The Overview of Human Pluripotent Stem Cells

#### **Chapter 1**

## How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone

*Vitaly Gursky, Olga Krasnova, Julia Sopova, Anastasia Kovaleva, Karina Kulakova, Olga Tikhonova and Irina Neganova*

#### **Abstract**

The application of patient-specific human induced pluripotent stem cells (hiPSCs) has a great perspective for the development of personalized medicine. More than 10 hiPSCs clones can be obtained from one patient but not all of them are able to undergo directed differentiation with the same efficiency. Beside, some clones are even refractory to certain directions of differentiation. Therefore, the selection of the "best" or "true" hiPSC clone is very important, but this remains a challenge. Currently, this selection is based mostly on the clone's morphological characteristics. Earlier, using methods of mathematical analysis and deep machine learning, we showed the fundamental possibility for selecting the best clone with about 89% accuracy based on only two to three morphological features. In this chapter, we will expand on how the morphological characteristics of various hiPSCs clones, the so-called "morphological portrait," are reflected by their proteome. By reviewing previously published data and providing the new results, we will highlight which cytoskeletal proteins are responsible for the establishment of the "good" morphological phenotype. Finally, we will suggest further directions in this research area.

**Keywords:** hiPSCs, hESCs, machine learning, best clone, morphological phenotype, proteome, cytoskeleton

#### **1. Introduction**

High-quality clones of human pluripotent cells (hPSCs) are of great importance for research in both basic and translational medicine due to their capacity to differentiate to all cell types of the human body and unlimited self-renewal. Unfortunately, currently available reprogramming methods to generate human induced pluripotent stem cells (hiPSCs) are stochastic, and that causes the presence of a large percentage of partially reprogrammed cells and cells with a low level of pluripotency [1]. The purification of culture is an important requirement to obtain high-quality clones. Usually, this includes either gene expression profiling or evaluation of the

cellular morphology by visual inspection or image analysis. However, both of these approaches have limitations. Namely, gene expression profiling gives a direct readout of stemness and differentiation, but it is destructive to cells. At the same time, visual morphological analysis of cells or their images is a nondestructive method, but it is prone to errors and misinterpretation. This explains the urgent need for the development of the noninvasive evaluation of the pluripotent cell cultures, that is. able to link cell morphology to the level of pluripotency. In the first part of the present chapter, we will discuss the morphological features of hPSCs and methods for their automated evaluation.

Currently, there are publications in the literature on the employment of live-cell imaging analysis along with deep machine learning for the development of automated software for the recognition of the best clones. Certainly, these scientific works, discussed in the first part of the chapter, represent only the first attempts of computer image analysis application for the selection of the best clones or identification of cells that have not undergone complete reprogramming. One question remains to be resolved in this method: to what extend can we make general conclusions based on the data from few studies, even with a large number of samples?

More than 500 distinct human embryonic stem cell lines (hESCs) have been generated to date, but only less than 100 lines are available now for general research as fully characterized lines (NIH stem cell registry, https://grants.nih.gov/stem\_cells/ registry/current.htm). In addition, there are multiple patient-specific human induced pluripotent stem cells (hiPSCs) lines and the list of these lines continues to grow. By 2020, about 131 studies were classified as clinical trials involving human pluripotent stem cells (hPSCs, comprising both hiPSCs and hESCs) [2]. The analysis published by Deinsberger and colleagues [2] revealed that the number of clinical trials involving hiPSCs was substantially higher than the one involving hESCs (74.8% vs. 25.2%). However, when counting only interventional studies, it appears that the majority (73.3%) was done with the use of hESCs. Application of patient-specific hiPSCs helps to overcome both ethical and immunological issues but hESCs are still widely used in the field of translational and regenerative medicine, disease modeling, and drug screening. Importantly, both hPSC types are very similar in their morphological characteristics not being molecular equivalents [3, 4].

Regardless of the common morphological features of hiPSCs and hESCs, it is welldocumented that major line-to-line morphological variability exists even in the same culture conditions and with the use of the same propagation technique [5]. This fact raises the question of whether there are common morphological features that distinguish a "good" hPSCs clone from a "bad" one. Finding the answer to this question is extremely important as the maintenance of hPSCs in culture is not only expensive but it is also very labor intensive. The development of an automated quality control protocol can improve the utility of the high-quality clinical-grade cells.

A noninvasive method of visual inspection of the morphological appearance remains the main criteria used to select the best hPSCs clone. However, until now, it was not clear which parameters of morphology are closely associated with the pluripotent state.

Recently, we analyzed morphological parameters of several hPSCs lines of various passages. We first extracted the parameters from phase-contrast images and constructed classification models of colonies by morphological phenotype [6], and then we used image analysis with convolutional neural networks (CNNs) [7]. Further to this, expression analysis of 11 pluripotency markers genes allowed us to identify phenotype-specific sets of genes that could be used for the selection of the

#### *How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

best clones, meaning the fundamental possibility of constructing a morphological "portrait" of a colony informative for its automatic identification. Additionally, we performed a proteomic analysis of several hPSCs samples from various lines used before for the computational analysis and showed that cells with different phenotypes from various lines cluster at the proteome level in accordance with their morphological phenotype [7].

Multiple studies have provided datasets of comparative proteomes of various hPSC lines. Several studies used proteomic approaches to find proteins that regulate pluripotency [8–10] or to conduct a comparative proteomic analysis of supportive and unsupportive matrix substrates for hESCs maintenance [11]. In addition, several papers described quantitative proteomic analysis of hESCs differentiation [12, 13]. However, only in 2019, the first paper appeared on the analysis and comparison of the proteomic landscapes of 20 hiPSCs lines classified as stable and unstable based on colony morphology. This study has shown that different morphological "portraits" of colonies are associated with different proteomic profiles and different competencies for directed differentiation [14]. Furthermore, it has been shown that a direct relationship exists between pluripotent markers (DNMT3B, DPPA4, SALL4, CD9) and morphological "portraits" of various lineages [6, 14].

In this chapter, we will review the current knowledge about how automated evaluation of the morphological portrait is used to control the hPSC phenotype, and how it is connected to the proteomic analysis. Next, we will present our own proteomic data analysis of hPSCs in respect to their morphological phenotype. We will pay special attention to the cytoskeleton proteins, as some of them turned out to be the top candidates in determining the best cell and colony morphology. The future will tell us if the hiPSC technology will ultimately overcome the current challenges and will finally make its way into routine clinical application with the help of automated recognition of the best clone based on the morphological selection.

#### **2. Morphological features of human pluripotent stem cells and methods for their automated evaluation**

Currently, work with hPSCs begins with the assessment of their morphology by an expert to determine if there are signs of spontaneous differentiation or other unwanted changes. Established standard criteria for morphological features of hPSCs during their expansion can be described as: (a) a high nucleus/cytoplasm ratio, (b) prominent nucleoli, (c) formation of compact and round colonies with flat and densely packed cells with scant cytoplasm. Additional important marker is the presence of a clear and smooth colony edge [6, 15, 16]. As hPSC colonies propagate in culture, cells might spontaneously deviate from pluripotency toward a differentiated state. In that case, cell morphology changes dramatically, and it is very noticeable; the cells in the colony start to distribute sparser, the distance between the cells expands and cells significantly increase in size, undergoing a characteristic shape change [6]. In addition, undifferentiated hPSCs have more relaxed chromatin than differentiated; during the differentiation process, nucleoli become unclear and invisible under phase contrast microscopy [16]. Notably, only a very skilled expert can notice this alteration; therefore, evaluation of the cultures by the observation of the colonies morphology by an expert obviously depends on the expert's skills. Undoubtedly, the safe application of hPSCs in the clinic requires the creation of a cell evaluation method, which would be less dependent of the expert's skills.

In recent years, several image analysis approaches have been developed. Machine learning, which involves pattern recognition and computational learning, is one of the most widely used strategies. In addition to pattern recognition, some of machinelearning algorithms classify cells into several quality classes, which are related to non-morphological image features, such as the distribution of luminance intensity. The fully automated system has been reported for morphology-based evaluation of iPSC cultures that consists of time-lapse microscopy and image analysis software [17, 18]. The system acquires low-light phase-contrast images of iPSC growth collected during a period of several days, measures geometrical- and texture-based features of the colonies throughout time, and derives a set of six biologically relevant features to automatically rank the quality of the cell culture. This method has shown that hiPSCs that are classified visually could be adequately distinguished with local binary patterns and an intensity histogram [18]. The classifier presented in that work successfully identifies different cell stages for a wide range of scenes that can include different-sized colonies, varying amounts of dead cells and debris, and differentiated cells within colonies [18].

As mentioned before, in case of cell differentiation, nuclear structures reconfigure dynamically. The method published by Tokunaga and colleagues [19] for discrimination of the bona fide hiPSCs from non-reprogrammed ones, is based only on the fine differences of the nuclear morphology between cells. Namely, this work has demonstrated that specific quantitative parameters contributing to morphological discrepancies reside in the nuclear sub-domains. Analysis of nuclear morphologies revealed dynamic and characteristic signatures, including the linear form of the promyelocytic leukemia (PML)-defined structure in hiPSCs, which was reversed to a regular sphere upon differentiation. Thus, this data confirmed that hiPSCs have a markedly different overall nuclear architecture that may contribute to highly accurate discrimination based on the cell reprogramming status [19].

Similarly, the paper by Kato et al. [20] demonstrated a noninvasive image-based evaluation method for detecting partially differentiated colony morphology in heterogeneous colony populations *via* live image analysis. The authors analyzed eight major parameters comprising 27 sub-parameters selected as essential for further analysis of 303 hiPSC colonies. The data showed a relationship between image features and gene expression by analyzing the expression of hiPSC colonies classified by using spatial frequency. Next, colony morphology classification based on the statistical analysis of the live-cell images with unbiased morphological parameters was compared with classification based on global gene expression profiles of individual colonies. Classification utilizing statistical analysis produced similar results as compared to classification based on gene expression profiles. Thus, authors concluded that quantitative morphological evaluation method facilitates the noninvasive analysis of hiPSC conditions and demonstrates its utility in recognition hPSCs heterogeneity.

The paper by Wakui and colleagues [17] aimed at establishing quality classification of hiPSC images into three classes (poor, moderate, and good) by evaluating the biological features used in the visual inspection. Three features associated with biological structures such as the number of nucleoli, the crack area rate, and the differentiating cellular nuclei area rate were chosen by the expert. Importantly, these features were effective for quality evaluation by the visual inspection. As mentioned before, the number of nucleoli is a feature indicating a non-differentiated state, and cells with many nucleoli are considered to be of good quality. In contrast, the crack area rate and the differentiating cellular nuclei area rate are indicators of deviation from

#### *How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

pluripotency. The method identifies three feature detectors and the cell quality classifier, the inputs of which are the outputs of the detectors. Then, in the image analysis method, the feature detectors and the classifier are applied to each of the regions of interest (150 pixels, 50 μm) of a phase contrast image. For machine learning of the nucleolus detectors, the nucleoli dataset was used as training data. The crack detector and the differentiating cellular nuclei detector were tuned with the masked dataset. The cell quality classifier was developed with the labeled dataset. Nucleoli observed in undifferentiated cells are nearly oval-shaped, 3- to 6-μm in diameter, and appeared black under phase-contrast observation. For confirming the classification capability of these three features, the distributions of the features for each cell quality class of a respective cell line were investigated and the accuracy for cell quality classification that was equivalent to visual inspection with respect to the three hiPSC lines was confirmed [17].

Interestingly, the paper by Nishimura and colleagues remains the only paper, which is based upon the morphology of a cellular organelle; it describes the use of the mitochondria distribution and state for distinguishing reprogrammed mouse PSCs [21]. The authors reported the development of an imaging system, termed phase distribution (PD) imaging system, which visualizes subcellular structures quantitatively in unstained and unlabeled cells. The PD image and its derived PD index reflected the mitochondrial content, enabling quantitative evaluation of the degrees of somatic cell reprogramming and mouse PSCs differentiation [21]. The dynamic changes in mitochondrial biogenesis and antioxidant enzymes are well-documented during the spontaneous differentiation of hESCs, as well as during the reprogramming process [22]. Unlike in PSCs, in the somatic cells mitochondria are numerous and large, reflecting their dependence on oxidative phosphorylation for efficient energy production. It is well-known that the reprogramming of the somatic cells into iPSCs is accompanied by a metabolic shift from oxidative phosphorylation to glycolysis, concomitant with changes in structure and function of mitochondria [23, 24]. Indeed, iPSCs that are reprogrammed to different degrees show an inverse relationship between their pluripotency and mitochondrial activity [25]. Thus, morphological changes of subcellular structures such as mitochondria may serve as an additional useful marker to evaluate the pluripotency of reprogrammed cells.

Our own data on morphological parameters from three lines (hESC line H9, hiPSC line AD3, and hiPSC line HPCASRi002-A) revealed that several morphological criteria can be used to distinguish between "good" and "bad" phenotypes (**Figure 1A**) [6], thus demonstrating that these are strong and reliable criteria for determining the phenotype of hPSCs. We tested seven morphological parameters in total as possible predictors in the neural network-based classification models of the colony phenotype. The models aimed to predict the probability of the colony phenotype (either 'good' or 'bad') and were trained on the morphological parameter values of colonies or cells. A minimal model was selected for each data type that contained a minimal number of predictors and still provided the prediction accuracy close to that in the model with all predictors included. For the colony morphology data, we found a minimal model of four input parameters (Perimeter, Minor Axis, Shape Factor, and AIS) that showed 74% accuracy on average, while only two parameters (Perimeter and Shape Factor) were enough to provide a 68% average accuracy in the minimal classification model for the cellular morphological data (**Figure 1A**).

As an alternative approach for the colony phenotype prediction, we applied convolutional neural networks (CNNs) directly to the phase-contrast images of colonies, omitting the intermediate step of extracting the morphological parameters from the

#### **Figure 1.**

*Two approaches to colony phenotype prediction using automatic classification [6, 7]. A: Minimal classification models based on neural networks use four morphological parameters of colonies or two morphological parameters of cells as predictors. The corresponding parameters are shown in bold and connected with the classifier input with blue or red lines for the colonial or cellular data-based models, respectively. The output of the models is the probabilities that the colony will have a good or bad phenotype (P(good) or P(bad), respectively). B: CNN trained directly on colony images. The convolutional part of the CNN extracts the most representative feature maps from the images. These features are then passed to the input of a fully connected network trained to separate the phenotypes. CNNs trained on datasets with images of various linear size L show the prediction accuracy that has a maximum at the size L ~ 144 μm, which can be interpreted as the most informative spatial scale. AIS, area of intercellular space (for colonies only).*

images (**Figure 1B**). CNNs extract the informative features, called "feature maps," from the images and use these features to predict the phenotype at the output. We trained the CNN-based classification model on phase-contrast images of the H9 hESC line colonies and obtained an 89% accuracy in phenotype prediction [7].

The morphological "portrait" of the colony that can be associated with clonality is a complex trait, with no clear spatial scale that could unambiguously separate the natural morphological variability within the colony from signs of clonality loss. Trying to answer the practical question about the most informative spatial scale at which the colony phenotype could be recognized by the automated classifiers most effectively, we trained CNNs on multiple datasets containing images of various linear size. We found an optimal image size of ~144 μm providing the best classification


*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*


*Advances in Pluripotent Stem Cells*

*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*



**Table 1.**

*Selected results of phenotype classification based on morphological features of hPSCs colonies.*

*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

performance (**Figure 1B**). This size is intermediate between typical colony and cell sizes, reflecting the fact that both cellular and colonial information should be taken into account.

We linked these results to the transcription studies by measuring the expression of 10 pluripotency markers (*DNMTB3, SALL4, IGFR1, CD9, DPPA4, OCT4, REX1, NANOG, SOX2,* and *KLF4* genes) in colonies from the hESCs (H9) and hiPSC (AD3 and HPCASRi002-A) lines with different phenotypes [6]. We found that the *SALL4*, *DNMTB3*, *REX1*, *DPPA4*, and *SOX2* genes demonstrated differentiated expression in colonies of different phenotypes, thus confirming that the phenotypes did represent the pluripotency status of the colony.

Finally, the question can be asked, whether the cultivation conditions, namely various culture media and matrixes, affect the morphological parameters important for morphological phenotype recognition. By other worlds, is evaluation method based on the morphologies of various hPSC lines applicable under different culture conditions? The paper by Harkness and colleagues [26] addresses the effect of five different media, namely, mTESR1, Essential E8, StemPro (SP), mouse embryonic fibroblasts conditional media (CM) and StemMacs iPS-Brew XF (SM), on the morphological parameters of the three established hESCs lines (MEL1, WA09, ESI-hES3). These lines were routinely grown on Matrigel (Corning) in mTESR1 media before switching to a different media. As a result, the authors observed distinct and measurable differences in nuclear and cell morphology between different culture conditions. In CM and SP cultures, authors noticed a looser colony structure and a flatter appearance when compared to mTESR1, E8, or SM media. The morphological parameters such as nuclear area, cell area, cell roundness, and cell spread in all three lines demonstrated an overall decrease, while in the least defined media, CM, in all cell lines the cells became larger. Moreover, the nuclear/cytoplasmic ratio varied between the lines, suggesting that media composition can affect the cell's parameters and may cause cytoskeletal remodeling. Furthermore, high content imaging demonstrated that hESCs grown in different media exhibit significantly different cytoskeletal architecture while maintaining their pluripotent status, suggesting that cytoskeleton has become more stable in xeno-free media [26]. Thus, the detailed analysis provided in this research let to conclude that morphological alterations of cell phenotype can be associated with the changes of cell culture conditions. However, it can be assumed that when changing from culture medium to another, cells undergo a period of adaptation and, perhaps, after a certain number of passages, they will restore their previous morphological parameters. However, this needs further verification.

Thereby, to create a reliable system for recognizing the best clones, further studies of different hPSC lines during their cultivation on various matrixes and media are required. The creation of a single database that combines data on morphological parameters from numerous lines will improve the methods of automatic clone's recognition for their reliable application in clinic.

In **Table 1**, we summarized some findings about phenotype classification based on morphological features of hPSCs colonies.

#### **3. Morphological phenotypes of the hPSCs reflected in different proteomic landscapes**

Proteomics analysis provides an excellent tool for large-scale quantification and benchmarking of cells and an opportunity to understand deeper the rules that govern hPSCs morphology. Compared to other ~omics, such as transcriptomics and genomics approaches, proteomics analysis measures the translated proteins. Most of the previous studies have used proteomics approaches to identify proteins important for stem cell pluripotency maintenance and for lineage differentiation [8–10, 13]. Some studies have explored the membrane proteins [32] or the hESCs phosphoproteome [8, 9] in comparison to hiPSCs proteome and phosphoproteome [33]. In addition, molecular differences of the proteome level between hiPSCs of different somatic origin were described [10]. A comparative proteomic analysis has been published comparing supportive and unsupportive extracellular matrix substrates used for hESCs maintenance [11]. All these studies revealed a huge number of proteins known to be important for hPSCs maintenance, namely, cell cycle and DNA damage repair proteins, proteins involved in integrin binding, intracellular vesicle trafficking proteins, RNA binding, adaptor proteins and histones, proteins of exosomes biogenesis and tumorigenesis, zinc finger proteins, mitochondrial proteins, and many others.

The goal of our study was to analyze the hPSCs proteome in accordance with the selected morphological phenotypes [6, 7]. Thus, we compared a proteomic "portrait" of the "true" or the best hPSC colonies versus the "bad" ones.

In the hESCs (H9) samples, we have identified in total 1791 proteins in a good agreement with the Van Hoof and colleagues [9] who have identified 1775 proteins from undifferentiated hES cell line HES-2. Our data demonstrated a clear separation of the samples in accordance with their morphological phenotypes [7], in agreement with the previously published data of Bjørlykke and colleagues [14], thus emphasizing that good and bad morphological populations are molecularly distinct. Comparative proteome analysis of the hESC (H9) colonies with the good morphological portrait compared to colonies with poor morphology and signs of spontaneous differentiation showed that 63 proteins are downregulated and 25 proteins are upregulated (**Figure 2**) [7].

In the context of the identified proteins that determine the morphology of hPSCs, we were especially interested in cytoskeletal proteins since they form the structural network of the cell. In addition, the migration and spread of motile cells, such as hPSCs, over the surface of the substrate accompanied by the reorganization of their actin network. Among 25 upregulated proteins, four belong to cytoskeletal proteins (MYH7, RDX, CNN3, and AIF1L). The other one is the tight junction protein 1, or ZO1 (TJP1), one of the functions of which is to organize the components of tight intermediate junctions and bind them to the cortical actin cytoskeleton. In our analysis, MYH7 appears on the top position among the upregulated proteins (**Figure 2**). The MYH7 gene, known as myosin beta heavy chain (MHC-β), is classified as a type I fiber. Myosins are a large family of proteins that share the common features of ATP hydrolysis, actin binding, and potential for kinetic energy transduction. They composed of a pair of myosin heavy chains (MYH) and two pairs of nonidentical light chains. At least 10 different MYH isoforms have been described in mammalian cells, and the role for the identified in hESCs MYHs, such as MYH16, MYH15, MYH10, MYH9, and MYH7, is aviating to be discovered. This protein is a critical component of the sarcomere's structure and interacts with other key cytoskeletal proteins such as actin, troponin, and myosin-binding protein C (MYBPC3). Its role was shown in directed differentiation of hiPSCs into cardiomyocytes [34] but has not been studied in hPSCs. The dynamics of actin-myosin contraction are directly regulated by the amount of alpha-actinin-3 (ACTN3), which forms cross-links, and the absence of ACTN3 disrupts the symmetry of the actin network in cells. Human ESCs exhibit basal-apical polarity, junctional complexes, integrin-dependent matrix adhesion, and E-cadherin-dependent adhesion, all of which are characteristics of the epiblast epithelium of a mammalian embryo.

*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

#### **Figure 2.**

*Z-score-ranked distribution plot for the proteins of the hESC (H9) colonies with the "good" morphological portrait compared to colonies with "bad" morphology. Cytoskeletal proteins are marked blue, and proteins identified via comparison of the "good" morphological hESC H9 samples versus two hiPSC lines with the same characteristics are marked red.*

When hPSCs are subject to enzymatic digestion during propagation of the colonies, epithelial structures are destroyed, which leads to programmed cell death; here, actinmyosin contraction is a critical effector of the cell death response to enzymatic dissociation [35]. With this regard, inhibition of the myosin heavy chain ATPase, inhibition of the myosin heavy chain, and inhibition of the myosin light chain (MLC) have been shown to increase the survival and cloning efficiency of individual hPSCs [36]. ROCK inhibition decreases phosphorylation of MLC, suggesting that inhibition of actinmyosin contraction is also the mechanism through which ROCK inhibitors increase cloning efficiency of hESCs [37]. In addition, ROCK1/ROCK2 silencing demonstrated that ROCKs regulate MYH function through MLC phosphorylation in hESCs, which, in turn, leads to membrane blebbing and cell death [36]. Lastly, MYH9 and MYH10

are the most highly expressed MYHs with the conserved sites in hESCs. Treatment of hESCs with MYH9/MYH10 siRNAs demonstrated severe phenotypic changes after 96 hours of transfection but increased cell attachment, survival, and cloning efficiency [36]. On the other side, as mentioned above, MYH7 is regarded as a mesenchymal and specifically myocardial marker gene [38]. Our data is also in a good agreement with the earlier work, which demonstrated that a high level of MYH7 protein was detected in hESCs but not in hiPSCs, while MYH9 was identified in both cell types [12]. Obviously, the role of MYH7 needs to be elucidated further. We were able to detect a very thin network of MYH7 colocalized with F-actin fibers but observed its destruction in hESCs (H9) clones with bad morphology (**Figure 3**); supporting the data from

#### **Figure 3.**

*Colocalization of the MYH7 with F-actin in the organized cytoskeleton network in colonies with "good" phenotype and complete loss of such structural organization and colocalization in "bad" hESCs. Thin white arrows are pointing on the colocalization of F-actin and MYH7. Immunofluorescence was performed using anti-MYH7 antibodies (Santa Cruz Biotechnology, sc-53,089), secondary anti-mouse IgG tagged with Alexa Fluor 488 (Abcam, ab150113), and Rhodamine-Phalloidin (Invitrogen, R415). Scale bar 50 μm.*

*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

proteome study, we did not detect MYH7 staining in hiPSCs. The emergence of MYH7 as a top candidate to support the best hESCs morphology might reflect the complexity of the hESCs proteome.

Radixin (RDX) was ranked fourth in our list (**Figure 2**). Radixin is a cytoskeletal protein that may play an important role in binding actin to the plasma membrane. Its exact role for hPSCs has not been explored. However, cellular functions, such as migration and adhesion, require a highly dynamic cytoskeleton behavior. Linker proteins of the ERM family (ezrin/radixin/moesin) can interact with both F-actin and several transmembrane proteins, providing a connection between extracellular cues and the cytoskeleton. The involvement of ERM proteins in a variety of cell functions in the embryonic and early postnatal brain, including axonal outgrowth, morphological rearrangement, cell migration, and signaling, have been described [39]. It is important to note that radixin has been shown to concentrate in the cleavage furrow of dividing cells and may have a role in proliferation [40, 41], the high speed of which is important for pluripotency maintenance [42].

Lastly, throughout the top represented cytoskeletal proteins in hESCs with a good morphological phenotype, we want to discuss CNN3, Calponin 3 (**Figure 2**). Calponin is an actin filament-associated regulatory protein expressed in smooth muscle and multiple types of non-muscle cells. It is capable of binding to actin, calmodulin, and tropomyosin. Three homologous genes, *CNN1*, *CNN2,* and *CNN3*, encoding calponin isoforms 1, 2, and 3, respectively, are present in vertebrates. All three Calponin isoforms are actin-binding proteins with functions in inhibiting actin-activated myosin ATPase and stabilizing the actin cytoskeleton, while each isoform executes different physiological roles based on their cell type-specific expressions. Calponin 1 (*CNN1*) is specifically expressed in smooth muscle cells and plays a role in smooth muscle contractility. Calponin 2 (*CNN2*) is expressed in both smooth muscle and non-muscle cells and regulates multiple actin cytoskeleton-based functions. Calponin 3 (*CNN3*) participates in actin cytoskeleton-based activities in embryonic development and myogenesis. Experiments with cytotrophoblasts from human placenta demonstrated that *CNN3* gene knockdown promoted actin cytoskeletal rearrangement, suggesting *CNN3* to be a negative regulator of trophoblast fusion [43]. With the course of trophoblastic cell differentiation, CNN3 undergoes downregulation. In the trophoblastic cells, membrane flexibility is necessary for membrane fusion [43]. However, whether *CNN3* expression affects the flexibility of the hPSCs plasma membrane is not known, but it may be suggested that regulation of actin cytoskeletal rearrangement by *CNN3* is required for hPSCs. Recently, Calponin 3 was studied in the U2OS osteosarcoma cells, where RNAi knockdown studies revealed that CNN3 is a dynamic component of stress fibers and is required for controlling proper contractility of the stress fiber network [44]. Importantly, the role for CNN3 was also shown for the maintenance of the lens epithelial phenotype where downregulation of CNN3 expression induced changes in cell shape, reorganization of actin cytoskeleton, and formation of focal adhesions resulting in activation of mechanosensitive transcription factor Yap in association with decreased E-cadherin and β-catenin expression [45]. Whether or not the high level of CNN3 in hESCs is associated with the focal adhesion and E-cadherin maintenance remains to be elucidated. Our immunofluorescence study supported obtained proteomic data and revealed a colocalization of the CNN3 with F-actin in the organized cytoskeleton network in colonies with good morphological appearance and complete loss of such structural organization and colocalization in "bad" hPSCs (**Figure 4**).

Among the top downregulated cytoskeletal proteins in hESCs with good morphology appeared DYNLL1 (Dynein light chain 1) and PLEC (PLECTIN) (**Figure 2**).

#### **Figure 4.**

*Colocalization of the CNN3 with F-actin in the organized cytoskeleton network in colonies with "good" phenotype and complete loss of such structural organization and colocalization in "bad" hPSCs. Thin white arrows are pointing on colocalization of F-actin and CNN3; thick white arrows show the absence of colocalization. Immunofluorescence was performed using anti-CNN3 rabbit antibodies (ATLAS, HPA051237), secondary anti-rabbit IgG tagged with Alexa Fluor 488 (Abcam, ab150077), and Rhodamine-Phalloidin (Invitrogen, R415). Scale bar 50 μm.*

Cytoplasmic DYNEIN1 acts as an engine for intracellular retrograde mobility of vesicles and organelles along microtubules. Plectin maintains tissue integrity and associate with intermediate filaments (IF). It acts as a cytoskeletal cross-linking agent and signaling scaffold, influencing both the mechanical and dynamic properties of the cytoskeleton. As a member of the cytolinker protein family, plectin has a multidomain structure that is responsible for its ability to bind to many cytoskeletal proteins. It binds not only to all types of IFs, actin filaments, and microtubules but also to transmembrane receptors, nuclear envelope components, and several kinases with known roles in cell migration, proliferation, and energy metabolism. The exact role of plectin in cytoskeletal dynamics is not studied for hPSCs, but in view of its downregulation for a good morphological phenotype, it can be assumed that lower level of protein expression may play a role in the cytoskeletal plasticity of these cells.

*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

In **Figure 5A**, we demonstrate biological processes that we have identified to be related to the upregulated proteins in hESCs H9 line with good morphology. As can be seen, among the most important of them are cellular component biogenesis and assembly, organelle organization, epithelium development, cytoskeleton organization with DNA packaging, and chromatin organization. It is important to highlight that among the most important processes are up-regulation of actin filament-based processes and actin cytoskeleton organization. Among biological processes associated with downregulated proteins (**Figure 5B**), we identified cellular metabolic processes, nitrogen compound metabolic processes, cellular localization, protein transport, DNA metabolic processes, and many others related to control of the cellular metabolism. Also, cellular component analysis (**Figure 5C**) revealed cytoplasm, actin cytoskeleton,

#### **Figure 5.**

*Biological functions, cellular functions, and molecular processes associated with the up- and downregulated proteins in hESCs H9 cell line. Red bars (A, C, and E) refer to upregulated proteins; blue bars (B, D, and F) refer to downregulated proteins.*

and adherence junction among most upregulated processes, while cellular processes related to cell, membrane-bounded organelle, vesicle, mitochondrion, mitochondrial, and organelle envelope appeared to be downregulated (**Figure 5D**). Importantly, molecular functions associated with "good" morphology include cytoskeletal protein binding, cell adhesion molecular binding, cadherin binding, actin binding together with chromatin and histone binding (**Figure 5E**), while among downregulated cellular functions appeared ribonucleotide, purine nucleotide binding, ATP binding, and GTP binding (**Figure 5E**).

#### **Figure 6.**

*Z-score-ranked distribution plot for the proteins of the hiPSC (AD3) colonies with the "good" morphological portrait compared to colonies with "bad" morphology. The EZRIN protein is marked blue, and proteins identified via comparison of the "good" morphological hESC H9 samples versus two hiPSC lines with the same characteristics are marked red.*

#### *How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

In addition to hESC line H9, we analyzed by the same proteomic approach two hiPSC lines of different origins, namely obtained from the neonatal fibroblasts line AD3 and a patient-specific hiPSCs line HPCASRi002-A (CaSR) [6, 7]. Morphological evaluation of the "good" and "bad" hiPSC clones, as well as comparisons of their proteomic landscapes was performed as for hESC and H9 samples [6, 7]. Interestingly, in the same analysis of proteins associated with cytoskeletal function among experimental groups of the hiPSCs lines, EZR (EZRIN) turned out to be the top-upregulated protein (**Figure 6**). Ezrin, also known as cytovillin or villin-2, is a cytoplasmic peripheral membrane protein and functions as a substrate for tyrosine kinase in microvilli. Its significance for hPSCs morphology has not been studied. Earlier, in support of our data, EZRIN was demonstrated as one of the most prominent cytoskeletal proteins by proteomic profiling of hESCs at the first 48 hours of the early differentiation stage [12], suggesting that it may be expressed differently in clones with "good" and "bad"

#### **Figure 7.**

*Association of the EZRIN cytoskeletal network with F-actin in hESC and hiPSC clones with "good" morphological phenotype and complete distraction of this network in "bad" clones. Thin white arrows are pointing on colocalization of F-actin and EZRIN; thick white arrows show the absence of colocalization. Immunofluorescence was performed using anti-EZRIN antibody 3C12 (Invitrogen, 35–7300, secondary), secondary anti-mouse IgG tagged with Alexa Fluor 488 (Abcam, ab150113), and Rhodamine-Phalloidin (Invitrogen, R415). Scale bar 50 μm.*

phenotype. In support of our proteome data, by employing specific anti-EZRIN antibody, we were able to detect the association of the EZRIN with F-actin in clones with good morphological phenotype and complete absence of such association and very weak pattern of staining in "bad" clones (**Figure 7**).

Interestingly, a very close in concept earlier work of Bjørlykke and colleagues [14] performed on 20 hiPSC lines different on their morphological appearance did not recognize the same cytoskeleton proteins among abundant upregulated or downregulated proteins. However, KERATIN 19 (KRT19), a member of the keratin family of the intermediate filament proteins responsible for the structural integrity of the epithelial cells was identified among upregulated ones as well as ADD2, a member of the cytoskeleton-associated proteins (ADDUCINS) that promotes the assembly of the spectrin–actin network [14]. Among abundant downregulated proteins PALLADIN (PALLD) and FIBRONECTIN1 (FN1) along with the MRC2, extracellular matrix remodeling protein, appeared as significantly downregulated [14]. Fibronectins bind cell surfaces and various compounds as collagen, fibrin, and actin. These proteins involved in cell adhesion and maintenance of the cell shape. Palladin as a cytoskeleton protein involved in the organization of the actin network, motility, and adhesion. Importantly, both named proteins have a role in cell morphology. Keeping in mind the importance of the colony-defined edge as meaningful morphological characteristic of a good hPSCs phenotype, one can recognize an importance of MRC2 for the establishment of the hPSCs phenotype as MRC2 is a member of the mannose receptor family proteins and plays a role in the extracellular matrix remodeling.

Importantly, it appears that only five proteins (SFXN1, LAMC1, RAP2B, NUP98, and EEF2) were identified *via* comparison of the "good" morphological hESC H9 samples versus two hiPSC lines with the same characteristics (**Figure 8**). Wherein, H9 samples contained 83 unique proteins and hiPSCs–116, but none of the identified proteins is a cytoskeletal protein.

Eukaryotic translation elongation factor 2 (EEF2), the GTP-binding translation elongation factor family member and an essential factor for protein synthesis appeared upregulated in hiPSCs while was downregulated in hESCs samples with good morphology (**Figures 2** and **6**). EEF2 is known as a positive regulator of apoptosis [46]. In a highly proliferative cells, EEF2 maintains genomic integrity by arresting the cell cycle at G2/M phase in response to ionizing radiation to prevent

#### **Figure 8.**

*Comparison of the "good" morphological hESC H9 samples versus two hiPSC lines with the same characteristics revealed five common proteins.*

#### *How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

mitotic catastrophe [46]. The rapid proliferation of hPSCs is due to their unique cell cycle regulation. The interplay between cyclins, cyclin-dependent kinase (CDK), and cyclin-dependent kinase inhibitors is important for tight regulation of cell cycle progression in these cells [47]. Moreover, the cell cycle regulation is not only tightly related with pluripotency but also the cell cycle regulators have important functions in DNA damage response (DDR) [42]. Since maintaining genomic stability in hPSCs plays a pivotal role in their self-renewal and stemness, the role of EEF2 should be assessed in the nearest future as in terms of therapeutic application, genomic stability is the key to reducing the risks of cancer development due to abnormal cell replication.

Laminin subunit gamma 1 (LAMC1) belongs to Laminins, a family of extracellular matrix glycoproteins, which are the major non-collagenous constituent of basement membranes. Basement membranes are thin sheets of specialized extracellular matrix (ECM), underlying all epithelia and some other cell types. Laminins are important regulators of cellular functions such as cell adhesion, differentiation, migration, signaling, and metastasis. Human PSCs not only have characteristics typical for epithelial cells [48, 49] but they also rely upon ECM proteins for the support of their niche [50]. Human ESCs produce Laminin α1, α5, β1, and γ1 chains and deposit them as Laminin-511 into hESC-produced ECM. Importantly, Laminin-511 supports hESCs growth in defined medium equally well as Matrigel [50]. Indeed, LAMC1, as well as LAMB1, have been detected in the hESCs by proteomic analysis [11]. However, in our analysis, LAMC1 appears among downregulated proteins in hiPSCs with good phenotype in contrast to hESCs (**Figures 2** and **6**) regardless that all cell lines were grown on the same basement membrane matrix-Matrigel (Corning) with mTESR1 media [6]. As much as hiPSCs are not identical to hESCs, the identified differences may indicate the need for further research in the direction of the hPSCs niche supporting factors for better support of their *in vitro* maintenance.

SFXN1, RAP2B, and NUP98 are expressed in both hESCs and hiPSCs with "good" morphological phenotypes (**Figures 2** and **6**).

Sideroflexin 1(SFXN1) is an integral component of the mitochondrial inner membrane, and it is important for D-serine and L-serine transmembrane transporter activity.

Ras-related protein Rap-2b (RAP2B) is a member of the Ras family of small GTPbinding proteins, and it is involved in innate immune response and ERK signaling, both of which are important players during the reprogramming process. Also, RAP2B may play a role in cytoskeletal rearrangements and may regulate cell spreading through activation of the effector Traf2- and Nck-interacting kinase (TNIK) [51]. Moreover, RAP2B is expressed at high level in various human tumors, where its involvement in cellular spreading and migration was demonstrated more recently [52].

Nuclear pore complex protein Nup98 (NUP98) plays a role in the nuclear pore complex (NPC) assembly and/or maintenance. Nuclear pore complex (NPC) proteins are well-known for their critical roles in regulating nucleocytoplasmic traffic of macromolecules across the nuclear envelope. Several findings suggest that some nucleoporins, including Nup98, have additional functions in developmental gene regulation. Nup98 exhibits transcription-dependent mobility at the NPC but can also bind chromatin away from the nuclear envelope, and it is frequently involved in chromosomal translocations [53]. Importantly, acting as transcription factor, Nup98, could interact directly with histone-modifying enzymes CBP/p300 and histone deacetylases (HDACs), the role of which for hPSCs is well established. However, while the role of Nup98 as a multifunctional protein in macromolecular export has been

studied extensively [53], its precise role in hPSCs has not been elucidated and awaits further discovery.

Thus, we can conclude that despite the significant differences in the protein content for the two studied pools (hESCs vs. hiPSCs), when comparing cells of different types within the same experimental group of "good" morphological phenotype, only five differentially expressed proteins were found out of 1933 reliably identified proteins. This may indicate the similarity of the mechanisms that regulate the "good" morphology of hPSCs.

#### **4. Conclusions and future perspectives**

The development of reliable methods for estimating the quality of the hPSCs cultures is an urgent requirement for their reliable use in the clinic. Currently, much attention is paid to the creation of the automatic methods for selecting the best clones based on their images, as noninvasive methods for their evaluation. The first section of our chapter is devoted to these methods with a particular emphasis of our approaches [6, 7] based on the analysis of the morphology of colonies and cells. However, the search for the new approaches to analyze morphological parameters should not stop and the question of the regulation of the cell morphology deserves a separate chapter.

Our proteomic data for the first time demonstrated cytoskeletal proteins as top effectors of the "good" morphological hPSCs phenotype. As discussed above, most of these cytoskeletal proteins have not been studied in detail in hPSCs. The molecular differences on the proteome level between hiPSCs and hESCs lines, as reported in multiple publications, may be related to many factors such as time in culture, methods of cells propagation, general culture conditions, as well as different somatic origin of hiPSCs, the level of pluripotency, and many others [10, 54, 55]. Regardless of the used approaches and cell lines, all proteomics results revealed a large proportion of cytoskeletal proteins, thus highlighting cytoskeletal remodeling as a prominent characteristic for hPSCs phenotype [8–10, 12, 13]. That is not surprising, as the actin cytoskeleton network, consisting of actin filaments and crosslinking and motor proteins, regulates the shape of the most cells.

Understanding the mechanisms responsible for the dynamic changes of the colony morphology from the "good" to the "bad" is an important prerequisite for the safe clinical application of these cells, not only because the differentiation potential of hPSCs is deeply associated with the colony morphology but also because the morphological changes occur quicker and well before significant changes in the pluripotency markers expression profiles can be detected [47, 56]. Human PSC colonies demonstrate fast changes of morphological parameters during the exponential growth, and essential differences in their structure associated with the colony area, mean nuclei area, and mean distance between nearest neighbors were shown to be good indicators to detect possible changes of the pluripotency status [57]. So far, we are only making the first steps toward the complete understanding of this process.

Based on our data, we propose to expand the panel of hPSCs markers used to identify the "best" morphology phenotype to include the cytoskeletal proteins, namely MYH7, RDX, and CNN3 for evaluation of the best hESCs and EZRIN for evaluation of hiPSCs. Obviously, the quest for the reliable markers for the identification of the best morphology has to continue.

Eventually, the development of more complex automated approaches for comparative analysis of cells will provide the best quality control of clones, which will thus ensure their continued safe application in regenerative medicine.

*How Morphology of the Human Pluripotent Stem Cells Determines the Selection of the Best Clone DOI: http://dx.doi.org/10.5772/intechopen.112655*

#### **Acknowledgements**

This study was funded by the Russian Science Foundation, grant number 21-75- 20132 for IN. Proteomic analysis was performed using the equipment of the "Human Proteome" Core Facility Center of the Institute of Biomedical Chemistry (Moscow, Russia). The authors express their deep gratitude to Prof. S.L. Kiselev and A.V. Panova for providing the hiPSC line HPCASRi002-A (СaSR), and to Dr. Ivan Diakonov from Imperial College London for valuable comments and suggestions during the paper preparation. We are also thankful to the staff of the Institute of Cytology core microscopic facility M.L. Vorobev and G.I. Stein.

#### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Vitaly Gursky1†, Olga Krasnova1†, Julia Sopova1 , Anastasia Kovaleva1 , Karina Kulakova1 , Olga Tikhonova2 and Irina Neganova1 \*

1 Institute of Cytology, Saint Petersburg, Russia

2 Institute of Biomedical Chemistry, Moscow, Russia

\*Address all correspondence to: irina.neganova@incras.ru

† These authors contributed equally.

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 2**

## Undifferentiated and Differentiated Spermatogonial Stem Cells

*Danial Hashemi Karoii and Hossein Azizi*

#### **Abstract**

Spermatogenesis is initiated and sustained by a rare population of singular spermatogonial stem cells (SSCs). These SSCs are connected to the basement membrane of the seminiferous tubules and possess distinctive morphological characteristics. They serve as a vital foundation for a robust stem cell system within the testis, crucial for spermatogenesis and reproductive processes. The isolation and cultivation of human SSCs would significantly enhance our understanding of germ and stem cell biology in humans. Although a challenging endeavor, the recent advancements in enriching and propagating spermatogonia carrying the male genome offer a significant stride toward future transplantation and the restoration of fertility in clinical settings.

**Keywords:** spermatogenesis, spermatogonial stem cells, stem cell, transplantation, fertility

#### **1. Introduction**

Male reproductive systems are characterized by spermatogenesis, which is a genital process. It is the spermatogonial stem cells (SSCs) that play the most important role in this system [1]. Somatic cells and Sertoli cells support SSCs in the basement membrane of seminiferous tubules. Seminiferous tubules support spermatogenesis by containing Sertoli cells (sustentacular cells) [2]. By secreting growth factors during spermatogenesis, these somatic cells support the fate of SSCs.

In spermatogenesis, genetic information is transmitted to the next generation. This process results in spermatogonia differing and proliferating into spermatozoa [3]. They can be subdivided into single spermatogonia (As), paired spermatogonia (Apr), or aligned spermatogonia (Aal) based on their topological organization [4]. Undifferentiated spermatogonia are called spermatogonia collectively. After proliferating throughout the seminiferous epithelium cycle, these cells become quiescent before they differentiate into A1 spermatogonia. An important subsequent division (A2-B) leads to the separation of differentiated SSCs into primary and secondary spermatocytes. As spermatozoa elongate following several meiotic divisions, secondary spermatocytes become round spermatids. Differentiated spermatogonia A1 through B are collectively referred to as spermatogonia A1 through B [5].

#### **Figure 1.**

*Spermatogenesis can be divided into five successive stages of germ cell development: (1) spermatogonia, (2) primary spermatocytes, (3) secondary spermatocytes, (4) spermatids, and (5) spermatozoa.*

Unipotent stem cells called spermatogonial stem cells (SSC) produce sperm throughout the male's lifetime. In the development, signaling pathway, growth regulation, and differentiation of cells, zinc finger, and BTB domain containing 16 (ZBTB16/PLZF) genes play several important roles. Sperm cells, embryonic stem cells, and pluripotent embryonic stem cells express PLZF [6]. Our study examined the expression of PLZF in testis, stem cells, pluripotent embryonic stem cells, and ES-like cells (**Figure 1**).

There are two types of activities shown by spermatogonia. To maintain the primary pool of stem cells, self-renewal by mitotic divisions is necessary, followed by spermatogenesis, defined as the process of dividing undifferentiated spermatogonia into differentiated ones [7]. As a result of cytoskeletal activity, germ cell movements are also regulated in size and shape. Actin, microtubules, and intermediate filaments are part of the cytoskeleton, which governs the activities of those cells. Vimentin is crucial to these spermatogenic processes because it plays a critical role in the cytoskeleton [8].

#### **2. Some gene expression between pluripotent stem cells and testicular germ cells**

#### **2.1 PLZF**

The PLZF protein, as detected by immunohistochemistry (IMH), was localized in the differentiated tubular cells of the testis tubule center, as well as in the basal compartment of the seminiferous tubules of the undifferentiated testis [9]. A significant difference was found in PLZF IMH-positive cells in adult testis compared to neonates

*Undifferentiated and Differentiated Spermatogonial Stem Cells DOI: http://dx.doi.org/10.5772/intechopen.112964*

#### **Figure 2.**

*Testis section with PLZF positive cells. Sections of spermatogonial stem cells from undifferentiated and differentiated spermatogonials (A) and from in vitro spermatogonials (B) were analyzed for PLZF positive cells. There was a greater number of PLZF positive cells in undifferentiated testes compared to differentiated testes and in vitro testes.*

when positive cells were counted in sections of seminiferous tubules from undifferentiated and differentiated testes (P < 0.05). PLZF germ cell marker was strongly ICC-positive for SSC colonies *in vitro* but negative for ES cells and ES-like cells.

In pluripotent germ cells, PLZF is downregulated, but it is a transcription factor associated with testicular germ cell proliferation. As a result, *in vitro* and *in vivo* analysis of germ cell development can be supported (**Figure 2**).

A marker of spermatogonial differentiation, KIT, is directly repressed by PLZF, according to Filipponi et al. In the testis niche, PLZF plays an important role in maintaining the self-renewal and maintenance of the SSC. In undifferentiated spermatogonia, PLZF is co-expressed with Oct4 [9]. PLZF loss leads to a limited number of normal spermatozoa and, after birth, a lack of respected germline due to the progressive loss of spermatozoa. The expression of genes regulating limb and axial skeletal development is regulated by PLZF during embryogenesis. There is a genetic relationship between PLZF and Gli3 and Hox5 genes during limb development [10]. Testis and SSCs expressed PLZF, making it a SSC marker according to previous studies. PLZF expression in neonate and adult testicular sections, isolated SSCs, ES cells, and ES-like cells of mouse testicular culture was examined to determine if PLZF expression is the same in both testicular germ cells and pluripotent stem cells. According to the results, plunging stem cells do not express PLZF [9].

#### **2.2 Vimentin**

There are two types of activity in spermatogonia. To maintain the primary pool of stem cells, self-renewal occurs through mitotic division, followed by spermatogenesis, which refers to the differentiation of undifferentiated spermatogonia into differentiated spermatogonia. Associated with these events are widespread adjustments in germ cell movements in relation to cytoskeletal activity. Microtubules, actin, intermediate filaments, and the cytoskeleton make up the cytoskeleton, which governs the activities of those cells [10–12]. During these spermatogenic processes, vimentin plays a critical role in cytoskeleton function. Spermatogenesis begins with the expression of vimentin, an intermediate filament. The filamentous intermediate filament of vimentin connects the tubulin and actin cytoskeleton to the nuclear periphery [13]. As spermatogenesis progresses, vimentin functions primarily to ensure cellular stiffness, to maintain actin position, to facilitate cell migration, to divide cells, and to organize organelles. Additionally, vimentin has a number of essential roles, including determining cell shape, differentiation, motility, maintaining cell junctions, contributing to the maintenance of ordinary spermatogonia morphology, and anchoring germ cells to the seminiferous epithelium to anchor them [14].

It has been suggested that vimentin plays an important role in the differentiation of SSCs. However, it has been unclear whether the vimentin intermediate filament is necessary during differentiation *in vitro*. Finally, vimentin is expressed in male germ cells in a few studies. Separating germ cells in the adluminal and luminal compartments of seminiferous tubules expressed high levels of vimentin, while undifferentiated cells located in the basal compartment expressed low levels of vimentin. Immunohistochemical analysis indicated that vimentin expression was associated with Sertoli cells near the basal membrane. Afterward, we differentiated germ cells from Sertoli cells using SOX9 specific markers. According to IMH analysis, Sertoli cells expressed SOX9 cytoplasmically and differentiating germ cells expressed SOX9 negatively (**Figures 3** and **4**). Growth factors were added to isolated cells after enzyme digestion. In a previous study, we characterized isolated testicular cells. Immunocytochemistry was used to examine vimentin expression in primary and secondary spermatocytes, round spermatids, and undifferentiated spermatogonia [11].

Using the DAZL specific marker, we distinguished primary and secondary spermatogonia as well as spermatids from undifferentiated spermatogonia. Undifferentiated spermatogonia expressed DAZL at a high level, while differentiated germ cells did not. Differentiating germ cells express high levels of vimentin, while

#### **Figure 3.**

*Characterization of vimentin intermediate filaments in adult mouse seminiferous tubules by immunohistochemistry. Differentiating germ cells located in the middle compartment of seminiferous tubules expressed high levels of vimentin, whereas undifferentiated cells located in the basal compartment expressed low levels. Merged image with DAPI. (A) Vimentin, red, (B) DAPI, blue (scale bar: 15 μm), and merge (C).*

*Undifferentiated and Differentiated Spermatogonial Stem Cells DOI: http://dx.doi.org/10.5772/intechopen.112964*

#### **Figure 4.**

*DAZL and vimentin immunocytochemistry. In (A), DAZL expression is marked by green fluorescence; in (B), vimentin expression is marked by red fluorescence; in (C), DAPI is shown merged with the green fluorescence. The white arrow represents undifferentiated spermatogonia, and the yellow arrow represents germ cells that are differentiating. DAZL is green; vimentin is red; and DAPI is blue (scale bar: 15 μm), and (D) bright field. (scale bar: 15 μm) (get this figure from our recent article [11]).*

undifferentiated spermatogonia display low levels. Last time, spermatogonia were differentiated from undifferentiated spermatogonia by utilizing the Ki67 specific marker. As expected, differentiated germ cells express a high level of Ki67, while undifferentiated spermatogonia display a low level of Ki67 expression [11, 14].

#### **2.3 POU5F1**

With the advent of SSC culture techniques and genetic analysis, important genes were identified that maintain the stem-cell function of SSCs. As, Apr, and Aal spermatogonia express POU5F1 (POU domain, class 5, transcription factor 1), one of the molecular markers of undifferentiated spermatogonia. The POU5F1 gene encodes a transcription factor that plays an essential role in controlling embryonic development and maintaining pluripotency and self-renewal. POU5F1A, POU5F1B, and POU5F1B1 are produced during alternative splicing, but only POU5F1A maintains stemness.

Developing and optimizing treatment methods for male infertility requires understanding how SSCs differentiate and how genes involved in spermatogenesis are expressed at different stages of SSC differentiation. Since male fertility depends on accurate spermatogenesis and the population of SSCs in the testis, understanding the mechanisms behind SSC differentiation is essential. The POU5F1 protein localization

in neonate and adult mice testis did not distinguish the populations of SSCs in our previous study using three antibodies [15]. A comparison between this study and the previous study in this article reveals differences in POU5F1 expression between the two populations of spermatogonia. Since SSCs play an important role in regenerative medicine, it is important to understand the differences between two different populations of SSCs in order to utilize each one appropriately. Our research has helped the scientific community gain a better understanding of what POU5F1 actually does during spermatogenesis [16].

In mouse seminiferous tubules, we analyzed the expression pattern of POU5F1 using immunohistochemistry. Using POU5F1 Proprietary Antibody, we identified SSCs using immunohistochemistry. After merging and staining mouse seminiferous tubules with DAPI, it was found that they express this marker. Seminiferous tubule basal spermatogonial cells exhibit the highest POU5F1 expression, as seen in fluorescent microscope images [17].

Images obtained from the bright field microscope demonstrated the difference between differentiated and undifferentiated spermatogonia when cells were extracted from two spermatogonial populations and cultured on their respective media. Spermatogonial cells that were undifferentiated did not grow much after culturing; they were also small and tended not to form specific shapes. Unlike undifferentiated spermatogonial cells, differentiated spermatogonial cells expand during culturing and tend to form clusters. Thus, spermatogonial cells can be diagnosed based on their morphology [17].

The comparison of two SSC populations shown that there are differentiated and undifferentiated. A bright field of spermatogonia cells during differentiation indicates that undifferentiated and differentiated spermatogonial cells differ morphologically. In the subsequent study, we examined POU5F1 expression in differentiated and undifferentiated spermatogonia by ICC. In two spermatogonia populations, DAPI staining was used to determine SSCs and to stain for the POU5F1 marker. According to ICC analysis of images obtained with a scanning UV laser microscope, undifferentiated cells expressed more POU5F1 than differentiated cells. According to IMH analysis, basement membrane cells expressed POU5F1 at high levels and differentiated cells expressed it at low levels (**Figure 5**).

#### **2.4 VASA**

It was discovered that the VASA gene plays a crucial role in the development of female germ stem cells (GSCs) in Drosophila [18]. The VASA gene is eliminated in

#### **Figure 5.**

*In the seminiferous tubules, immunohistochemistry (IMH) analysis revealed the expression pattern of POU5F1. POU5F1 (A), DAPI (B), and 4*′*,6-diamidino-2-phenylindole (POU5F1) (C) show the mixed images and sharp expression of POU5F1 in the basal membrane (scale bar: 15 μm).*

#### *Undifferentiated and Differentiated Spermatogonial Stem Cells DOI: http://dx.doi.org/10.5772/intechopen.112964*

mice with a systematic genetic deficiency that results in a loss of sperm production in the males. During meiosis phases, GSCs in males appear to die at the zygotene stage, but the ovary appears to function normally [19]. On embryonic day 12.5 and subsequent to entry into the gonadal anlage, mice show localization of VASA in PGCs. PLZF has been implicated in direct repression of Kit transcription, a spermatogonial differentiation marker, in previous studies. The loss of the PLZF gene also causes limited numbers of normal spermatozoa to be produced, resulting in an impaired germline after birth. In embryogenesis, PLZF regulates gene expression during the patterning of the limbs and axial skeleton. Two types of cell populations present in seminiferous tubules were analyzed for co-expression of PLZF and Oct4.

Spermatogenesis defects are often responsible for infertility in humans. It is essential to understand normal spermatogenesis in order to develop subfertility and infertility in humans. RNA-binding proteins play a crucial role in the formation of germ cells. In addition to rhesus macaques, goats, cattle, pigs, and other animals, VASA is expressed in germ cells [20]. ATP-dependent RNA helicases and RNAbinding proteins are encoded by the VASA gene. Spherical spermatids, spermatogonia, and spermatocytes can be identified in human testicular tissues based on the expression of the VASA protein. Human spermatogenesis might be better understood by understanding how these proteins are expressed in different germ cells at different stages [21, 22].

Drosophila cells dispersed VASA protein evenly throughout their cytoplasm. VASA proteins function as RNA chaperones and are connected to chromatoid bodies. Various studies have shown that VASA functions as the mRNA transcript and CB in spermatozoa when the genome is inactive. In addition to spermatogenesis, VASA is essential for the differentiation of embryonic stem cells into primordial germ cells (**Figure 6**).

#### **2.5 SOX2**

In stem cells and progenitor cells, Sox2 plays an important role in maintaining pluripotency and differentiation. Furthermore, it plays a role in cell reprogramming in the inner cell mass (ICM) and ectoderm of blastocysts (10). As well as regulating Sall4, Plzf, Gfra1, Oct4, Klf4, Foxm1, Cux1, Zfp143, Trp53, E2f4, Esrrb, Nfyb, and c-Myc, Sox2 can also convert somatic cells to pluripotent stem cells. Reprogramming and pluripotency are dependent on each other for the production of induced pluripotent stem cells (iPS) [23]. Mice's primordial germ cells also expressed Nanog and Sox2. Human primordial germ cells may still contain Sox2, but further research

#### **Figure 6.**

*Immunohistochemy image VASA and vimentin in vitro. (A) Immunohistochemy of VASA, (B) Immunohistochemy image of vimentin, (C) DAPI, and (D) merge (scale bar: 100 μm).*

is needed to confirm this. The number of pluripotent cells decreases when Sox2 expression is reduced, and cell differentiation begins. There have also been reports of high levels of Sox2 expression in brain cancers that can cause pituitary tumors and decreased levels of Sox2 expression in patients with ocular abnormalities [24]. Breast cancer, colorectal cancer, and glioblastoma have been linked to increased Sox2 expression, while gastric cancer is associated with its decrease. Different cell types and tissues express Sox2 differently in humans. Bone marrow, endometrium, heart, kidney, liver, and pancreas have lower expression levels than the lung, prostate gland, stomach, testis, and fallopian tube. Sox2 gene interaction with spermatogenesis genes was the goal of this study. We investigated and compared gene expression in differentiated and undifferentiated spermatogonial stem cells. Moreover, this gene was examined in differentiated and undifferentiated spermatogonia to determine its quantity and mode of expression. To improve male infertility treatment, this research aimed to understand the mechanisms involved in sperm generation [25].

The interaction network between Sox2 protein and some other proteins involved in spermatogenesis was analyzed in this experiment, as shown in **Figures 1** and **2**. Using STRING and Cytoscape databases, key genes were identified that are not connected to Sox2, including Sim2 and Rfx4. These two figures illustrate the origins of these genes, the sources of measurement and connection, as well as where each gene is expressed in each testicle and its biological function. A greater or lesser degree of relationship was also detected between genes. Oct4, Nanog, and Klf4 are strongly connected with Sox2, but Smad1, Gdnf, Egr2, and Stra8 are poor connections. In addition to POU5FA, Stra8 and Gdnf, Sox2 appears to be related to Pou5f1, Stra8, Klf4, and Bmp8b. In addition, Sox2 is connected with Pou5f1, Klf4, Kit, and Nanog in stem cell population maintenance. The expression of Sox2 was examined by immunohistochemical analysis of cross sections of seminiferous tubules. According to immunohistochemistry analysis by confocal scanning UV laser microscope, Sox2 nuclear expression increased during spermatogenesis *in vivo* over time (**Figure 3**). In this figure, single undifferentiated cells can be seen along with a group of spermatogonial stem cells. Different expressions of Sox2 were observed in isolated spermatogonia that had been cultured and differentiated *in vitro* (**Figure 4**). The expression of Sox2 in differentiated cells exceeded expectations when considered as a pluripotency factor. A Fluidigm PCR analysis of spermatogonia grown *in vitro* showed that differentiated spermatogonia expressed much more Sox2 than undifferentiated spermatogonia. A high level of Sox2 expression was observed in differentiated cells, and a significant difference in Sox2 expression between differentiated and undifferentiated cells was also observed (p < 0.05). Undifferentiated spermatogonia under *in vitro* conditions also expressed high levels of Sox2. However, Sox2 was highly expressed in differentiated spermatogonia, similar to *in vivo* conditions (**Figure 7**). The study's most interesting finding was that undifferentiated cells expressed high levels of Sox2 cytoplasmically and differentiated cells expressed high levels of Sox2 [24].

Sox2 expression is essential for stem cells and SSCs to remain pluripotent, and it also plays an important role in maintaining, increasing, and specializing SSCs and PGCs. Additionally, both *in vivo* and *in vitro* spermatogonia expressed Sox2. The nucleus of differentiated spermatogonia expressed more Sox2 than the cytoplasm of undifferentiated spermatogonia in *in vitro* experiments. Differentiated spermatogonia expressed more Sox2 in *in vivo* experiments than undifferentiated spermatogonia. Still, this gene expression played a critical role in maintaining stem cell pluripotency, which is crucial for spermatogenesis. By studying Sox2 expression in spermatogenesis, we may be able to improve male infertility treatments.

*Undifferentiated and Differentiated Spermatogonial Stem Cells DOI: http://dx.doi.org/10.5772/intechopen.112964*

**Figure 7.**

*The pattern of Sox2 expression. (A) the expression of Sox2 in seminiferous tubules, (B) the DAPI (blue) staining shows the nuclear cells, and (C) merged images (scale bar 50 μm).*

#### **3. Gene expression profiling of SSCs seems to be age dependent**

An embryonic germ layer can be differentiated into ectodermal, mesodermal, and endodermal cells using pluripotent stem cells (PSCs). PSCs have been generated using several different approaches, including ESCs obtained from embryonic blastocysts after fertilization. The so-called induced pluripotent stem cells (iPSCs) were also obtained by enforcing the expression of pluripotency genes in somatic cells; SSCs have proven to be a promising method for establishing PSCs in a more natural and ethically acceptable manner, especially for therapeutic approaches in medicine [26].

#### **Figure 8.**

*Neonatal SSCs (colored dark blue), adult SSCs (colored light blue), and adult SSCs (colored blue-green) express pluripotency and germ cell genes differently. The red arrows indicate genes downregulated in adult SSCs, while the purple arrows indicate genes upregulated (more than twofold). Note the downregulation of Oct4, Nanog, and Sox2 in 12-week-old mice's SSCs) (get this figure from our recent article [29]).*

It is possible to isolate and expand SSCs *in vitro*, as they are found in small numbers in the testis. They are unipotent stem cells under the control of their stem cell niches, but under specific culture conditions outside the niche and without exogenous pluripotency genes, they are capable of converting into ESC-like cells at various times after culture or isolation of SSCs [27].

In neonatal and adult SSCs obtained from 7- and 12-week-old mice, real-time PCR was used to quantify and analyze the expression of important germ cell-enriched genes (LHX1, Stella, VASA, DAZL, CD9, EPCAM, GPR125, GDF3, THY1, STRA8, GFRa1, 1ITGB1, KIT, ETV5, and BCL6B) and pluripotency associated genes (Oct4, Nanog, Sox2, TDGF4, KLF4, MYC, LIN28, SALL4, and DPPA3).

**Figure 7** shows how neonatal SSCs and adult SSCs were grouped according to hierarchical clustering (dendrograms) and principal component analysis (PCA). There was a significant difference between neonatal and adult SSC clusters in the heat map analysis of pluripotency and germ cell genes [28].

The Oct4, NANOG, TDGF1, and Sox2 expression levels of neonatal SSCs were significantly higher than those of adult SSCs. MYC, NODAL, LHX1, GDF3, GPR125, CD9, ITGB1, VASA, TAF4b, EPCAM, BCL2L2, ETV5, DAZL, KLF4, RET, and THY1 were significantly higher expressed in adult SSCs (fold change >2 and -test) than in neonatal SSCs [28].

These differences became even more apparent when neonatal SSCs were compared to SSCs obtained from 12-week-old mice (see Supplementary Tables). In addition, 7-week-old mice have significantly higher expression levels of pluripotency genes than 12-week-old mice (**Figure 8**).

#### **4. An analysis of haGSCs with predefined gene sets related to germline, pluripotency, fibroblasts, and mesenchymal stem cells**

There has been some evidence that human adult germ stem cells (haGSCs) derived from highly enriched spermatogonia isolated from adult human testicular tissue are highly versatile and share some similarities with human embryonic stem cells (hESCs). They can be differentiated *in vitro* into a variety of cell lineages comprising the three germ layers and express genes associated with pluripotent cells. Based on some studies, mesenchymal stem cells or cells similar to MSCs may have been the source of cells expressing markers of pluripotency. HaGSCs may also be low-differentiated testicular fibroblasts, according to some studies. The stem cells from human testis biopsy, on the other hand, were derived from both germ and mesenchyme and could differentiate into cells from all three germ layers. Other research has shown that haGSCs can produce small teratomas similar to hESCs. Each of these studies raised new questions regarding the true nature of pluripotency in haGSCs. Generally, the activation of a transcriptional regulatory network is required for the pluripotency of cells, which has been observed in ex vivo cultures of early embryonic cells, as well as germ cells. Members of the pluripotency network are normally active in these cells, including morula and blastocyst-stage (inner cell mass) embryonic cells, epiblasts, primordial germ cells (PGCs), and germline stem cells.

Fluidigm real-time PCR analysis was performed on the following germ cell- and pluripotency-associated genes based on microarray results in addition to the initial panel of germ cell- and pluripotency-associated genes: L1TD1, SALL4, JARID2, HOOK1, EPCAM, PROM1, SALL2, IGFR2BP3, REX1, and GATA4. A similar pattern of gene expression was observed in haGSCs derived from two additional patients,

#### *Undifferentiated and Differentiated Spermatogonial Stem Cells DOI: http://dx.doi.org/10.5772/intechopen.112964*

with VASA, DAZL, and PLZF predominant. VASA, DAZL, and PLZF expression in haGSCs was significantly lower than in hSSCs. While STELLA and GFR1 were strongly expressed in haGSCs, the other two germ cell-specific genes were not. As compared to hSSCs, haGSCs expressed REX1, LIFR, and NANOS in similar ranges, while CD9 expressed at a higher level. In hFibs, neither DAZL nor LIFR were expressed. Compared to hFibs, hSSCs and haGSCs showed significantly higher expression of germ cell-associated genes. Similar to hESCs, haGSCs possess a rudimentary gene expression profile associated with germ cells. There were higher levels of CD9 and GFR1 expression in haGSCs than in hESCs (**Figure 9**).

Cell culture produces haGSCs from spermatogonia and MACS enriched in CD49f but never from negatively selected fractions or from patients without spermatogonia. A central cluster of haGSCs with outgrowing "epithelial"-like cells characterized these colonies from hFibs. The expression of germ- and pluripotency-related genes was quite different in haGSCs compared to hFibs based on single-cell Fluidigm analysis. The majority of outliner hESCs and haGSCs did not share any similarities with hFibs.

#### **Figure 9.** *Based on microarray data, haGSCs are upregulated by pluripotency-associated genes compared to hFibs.*

*Advances in Pluripotent Stem Cells*

In addition, it became clear that haGSC colonies were heterogeneous, displaying similar characteristics to pluripotent states. Moreover, different haGSC colonies showed a relatively heterogeneous expression of germ- and pluripotency-associated genes in the microarray study. In comparison with hESCs and hFibs, the haGSC transcriptome and high variance genes showed a distinct separation from hFibs.

#### **5. Spermatogonia stem cell gene ontology and signaling pathway bioinformatics analysis**

Statistic and bioinformatics analyses are the main bottleneck in transcriptomic studies. Candidates were usually identified using widely accepted statistical criteria (such as P values and fold changes). In order to translate the gene list into biomedical significance, automatic functional annotation was performed using knowledge bases, such as Gene Ontology (GO) and KEGG pathways. We have recently proposed a framework for revising candidate protein lists and identifying novel proteins based on reanalysis of published proteomics data. We also believe that reanalyzing transcriptomes using optimized bioinformatics methods would help us interpret the results better.

A previously published dataset was used to extract the expression data for two cell types (primitive and differentiated type A spermatogonia) from a previously published study. In the next step, eight canonical markers were evaluated using RNA-Seq data. As well as the expression index, we proposed a new parameter for integrating absolute and relative expression abundances. Our statistical model used this parameter to dynamically select the best cutoff considering biological relevance. To understand and study the maintenance of SSCs, we constructed a refined network by combining information about physical interaction, expression change, biological function, and disease association.

Despite transcriptomics' ability to profile gene expression and regulation, bioinformatics analysis is crucial for translating gene lists into functional biomedical applications. The two groups are usually screened using a one-size-fits-all cutoff using statistical inference. Considering both absolute abundance and relative change, we ranked genes using the expression index proposed in this study. By taking wellstudied genes associated with SSC self-renewal as a positive reference, we developed a statistical model that dynamically screens for the best cutoff to prioritize candidate genes. Based on predicted genes involved in cell proliferation or differentiation, an optimal cutoff was determined for identifying functionally important genes [29].

SSCs are thought to be proliferating and surviving by activating and silencing various endogenous genes in response to exogenous factors. The mechanism of SSC self-renewal *in vivo* is still poorly understood despite the identification of a few key regulators and signaling pathways. Our transient model for self-renewal versus differentiation is based on SG-A cells (primitive versus differentiated). Based on the expert knowledge-guided and dynamic statistical model described above, we identified 1119 candidate genes with the best enrichment of canonical markers. By combining physical interactions, expression changes, cellular function, and disease associations with these genes, we finally created a refined network [30]. A high quality and relevance of gene prioritization can be seen in this network, which contains five of the eight canonical markers. As well as finding novel regulators of SSC self-renewal, we suggest the refined network could be used to identify target genes for male infertility and testicular cancer treatment [16, 29, 31–33].

*Undifferentiated and Differentiated Spermatogonial Stem Cells DOI: http://dx.doi.org/10.5772/intechopen.112964*

**Figure 10.**

*In silico analysis in spermatogonial stem cell genes. (A) PPI network in spermatogonial stem cell genes, and (B) gene ontology in spermatogonial stem cell.*

The protein–protein interaction network with 945 genes was visualized using the STRING (v.11) database. Spermatogenesis was largely regulated by vimentin interaction and regulation, according to the study. There is a strong interaction between vimentin and Stat3, Mmp2, Trp53, Casp7, AURKB, Pik3r1, Ctnnb1, Lgals3, Cdkn1a, and Snai1. There was also a clear association between Trp53, Mmp2, Casp7, Stat3, and Pik3r1. Reactome and KEGG selected any spermatogenesis-related signaling pathway as the master regulator of the pathways involved in spermatogenesis. **Figure 10** shows a strong correlation between the highlighted genes.

#### **6. Conclusion**

A large number of spermatogonia are produced during each epithelial cycle when undifferentiated spermatogonia proliferate. When these Aal spermatogonia are in quiescence, they do not divide and develop into the AJ spermatogonia, the first generation of differentiating spermatogonia. Testis undifferentiated regions and the basal section of the seminiferous tubule are strongly expressed with POU5F1, VASA, and PLZF factors, according to the investigation. A comparison of differentiated and undifferentiated populations of spermatogonial stem cells was also conducted. It was found that POU5F1, VASA, and PLZF levels decrease with differentiation, whereas vimentin and sox2 levels increase in differentiated spermatogonial stem cells. In light of the use of SSCs for clinical and therapeutic purposes, such as male infertility, the study of spermatogonial stem cells will provide better insight into the regulation of stem cells in the testis. Also, molecular research and analysis, as well as improved understanding of how genes such as these genes contribute to male infertility, can

lead to new treatments or improvements to existing ones. The laboratory can also be used to treat Azoospermia and Oligospermia, abnormal sperm function, and blockages that prevent sperm delivery by investigating the differentiation process of spermatogonial stem cells and better understanding the methods of differentiation.

#### **Acknowledgements**

This study was funded by Centre for International Scientific Studies and Collaboration (CISSC), Tehran University and Amol University of special modern technologies.

### **Conflict of interest**

The authors declare no conflict of interest.

#### **Abbreviations**


#### **Author details**

Danial Hashemi Karoii1,2 and Hossein Azizi2 \*

1 Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran

2 Faculty of Biotechnology, Amol University of Special Modern Technologies, Amol, Iran

\*Address all correspondence to: h.azizi@ausmt.ac.ir

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### Section 2
