a suitable drug candidate was identified, its ADMET (absorption, distri-bution, metabolism, excretion and toxicology) can be also studied using such models [5, 16]. The whole pre-clinical studies often requires several years and notable investments of money for selecting a new compound and assure its safety before study on patients. Thus, the possibility to have more relevant and predictable in-vitro models could allow a remark-able reduction of cost and time for developing new drugs, as well as the minimization of animal experiments according to the 3Rs principle [17]. In fact, it is well established that 3D dynamic culture can recreate the interaction between cell, matrix and their microenvironment, which crit-ically affect cellular response to active compounds [18].
In drug studies, three tissues are particularly relevant to prevent the AD-MET or side effects of molecules:
1. the liver, which is the primary detoxification organ in the human body and it metabolizes most of the drugs assumed orally;
2. the heart, because many approved drugs have been removed from the market because of cardiotoxic side effects, not predicted using traditional in-vivo and in-vitro tests;
3. the epithelium, as this barrier tissue is main responsible for the absorption of substances.
Liver models should maintain hepatic specific functions and enzymatic activities for predictive drug metabolism. Unfortunately, traditional in-vitro models are often poorly representative of hepatic response, giving for instance false-positive results for hepatotoxicity because static 2D cell cul-tures are less able to withstand cytotoxic agents [19]. For these mistakes, potentially safe and efficient compounds may be removed unnecessarily from drug development programs.
Several in-vitro cardiac tissue models have already been developed for the study of cardiac diseases and in the prediction of drug cardiotoxicity [20]. In particular, more relevant cardiac tissue models and organotypic cell cultures with cross-talk between different cells could be more repre-sentative of cardiotoxic side effects.
Finally, given that the oral administration is the most common way to take drugs, the intestinal tract plays a key role in the absorption and dis-tribution of substances in the blood circuit. In fact, kinetic and metabolic profiles of active compounds can be modified passing through the epithe-lial barrier, as well as they can alter barrier functionality and permeability [21]. In-vitro models able to simulate the whole absorbing pathway for chemical substances can be a relevant step forward the development of more predictable cell response.
1.2.3 Disease Models and Personalized Therapy
An other important application of in-vitro models in the pharmaceutical field is the possibility to recreate some specific pathological conditions, in order to investigate human disease progression.
Correlated to disease models is the concept of “personalized medicine”, which can represent a remarkable progression of medical science toward
greater understanding of health, disease, and treatment specific for each patient. In fact, there is an urgent need to improve the efficiency, in-formation yield, and predictability of drug development, as well as the safety and effectiveness of pharmaceutical treatments [22]. For instance, several tumor tissue models have been studied, in order to test in-vitro anti-cancer drugs with much higher efficacy than using animal models, and for developing new anti-cancer therapies [23, 24]. Cancer pathogen-esis, development and treatment response critically depend on several factors like cross-talk signals between the different cell types within the tumor mass and changes in neighbors tissues endocrine responsiveness of the patient [25]. Personalized therapies based on individual cancer cells extracted from tumor samples can represent a suitable model for in-vitro studies of the pathology. Tumor samples can be extracted from the patient and processed in order to isolate different cancer cells. These cells should be maintained in the 2D Petri dishes for some days, in order to identify metabolic molecular targets for optimizing the efficacy of the therapy in each patient. Several studies showed that tumor cells can be cultured for longer time in-vitro if maintained in the appropriate micro-environment [26].
Applications of in-vitro models can also concern the study of infectious diseases, providing novel platforms to explore the complex nature of host-pathogen interactions and to investigate the drug resistance strategies [27]. The use of advanced in-vitro models as surrogates for human tissues in the early stages of the drug design process can potentially aid in re-ducing the number of inadequate drug candidates that enter into clinical trials. Moreover, they can give a complete understanding of the physical and chemical interactions that occur between microorganisms and patient tissues, as well as the possibility to study the increased susceptibility of the host organism to secondary infections, to improve strategies for
pre-venting and treating infectious diseases [28].
More relevant and predictive in-vitro models can be the first step for improving the diagnostic modality, evaluating inter-individual variations in drug exposure that result from polymorphisms in drug metabolism or cellular targets for drug intervention [29].
1.2.4 Toxicity tests
Toxicology is the study of all possible harmful effects of a toxic substance that can represent a risk for humans, animals and the environment. As for drug development, these studies can be implemented using several methods with different levels of complexity, from in-vivo experiments to computational simulation. Simple in-vitro models are commonly used in toxicology experiments to identify the biological effects of chemicals and drugs, as well as to screen the relative toxicity of substances.
In-vivo drug toxicity is a multi-factorial, dynamic, and complex sequence of physiological events which can be specific for every organ, depending from interactions between multiple cell types. These events are poorly recapitulated for risk assessment in too simplified in-vitro models. In ad-dition, toxic effects can be also due to active metabolites produced via conjugated mechanisms, for instance in the liver. Current methods of cellular toxicity assessment are most appropriate for identification of spe-cific targets, but the prediction of systemic response to toxic compounds is still difficult to achieve.
One of the main difficulties related to toxicity studies is the prediction of the toxic dose for humans from animal experiments or in-vitro tests, because unfortunately the dose-response relationship is not always lin-ear, and depends critically on absorption, distribution, metabolism, and elimination characteristics of the compound in the organism. For this rea-son, current researchers are using toxicokinetical methods in preclinical
safety assessments to better predict human toxicity profiles from in-vitro or laboratory animal data [30]. The “toxicokinetic” relates drug dose to exposure levels, correlating both to development of toxicity indicators like cell dead or markers for stress. Unfortunately, even if certain doses (or concentrations) are found to be toxic, there is no way to know if the toxi-city range overlaps with the effective dose in humans without established methods of relating in-vitro and/or animal dose data to human in-vivo doses. Moreover, repeated dose tests are still very difficult to be imple-mented using traditional static cellular models, because they are strictly correlated to the dynamic environment present in-vivo, for long term ex-posure of cell and tissue to toxic compounds.
In this scenario, new improved tools and technology for in vitro toxicol-ogy testing are urgently required by industries and governments as an alternative to animal testing. Improvements in systems biology, bioin-formatics and rapid assay technologies are helping scientists to better understand how cellular networks or pathways in the human body carry out normal functions that are key to maintaining health. An approach to overcome in-vitro test limits is to develop a more realistic model that mimics animal and human response accurately, providing chemical and physical cues typical of in-vivo cell environments [31]. A new toxicity test system that relies mainly on understanding “toxicity pathways” of cel-lular response would evaluate biologically significant alterations without relying on studies of whole animals.
1.3
Advanced in-vitro models
In this context, it is increasing evident that more relevant and predic-tive in-vitro models should replicate all the stimuli which act on the cells in the human body. These systems should be able to combine the 3D structure with the application of physical stimuli and the maintenance
Figure 1.3: Comparison between the microfluidic with the milli-fluidic.
of cross-talking signaling between different cells. Currently, two different approaches have been proceeded: i) organotypic models in which different cells are seeded and grown in the same scaffolds (either natural or syn-thetic), in order to completely recapitulate the cell-cell and cell-matrix interactions; ii) connected cell culture, in which a common media passes through different compartments containing different cells, thus allowing to simulate the systemic response. In the latter approach, the most used systems are the so-called “organ-on-chip”, that apply microfluidic systems to several tissue and organ models, maintaining small dimensions. Alter-natively, larger system based on the milli-scale can be used, as further described. A critical comparison between the micro and milli approach is summarized in Table 1.3.
1.3.1 Organotypic models
In organotypic cellular models (OCM), researchers try to replicate the complex 3D network of interactions between cells present in-vivo in the tissue and organs. These interactions are crucial for regulating different cellular processes related to proliferation, differentiation, survival and im-mune function. For instance, we speak about OCM for models of lung epithelium which combines layer by layer endothelial cells, fibroblasts
and epithelial cells with the appropriate air-liquid interface [32]. There are three main way to realize OCM (Fig.1.4): i) using engineered scaf-folds, cellular spheroids or microbeads with appropriate combination of cells and/or ECM; ii) using tissue slices; iii) using whole decellularized organs.
Spheroids and microbeads, typically in the range of 100-400 µm, are par-ticularly useful for in-vitro cancer models [27] or for differentiation of stem cells, to create the so-called “embryoid bodies” [33]. Cell differentiation is influenced by a variety of factors in the intercellular microenvironment, like ECM interactions and cell adhesion. These cell structures are often cultured in dynamic systems like rotary orbital culture, stirred/rotating culture vessels, or spinner flasks, to enhance cell aggregation and uniform growth.
Tissue slices are sometimes used as short term in-vitro models to study pathological models or to answer specific questions about tissue response to treatments, in particular in neural and metabolic studies in which the complex architecture of organs plays a crucial role [34, 35]. The use of tissue slices came from the early stages of tissue engineering, but it was for long time unsuccessful because of the difficulty to maintain a good cell viability and intact phenotype for several days. Recently, a new ap-proach was used, integrating this technique with the microfluidic: slices were submerged in the microchambers and perfused with media, to im-prove oxygen/nutrient penetration into the tissue [36, 37].
In the last years, a new and promising OCM has been developed, based on decellularized matrix scaffolds which was dynamically re-seeded in order to artificially create a functional organ [38, 39]. At the present, several different organs have been successfully decellularized (i.e. heart, liver, kidney, pancreas, blood vessels), as reported by Ott in his review [39]. However, the re-seeding process is still at the initial stage, and
tech-Figure 1.4: Organotypic cellular models: A) microbeads; B) tissue slices (kidney) before and after 7 days long culture; C) whole rats organs before and after cell re-seeding.
niques currently employed in recellularization of whole-organ grafts are essentially adaptations of the approaches employed in cell-transplantation therapies, where cells are either injected directly into the organ or injected into the circulation with the expectation that the cells will home to the injury site. The recellularization process can be considered in two ma-jor steps: cell seeding, where the goal is redistribution of cells similar to their in-vivo spatial configuration, followed by perfusion culture, which is typically utilized to prepare the cells for in-vivo function. The whole or-gan regeneration still remains one of the most promising and investigated techniques for in-vitro models and regenerative medicine.
1.3.2 The organ-on-chip approach
Microfluidic systems are increasingly being used in cell cultures, in partic-ular for applications related to biological studies, drug testing and tissue engineering. Novel platforms based on microfabrication and microfluidics were developed to offer a representation of the complexity of the in-vivo situation, as they could provide a greater control of environmental
pa-rameters [40]. They offer several advantages, like the very low reagent consumption, as well as the scale and transparency of lab-on-a-chip sys-tems for on-line imaging of cellular processes, which makes these syssys-tems particularly suitable for high-throughput screening. A wide range of sin-gle organs and tissues have been studied using micro-devices, like liver, kidney, intestine, lung, heart, cornea or brain [40, 41, 42, 43].
In microfluidic systems, media flow in each channel or chamber is very slow, to not apply high shear stresses on cell surfaces. These conditions strictly affect the oxygen concentration, in particular if used in combina-tion with highly demanding cells like hepatocytes or cardiomyocytes. For this reason, a large number of works were focused on the characterization of the oxygen profiles in such systems, using computational methods or direct measurements in cell culture chambers with respect to different fluid flow profiles [44, 45, 46]. These works pointed out that, in order to optimize oxygen availability in microchannels, a number of factors should be controlled, like: i) the material from which the device is fabricated, ii) the architecture, iii) the rate at which the media is perfused through the channels. One of the key factors in the microfluidic systems is the use of PDMS (polydimethylsiloxane), a biocompatible material with high oxygen permeability (D = 3.4 × 10−5 cm2/s) and easy to be processed with microfabrication techniques. However, some studies reported that residual uncrosslinked oligomers may leach from PDMS during the man-ufacturing of micro-devices, and interact with cells or culture media [47]. A relevant upgrade of these systems in term of in-vivo similarity is represented by the Shuler’s works, with the “Body-on-Chip” devices con-taining multiple microchambers, each of which with a different type of cells connected by a network of microfluidic circuit that mimics the blood circulation [43, 50]. The fluid pattern in this system was precisely cal-culated from the PBPK (Physiologically Based PharmacoKinetic) model
Figure 1.5: A) Design of Body-on-Chip systems, with different compartments inter-connected by the common cell culture media; B-C-D) Some examples of organ-on-chip system (from [48, 49])
[51], in order to predict the time course of pharmacological effects. Despite the variety of beneficial features, microfluidic systems still present several disadvantages, besides the oxygen/PDMS problem previously dis-cussed [52, 53]. First of all, due to the large surface/volume ratio of cell cultures, surface properties become more relevant than in-vivo, and also cause non-physiological accumulation or washout of extrinsic factors [44]. An other important limit of these systems is that they often require specific and expensive fabrication technologies, like soft lithography, in order to create connected chambers with a fixed topology. So, consider-ing that the main criteria for advanced in-vitro models is to recreate the physiological environment typical of each tissue, models for different or-gans will require the design and fabrication of purpose-done devices, not suitable anywhere else. Moreover, microfluidic systems present some diffi-culties during the cell seeding, which is usually performed introducing the
cell suspension through the microchannels. During this procedure, cells withstand very high pressures in order to allow the cell culture media to overcome the surface tension against the PDMS. Moreover, in this way is very difficult to control the cell concentration and distribution in the channel or chambers [54]. Finally, even if the dimension of the cell culture system is small, the tools required for controlling the fluid flow and pres-sure in the microchannels are not similarly scaled, thus they are often too cumbersome to be used in traditional laboratories for cell cultures [40].
1.3.3 System-on-a-plate
The system-on-a-plate approach was developed to overcome the disadvan-tages of the body-on-chip systems, in order to combine the cross-talking between different cells and tissues with the geometry of traditional tools for in-vitro technology like the 24-well plate [55]. Starting from the con-sideration that one of the main limits of the microfluidic systems was the fixed topology of the fluid circuit, the system-on-a-plate devices were designed as modular chambers that can be combined in different ways, also allowing to adapt the length of tubing between different cells accord-ing to the PBPK models [56]. Moreover, principles of allometry scalaccord-ing can be applied on cell numbers and mean residence times of molecules in metabolic tissues, to establish physiologically inspired system models [57]. One important feature of these systems was the possibility for bi-ologists to apply the same protocols used in traditional static multiwells to the bioreactor modules, thanks to the similar geometries and volume of media. These systems were used to simulate several physiological or pathological conditions, starting from the single tissue model using highly metabolic cells like hepatocytes [56, 58, 59]. Then, the possibility to con-nect together different chambers and build up systemic models was tested, combining hepatocytes with different target tissues for drug studies like
adipocytes or endothelial cells [60, 61, 62, 63]. Several diseases were sim-ulated using the appropriate combination of bioreactors: for instance, metabolic system was recreated in-vitro in [60], in order to investigate the role of flow in metabolic cell response as well as the importance of cross-talking. Results showed an increase in glucose uptake and an overall increase in free fatty acid and lactate release in the connected cells cul-ture. Moreover, an other study demonstrated that this systemic model was able to mimic metabolic disorders with the implementation of two different conditions like normoglycaemia and hyperglycaemia, by altering the composition of the common cell culture medium [63]. A recent study also showed the ability of these systems to study the toxicity effects of nanoparticles on human epithelial cells, simulating the oral absorption of these compounds and their passage through the liver and the intestine epithelium [61].
1.4
Workflow of the thesis
The aim of this thesis is to develop new tools and methods for realiz-ing advanced in-vitro models of different tissues, focusrealiz-ing on the role of physical stimuli on cell cultures. The work was structured as reported in Fig.1.6. The thesis is divided in two parts: in the first, an environmental control unit called SUITE (Supervising Unit for In-vitro TEsting) was optimized (Chapter 2) and used to perform two different models using the ability of the systems to actively control the hydrostatic pressure and the pH in a fluidic circuit. SUITE was used in combination with two different flow bioreactors, in particular in Chapter 3 a disease model of portal hypertension was implemented using hepatocytes and endothelial cells in the LiveBox1, a transparent single flow bioreactor, whereas in Chapter 4 a physiological model of human intestine was developed using the LiveBox2, a double flow bioreactor with membrane designed for
sim-Figure 1.6: Structure of the thesis
ulating epithelial barriers.
In the second part, a highly independent bioreactor called S2PR (Sen-sorized Squeeze PRessure bioreactor) able to apply a hydrodynamic cyclic stimulus was developed (Chapter 5) and tested on cardiac cells seeded on different scaffolds (Chapter 6), in order to implement a more relevant cardiac model.
SUITE for
physio-pathological in-vitro
models
Chapter
2
Optimization of the
Environmental Control
Abstract
The first part of this thesis starts with the optimization and improvement of an already existing control unit for cell cultures named SUITE. This system is able to control local environmental variables such as pH, tem-perature and pressure over long periods, in order to provide the optimal environment for cells outside the classical incubator and at the same time to apply mechanical and chemical stimuli to simulate the physiological mi-lieu. The work was focused on the optimization of the control algorithm for a stand-alone regulation of the pH with applying different hydrostatic pressures on the cell cultures. Moreover, the real-time monitoring of the oxygen concentration in cell culture media was implemented, as well as a small and independent heating module compatible with different bioreac-tors.
2.1
Introduction
Environmental parameters are essential to realize good and more rele-vant in-vitro models, as cell cultures are extremely sensitive to chemical as well as physical stimuli, like pressure and pH. As described in the in-troduction, traditional in-vitro models are performed in static conditions maintaining cells in appropriate incubators, with a physiological tempera-ture of 37 and a controlled partial pressure of CO2 (5%) and O2(21%).
These conditions are poorly representative of the human physiology, even if the static culture is replaced with bioreactors for applying physical or mechanical stimuli. In fact, cell response depends on the combination of several different stimuli, although the evaluation of the effect of single stimulus can be useful for basic research.
An other crucial point of in-vitro research is the need to maintain the culture in a constant and isolated environment, in particular with highly sensitive cells like stem cells or undifferentiated cell-lines. In fact, it is well known that small and short changes of the physical and chemical conditions of cell environment can activate the signal processing for cell differentiation [33], compromizing the results of the experiment. These alterations can append during the opening/closing of the incubator door, which briefly modified the gas levels as well as the pressure in the whole chamber. Cells in-vitro are incredibly sensitive and react to different conditions with instability and changed vitality. In order to keep exper-imental results comparable, the highest possible level of standardization is necessary.
The SUITE (Supervising Unit for In-vitro TEsting) system is a control unit able to monitor and adjust local environmental variables such as pH, temperature and hydrostatic pressure over long periods in cell cul-ture medium [64, 65]. This system can provide the optimal environment for cells outside the classical incubator and at the same time apply
me-chanical and chemical stimuli to simulate the physiological milieu. The SUITE platform was composed of a mixing chamber for pH regulation and media reservoir, a heating system, a peristaltic pump and an elec-tronic circuit and electro-valve box linked to a PC. The mixing chamber also contained the pHmeter, and could be connected to several bioreac-tors by the common media that flows through the whole circuit thanks to a peristaltic pump. The pH regulation was performed by controlling the injection of two different gases, the CO2 and Air: the diffusion of O2
(Air) tends to raise the pH, whereas the CO2 tends to lower it. The two
gas lines were also connected to a pressure regulator, an electrovalve and a pressure sensor (Fig.2.1.B). Notably, this strategy for active control of pH needed to have an air outlet with high flow resistance, because of the continuous injection of air. In the old control unit, the heating system was a Plexiglas box where the mixing chambers and the bioreactors are inserted. The box was filled with water, heated by a resistor placed under the cell culture units, and the temperature of the water was monitored with two thermistors, connected to the control unit for a feedback control. The control box was composed of a custom made electronic block that acquired signals from sensors (i.e. pHmeter, pressure sensor and NTC thermistor) and driven the actuators for actively controlling the environ-mental parameters. The control software was the core of SUITE, and it was completely described in [64, 65]. It is based on a programming framework called Robotics.NET developed in F#, with an architecture inspired by the human nervous system. The bioreactor was controlled with a dedicated library, which read data from the sensors and commu-nicated with the system brain, as the peripheral organs with the human brain.
The user could set and monitor the experimental parameters using a Graphical User Interface (GUI) developed in C#.
Figure 2.1: A) CAD drawing of the inlets and outlets in the mixing chamber. Media flow (in/out) refers to the fluidic circuit in the SUITE system. B) Block diagram for the pH regulation and generation of hydrostatic pressures (modified from [64])
Prior to this thesis, the SUITE presented several limitations:
the pH control algorithm was not able to maintain the target value in all the conditions, in particular applying different hydrostatic pressures;
the injection of gases directly in fluid avoided the use of standard cell culture media with serum;
the simple heating box was not compatible with advanced devices like transparent bioreactors or sensing elements;
the oxygen concentration was not monitored in the system, despite this parameter is considered one of the most critical in cell cultures. Thus, the first step of this work was aimed to improve SUITE in order to allow the realization of relevant in-vitro models further described.
2.2
Material and Methods
2.2.1 Generation of Hydrostatic Pressures and pH control
In traditional incubator, the pH value in cell culture medium is main-tained at a constant level (generally within a neutral range) through the combination between chemical buffer and high CO2 supply. It is
a critical parameter because even the slightest hyperacidity through the metabolism products of the cell can lead to heavy impairment of cell vi-tality and irreversible loss of function. The alkalinisation of medium in turn usually causes the cells death immediately.
In the SUITE system, pH is strictly regulated by the controlled injec-tion of pressurized air (O2) and carbon dioxide in the mixing chamber
containing cell culture media. These gases diffuse in the fluid and re-act with the sodium bicarbonate buffer, causing a pH decrease for high CO2 concentration and pH increase when the O2 percentage is higher.
Because of the delays between gas injection and ionic dissociation, and the elevated number of variables acting in this system, a mathematical or computational model of pH regulation is extremely complex to achieve. To predict the pH value as function of CO2 and air inserted, four different
kinematics should be considered: i) convective and diffusive transport of gases in the top part of the mixing chamber; ii) air-liquid passage of gas molecules, following the Henry’s law; iii) diffusion of gases in cell culture media; iv) reaction of gases with the buffer. Because of this complexity, the SUITE control follows a specific algorithm: the system continuously inject air in the mixing chamber until the pH is below a set threshold, then it alternatively insert CO2 and air for an amount of time defined by
the proportional-derivative (PD) algorithm [65]. Because of the presence of pressure regulator and gas valve, this control strategy was also used to generate hydrostatic pressure in fluidic circuit of the SUITE.
In the previous strategy, the pH regulation was speeded up flowing the gases in cell culture media, like a bubbles oxygenator. This method had several disadvantages, as the generation of foam if standard media with serum was used, or a quick evaporation of fluid with a rate proportional to the hydrostatic pressure generated (25% evaporation after 24 h at 40 mmHg). These problems compromise the flexibility and usability of the system, because they make very difficult to perform experiments for more than 12 h. Moreover, the pH value has high fluctuations around target value, mainly because of the PD control and the slow reactivity of the system. The last limitation of the old control was the difficulty for users to find the starting values for the algorithm, because they strictly de-pended on the target pressure applied, and they affect the whole control process. For these reasons, two main changes were introduced:
the gas inlet tube in the mixing chamber was reduced, in order to insert gases only above the media;
the control algorithm was changed in order to calculate both injec-tion times, taking into account the pressure and the temperature in cell culture media (Fig.2.2). Moreover, a simple proportional control was used instead of the PD one.
Figure 2.2: Block diagram for the pH control algorithm, with the gas injection time depending on the other environmental parameters of the system.
These changes were introduced step by step in the system, for evaluating the contribution of each one in the control efficacy. At first, the gas inlet tube in the mixing chamber was modified, and the DelayTime (Air in-jection) was changed in order to be adjusted on the base of the pH with the proportional control algorithm. Then, in order to understand the role of hydrostatic pressure in pH regulation, several different conditions were generated in the SUITE system, for the evaluation of: i) correla-tion between pressure and gas flux in the mixing chamber; ii) correlacorrela-tion between pressure and CO2/Air injection time during the oH control; iii)
evaporation of cell culture media. As we can see in Fig.2.3, there is a good linearity (R = 99%) between hydrostatic pressures created in the fluidic system and gas flux in the mixing chamber. Moreover, no evaporation of media was detected in the mixing chamber in all the conditions tested.
The second analysis pointed out an inverse relation between pressures
Figure 2.3: Correlation between gas flux in the mixing chamber and hydrostatic pres-sure.
and injection times of both gases when the pH control algorithm reached the full speed state. In fact, from the ideal gas law and the Henry’s law, we assume that gases were more concentrated, as well as their increase their solubility according to the hydrostatic pressure. For this reason, the control strategy was further modified in order to take into account all the environmental parameters. As shown in the code below, the SprayTime
(CO2 injection) and the DelayTime (Air injection) are now adjusted
ev-ery computational step on the base of the actual pH value, the pressure and the gas flow rate present in the mixing chamber. The pH control was
finally tested for 24 h using experimental conditions: the SUITE system was connected to two single flow bioreactors, filled with 10 mL of media, and a fluid flow of 250 µL/min was applied in the circuit. Different hydro-static pressures (15 - 50 mmHg) were evaluated and data were recorded in order to analyze the capability of the system to automatically reach the target values by modifying the injection times of gases.
2.2.2 Integration of the oxygen sensor
The oxygen concentration is a critical parameter for cell cultures. Oxy-gen is essential to cell growth and differentiation and the level of oxyOxy-gen that cells are exposed to is critical for cell function, in particular for highly metabolic cells like hepatocytes or cardiomyocytes [66, 67].
More-over, real-time monitoring of such parameter is advantageous for assessing growth rate, the state of bioreactions, and to obtain early indications of negative events like contamination or cessation of growth. Because of the profound effect oxygen has on biological systems, controlling and moni-toring oxygen concentrations is useful in many cell culture applications. In addition to the simple oxygen-sensing application, oxygen sensors can also be adapted for the measurement of glucose concentrations through the addition of glucose oxidase, which allows glucose levels [68]. This further increases the applicability of oxygen sensors.
For all these reasons, a remarkable improvement of the SUITE system is the possibility to monitor the oxygen concentration in parallel with the other environmental parameters. A commercial system (Neofox Phase Measurement system, Ocean Optics Inc, Ostfildern, Germany) for oxy-gen measurement was integrated in the control unit. The Neofox system is compatible with both probe and patch sensors, thus allowing the use of the probe in the mixing chamber as well as real-time oxygen monitor-ing inside transparent bioreactors. The fiber optic oxygen sensor system is a spectrometer-coupled chemical sensor for quantitative measurements of dissolved and gaseous oxygen pressure without consuming it, using a fluorescence method [69]. Optical fiber carries excitation light produced by the blue LED to the thin-film coating at the probe tip. The probe col-lects fluorescence generated by a ruthenium complex trapped in a solgel matrix at the tip and carries it via the optical fiber to the high-sensitivity spectrometer. When oxygen in the gas or liquid sample diffuses into the thin film coating, it quenches the fluorescence, and the degree of quench-ing is correlated to the level of oxygen pressure. The optical fiber probe (AL300, tip diameter 410 µm) is inserted in the mixing chamber of SUITE and connected to the Neofox unit, which calculates the partial pressure of oxygen from known values of tau and temperature. A “two point”
calibration need to be previously performed, using the oxygen-tau pairs to approximate a linear relationship between the reciprocal of tau and oxygen. For the calibration in the SUITE system, oxygenated cell cul-ture media is used as one of the two reference points, since the percent oxygen can be reliably assumed to be 20.9% of 1 ATM. The second point must be a sample taken at 0% oxygen, obtained with a solution of sodium sulfate in deionized water (10 mg/mL). The probe is very sensitive to the
Figure 2.4: A) Block diagram for the integration of the oxygen sensor in the SUITE system B-C) Analog communication between Neofox board and the control unit (figures adapted from the sensor datasheet)
convective flow of medium, so it must be adequately shielded and pro-tected from media recirculation in the mixing chamber. For this reason, a new Teflon tube (external Ø4 mm, internal Ø3 mm) in inserted in the chamber until the base of it, leaving the sensor tip in contact with the cell culture media only where convective forces are low. As summarized in Fig.2.4.A, the oxygen probe is connected to the commercial Neofox board, for signal processing and analysis, and this unit communicates with the SUITE system through the analog connector (Fig.2.4.B) with a 4-20 mA current output. Signal is read with the standard current loop connection, following the Ocean Optics’s instructions (Fig.2.4.C), using commercial
4-20 mA adapter (1132 board, Phidgets Inc.) with external 12 V voltage supply.
Several tests were performed in order to assess the behavior of the oxy-gen sensor in the SUITE system, and also evaluate the effect of different hydrostatic pressures on oxygen concentration as well as the pH active control. In each experiment, data were acquired for 30 minutes at 1 Hz frequency, and reported as average ± standard deviation.
2.2.3 Heating module
As described in the Section 2.1, one of the main problems related to the SUITE system was the heating box, designed for connecting the control unit to the MCmB (Multi-Compartmental Modular Bioreactor) [56] using a temperature controlled bath with water. With this heating strategy, it is not possible to connect the SUITE with more advanced bioreactor, with integrated electronics or sensing elements. Moreover, the new version of the MCmB (Live Box 1, LB1) presents a transparent bottom surface, thus enabling a real time monitoring of cells during the experiment under the inverted microscope. This advantage cannot be used in such heating bath.
For this reason, a new heating module was designed in order to be com-patible with several modular bioreactors and connect the SUITE with different systems. In this way, the real-time monitoring and control of environmental parameters can be applied to perform different in-vitro models, choosing the appropriate bioreactor for simulating the desired physiological conditions. General requirements for the heating module are:
maintenance of a stable and uniform temperature in the whole biore-actor;
small dimensions, for allowing the real-time imaging under the stan-dard inverted microscope (if used with transparent bioreactors); allowing both the integration in the SUITE and an independent
control of temperature
Following these specifications, the heating system was realized as de-scribed in the block diagram in Fig.2.5.A. On the basis of the actual temperature, a PID control drives the heating of two identical resistors (15 Ω, 1 W) [70], in order to maintain a stable temperature of 37 . The PID (Proportional-Integrative-Derivative) controller is a control loop feedback mechanism which attempts to minimize the error between a measured process variable and a desired setpoint, adjusting the process control outputs. In this system, the process value is the temperature of the module, measured with the SUITE sensors or with independent sensors (AD22100KT, Analog Devices Inc, USA) in case of stand-alone heating. The AD20100 sensors directly give a voltage output propor-tional to the measured temperature, with a transfer function provided by the manufacturer. The PID control is implemented in the Arduino Microcontroller [71], using the “PID v1” library [72]. The PID algorithm generates a PWM (Pulse with Modulation) output signal between 0-5 V that drives the gate terminal of a Mosfet (IRLB3813PbF, HEXFET Power MOSFET, IRF International Rectifier, Italy), in order to modu-late the drain current IDS in resistors (Fig.2.5.B). The latest components
are placed at opposite sides of a case in Aluminum (Fig.2.5.C), with high thermal conductivity (205 W/(m K)) [73], so allowing a quick and uni-form distribution of the heat.
Performances of temperature sensors in term of response time and ac-curacy were also evaluated because they affect the PID control, and they strictly depend on the environmental working conditions. For these tests, the temperature of a heating plate was recorded between 22 and 40
Figure 2.5: A) Block diagram showing the working principle of the heating module. B) Electronic circuit for controlling the heating module. C) Schematic view of the heating module with all its components.
using both the AD22100 sensor and a digital handheld thermometer with temperature probe (Tekpower KT300, resolution 0.1 , accuracy ± 0.1 %). Preliminary experiments pointed out that the accuracy of the sensor can be improved considering the self-heating effect of this component, quantified as 0.4 . This value was quantified as power consumption as well as a constant drift in the calibration curve. After this correction, the accuracy of the sensor is less than ± 0.5. Moreover, the response time at working conditions was evaluated by placing the AD22100 sensor in contact with a plate constantly maintained at 37, and acquiring data from the sensor at 1 Hz frequency (Fig.2.6). Data were then analysed in Matlab (Mathworks, Inc) using the exponential function 2.1, in order to calculate the time response as t* = 1/b:
Three repetitions of these measurements were performed in order to allow statistical analysis, and results pointed out a time response of 11 ± 0.14 s. These results were considered during the testing of the PID control, in particular for defining the sampling time of algorithm (how ofter the PID control evaluates the state of the system).
Finally, tuning parameters of the PID (Kp, Ki, Kd) were adjusted in order
to improve the dynamic behavior of the control. A low overshoot is required to not overheating the bioreactor, because a temperature over the 38 can seriously compromise the cell culture. Additionally, a fast attenuation of oscillations around the target value is desired.
Figure 2.6: A) Evaluation of response time of the temperature sensor, performed ac-quiring data from AD22100 place in contact with a heating plate at 37 C at time t=0. B) Heating system combined with the LiveBox1 (CAD drawing)
2.3
Results
The ability of the new control strategy to adjust and maintain physio-logical values of pH in different conditions was tested. At first, several pressures were applied in SUITE using the same starting values for the control algorithm. The system was filled with 10 mL of cell culture media, and a flow rate of 250 µL/min was maintained in the fluidic circuit. As
shown in Fig.2.7, the system quickly reduces the pH from the basic initial value to the target range of 7.3 ± 0.1 (around 20 s), and it maintains a very constant pH. Moreover, Table 2.1 pointed out how the system reacts for controlling the pH in different conditions: the new algorithm automat-ically adjusts the injection time of the gases, thanks to the correlation to current environmental parameters in the mixing chamber.
The control system was finally tested with pH perturbations due to
ex-7.3
Figure 2.7: Evaluation of pH control at different hydrostatic pressures, setting the same initial conditions (CO2: 3500; Air: 1000)
ternal stimuli (0.1 mL of basic solution pH 10). As we can see in Fig.2.8, the system reacts to the altered pH increasing both the injection time of CO2 and Air, and the pH was brought back to the target value in about
50 seconds. When the pH was again under the set threshold, the injection times returned to the initial values.
These tests demonstrated that changes introduced in the SUITE con-trol improved the stability of the system, obtaining results comparable with previous works, which used much more complex control strategies [74, 75].
Then, the correlation between the oxygen concentration and hydro-static pressures or pH variation was evaluated. From the Henry’s law,
Pressure Flux Air Inj CO2 Inj
(mmHg) (mL/min) Time (s) Time (s) 15 39.03±1.41 1.94±0.22 4.06±0.18 20 48.86±2.17 1.54±0.23 3.15±0.28 25 58.58±2.12 1.46±0.21 2.88±0.24 30 68.99±2.29 1.10±0.09 2.11±0.47 40 86.76±2.04 0.78±0.06 2.03±0.23 50 104.25±1.86 0.83±0.18 1.57±0.16
Table 2.1: Evaluation of the better “Finding Home” procedure with different scaffolds traditionally used for cell cultures.
we expected that oxygen percentage increased or at least it was main-tained at atmospheric values with the increase of pressure. Figure 2.9.A shows instead an opposite trend, as the oxygen concentration tends to de-crease with higher hydrostatic pressures. This behavior is probably due to the higher solubility of CO2 (1.45 g/L/atm) with respect to O2 (0.032
g/L/atm), and this difference seems to became more relevant for higher pressure in the mixing chamber. The same explanation can be provided for the relation between pH value and oxygen percentage (Fig.2.9.B). In fact, a lower pH is obtained increasing the injection of CO2, that diffuses
more quickly in the media and thus tends to decrease the partial pressure of oxygen. Anyway, in all these conditions, the partial pressure of oxygen in the culture media was maintained at good values for cell cultures.
Finally, Fig.2.10 reported the results of temperature tests in the heating module. In particular, from Fig.2.10.A we can observe the optimization of tuning parameters for PID control and the sampling time in order to re-duce the overshoot around target temperature as well as for respecting all the requirements indicated in the previous section. The best control was identify using a Kp=0.1, Ki=5 and Kd= 0.001, with a sampling time of 5
Figure 2.8: Testing of the pH control: a base was inserted in the media to evaluate the system capability to react to external stimuli
Figure 2.9: Oxygen concentration evaluated in the mixing chamber at: A) different hydrostatic pressures at pH 7.4; B) different pH values, at a pressure of 20 mmHg. Both the experiments were performed at 37 C.
connected to LB1 with the heating module. The optimized control sys-tem allow the maintenance of the transparent bioreactor under standard inverted microscope during the experiment in order to perform real time imaging. In this way, the effect of the stimulus applied by SUITE, as well as fluid-induced forces, can be easily monitored and quantified.
Figure 2.10: A) Effect of the tuning parameters and sampling time on the dynamic behavior of the heating box. Test 1 (green): Kp = 1, Ki = 5andKd = 0.001, with a
sampling time of 10 s; test 2 (blue): Kp= 0.1, Ki= 5andKd= 0.001, with a sampling
time of 10 s; test 3 (orange): Kp=0.1, Ki=5 and Kd=0.001, with a sampling time of
5 s. B) Heating module inserted in the frame of the LB1 bioreactor.
2.4
Discussion
The SUITE control system has been optimized and improved in order to allow the application of several hydrostatic pressures with maintaining a stable physiological pH in the cell culture media. Results also pointed out that the new control strategy reduced the user-dependence of the system to properly adjust the pH value, as the adaptive control algorithm regu-lates the injection of gases on the base of the environmental parameters in the mixing chamber. Moreover, the introduction of the oxygen sensor in
SUITE allowed real-time monitoring of oxygen concentration during the whole experiment in a non-invasive way. All these features, together with the small heating module, have made the SUITE a plug-and-play and re-liable system for developing long term experiments outside the traditional incubator, using different bioreactors on the base of the in-vitro model to be implemented. In the next two chapters of this Part I, the control unit was tested in combination with two different fluid flow bioreactors, in order to mimic two tissues: liver and intestine.
Chapter
3
In-vitro model of
portal hypertension
Abstract
In this chapter, the SUITE system is used in combination with single flow modular bioreactors in order to simulate a liver disease known as portal hypertension. This disease affects both the endothelial cells of the portal vein and hepatocytes, and it is mainly due to the increase in vascular resis-tance to portal vein flow. Very few in-vitro models of this pathology have been developed at the present, because of the complexity of this syndrome, which affects several different organs and tissues. Here, a preliminary study was performed, to evaluate the effect of increasing hydrostatic pres-sures on two different cells involved in portal hypertension: endothelial cells and hepatocytes. These data represent a baseline for future studies, taking advantage of the modularity of the bioreactors for creating a more complex model able to mimic the cross-talk of this pathology.
3.1
Introduction
Portal hypertension is hemodynamic abnormality defined as elevation of hepatic venous pressure up to 20 mmHg [76], frequently associated with the most severe complications of chronic liver disease like cirrhosis. In hu-man anatomy, a portal venous system refers to a capillary bed which pools into another capillary bed through veins, without first going through the heart. Thus, both capillary beds and blood vessels that connect them are considered part of the portal venous system. The main cause of por-tal hypertension can be identified in the increased resistance to porpor-tal blood flow, which compromises liver architecture and causes increasing hydrostatic pressure in intra-hepatic vessels. This increased resistance can be pre-hepatic (portal vein thrombosis), intra-hepatic (liver cirrho-sis) or post-hepatic (Budd-Chiari syndrome) [77].
Although this pathology has been intensively studied since 1980s, there is still significant scientific interest because there is no simple way or test to identify if a person has portal hypertension. In order to give an idea of the importance in this pathology, we have to consider that the National Institute on Alcohol Abuse and Alcoholism (NIAAA) reported 30 000 deaths in United States only in 2007, whereas the European Association for the Study of the Liver (EASL) showed that hepatic cirrhosis cause more than 170 000 deaths per year, where 30-40% are due to portal hy-pertension.
However, despite the increasing interest in the reduction of animal mod-els, in-vivo tests on rats, mice and rabbits are used for studying this pathology [77, 78, 79]. This is due to the difficulty of traditional in-vitro models to predict and simulate different manifestations of the syndrome, which affects several tissues and organs like blood vessels (endothelial cells), liver cells (hepatocytes, stellate cells), as well as peripheral organs like the kidney and the gastro-intestinal tract [80].
Changes in portal vein resistance are due to several factors, like the de-crease contractility of the wall of vessels, inde-crease in vascular tone, colla-gen accumulation in the space of Disse and also changes in hepatocytes morphology [81]. On the other hand, as shown in Fig.3.1, alterations in portal vein pressure can directly affect several cells, in particular endothe-lial cells, hepatocytes and stellate cells. At present, it is well know that the main mechanism mediating abnormal vascular responses is endothe-lial dysfunction with insufficient nitric oxide (NO) production and the increased production of vasconstrictive factors [78]. For this reason, the few in-vitro models that mimic portal hypertension are mainly focused on understanding the correlations between dysfunctions in sinusoidal en-dothelial cells and liver diseases, evaluating both NO and endothelin-1 production [82]. However, previous studies also reported a relationship between hepatocyte enlargement and intra-hepatic and portal pressures in patients with severe liver disease, presenting liver biopsy abnormalities [76].
The general purpose of the thesis was to develop and investigate
vative tools for realizing advanced in-vitro models. The specific aim of this chapter is to evaluate the possibility of using the SUITE system for developing more relevant in-vitro models of portal hypertension. For this reason, two different proofs of concept cell experiments were performed, in order to investigate if the function of endothelial and liver cells can be af-fected by hydrostatic pressures generated in the SUITE system. HUVEC (Human Umbilical Vein Endothelial Cell) [84] and C3A [85], a hepatic cell line, were cultured in single-flow bioreactors (LiveBox1, LB1) connected to the control unit, and were subject to different levels of hydrostatic pressure in combination with fluid flow. Cell viability was evaluated with respect to the stimulus applied, and compared with both static condi-tions as well as with the bioreactor maintained in the incubator for cell cultures (37 , 5% CO2) at atmospheric pressures. These results were
also used to verify the SUITE system as a more flexible alternative to the traditional incubator.
3.2
Material and Methods
3.2.1 Engineering of in-vitro environment
In order to provide the optimal dynamic environment for the cells, the SUITE system was connected to the LiveBox1 (LB1), a transparent single-flow bioreactor able to apply high single-flow rates with low wall shear stress. This bioreactor is an upgrade of the previous MCmB (Multi-Compartmental modular Bioreactor), which has been successfully used with hepatocytes [56, 58]. The LB1 maintains the same inner size of the fluidic chamber of the MCmB, thus all the previous fluid-dynamics characterizations are still acceptable, but they enable optical imaging thanks to the glass bot-tom. In these experiments, the SUITE platform is composed of two LB1 chambers, the mixing chamber for pH and oxygen regulation, the heating
