1.1 Introduction
It has long been known that cells express all their functions in response to stimuli deriving from the surrounding microenvironment; these stimuli can be electrical (galvanotaxis), chemical (chemotaxis) and mechanical (durotaxis).
In biology such stimuli are often characterized by gradients, which represent the variation of a certain physical constant in space and time, such as electrical charge, chemical species or in the case of the mechanical properties variation in stiffness.
1.1.1 Durotaxis
Durotaxis describes cells movement as consequence of substrate
stiffness, for example moving from the soft to the stiff side of a material. A very
interesting study done by Lo et al. [1], showed that cell movement is guided by
the rigidity of the underlying substrate. By plating NIH/3T3 fibroblasts on PA gel
substrates, which had a transition in the rigidity, they showed that cells from the
soft side could easily migrate to the stiff side of the gel while simultaneously
increasing their area on the stiff side. However, the reverse of this phenomenon
was not true. Cells, which were present on the stiff side of the gel, turned around
or retracted as they reached the boundary and never crossed the boundary to go
to the soft side of the gel. As cell migration or movement plays a critical role in
many physiological processes like wound healing, immune response, and tissue
morphogenesis this study highlighted the importance of substrate rigidity in
controlling cell locomotion.
1.1.2 Chemotaxis
Chemotaxis describes the cellular movement of cells when they move up or down a chemical gradient [2]. This movement allows the cells to explore their extracellular environment. Cells move randomly, away from repellents and towards attractants. Questions have arisen on how cells can detect small changes in their extracellular environment. Usually the cells will undergo a random walk, consisting of smooth swimming and brief direction changes (tumbles). By increasing the attractant, the tumbling is suppressed, which leads to a biased random walk. Chemotaxis movement are widely study in many biological aspects such as movements of melanocyte cells toward a particular hormone [3] or to study the cell movements occurring in morphogenesis [2].
1.1.1 Galvanotaxis
The movement of cells due to an electric field is called galvanotaxis.
Normal physiological range of electric fields is between 2 and 600 V/m [3]. It has been shown that cells in vitro can react to an electric field up to 1000 V/m.
Electric fields in the body are considered to be caused by ions, such as sodium, potassium and chloride [4-‐5]. Their concentrations vary over space and time.
These different concentrations can be presented between hours and days. This is considered to give rise to galvanotaxic behaviour. There are several examples of where galvanotaxis could be a reason of cell movement in the body. Some examples are embryo development, directing nerve cell growth, wound healing, angiogenesis, directing metastatic cancer cells [4-‐6]. It has been seen that different cell types respond differently to an applied electric field, which then could be used to separate cells [6]. A large interest has been shown to see the effects of galvanotaxis in the area of cancer and its metastatic behaviour.
Metastasis is where cancer cells move from the original tumour to form
daughter tumours. The connection between galvanotaxis and metastasis is that
it has been shown that highly metastatic cancer cells give a stronger response to an electric field than healthy or weakly metastatic cancer cells [5].
1.2 Stiffness has a biological role
Cells sense and respond to differences in substrate rigidity, which has been shown to influence behaviours such as growth [7-‐9] and differentiation [10-‐12]. However details of the mechano-‐chemical mechanisms have not been fully elucidated.
In any case, it is accepted that a major role in cells regulation is played by the stiffness of the tissue in which cells reside. The stiffness of an object is characterized by its ability to resist deformation, and is generally defined by its Young’s modulus or elastic modulus [13]. A rigid substrate resists deformation more readily than a soft substrate and thus possesses a larger elastic modulus.
The elastic modulus (E) of a substrate can be determined by applying force to it (stress), and then measuring the resulting change in length (strain). The stiffness of tissues within the body varies greatly, with brain tissue being among the softest (several hundred Pa) and mineralized bone among the most rigid (several million kPa) (Figure 1) [14].
Figure 1: Range of stiffness of biological tissues. Soft tissues within the human body range from 0.1 kPa up to 100 kPa. Brain tissue is one of the softest tissues, and bone is the
hardest.
In multicellular organisms, and specifically in tissues, cells function may
happens: during development [15], with the progression of diseases including cancer [16], fibrosis [17], and atherosclerosis [18], and during aging [19-‐20].
Diseases often result in the stiffening of the tissue: tumour formation and tissue fibrosis markedly increasing tissue stiffness mostly due to collagen production.
For example, healthy adipose tissue ranges from 0.2-‐2 kPa, but as a breast tumor develops, the surrounding tissues increase in stiffness to 4-‐12 kPa [16,20-‐21].
An increase in tissue stiffness can also correlate with the degree of disease progression. For example, Yin et al. reported a correlation between the stiffness of liver tissue and the progression of fibrosis, starting with measurements of 2 kPa for normal liver up to 12 kPa for the most advanced stages of fibrosis [22].
Tissue rigidity plays an important role in cancer development, progression and metastasis, with increased rigidity promoting an oncogenic phenotype [14,16]. Tumours often result in an increase in tissue stiffness, and this characteristic is commonly used to detect their presence [24-‐25]. Increased tumour rigidity has been found to correlate with an increase in metastasis [26-‐
27] and a decrease in successful treatment [28].
Importantly the value of the stiffness is not homogeneous in space, and as exemple there are some tissues [29-‐30] that possess a spatial distribution of stiffness. In pathological conditions changes in the extra cellular matrix (ECM) composition were observed: the normal distribution of protein, such as collagen or fibronectin, was modified resulting in an increased deposition of ECM proteins, thus and increase in stiffness. For example stiffening of tumorogenic tissue is largely due to an increase in the deposition and crosslinking of collagen [14,31-‐32]; however, the amount of other ECM proteins such as fibronectin have also been found to dramatically increase in breast tumours [33-‐34], and fibronectin has been implicated in the progression of other cancers, including prostate cancer, melanoma, and lung carcinoma [35-‐36].
As already stated, there are examples of stiffness variation in tissues
during aging processes. Cosgrove at al. and Keung et al. report an increment in
mechanical properties associated with aged skeletal muscles and in brain [19-‐
20].
Stiffness gradients within tissues can result from pathological conditions, but also occur through normal variations. For example the tissues stiffness varies naturally at interfaces, e.g. hard, calcified bones are connected to soft cartilage or as MSCs egress from bone marrow and hone to these interfaces or migrate through tissue [37], they may encounter such stiffness gradient(s), and it is not clear whether the MSC response to these stimuli is to remain in place and differentiate, as with static materials [11], [38] or migrate in response to the stiffness gradient as with fibroblasts [1-‐39].
1.3 Aim of this Work
All the cellular behaviour presented above are influenced by tissues stiffness, and for this reason the generation of systems able to reproduce such stiffness variation allow a more representative reconstruction of the pathophysiological state. An ideal approach would also include an achievement of stiffness variation using the same mechanism as it happens in a specific area of interest of the target tissue. In any case, obtaining a surface with a known variation of mechanical properties in space represents an ideal platform for the study of several cellular responses, such as migration and or differentiation.
In order to fabricate engineered microsystems resembling the pathophysiological state of a target tissue, the aim of this thesis is the development of a milli-‐sized substrate with controlled stiffness at the micro-‐
scale. Hydrogels will be used for its capacities to reproduce the mechanical
properties of soft tissue and for their easiness in tune the resultant stiffness. By
increasing the number of cross-‐linking using a cross-‐linker molecule it is
hypothesised to control the overall stiffness. Moreover to replicate the stiffness
gradients of soft tissues (elastic modulus ranging between 1–12 kPa) in a micro-‐
scaled manner (with a stiffness rate of about 2.5 kPa/mm), an inkjet system is used in this work to modify such hydrogels.
Starting from the theoretical aspects necessary to improve the
mechanical properties of the hydrogel (i.e. use of cross-‐linker molecule), a
prediction of the physical variables of the system will be characterized by the
implementation of computational models, describing and controlling the
reaction and diffusion of a crosslinking molecule within a hydrogel domain
(monitoring the concentration as function of time and space). Afterwards
experimental tests will be performed to validate the solutions obtained by these
models. Particular attention will be then dedicated to the generation of
concentration profile in space (among a single axis) on the surface of a hydrogel
substrate. The concentration profile will lead to obtain hydrogel with a
crosslinking density able to promote a material stiffening in the range of soft
tissues mechanical properties (1–12 kPa). Again, once a computational model
will return the most suitable initial conditions to be used to generate the desired
gradient, this will be experimentally validated using the same procedure used
before (assessing a fine control over the crosslinking density and its spatial
distribution, pixel size < 1 μm). Finally, mechanical tests will be performed on
several hydrogels to measure the elastic modulus as function of the crosslinking
degree. In this way it will be possible to design functionalized substrates having
a known cross-‐linker concentration, thus a spatially distributed elastic modulus
in a specific mechanical range.
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