Identification of Activated Valvular Interstitial Cells Via Harmony Software Analysis

Transcription

1 University of Colorado, Boulder CU Scholar Chemical & Biological Engineering Graduate Theses & Dissertations Chemical & Biological Engineering Spring Identification of Activated Valvular Interstitial Cells Via Harmony Software Analysis Eduard Jimenez Castano University of Colorado at Boulder, Follow this and additional works at: Part of the Chemical Engineering Commons, and the Medicine and Health Sciences Commons Recommended Citation Jimenez Castano, Eduard, "Identification of Activated Valvular Interstitial Cells Via Harmony Software Analysis" (2017). Chemical & Biological Engineering Graduate Theses & Dissertations This Thesis is brought to you for free and open access by Chemical & Biological Engineering at CU Scholar. It has been accepted for inclusion in Chemical & Biological Engineering Graduate Theses & Dissertations by an authorized administrator of CU Scholar. For more information, please contact

2 IDENTIFICATION OF ACTIVATED VALVULAR INTERSTITIAL CELLS VIA HARMONY SOFTWARE ANALYSIS by EDUARD JIMENEZ CASTAÑO B.A., Autonomous University of Barcelona, 2015 A master s thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment Of the requirement for the degree of Master of Science Department of Chemical and Biological Engineering 2016 i

3 This thesis entitled: Identification of activated Valvular Interstitial Cells via Harmony software analysis written by Eduard Jimenez Castaño has been approved for the Department of Chemical and Biological Engineering Kristi S. Anseth Stephanie J. Bryant Leslie Leinwand Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. ii

4 Jimenez Castaño, Eduard (M.S., Department of Chemical and Biological Engineering) Identification of Activated Valvular Interstitial Cells via Harmony Software Analysis Thesis directed by Distinguished Professor Kristi S. Anseth An automated image analysis method was developed to characterize and quantify the fibroblast and myofibroblast phenotypes of Valvular Interstitial Cells (VICs) when embedded in hydrogel scaffolds. A spinning disk confocal microscope was used for imaging the 3D cell-laden gels, and the imaging analysis used the instrument s Harmony Software. VICs cells were encapsulated in poly(ethylene glycol) (PEG) hydrogel matrices with varying properties, specifically varying; degradability and stiffness. 10 μl/gel were spotted with the liquid handling system (EpMotion M5073, Eppendorf) to achieve a thickness of 0.9mM. For the 2D experiments different hydrogel compositions were used to analyze the cell in different hydrogel formations. 8-arm PEG-nb hydrogels with different linkers, ester and amide, were used as the culture platforms for the 2D study. 2D study of VICs with different concentrations of Endothelin 1 (ET-1) was also made. 3D culture platforms were designed with different stiffness by varying the PEG molecular weight (20-40 kda) and varying the presence or not of TGFβ. At various time points (3 and 5 days for the 2D and the 3D respectively) images were collected, and a protocol was developed to differentiate the activated myofibroblast VIC phenotype from the quiescent fibroblast phenotype. First the analytical method, identifies the nuclei and cytoplasm regions of the VICs and calculates the fluorescence intensity of F-actin, as a measurement of stress-fiber formation, and alpha-smooth muscle actin (αsma), an activated myofibroblast marker. Next by calculating the ratio of αsma expression relative to F-actin expression, the method is able to identify the coexpression of the αsma myofibroblast marker with actin, thereby providing a measure of ; αsma organization into stress-fibers. Based upon αsma to F-actin, a threshold can be set, here a values between have been used, that provides a quantitative, objective, and automated method to determine whether or not the VICs are activated myofibroblasts. To validate this image analysis method, results were compared to a series of well-established data sets that used the same hydrogel formation, but manually quantified VIC myofibroblast formation by counting αsma stress fiber positive cells. The method here developed show a high accuracy in characterizing activation of VICs. The analytical method validation present the same results as the published data within a 5±5% of difference when quantifying myofibroblast activation for the 3D culture platforms and a 9±5% of difference when quantifying myofibroblast activation in the 2D culture platform studies. The software and protocols that were developed as part of this thesis should provide a general methods that will be useful for analyzing intracellular protein expression co-localized with a cells actin cytoskeleton. iii

5 Acknowledgement Many people have contributed significantly to the work contained within this thesis. First, I have to thank my research advisor, Kristi Anseth. Throughout my short time here and research under her advice, guidance, and extreme dedication, she displays are truly admirable. I am extremely grateful to her understanding and support, when some personal circumstances required me to work harder and faster, she always has been putting that extra effort and making me feel confident and appreciated. In addition, I would love to thank my committee members: Leslie Leinwand and Stephanie Bryant for their help and understanding in a very extraordinary situation, and for allow me to achieve my personal goals. I really appreciate their time, guidance and feedback. To my research group members Andrea González and Megan Schroeder, thank you for of the help that you have given me with the Thesis. Your guidance and advice while helping me with the written part and for helping me choosing my Thesis focus. I would specially thank Andrea again for introduce me to this wonderful research group and for all of her assistance and support during my experience here. I am especially grateful to Elena Fernández Rubio, who makes me feel at home wherever I am. I would also like to thank my family for all of their support and love. Pepitilla: because who I am is just your work, sweat, blood and tears. Albert: because I can only feel proud of my brother, for being my friend, support, confident and teacher. Finally, Rosita: because your smile and way of understand this world are eternal, and will always live in me. Thanks. iv

6 Table of Contents List of Tables.vi List of Figures...vii Chapter 1. Introduction Anatomy of Heart Valves Cardiac Valves cell types Tunable scaffolds to study VICs matrix signalings Thesis approach...12 Chapter 2. Objectives...13 Chapter 3. Materials and Methods Hydrogel formation and characterization Harmony Software Analysis Protocol Development...18 Chapter 4. Results VIC myofibroblast activation on 2D hydrogel matrices of varying stiffness VIC myofibroblast activation on 2D hydrogel matrices of varying ET-1 concentration VIC myofibroblast activation on 3D hydrogel matrices of varying matrix degradability VIC myofibroblast activation on 3D hydrogel matrices of varying TGF-β Bland-Altman method...26 Chapter 5. Conclusions and Future Recommendations...31 Chapter 6. Bibliography...33 v

7 List of Tables Table 4.1. Hydrogel composition and concentrations of reactants for the 2D cell culture platforms 21 Table 4.2. Hydrogel composition and concentration of reactants for the 2D cell culture platform with ET-1 22 Table 4.3. PEG-N molecular weights and Monomer concentrations for 3D platforms used as a cell culture..24 Table 4.4. PEG-N molecular weights and Monomer concentrations for 3D platforms with TGF-β vi

8 List of Figures Figure 1.1 Cross-sectional view of the human heart...3 Figure 1.2 Three layers of a normal aortic valve leaflet..5 Figure 1.3 Cross-sectional view of the aortic valve presenting severe stenosis..5 Figure1.4 Cellular functions of VICs. Differentiation steps of fibroblast towards myofibroblast subtypes...7 Figure 1.5 VICs function in Healthy and Disease valves 8 Figure 1.6 Alpha-smooth muscle actin activation in porcine VICs 9 Figure 1.7 Effects of substrate stiffness in VICs focal adhesions..11 Figure 1.8 Activation and morphology of VICs; 2D TCPS plates and 3D Hydrogel 11 Figure 3.1 Example screenshot of the Harmony software showing a training assay.16 Figure 3.2 Flowchart of an image analysis sequence performed with Harmony software 18 Figure 3.3 Screenshot of the Harmony; interface appearance in the different building blocks (1)...19 Figure 3.4 Screenshot of the Harmony; the interface appearance in the different building blocks (2) 20 Figure 4.1 Percentage of VICs activated to the myofibroblast phenotype for the 2D culture platform.22 Figure 4.2 Percentage of VICs activated to the myofibroblast phenotype for the 2D culture platform with ET-1.23 Figure 4.3 Percentage of VICs activated to the myofibroblast phenotype for 3D culture platform.24 Figure 4.4 Percentage of VICs activated to the myofibroblast phenotype for 3D culture platform with TGFβ1..25 Figure 4.5 Screenshot of Harmony;the specific FOV that is being analyzed 27 Figure 4.6 Bland-Altman plot for the 2D soft with 2.5μm experiment...28 Figure 4.7 Bland-Altman plot for the 2D culture platform Blank experiment 28 Figure 4.8 Bland-Altman plot for the 2D culture platform 1nM and 100nM ET-1 conditions experiment 29 vii

9 CHAPTER I Introduction Heart valves must withstand large stresses and mechanical forces during blood flow regulation. Proper movement and function of valves are critical for heart health. Under a homeostatic state, heart valves open and close accurately regulating blood flow within the heart, as well as enabling a proper supply of blood throughout the body. While the human heart has four valves, the aortic valve is often implicated in heart disease. As an example, if a narrowing of the aortic valve opening occurs, blood flow across the heart can become restricted, affecting the pressure in some of the heart chambers. This disease is called aortic stenosis and can cause symptoms such as breathlessness, chest pain, palpitations and a reduction in the ability to do normal activities. Disease of cardiac leaflets is a significant cause of mortality and morbidity in the United States 1. For example, Calcific Aortic Valve Disease (CAVD) affects a large portion of population in America, with a range of 2-4% 2 of adults over 65 years old being diagnosed with CAVD and around 70,000 people undergo aortic valve replacement each year in the United States due to valvular disease complications 2,3. In the past, it was thought that heart valve malformations and disease were primarily a consequence of aging or congenital defects that occurred during development 1. Now it is appreciated that the cells that reside in the valve: VICs and VECs are known to play an active role in suppressing or promoting valve disease progression 4. During late stages of valve fibrosis, the issue can calcify, resulting in severe stenosis and disrupted heart function. Currently, the only treatment for late stage valve disease is valvular replacement surgery. With mechanical or tissue based valve 5. Despite being the preferred treatment, surgery can lead to post-operative complications, such as the necessity for ongoing medication and, fatigue even after mild exercise 6. The main cellular components of heart valves are, valvular interstitial cells (VICs) and valvular endothelial cells (VECs). Their role in aortic valve stenosis, as well as the complex signaling between VICs and VECs, are still not fully understood, but are thought to play a key role in maintaining the valve homeostasis and their misregulation is often implicated in valve disease. Aortic VICs are studied in this thesis. The different VIC phenotypes include fibroblasts, activated myofibroblast, and osteoblastic cells 7. When activated, VIC myofibroblasts have a tissue repairing and wound healing function that contributes to overall valve health. Among other functions, myofibroblastic VICs contribute to contraction and matrix secretion (e.g., collagen deposition) If the activated phenotype persists, such as under prolonged disease or repeated injury, VICs are known to express genes associated with osteogenesis( e.g., related to calcification), leading to increased valve stiffness and eventual valvular fibrosis and stenosis

10 Currently, there are no drug therapies or treatments available to reverse VIC activation once it has occurred. This fact motivates a better understanding of VIC myofibroblast regulation to identify a functional aortic valve treatment and, in some cases, avoid valve replacement. Despite the fact that, several approaches have been made to a better understanding of VIC myofibroblast activation, current methods for quantifying and characterizing these phenotypes are still potentially improvable. Common methods rely on multiple methods and techniques, often integrating gene and protein expression with functional assays. However, for high throughput screening methods, it would be desirable to have a reliable marker of activated myofibroblast phenotype, and the Anseth and Leinwand groups routinely rely on αsma stress fiber organization, as a hallmark of the VIC myofibroblast. While numerous publications 16,17 use this as a reliable marker, the image analysis typically relies on individual assessment and counting of numerous fields of view, a time consuming and somewhat subjective process. This motivates the focus of this thesis, which is to develop an automated image analysis protocol to characterize the VIC phenotype. The Anseth laboratory uses a Perkin Elmer Operetta high-content confocal microscope system as a high throughput, real time imaging system for VICs. For this reason, Harmony, an image analysis software developed by Perkin Elmer was selected to establish and validate a protocol for experiments to automatically discern, quantify and characterize VIC myofibroblast by quantifying αsma stress fiber positive cells. Successful establishment of the protocol will allow users to screen through a plethora of microenvironmental conditions that regulate dynamic VIC differentiation between the myofibroblast and quiescent fibroblast phenotypes in a precise and objective manner. While the protocol sequence is focused on VICs, the ability to measure multiple parameters together with the open-organization of the analytical method makes it readily translatable to other cell types and applications. The following sections of this introductory chapter first review the anatomy, structure, and biology of the aortic heart valve, focusing on the differences between healthy and diseased valves. The final sections reviews the state of art in VIC myofibroblast identification, as well as the possible future directions for the application Anatomy of Heart Valves Heart Valve Anatomy The heart is composed of four chambers, two upper chambers, called atria, and two lower chambers, called ventricles (Figure 1.1, Panel A). The chambers are separated by four valves that maintain unobstructed and unidirectional flow of blood throughout the body. Blood passes through a valve before leaving each one of the heart chambers. The valves function is to prevent regurgitation of blood flow. The valves themselves are composed of leaflets that act as unidirectional blood inlets for blood that comes into a ventricle and unidirectional outlets for blood leaving a ventricle (Figure 1.1, Panel B) 18,19. When the heart muscle contracts and relaxes, the valves open and close, allowing blood flow into the ventricles and atria at different times. The phase of the heartbeat when the heart muscle contracts and pumps blood from the chambers into the arteries is known as systole, while the relaxation of the muscle to fill the heart again with blood is known as diastole. 2

11 Deoxygenated blood flows from the right side of the heart into the lungs in order to receive oxygen. On the left side, oxygenated blood come back from the lungs and is delivered into the body through the aorta. When the left ventricle relaxes, the aortic valve shuts and the mitral opens, allowing blood to go from the left atrium into the left ventricle. Once the left atrium contracts, more blood flows into the left ventricle. Then the left ventricle contacts again making the mitral valve close and the aortic valve open, and blood flows into the aorta to deliver freshly oxygenated blood into the body. The ventricles responsible for most of the pumping action, while the atria are receiving areas. Blood flows between the ventricle and atria through two atrioventricular valves. The one on the left side is called the mitral valve and its right side analogue is the tricuspid valve 20. Valves of the heart (Heart shown as a cross-section) Aorta Aortic valve Right atrium Tricuspid Mitral valve Pulmonary tank Pulmonary valve Left side of heart Right side of heart Left atrium Mitral valve Pulmonar Aortic Tricuspid valve Right ventricle Posterior Anterior Left ventricle Figure 1.1 (A) Cross-sectional view of the human heart (adapted from: a/cardiovascular_diseases) When the left ventricle relaxes, the aortic valve shuts and the mitral valve opens, allowing blood to flow from the left atrium into the left ventricle. Afterwards, the left atrium contracts, allowing more blood to fill up the left ventricle. Subsequently the left ventricle contracts, the mitral valve closes and the aortic valve opens, so blood flows into the aorta. Finally, when ventricle pressure exceeds the aortic pressure the pulmonary and aortic valves open ejecting blood, followed by decreased pressure in the ventricles and consecutive closing of the pulmonary and aortic valves. (B) Top view of the heart showing a cross-section of the four heart valves. Adapted from Anatomy & Physiology 20 As mentioned before, the left side of the heart receives blood returning from the lungs. Therefore, it has to endure higher strains and pressures as compared to the right side. As a consequence, left-side valves, the mitral and aortic, experience the most deterioration 21. Note that aortic valve resides between the main pumping chamber of the heart (left ventricle) and the main artery to the body (aorta). Due to this location and the high stresses and strains that occur in this region, a greater number of dysfunctions occur at the aortic valve. Because of this fact, this thesis focuses on characterizing VICs isolated from the aortic valve. 3

12 1.1.2 Structure of the aortic valve The aortic valve controls the flow of blood from the left ventricle to the aorta, and its structure is comprised of three separate leaflets attached to the base of the aortic root at a dense band of fibrotic tissue called annulus. It is essential for proper function that the valve is suitably formed and flexible. In addition, the valve should open all the way in order to allow blood flow through. Consistently, the aortic valve must close tightly so that no regurgitation occurs. During systole, the flow direction allows the valve cusps to open as the blood flows across the open aortic valve leaflets. Conversely, in diastole, the valve shuts completely during atrial filling. When closed, the valve resists a pressure up to 20 kpa just before it opens to allow flow to the aorta 21. In order to withstand such a severe stressful environment, the aortic valve tissue is arranged into three different layers of tissue, called the fibrosa, spongiosa and ventricularis (Figure 1.2.). The human aortic heart valve is mostly composed of glycosaminoglycan, collagen and elastin 21. All three layers are enriched in a certain set of extracellular matrix (ECM) components. The fibrosa is the layer closest to the outflow surface, is densely rich in collagenous fibers and it is in charge of valvular supporting. This fact contributes to the strength of the valve. The way that the fibers are arranged allows pressure to be transferred to the base of the valve during contraction 22,23. The middle layer is a central core of loose connective tissue, composed primarily of glycosaminoglycans (GAGs) with randomly aligned collagen fibers. This layer, named the spongiosa imparts cushioning between the layers during the valve opening and closing. Hyaluronic acid is the major component of the GAGs in the spongiosa, where it helps a high water content and sustains flexibility against the constant cardiac cycle 21,23,24. The ventricularis is the outermost layer closest to the ventricular side of the valve. This layer is rich in elastin and collagen fibers that are radially aligned, and allows for expansion of the valve in response to the pressure rise in the heart after blood filling. 4

13 Figure 1.2 (A) Three layers of a normal aortic valve leaflet. Fibrosa is a dense collagenous layer that extends toward the aortic surface. Spongiosa is a layer rich in proteoglycan, confers most of the cushioning for the valve. Ventricularis has a black-staining elastic layer and is rich in elastin and collagen, which are radially aligned. Adapted from Hara et. al. 23. (B) Trilayered Aortic Valve Leaflet diagram showing a cusp free edge, fibrosa, spongiosa, ventricularis. Adapted from Schoen et. al. 22 The ECM, together with the microstructure of the valve plays a critical role in VIC proliferation, differentiation, migration and apoptosis through direct cellular binding, as well as being a reservoir for some signaling molecules. Valvular disease and mechanical deterioration can occur if the structure of any of the three layers is disrupted. Calcific valve disease is closely related with tissue calcification and angiogenesis of the different valvular cell types, such as VICs 18. Under external stimuli, such as scare damage, VICs, differentiate into an activated phenotype that lead to regeneration and remodeling of the valve tissue, however if the activated phenotype persists could undergo to calcification of the valve leaflet (Fig. 1.3) 25. Figure 1.3 (A) Cross-sectional view of a bicuspid aortic valve with a short segment of the aorta around it. (Adapted from: (B) Cross-sectional view of the aortic valve presenting severe stenosis. (Adapted from National Institutes of Health) 5

14 1.2 Cardiac valve cell types Valvular Endothelial Cells Valvular endothelial cells (VECs) are present on the entire cardiovascular surface and on the surface of the valve, at the blood-contacting surfaces. VECs separate the valve interior matrix from the blood flow and, show a response to shear stress 21,26. Due to their proximity, VECs interact with VICs in order to, maintain the integrity of valve tissues, and mitigate disease 27. VECs are important for hemo-homeostasis, by preventing coagulation of the blood, alleviating inflammatory responses, and producing hormones that signal interior cells 27,28. Signaling from VECs to interstitial cells showed a direct correlation with VICs response to changes in microenvironmental stiffness and activation to a myofibroblast nature. When cocultured together, the presence of VECs help in suppressing VICs activation due to stiffness-induced matrix Valvular Interstitial Cells VICs are present in all three layers of the aortic heart valve and play a critical role in maintaining their valvular function 8. They are responsible for controlling the extracellular matrix that provides the mechanical characteristics required for the dynamic behavior of the valve. Among other cellular functions, VICs are capable of synthesizing ECM and secreting matrix degrading enzymes (such as matrix metalloproteinases [MMP]), that control and regulate the remodeling of collagen, elastin and other ECM proteins found within valve tissue (Fig. 1.4) 10,29,30. VICs comprise a diversified and dynamic population of cells, with some fibroblast-like cells and others that appear more myofibroblast-like 10,31. Each phenotype plays a specific role for a proper valve function. A characterization of the cellular composition of valve leaflets from porcine and human valves has demonstrated that different phenotypes, with different characteristics, of VICs have been found in all 4 different valves of the heart 30. Each of these phenotypes help modulate function of the valve as a response to external environment signals. Two different cell morphologies have been observed when culturing VICs, small islands of cuboidal cells and spindle shaped elongated cells 32. The latter shows a swirling pattern, characteristic of fibroblast cells and start to accumulate on each other in layers. The fibroblast are quiescent and do not have the ability to synthesize or degrade ECM 18. Despite the fact that fibroblastic cells are the most common cell type in the heterogeneous VIC population, they have phenotype plasticity, conferring VICs the capacity to change or modify under certain valvular conditions. Upon sudden changes in the mechanical stress state or disease states, such as injury, VICs can shift to an activated phenotype with myofibroblastic characteristics 9,14,33. While VICs are activated, they continuously repair injuries to the ECM and help in tissue remodeling. When healing is complete, VICs in a healthy valve are supposed to undergo apoptosis or return to the inactivated phenotype 34. This ability of fibroblasts to adapt and change to their environment makes them valuable in the design of tissue engineered cardiac valves 32,33,35. 6

15 Furthermore, (mis)regulation of the fibroblast to myofibroblast transition has important implication in valve repair versus disease progression. These are among the reasons that characterization and quantification of the VIC phenotype is a focus of this thesis. Figure 1.4 (A) Cellular functions of VICs. Adapted from Taylor et. al.32. (B) Differentiation steps of fibroblast towards myofibroblasts subtypes. The protomyofibroblast does not yet contain the contractile αsma thus representing an immature myofibroblast. Mature myofibroblasts are characterized by the presence of αsma. The transition from fibroblasts to protomyofibroblasts is reversible (solid line), but it is not known whether myofibroblasts can also reverse back into fibroblasts (dashed line). Adapted from Falke et. al. 35. However, under certain circumstances, activated myofibroblast do not undergo to apoptosis or reverse back to a fibroblastic phenotype. This irreversible phenotype can lead to severely impaired valve function due to excessive collagen deposition, ECM protein secretion or expression of osteoblastic cells (Fig. 1.5) Nevertheless, the complete factor and processes that cause aortic valve stenosis are still unknown. 7

16 Figure 1.5 (A) In healthy valves (left), most of VICs and VECs are in a nonactivated phenotype, there is no need to repair local tissue damage. Collagen fibers are circumferentially aligned and activated VICs undergo continual turn over into deactivated fibroblasts and other cell fates (such as apoptosis). In diseased valves (right), VICs and VECs are recruited to remodel the tissue, contributing to excessive collagen accumulation, a degraded and disarrayed matrix, which in some cases could undergo to calcification. Valve cells lose the regulation of VIC phenotype, the homeostatic equilibrium and remain in an activated state. Adapted from Anseth et. al Myofibroblast phenotype This thesis defines a VIC that has a myofibroblast phenotype as activated. Myofibroblast are unique mesenchymal cells present in almost all tissues and have many similarities with both fibroblasts and smooth muscle cells (SMCs) 39,40.. Among many others they are present in lung 41, skin 42, liver 38, and even though their origins can be different, myofibroblasts have a similar set of properties. It is widely known that myofibroblast play a key role in wound healing and tissue regeneration Some of the most remarkable characteristics include expression of muscle and non-muscle proteins, secretion of extracellular matrix and contractile properties. Myofibroblast are readily differentiated from SMC, as they do not express many SMC markers, such as smooth muscle myosin heavy chain and do not express smoothelin, a late marker of SMC differentiation 46. However, several studies showed evidence that many of the myofibroblast cells express alpha smooth muscle actin (αsma), as do SMCs 4,31,47,48, and αsma is one of the classic markers used to define the myofibroblast phenotype 49. Myofibroblast cells synthesize collagen, some other matrix proteins, and possess a contractile cytoskeletal apparatus 29,50. They show an αsma organization into stress-fibers (Fig. 1.6). The activation of VICs to myofibroblast phenotype is essential for proper valve repair, such as tissue remodeling in response to injury. However, the fibroblast to myofibroblast transition must be properly regulated in order to maintain valve homeostasis. Regulation is executed by cytokines, mechanical cues and ECM structure. 8

17 Figure 1.6 Alpha-smooth muscle actin activation in porcine valvular interstitial cells. Immunostaining of VICs for αsma (green) and F-actin (red) with blue counter-stained nuclei (DAPI). Adapted from Anseth Research Group Theoretically, during wound healing, fibroblasts differentiate in to activated myofibroblasts as a response to injury and inflammatory factors at the site of the damage, and afterwards, VICs myofibroblast undergo apoptosis or revert back to the quiescent inactivated fibroblast state 8,13. If this transition is misregulated and the myofibroblast phenotype persists,, VICs can increase the global valve mechanical properties and stiffness through excess remodeling of the valve matrix which results in tissue scarring and loss of functionality. This malfunction can lead to disease like valvular sclerosis and stenosis 4,18,51,52. The VIC myofibroblasts have many similarities and markers with both fibroblasts and SMCs, which can make them difficult to identify. Due to their similarities with fibroblasts and SMCs, there are no surface or membrane markers specific for myofibroblast to distinguish from fibroblast or SMCs. Another peculiarity from myofibroblast is their matrix secretory and degrading functions. However, other cell types can also produce large amounts of ECM, such as collagen, limiting the ability to characterize myofibroblast exclusively by this manner 53. Myofibroblasts also express MMPs, whose expression changes with wound healing 54. In order to differentiate myofibroblasts from SMCs, it has been observed that even though both of them express αsma, SMCs also express additional markers that are not present in VIC cultures, such as smoothelin and smooth muscle myosin 55. On the other hand, when making a distinction between myofibroblasts and fibroblast, myofibroblasts possess several different characteristics to discriminate from fibroblasts: intracellular junctions, bundles of contractile microfilaments and an extensive cell-matrix attachment 43,56. One of the main characteristic used to identify myofibroblast from fibroblasts are their stress fibers 57. Using just light microscopy, these stress fibers can be discerned by immunostaining for the presence of αsma. It is important to emphasize that to properly identify myofibroblasts from diverse populations one should examine numerous protein markers and functional properties. However, one of the purposes of this thesis was to identify the emergence of the myofibroblasts phenotype in VICs as a function of the numerous extracellular signals that could be introduced in a high throughput screen. Thus, expression of αsma organized into stress fibers, was used as a complementary high throughput readout in order to distinguish the myofibroblasts phenotype. 9

18 VIC Myofibroblast activation Regulation of VIC myofibroblast phenotype activation is carried out by both biochemical and mechanical signals. Persistent of the myofibroblast phenotype after wound healing, is highly correlated to every fibrotic pathology, including valvular sclerosis 58. Due to of this fact, a better understanding of VIC myofibroblast activation and deactivation in response to matricellular cues will contribute to strategies and methods to treat fibrotic valve disease. One of the biochemical cues that has been widely studied and implicated in myofibroblast activation is TGF-β1, an inflammatory cytokine, that promotes VIC myofibroblast differentiation and αsma expression 59,60. When TGF-β1 is synthesized by the cell, it is encapsulated in a large active complex called the TGF-β1-binding protein (LTBP) complex. This inactive complex remains stored in ECM tissues until activated. There are several circumstances that trigger TGF-β1 activation, such as acidic conditions 61, interactions with MMPs 62 64, and generation of mechanical stress to the inactivated complex 65. Beyond soluble cytokines, the matrix environment is also known to influence VIC activation, and mechanical signaling, has a direct impact on myofibroblast differentiation 66,67. VICs cells have shown phenotypic response to mechanical and chemical properties of the culture microenvironment. VICs cultured on culture platforms of increasing stiffness, Young s modulus (3-27kPa), show an increase in the percentage of myofibroblasts identified by the expression of αsma organized in stress-fibers(from 36 to 72%) 4. Current research is beginning to explore the effects of the synergy between biochemical and biomechanical cues, as some cytokine receptors can be mechanically activated and receptor dynamics and cytokine concentration can be quite different in two versus three dimensions Tunable scaffolds to study VICs matrix signaling Fibroblasts use integrin-matrix attachments sites, called focal adhesions, which anchor the cell to the substrate. Through these attachments, fibroblast sense mechanical signals from the surroundings. Focal adhesions anchor actin and stress fibers to ECM proteins. (Fig. 1.7). Stiffer substrates lead to a higher number of focal adhesions which consequently increases myofibrotic pathways 4. Inhibition of αsma function, therefore blocking myofibroblast differentiation, results in a lower adhesion strength of established myofibroblasts. VICs cells treated with an αsma inhibitor showed a 70% decrease of number of focal adhesions after just 30 min 70. On stiffer substrates, myofibroblast likely form supermature focal adhesions that are longer (8-30 μm) than normal focal adhesions (2-6 μm) 71. This supermature and large number of focal adhesions is thought to be a critical point in myofibroblast ability to sense stress

19 Figure 1.7 Effect of substrate stiffness in VICs focal adhesions. A higher number of stable focal adhesions contribute to an increase in the VIC myofibrotic phenotype. Images adapted from Gould et. al.4 Traditional culture methods used by most scientists employ polystyrene surfaces. Tissue culture polystyrene (TCPS) is preferred for many experiments due to ability to culture numerous adherent cell types, the ease of passaging these cells, and the ability to image and introduce numerous biological signals into the culture media. Despite these advantages, TCPS is a 2D surface with a physiologically high stiffness, yet many cells reside in a soft 3D tissue matrix. 2D environments, can polarize the cells to one surface, which constrains cell-cell contact to a plane and can alter the organization of membrane surface receptors. Furthermore, cell secreted molecules are rapidly diluted in the sink of the serum media,. As already mentioned, TCPS culture platforms are supra physiologically stiff for VICs (i.e. >5 orders of magnitude stiffer than compliant valves 73) which alters many of the signals that the cells receive 74. VICs cultured on TCPS show approximately 75% activated myofibroblast population after just 48 hours of culture, making it nearly impossible to study the quiescent fibroblast phenotype (Fig. 1.8) 74,75. Figure 1.8 Activation and morphology of VICs on different substrates; twodimensional tissue culture (TCPS) plates and 3D Hydrogel. (A) VICs show elongated and sharp morphology due to TCPS stiffness. Stress fibers formation for αsma. (B) VICs cultured on 3D Hydrogels do not show an activated myofibroblast phenotype. Immunocytochemical staining of VICs for αsma (green) and F-actin (red) with blue counter-stained nuclei (DAPI). Images adapted from Mabry et. al. 75 These studies, suggest that dimensionality and stiffness play a key role in dictating VIC phenotype. 11

20 Mabry et al 75 have compared expression profiles of VICs cultured on different microenvironment platforms, PEG gels and TCPS, to better understand and quantify the microenvironmental influence on VIC phenotype and myofibroblast activation. Mabry et al showed how VICs cultured in hydrogel platforms had lower levels of activation (<10%), similar to levels seen in healthy valve tissue, while VICs cultured on TCPS were 75% activated myofibroblasts 75. Thus illustrating that stiffness alone can induce VICs myofibroblast activation.. By cultivating VICs cells in 3D hydrogels, it is possible to create a more physiologically similar microenvironment for VICs cells. For these reason, a main focus of the Anseth and Leinwand efforts has been the development of biocompatible materials that allow the encapsulation and cultivation of VIC cells in more mimetic 3D environment. These engineered 3D materials provide a valuable platform essential for the study of therapeutic strategies for heart valve disease, as well as for those interested in regenerative valve tissue for the purposes valve replacement Thesis Approach The main objective of this thesis is to develop a robust high throughput, and quantitative image analysis method to characterize the VIC myofibroblast phenotype, as defined by the presence of αsma stress fibers. Images of cell-laden hydrogels are collected from Perkin Elmer spinning disk confocal microscope (Operetta), which allows tracking of VIC-matrix interactions in real time. By using Harmony Software and an extension of building blocks, the goal is to develop and validate a protocol that will lead to an objective, automated and quantitative characterization of the different VICs phenotypes and, therefore improve the current state of characterization. Some of the principal features used to identify the different VICs phenotypes, are outlined in chapter Myofibroblast phenotype. Chapter 2 describes the objectives and main goals of the present thesis, as well as the principal aims expected to be validated in this work. Chapter 3 describes the culture platform compositions and synthesizes processes for the different hydrogels used to cultivate VICs. A detailed sequence of steps is explained for the hydrogel formation. Chapter 3 also specifies the development of an analytical method used to automatically characterize and identify the VIC myofibroblast phenotype. A precise explanation of the analytical method is meticulously illustrated by images extracted from Harmony, the image analysis software from Perkin Elmer s spinning disk confocal microscope (Operetta). Chapter 4 comprises the validation of the analytical method. Published data sets characterizations of VICs myofibroblast images are compared with the results obtained with the analytical method here developed. 2 different experiments are tested; VICs cultured in 2D and 3D hydrogels respectively. Chapter 5 concludes with a summary of the main conclusions and recommendation for future work. 12

21 CHAPTER 2 Objectives Valvular Interstitial Cells (VICs) are known to play a key role in maintaining aortic heart valve homeostasis. The study of VICs is crucial for a better understanding of aortic heart valve diseases, such as valvular stenosis. While the main characteristics of VICs different phenotypes are well understood, the influence of the external microenvironment and the facts that regulate phenotype differentiation remain somehow understudied. Current methods for characterization of VICs myofibroblast are still based on hand-counting and individual identification, requiring a laborious and extensive work. The overall goal of this thesis is to develop an analytical method that characterize and identifies quantitatively VICs myofibroblast in an automatic, rapid and objective manner. To test this hypothesis, the specific objectives of this thesis are to: Objective 1: Develop an analytical method that automatically characterizes and identifies VICs myofibroblast by measuring expression of αsma organized in stress-fibers. Objective 2: Test and validate the analytical method developed using well-established data sets of VICs myofibroblast images. This project pretends to develop a useful method to objectively characterize VICs myofibroblast activation basing its identification in αsma organization in stress-fibers. As a future objective, this thesis pretends to extend the applicability of the analytical method here develop to characterize and quantify other cell populations or phenotypes. 13

22 CHAPTER 3 Materials and Methods 3.1. Hydrogel formation and characterization For the significance of this thesis, the methods from Mabry et al 75 have been followed. For the 2D hydrogel synthesis, 8arm PEG-Norbornene with 20 and 40kDa molecular weight were used as the stiff and soft conditions respectively. For the ET-1 experiment, 8arm PEG-Norbornene with 40kDa molecular weight was used. These hydrogels possess tissue-like elasticities together with cytocompatible conditions that bring it as a suitable scaffold for the study of VICs. In a sterile cell culture hood 8-arm PEG-nb was cross-linked with a dithiol-containing, matrix metalloprotease (MMP)-degradable peptide KCGPQG IWGQCK (American Peptide Company, Inc.). To promote cell adhesion to the hydrogel, a peptide called CRGDS (Arg-Gly-Asp-Ser), (American Peptide Company, Inc.) was also added to the mixture. The photoinitiator lithium phenyl-2,4,6trimethylbenzoylphosphinate (LAP) was incorporated to the prepolymer mix solution. Three replicates of each condition were used for each of the two stiffness conditions. A 2.5μm and 40μm treatment of an EGFR-tyrosine kinase inhibitor (Santa Cruz Biotechnology, Inc.) as well as a blank were the conditions for this experiment. All components were dissolved in phosphate buffered saline (PBS, Life Technologies). Non-stoichiometric ratios of the thiol and ene functionalities were used to control the final crosslinking density, and ultimately, the gel connectivity and shear modulus to permit cell spreading in cell-laden hydrogels 75. The 2D hydrogels platforms were manufactured on glass coverslips that had been thiolated to facilitate covalent anchoring of the gels to the coverslips. First, drops of the monomer solution were pipetted onto a SigmaCote (Sigma Aldrich) hydrophobic treated glass, slide and covered with the thiolated coverslip. All hydrogels were exposed to UV light ( 2 mw/cm2 at 365 nm) for 3 min to allow polymerization, as determined by monitoring the evolution of the elastic modulus and its plateau. Gels were then located in wells containing PBS. 2D gels were allowed to swell overnight before seeding with cells. To characterize the materials properties of the hydrogels, the shear elastic moduli (G ) of the swollen hydrogels were measured using a DHR3 rheometer (TA Instruments) and a parallel plate geometry. For the Young s modulus (E) characterization, the shear modulus (G) of the hydrogels was measured by parallel plate rheometry and converted to Young s modulus via the rubber elasticity theory (E = 2 (1+v) G = 3 G), assuming Poisson s ratio (v) equal to 0.5 (incompressible material; water) 76. The second 2D hydrogel data sets, were fabricated following the same indications previously indicated, just varying the addition of the protein ET-1. After seeding 24 hours the hydrogels were treated with on ET-1 of 1nM 100nM respectively. 48 hours after the ET-1 treatment, the hydrogels were fixed with 4% paraformaldehyde (PFA). For the 3D hydrogels, 8-arm PEG-Norbornene was previously synthesized with two different ligands in order to test hydrogel degradability rates, a PEG functionalized with an ester link, and PEG functionalized with an amide link. The same preoplymer mixture conditions used for the 2D culture platform was also used for the 3D culture platform, but varying the kind of PEG. A solution composed of the PEG-Norbornene, the MMP degradable cross-linking peptide, and cells in phosphate14

23 buffered saline (PBS, Life Technologies) was robotically mixed. The final prepolymer solutions contained 10 million cell/ml and was pipetted in 10 μl droplets per gel. Encapsulations were performed on an automated liquid handler (EpMotion M5073, Eppendorf) to ease the high-throughput production of cell-laden hydrogels. In order to allow the covalent anchoring of the hydrogels to the bottom of a 96-well glass bottom plate (PerkinElmer), the plate was treated with 95% ethanol (ph 5.5) with 0.55 vol % (3- mercaptopropyl) trimethoxysilane for 5 min, and was then rinsed with 95% ethanol. The monomer solutions were exposed to 2.5 mw/cm2 of 365 nm light for 2 minutes to photoinitiate the polymerization. After polymerization, 75mL of 15% FBS media, 2mL of fungizone and 6mL of Pen/Strep were added to each well. Last, for the second 3D culture platform analysis, PEG hydrogels were fabricated using 8-arm, 40 kda norbornene functionalized PEG molecules, a 5 kda dithiol PEG cross linker, and 2 mm CRGDS peptide at a 1:1 thiol-ene crosslinking ratio as previously described. Gel precursor solution was layered between a silicon-coated glass slide and a thiolated 12 mm coverslip, and exposed to UV light at 10 mw/cm2 for 3 minutes to form gels. Gels were swollen overnight in VIC medium (Media 199, 1% FBS, 1% penicillin/streptomycin, 0.5 μg/ml fungizone) prior to cell seeding. Serum was collected from AVS patients. Serum from a healthy male patient without AVS served as a control, healthy case. Serum from healthy patients were diluted to 1% in VIC medium. Media supplemented with transforming growth factor beta (TGFβ1, 10 ng/ml) was used as positive controls. VICs isolated from porcine aortic valves were harvested and seeded on PEG hydrogels at a density of 20,000 cells/cm2. After one day of culture, media samples containing TGFβ1, or patient serum were used to treat VICs for 1 day. After treatment, cells were fixed and stained for αsma and cell nuclei using standard immunofluorescence techniques. Images were acquired using a high-throughput Operetta confocal microscope and αsma stress fiber formation was assessed as a percentage of total cells. This synthetic material platform offers a wide spectrum of advantages, besides being a reliable mimic microenvironment for VICs. 8arm PEG-N hydrogels have tunable characteristics that allow one to recreate the microenvironment of different aortic heart valve states, by modifying the stiffness of the gel. Due to their low protein adhesive properties, the hydrogels used provide an inert platform in which to encapsulate cells. The purpose of this study is to validate well-established data sets and, therefore the analysis of experiments performed with different kinds of hydrogels allows to test the analytical sequence efficiency under several conditions and environments. 15

24 3.2. Harmony Software The Harmony High-Content Imaging and Analysis Software was designed by Perkin Elmer to complement its Operetta High Content Imaging System. The Harmony Software allows the user to work with an image analysis sequence by identifying subpopulation characterization tool regions for the target or by optimizing different parameters. The most important benefit that this software provides is that it contains all of the general features required for complete image analysis, such as the capacity to set up assays, automate experiment analyses, acquire images and examine data. It also has a workflow-based interface that allows the user to work easily on the experiment event (Fig. 3.1). Figure 3.1 Example screenshot of the Harmony software showing a training assay. An image of a sample of VICs encapsulated in a hydrogel is shown, where different phenotypes are evident. 1 Analysis and Measurements selected. 2 Training control elements. 3 Channels activated for markers detection. 4 Different VICs phenotype PhenoLOGIC Machine Learning Technology The PhenoLOGIC algorithm is an image analysis method based on a machine learning process. First a training phase is carried out, where the user selects and defines the measurement of interest, such as clicking on live cells during a live/dead viability assay. After prior training, the software is able to identify similar objects and regions of interest and report quantifiable, valuable data about the image. The PhenoLOGIC tool was selected as a useful training tool when trying to characterize a population of VICs. It goes beyond the ability to identify similarities in cell shape or morphology, but also has the capacity to identify similar textures and variations in fluorescence intensity, such occurs during analysis of the αsma fluorescence marker, among other many features. In general the sequence of steps needed for a PhenoLOGIC image analysis are as follows: 1. Choose the experiment measurement desired 2. Decide how many classes you want to detect (e.g. fluorescence intensity, cell morphology, etc.) 3. Training phase: Select training points of regions of interest or objects with similar properties or features. 4. The software segments the rest of the image based upon the training samples previously selected. 16

25 One important advantage of using PhenoLOGIC is that the training can be adjusted and redefined until the results match the user s expectations. The adjustment can be made at any time by using more training points, more images or by tuning parameters of the algorithm, such as size of the region of interest. Successful application of the PhenoLOGIC technology relies on highly similar, reproducible, experimental results. This precision means that once the training phase is created, the software will be able to recognize, classify and characterize future experiments images. However, this analysis is always based on the previous training sequences. For the basic sets of images, such as live/dead cell counting, it is very useful quantification tool for quantifying experimental images.. As mentioned above, for a case when the experiment is easy to control and predict, and the results are less variable, PhenoLOGIC is an extremely valuable tool. However, for situations when parameters such as fluorescence intensity, texture, and cell morphology are less predictable and can be quite heterogeneous with a cell population, PhenoLOGIC alone is not robust enough. As an example, if the study s output relies on the activation of a certain population, and the activation is measured through the expression of a protein detectable by a fluorescence marker, once the training phase is completed, the software has been taught to call activated cells that show a fluorescence intensity higher than a background value. Nevertheless, this is not a categorical value, it is just the comparison of a punctual result of one experiment. Further experiments could show fluorescence activity, but if they fall below the training value, the software will not call the objects as activated, even though they were clearly activated. In that case, the analysis would be based upon a comparison between fluorescence activities of a certain population and, therefore would be relative to the single experiment in study, and would not provide an exact value to discern around activation. With all of that being said, it is not possible to set up a single training phase using PhenoLOGIC that would be able to automatically characterize all the VICs measurements. Therefore it still remains necessary to find an objective and feasible way to analyze the experiment images. An analysis method that takes into account the necessities and impediments of the image analysis has been developed in order to satisfy the need of a more objective and automatized characterization of VICs phenotypes. 17

26 3.3. Analysis Protocol Development Following the same directions as mentioned in Section PhenoLOGIC Machine Learning Technology, a sequence of analysis steps was established for the myofibroblast identification protocol. Because of the heterogeneous VIC population, the first step is to establish the training phase for each experimental plate and subsequent image analysis. A generic flowchart of the image analysis sequence is shown in Figure 3.2. Input Image Image Segmentation Define Regions of Interest A Quantify Properties in Regions Calculate Properties Identify Subpopulations Calculate readout values Readout values B Quantification of features of interests within the channels and regions of interests (here: intensity of a marker in two regions, A: cytoplasm and B: nuclei) Calculation of new properties (here the ratio of two measured intensities) Find subpopulation (here: with Ratio ) Combine individual cell results to obtain whole image readout values Value 1: total number of cells Value 2: fraction of cells with Ratio > 1.0 Figure 3.2 Flowchart of an image analysis sequence performed with Harmony Software (left column). Right column show a schematic example of the different operations that the software carry out on each cell during the analysis sequence. Another example related to VIC characterization is presented in Figure 3.3, in order to show the sequence of steps followed to set up the image analysis. For the experiments presented in Figure 3.3, the following immunostaining was performed: Nuclei-DAPI, F-actin-Alexa 546, αsma-alexa 488. Figure 3.3 show the schematic view of the steps process. 18

27 1. Find Nuclei To identify the nuclei, only the DAPI channel is turned on. The areas recognized by the software are selected and called nuclei. 2. Find Cytoplasm Similarly, for the cytoplasm identification, a phalloidin stain was used for F-actin, only the Alexa 546 channel is turned on. The areas identified by the software are selected and called cytoplasm. 3. Select Population After identifying the nuclei and cytoplasm, select and name the population of interest. One important point to consider is the border objects. The objects or cells that do not appear completely in the image must be removed. One cannot know or predict, the shape or properties of the cell beyond the limits of the image. Once the border objects are removed, the remaining objects are called VICs. 4. Calculate Intensity Properties (1) By switching on the Alexa 488 channel, selecting the population VICs and the cytoplasm region, αsma expression is quantified. The output name for that property is called αsma Intensity Cytoplasm. 5. Calculate Intensity Properties (2) Proceeding exactly as in Calculate Intensity Properties (1), but swapping the Alexa 488 channel for the Alexa 546, the F-actin expression is measured. The output name for the property is called F-actin Intensity Cytoplasm. 6. Calculate Properties Since we define, activation of VICs by the expression of the αsma protein and its localization into stress-fibers, measuring the expression of αsma in organized stress fibers is critical. Because of this, a building block that calculates the ratio of the αsma intensity over the F-actin intensity in the cytoplasm region was developed. A B C D Figure 3.3 Screenshot of the Harmony software presenting the interface appearance in the different building blocks. (A) The software identifies the cells nuclei by the fluorescence marker DAPI. (B) Similarly, cytoplasm is identified through measurement of F-actin. (C) The software quantifies the fluorescence intensity of αsma and F-actin in the cytoplasm region. (D) An operational building block calculate the ratio of αsma expression over F-actin expression. 19

28 7. Select Population By clicking on any cell, the software calculates the value of the αsma/f-actin ratio mentioned above. Based on previous experience and results from VICs activation, identify select a clearly activated cell, expressing αsma organized into stress fibers, and set up the value for the αsma/f-actin ratio from this cell as the threshold corresponding to activated VICs. A typical threshold value might range between 0.7 and 1.7 the software then filters and analyzes the VIC population by this ratio threshold. All the cells above the threshold ratio are binned as Activated VICs. 8. Define Results The software automatically provides a summary of the Property measurements and defines a formula through which the software has calculated the activation quantification. Figure 3.4 illustrates the process mentioned in the latest steps. A B C D Figure 3.4 Screenshot of the Harmony software presenting the interface appearance in the different building blocks. (A) The software reports positive and negative objects based upon the configuration previously set up. (B) By clicking on a cell the software reports the readout values for the selected object. (C) Decide a threshold that will make the distinction between the different phenotypes. (D) The software report the results, the per cent of activation for the population studied. 20

29 CHAPTER 4 Results To validate the analytical method, image analysis protocol to identify VIC myofibroblast, four different data sets were analyzed. First, a 2D culture platform was used to encapsulate VICs. 8-arm PEG-nb of 20 and 40 kda molecular weight were used for the stiff and soft conditions respectively. Second, another 2D culture platform was used to encapsulate VICs. 8-arm PEG-nb of 40 kda molecular weight, this time with different concentrations of protein ET-1. Third, 3D culture platform PEG hydrogels with different ligands attached, were employed. Last, 3D culture platforms PEG hydrogels with addition of TGF-β1 were used. These data sets were selected as they had already undergone a robust study using human analysis of the images, using αsma fiber formation as the definition of the myofibroblast. These data sets were selected to test the results from the automated image analysis sequence to the traditional human interface and counting of myofibroblast VIC myofibroblast activation on 2D hydrogel matrices of varying stiffness A 2-dimensional culture platform for VICs was used to study the response of VICs to matrix stiffness; the results were analyzed using manual counting of immunostained images. Table.4.1 summarizes the composition of the hydrogel culture platform, and provides the specific composition of the various formulations and the Young s modulus of the resulting gels. 25 μl of the hydrogel mixture were used per gel. Table 4.1 Hydrogel composition and concentration of reactants for the 2D cell culture platform. PEG-N Mn (kda) PEG-N (mm) LAP (mm) MMPs (mm) RGDS (mm) Young s modulus (kpa) 27 3 Human visualization of images provides many advantages, including the ability to discern differences that are difficult for computer algorithms. While laboratories can develop standard protocols and experienced eyes are valuable, this approach has disadvantages. First analysis of hundreds of images is tedious and laborious, requiring extended periods of time for a task that could be automated. Second, many experiments needs to be repeated multiple times to provide replicates and allow statistical analysis, thus making the task even more repetitive. One must also be careful to blind the evaluator to the experimental conditions, as this can lead to an unintentional bias. While the human eye can interpret subtleties in images more easily than automated methods, the speed, objectivity, and time to analyze the results are all limitations. For these reasons, we first began to test the automated algorithm on relatively simple experimental data sets related to VIC myofibroblasts with αsma positive stress fibers. Numerous experiments and images have been conducted over the past decade, and this provided the first test of analytical method protocol. A direct comparison to the manual versus automated characterization of VICs myofibroblast activation as a function of microenvironmental stiffness are shown in Figure

30 Figure 4.1 Percentage of VICs activated to the myofibroblast phenotype for the analysis sequence developed (light green) and for the one-by-one counting (dark green) in a 2-Dimensional culture platform study. As observed in Figure.4.1 the results show a high accuracy from the analysis sequence here developed. For the soft condition, the analytical method shows to be remarkable precise being the Soft with 40 m of inhibitor the most exact, with a percentage of myofibroblast activation of 37±12%, while the manual characterization for the same experiment is 31±9%. For the stiff condition the analytical sequence exhibits to be even highly robust. The results for the developed protocol and the manual counting of myofibroblast activation for the stiff blank case are 69±8% 72±7% respectively, demonstrating a valuable precision from the automated method. For the Stiff 40 m of inhibitor case, the results are not enough accurate due to the fact that some points of view of the data images contained no cells, making the statistical analysis not rigorously credible. To further study the behavior of the developed analysis sequence, another test was conducted on 2D culture platforms with different presence of protein ET VIC myofibroblast activation on 2D hydrogel matrices of varying ET-1 concentration A 2-dimensional culture platform for VICs was used to further study the response of VICs to ET-1 protein; the results were analyzed using manual counting of immunostained images. Table.4.2 summarizes the composition of the hydrogel culture platform, and provides the specific composition of the various formulations. 25 L of the hydrogel mixture were used per gel. Table 4.2 Hydrogel composition and concentration of reactants for the 2D cell culture platform with ET-1 PEG-N Mn (kda) PEG-N (mm) LAP (mm) MMPs (mm) RGDS (mm) Young s modulus (kpa) ET-1 (nm)

31 Similarly, a direct contrasting to the manual versus automated characterization of VICs myofibroblast activation as a function of ET-1 presence are shown in Figure.4.2. Figure 4.2 Percentage of VICs activated to the myofibroblast phenotype for the analysis sequence developed (light green) and for the one-by-one counting (dark green) in a 2-Dimensional culture platform with presence of ET-1 study. As shown in Figure.4.2 the results contrasts the accuracy from the previous analysis. For the different conditions, the analytical method demonstrates to be precise. For the 1nM ET-1 case, the analytical sequence obtained a percentage of myofibroblast activation of 36±10%, while the manual characterization for the same experiment is 33±15%. For the other conditions the analytical sequence exhibits to be also highly solid. The results for the developed protocol and the manual counting of myofibroblast activation for the blank and 100nM ET-1 cases are 18±8% 12±11% and 24±8% 16±7%, respectively, presenting a valuable accuracy from the automated method. In a similar manner, the deviation of the data values for the blank and 1nM ET-1 conditions, are not enough rigorous, due to the low cell density in some points of view of the data images. An important thing to note is that the analysis sequence and the hand counting of both experiments were carried out independently, by different people and without knowing the counterpart results. By this blind analysis either the hand counting or the automatized protocol could be biased or influenced by the other results. Moreover, both analyses were performed without being aware of the conditions of the populations (i.e. soft gel, density, cells already activated) making the analysis objective. Thus, the results are yet more promising. To contrast the veracity of this results another study, was conducted using 3D culture platforms. 23

32 4.3. VIC myofibroblast activation on 3D hydrogel matrices of varying matrix degradability While it is relatively easy for a human observer to delineate αsma stress fibers in 2D culture systems, this becomes much more difficult in 3D cultures, when VICs are embedded in hydrogel matrices. Thus, using similar hydrogel systems, VICs have been encapsulated and cultured in three-dimensional systems and their myofibroblast activation studied over time. Important for 3D cultures is the ability of the VIC fibroblasts to remodel their microenvironment, degrading the local surroundings to allow for spreading, attachment, proliferation, and even migration Here, the data set includes crosslinking functionalities with different linkers rendering them more (-ester linker) or less (-amide linker) susceptible to hydrolysis and degradation. Accordingly, Table.4.3 shows the composition of the hydrogels, which only differs in the linker of the PEG-n. Table 4.3 PEG-N molecular weights and Monomer concentrations for the final platforms used as a cell culture. PEG-N Mn 40 kda (mm) 0.75 PEG-N (mm) LAP (mm) MMPs (mm) RGDS (mm) Young s modulus (kpa) The procedure for both the hand counting and the analysis sequence run by Harmony software was the same as followed for the 2-D study. The percentage of VICs myofibroblast activation in the 3D culture platform studied are shown in Figure.4.3. Figure 4.3 Percentage of VICs activated to the myofibroblast phenotype for the analysis sequence developed (light green) and for the one-by-one counting (dark green) in a 3-Dimensional culture platform. Consistently, the results shown in Figure 4.3 present a high accuracy and exactitude from the analytical method. For the hydrogel with the ester linker the manual characterization show a 45±7% of myofibroblast activation, while the analytical method presents a 46±6%. Similarly the robustness of the method is evident in the amide linker case, where the manual counting and the analytical method show a VIC myofibroblast activation of 23±4% and 21±7% respectively. 24

33 4.4. VIC myofibroblast activation on 3D hydrogel matrices of varying TGF-β1 presence Accordingly, VICs have been encapsulated and cultured in three-dimensional systems and their myofibroblast activation studied with presence or not of TGF-β1 in 4 well plates for each condition. As mentioned in VIC Myofibroblast activation, TGF-β1 promotes VIC myofibroblast differentiation and αsma expression 59,60, therefore, a different VIC activation will be observed in presence of TGF-β1. Hence, the data sets include VICs encapsulated with serum from healthy patients diluted to 1% in VIC medium and VICs encapsulated with media supplemented with 10 ng/ml of TGF-β1. Correspondingly, Table.4.4 shows the composition of the hydrogels. Table 4.4 PEG-N molecular weights and Monomer concentrations for the final platforms used as a cell culture. PEG-N Mn 40 kda (mm) PEG-N (mm) LAP (mm) MMPs (mm) RGDS (mm) Young s modulus (kpa) TGF-β1 (ng/ml) The procedure for both the hand counting and the analysis sequence run by Harmony software was the same as followed for the previous studies. The percentage of VICs myofibroblast activation in the 3D culture platform studied are illustrated in Figure.4.4. Healthy Healthy + TGFβ Figure 4.4 Percentage of VICs activated to the myofibroblast phenotype for the analysis sequence developed (light green) and for the one-by-one counting (dark green) in a 3-Dimensional culture platform with TGF-β1. Figure 4.4 also presents an outstanding precision from the analytical method. In most cases, the difference between the two measurements falls into a ±17% VIC activation divergence, being that value the worst case scenario. For the healthy well plates studied, only existed a 5% of myofibroblast activation difference, being the D1 well plate the most accurate case. Thus, the solidness of the method is remarkable in the TGF-β1 presence case, where the manual counting and the analytical method 25

34 show a VIC myofibroblast activation of 68±11% and 51±25% respectively in the utmost condition. All together, these results prove again a high potential for the analysis sequence here detailed. One thing that recurrently happen to some of the data set images analyzed, is a high deviation of the VIC activation percentage values. This considerable variation, is due to cell density issues. A too high confluence of cells or a lack thereof in some fields of view (FOVs). In some images there were either not enough cell density or too confluency and that made an inconsistent statistical analysis. However, in a positive manner, this fact happens indistinctly in both measurements, hand counting and analytical sequence, demonstrating a no relation between this value divergences and the analysis protocol here developed Bland-Altman Method To further test and validate the analytical sequence here developed A Bland Altman plot was performed. The BlandAltman plot is widely used for any two methods that are designed to measure the same parameter, or property 80. As in the goal of this thesis, the identification of VICs activated phenotype was studied and compared between a hand-counting and individual characterization and the analytical sequence developed. The Bland-Altman method, normally compares a new measurement technique with a gold standard, widely accepted method, even though a gold standard does not necessarily imply it to be without error. In this case, there is no a well-standardized and easy to contrast gold technique. However, the hand counting method was established as the gold standard due to its previous and extended use. A good correlation and agreement states that one can indistinctly employ any of both methods to measure the property in question. Bland-Altman plots allow to examine the existence of a possible systematic difference between the measurements (i.e., constant bias) and to identify possible outliers. The estimated bias is calculated with the mean difference, and the standard deviation (SD) of the differences measures the random fluctuations around this mean. If there is a consistent bias, it can be adjusted for by subtracting the mean difference from the new method. In most cases, a Gaussian distribution is used to compute 95% limits of agreement for each comparison (average difference ± 1.96 SD of the difference), which tell us how far apart measurements by 2 methods were more likely to be for most individuals 80. In this point a personal criterion needs to be set and decide whether the differences within mean ± 1.96 SD are experimentally important or relevant. If so, the two methods may be used interchangeably. 26

35 Bland-Altman Plot Bland-Altman Plot was performed for all the experiments previously discussed. One important aspect that one should notice is that Bland-Altman method is meant to compare two different analytical measurements for the same experiment, and that implies a comparison of the same conditions, one by one. The Bland Altman method does not compare the final average but each individual result. In some of the data images analyzed in the present thesis, the hand counting of the VIC activation was quantified indistinctly, without a record of which specific FOV was being quantified (Figure.4.5). Because of that, BlandAltman method is not usable for experiments where, there is not a tracking of the specific FOV analyzed. Fig 4.5 Example screenshot of Harmony software indicating the specific FOV that it is being analyzed. As an example, Figure 4.6 shows the 2D soft 2.5μm experiment. In this case, the hand counting analysis did not track the FOVs that were being measured and therefore the data set values cannot be compared between the two analytical methods. The difference in the percentage of VICs phenotype activation in both methods (analytical-manual) is plotted versus the average of percentage of activation of both methods. 27