摘要
三維細胞培養(3D cell culture)提供一個方便的體外研究工具，可用於了解腫瘤微環境和腫瘤存活機制。由於傳統的二維細胞培養無法模擬實體腫瘤異質及多種細胞的複雜結構，所以對於能大量進行三維細胞培養的實驗方式仍存在龐大的市場需求，希望藉以模擬並複製在實體腫瘤臨床上觀察到的複雜細胞反應和動態變化。在本研究論文中，利用微流體裝置建立了大量懸浮、三維骨肉瘤細胞 (MG-63) 團塊培養的方式，並應用於腫瘤微環境的體外模式比較分析。研究中，對於細胞團塊分泌的A型血管內皮細胞生長因子(VEGF-A)進行研究，比較正常生長和壓力條件下的表現差異。結果顯示三維細胞團塊的VEGF-A在低生長壓力時濃度較低，但在高生長壓力下則增加。這些觀察證實了我們的三維細胞團塊培養模式具有和二維模型相異的生物功能，可以觀察到在對細胞團塊施加壓力情形下，細胞反應並產生VEGF-A的表現變化，類似於在促血管生成的無血管腫瘤中所發現的反應。文獻顯示，VEGF-A的刺激通常是由實體腫瘤核心內缺氧區或低氧區的形成引起的，為了研究在三維細胞團塊中的低氧核心，我們運用多光子雷射掃描顯微鏡（MPLSM）並配合特別撰寫的程式碼來進行分析。使用市售的低氧敏感染料對三維細胞團塊的活細胞進行三維螢光影像進行比較，比較球體內部在貼附條件下以及接觸人類臍靜脈內皮細胞（HUVECs）層時，兩者的氧氣濃度差異。這個實驗結果發現了低氧核心在三維細胞團塊內的存在以及在內皮細胞的影響下會量測到更高的氧氣濃度。藉由此種間接螢光檢測，本研究提出了一種可以在活腫瘤球體內，研究氧氣微環境的有效方法。腫瘤核心的低氧狀態具有調節促血管生成之細胞訊號和內皮細胞增生的功能，根據臨床研究報告，我們知道通過細胞激素VEGF-A和低氧誘導因子(HIF)的影響，無血管性的實體腫瘤通過血管新生可以轉變成更具侵略性的具有血管的腫瘤階段。基於單培養腫瘤球體中低氧狀況下與VEGF-A調節的成功觀察，本研究更進一步建立了三維混合腫瘤/內皮共培養細胞團塊模型，以探索血管腫瘤中的內皮細胞生長。在實驗中，將標有綠色螢光蛋白的HUVECs（GFP-HUVECs）添加到預先形成的MG-63球體培養中，形成腫瘤細胞/內皮細胞混合細胞團塊。通過顯微鏡測量細胞團塊直徑和綠色螢光蛋白強度的變化觀察內皮細胞增生的情形，並觀察類似於血管腫瘤微環境VEGF-A分泌量及相對應濃度上升的現象。在本研究中開發的共培養實驗模型具有探索影響體外血管生成許多不同因子以及藥物測試和生物醫學應用的潛力。本論文中開發的三維細胞團塊培養模式，可作為癌症研究中探究更多促血管生成因子和細胞反應的有利工具。

Three-dimensional (3D) cell culture provides a convenient in-vitro tool to study tumor micro-environment and tumor survival mechanisms. Since traditional 2D in-vitro cell cultures are incapable of representing the multicellular, heterogenous complex structures of solid tumors, there is an unmet market demand for 3D cell culture models on a large scale to replicate the complex cellular responses and dynamic changes observed clinically in solid tumors. In this work, large scale production of floating, 3D osteosarcoma cell clusters or spheroid culture, is established using microfluidic devices, for comparative analysis of tumor microenvironment in-vitro. Vascular endothelial growth factor of type A (VEGF-A) of the spheroids, is studied under normal growth and stress conditions to investigate their roles in spheroid growth. The results show the VEGF-A concentration for the 3D spheroids, decreases for low stress levels but increases at high stress levels. These observations substantiate the functional capabilities of our 3D spheroid model to show cellular stress responses and generate VEGF-A variations, similar to responses found in pro-angiogenic avascular tumors.
Studies have shown that stimulation of VEGF-A is often triggered by formation of oxygen deprived zones or hypoxia zones inside the core of solid tumors. To study such hypoxia cores, in our 3D spheroids, we use multi-photon laser scanning microscopy (MPLSM) combined with a specific analysis code. 3D fluorescence-based live imaging of the spheroids is achieved using a commercially available hypoxia sensitive dye to compare the relative oxygenation within the spheroids under conditions of adhesion, and contact with monolayers of human umbilical vein endothelial cells (HUVECs). The work emphasizes the presence of hypoxia cores in 3D spheroids and better oxygenation under influence of endothelial cells. It also presents an efficient method to study oxygen microenvironments, within live tumor spheroids by indirect fluorescence detection.
Hypoxia in tumor cores, are held responsible for regulating pro-angiogenic cell signaling and endothelial proliferation. Clinical studies have reported the transformation of avascular solid tumors to a more aggressive vascular stage through angiogenesis through the influence of cytokine VEGF-A and hypoxia inducible factors. Based on the successful observations of VEGF-A modulations and hypoxia in the monocultured MG-63 tumor spheroids, a 3D mixed tumor/endothelial co-cultured spheroid model is established in our work to explore the endothelial growth found in vascular tumors. For the experiments, Green Fluorescence Protein tagged HUVECs (GFP-HUVECs) are added to pre-developed MG-63 spheroid seeds to form tumor/endothelial mixed spheroids. The variations of diameters and GFP intensity as measured by microscopy, suggest endothelial proliferation to occur, corresponding to surges observed in VEGF-A secretions, similar to events reported in microenvironments of vascular tumors. The coculture model developed in this study has the potential to unravel many different aspects of angiogenesis in-vitro, along with other drug testing and biomedical applications. The 3D spheroid models developed in this research work is suitable tool to study many more angiogenic factors and cellular responses for cancer research.

Contents

List of figures iv
List of tables viii
Acknowledgement ix
Abstract x
Abstract (Chinese) xii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Literature Review 4
1.3.1 Tumor Microenvironments 4
1.3.2 Cell Spheroid Cultures in Microfluidic Device 7
1.4 Thesis Objective 12
1.5 Thesis Outline 14
Chapter 2 Developing Three-Dimensional Spheroid Models in
Microfluidic Devices for Analysis of VEGF-A Secretion under Cellular Stresses 16
2.1 Introduction 16
2.2 Materials and Methods 20
2.2.1 Fabrication of Spheroid Culture Microfluidic Chip 20
2.2.2 Cell Culture 21
2.2.3 Cellular Stress Application 23
2.2.4 Cell Analysis 24
2.3 Results and Discussion 25
2.3.1 Formation and Growth of Cell Spheroids 26
2.3.2 Comparison between Culture Formats 28
2.3.3 Effect of Nutrient Deficiency: Reduced Serum Culture 30
2.3.4 Effect of HIF Inhibition: Drug Treatment 35
2.4 Conclusion 41
Chapter 3 Study of Hypoxia within Live Tumor Spheroids
using Microfluidic Devices and Multiphoton Laser Scanning Microscopy 43
3.1 Introduction 43
3.2 Materials and Methods 45
3.2.1 Fabrication of Spheroid Culture Chip 45
3.2.2 Cell Culture 46
3.2.3 Spheroid Formation and Harvesting 46
3.2.4 Viability Assay 47
3.2.5 Estimation of Response of Reagent to Oxygen Tensions 48
3.2.6 Imaging Oxygen Tension within Spheroids 49
3.2.7 Image Analysis 51
3.3 Results and Discussions 52
3.3.1 Characterization of Hypoxia Reagent 52
3.3.2 Cell Spheroid Culture and Imaging 53
3.3.3 Analysis of Oxygen Tension within Monoculture Spheroids 55
3.3.4 Coculture of Spheroids and Monolayer Endothelial Cells 58
3.3.5 Effects of Surface Interactions and Endothelial Conditioned medium 59
3.4 Conclusion 61
Chapter 4: Study of Endothelial Growth in MG-63 Spheroids:
Development of Mixed Coculture Spheroid Model 63
4.1 Introduction 63
4.2 Materials and Methods 64
4.2.1 Fabrication of Spheroid Culture Chip 64
4.2.2 Cell Culture 65
4.2.3 Cell Analysis 66
4.3 Results and Discussion 68
4.3.1 Assembly and Formation of MG-63/HUVEC Mixed Coculture Spheroids 68
4.3.2 Growth of Spheroids in Device 69
4.3.3 VEGF Secretion and Growth Kinetics of Spheroids 72
4.4 Conclusion 75
Chapter 5: Conclusion and Future Works 77
Appendix: Supplementary Information 80
List of Publications 82
References 83

List of Figures

Figure 1. (A)Metastatic pathways and regulators at cell-response signaling as observed in typical colon carcinomas and (B) molecular responses in tumor under oxidative stress (hypoxia)…………………………………………………………………………………….. 3
Figure 2. Common techniques of spheroid cultures. (A) Suspension cultures, with cells allowed to aggregate in a suspension, in a single hanging drop or continual flow bioreactors. (B) Matrix immobilization with cells adhered into semisolid gels like Matrigel or scaffold material. (C) Using electromagnetic waves like ultrasound or magnetic fields for causing cell clusters. (D) Using microfluidic devices for flow control .......…………………………………………. 9
Figure 3. (A) Design and fabrication of the microfluidic device used for 3D cell spheroid culture. The two layered device is made of gas permeable PDMS. The top layer is designed for fluid flow and the bottom channel is exploited for cell entrapment and subsequent formation of 3D cell spheroids. (B) Experimental photos of the fabricated device. The inset shows a brightfield microscopic image of the cell culture chambers in the bottom layer. Scale bar is 200 µm………………………….………………………………………………………………. 19
Figure 4. Schematics of experimental workflow. The MG-63 cells are cultured in T75 flasks until confluency to form monolayer cell culture model while same population of cells is used to form 3D spheroids using the microfluidic device. Conditioned medium from the cultures are collected to quantitatively measure the VEGF-A concentration using ELISA and the cell viabilities are characterized using image analysis. The analysis results obtained from the monolayer cell culture and 3D spheroid culture models are further systematically compared ....……………………………………………………………………………………….…. 22
Figure 5. Growth kinetics of the spheroids in the microfluidic device. (A) Brightfield images of the MG-63 cell spheroids, cultured in the microfluidic device under 10-day observation period, including: cell seeding (Day 0), formation of the 3D spheroid structure (Day 2) and subsequent growth phase (Day 2 to Day 6), stationary phase (Day 6), and decline phase (Day 8 to Day 10). Scale bar is 200 µm. (B) Confocal images of a typical MG-63 spheroid stained with Hoechst for nuclei, Cell Tracker Green for cytoplasm, and Sytox Red for dead cells from different viewpoints. Scale bar is 10 µm. (C) Plot of the diameters of the spheroids at different days, normalized to the spheroid diameter measured on Day 2. Data are presented as mean±s.e.m. One-way ANOVA is performed for statistical analysis, and the data with statistically significantly difference are labelled with different letters (a, b, c = p <0.05). (D) Brightfield and fluorescence images of a typical MG-63 spheroid stained with Hoechst for nuclei and Cell Tracker Red for cytoplasm. Scale bar is 100 µm. ……………….…………. 27
Figure 6. VEGF-A concentration measured from the conditioned medium, collected from the monolayer and 3D spheroid cell cultures, at different days of growth, using ELISA. Data is presented as mean±s.e.m. One-way ANOVA is performed for statistical analysis, and the data with statistically significantly difference are labeled with different letters (a, b, c, d = p<0.05) ………….………………………………………………………………………………...... 29
Figure 7. Growth of cell monolayers and 3D spheroids under different serum concentration in growth media. The treatment groups include samples cultured in growth media containing 5% and 1% FBS while samples grown in 10% FBS is control group. (A) and (B) show the brightfield and fluorescence images collected for estimation of cell viability for monolayer and 3D spheroid cell cultures, respectively. The cells are stained with Calcein Violet AM as a live cell marker and Sytox Red as an apoptotic cell marker. Scale bar is 200 µm. ……………………………………………………………………………………………... 31
Figure 8. (A) and (B) show the VEGF-A concentrations, measured at Day 4 normalized to those at Day 2, from the conditioned medium collected from the monolayer and 3D spheroid cell culture, respectively. Data are presented as mean±s.e.m. One-way ANOVA is performed for statistical analysis, and the data with statistically significant difference are labelled with different letters (a, b, c = p <0.05) ………………………………………………………… 35
Figure 9. Growth of cell monolayers and 3D spheroids under different concentrations of HIF inhibitor YC-1 in growth media. The treatment groups include samples cultured in growth media containing 20, 40 and 60 mg/ml of YC-1 while samples grown without YC-1 is control group. (A) and (B) show the brightfield and fluorescence images collected for estimation of cell viability for monolayer and 3D spheroid cell cultures, respectively. The cells are stained with Calcein Violet AM as a live cell marker and Sytox Red as an apoptotic cell marker. Scale bar is 200 µm. ………………………………………………………………………………… 37
Figure 10. (A) and (B) show the VEGF-A concentrations, measured at Day 4 normalized to those at Day 2, from the conditioned medium collected from the monolayer and 3D spheroid cell culture, respectively. Data are presented as mean±s.e.m. One-way ANOVA is performed for statistical analysis, and the data with statistically significant difference are labelled with different letters (a, b, c = p <0.05) ………………………………………………………… 39
Figure 11. (A) Schematic design of the microfluidic device for spheroid formation and culture. The device consists of two PDMS layers with microfluidic channel patterns. The top layer is designed with a serpentine shape channel, and the bottom layer is designed with 4000 cell culture chambers for cell aliquot and spheroid culture. (B) A photograph and a microscopic image of the fabricated PDMS device …………………………………………………….. 45
Figure 12: Schematic flowchart showing the experimental steps of the integrated approach…………………………………………………………………………………… 48
Figure 13. Schematics of the algorithm exploited for image processing and analysis to measure radial distribution of HRF intensity in a spheroid from experimental images……………… 52
Figure 14. (A) Merged fluorescence images of monolayer MG-63 cells stained with Cell Tracker Blue (blue) and Image-iT Red hypoxia reagent (red), under various oxygen tensions. Scale bar is 100µm. (B) Average fluorescence intensities of the hypoxia reagent, stained MG-63 cells cultured under different oxygen tensions analyzed from the fluorescence images shown in (A). Data are expressed as mean ± standard deviation (n = 3). ……………….………… 52
Figure 15. (A) Microscopic images of seeding and forming MG-63 cell spheroids within the PDMS device. Scale bar is 200 µm. (B) Fluorescence images of MG-63 cell spheroids during Live/Dead cytotoxicity assay with live (green)/dead (red) stained cells, in the device and after harvesting from the device. Scale bar is 100 µm……………………………….………….. 54
Figure 16. The flow cytometry results of the MG-63 spheroids with diameters of 140±10 µm cultured in the microfluidic devices for 48 hours. The flow cytometry analysis is performed according to the reported protocol47. (A) Plot of the side scatter and forward scatter signals. (B) Plot of the propidium iodide (PI) and Annexin V signals. The results show that the cells within the spheroids have high viability with very small amount of the cells are apoptotic or necrotic……………………………………………………………………………………. 55
Figure 17. (A) Bright field image and central slice of MPLSM image of monoculture MG-63 spheroids stained with Image-iT Red hypoxia reagent (red) and Hoechst 33242 (blue). Scale bar is 50 µm. (B) The 3-D image of a hypoxia reagent stained MG-63 spheroid reconstructed from a series of MPLSM images, and HRF plot of the same spheroid showing radial distribution of HRF intensity. (C) Plot of normalized HRF intensity within spheroid core regions with different spheroid diameters. Data are shown as mean ± standard deviation (n = 4). There are no statistical differences between the three sets of data…………………………………….. 57
Figure 18. (A) Bright field image of co-cultured MG-63 spheroids and HUVEC monolayers. (B) Live/Dead cytotoxicity assay of co-cultured spheroids with HUVECs stained with Cell Tracker Blue. Result shows similar live/dead cell ratio as in mono-culture MG-63 spheroids. Scale bar is 60 µm. (C) HRF plot from MPLSM of MG-63 mono-cultures and MG-63/HUVEC co-cultures, showing decrease in hypoxia as noted by fluorescence intensity of marker dye. Data are expressed as mean± standard error (n=3)……………………………………………….. 58
Figure 19. (A) Bright field and fluorescence images (central slice) of the Image-iT Red hypoxia reagent stained MG-63 spheroids under 4 different conditions. Scale bar is 30 µm. (B) HRF plot of the spheroids shown in (A) under different culture conditions. (C) Plot of normalized HRF intensity within spheroid core regions with different culture conditions. Data are shown as mean ± standard deviation (n = 3)……………………………………………………….. 60
Figure 20. (A)Formation of MG-63/ HUVEC mixed cocultured spheroids in microfluidic device with. 200 µl of MG-63 cell suspension of 1.2X 107 cells/µl cell density, is seeded into the device and allowed to settle down into the culture chambers. Cells begin to aggregate into 3D clusters within1 day, after which 60 µl of HUVEC cell suspension is additionally seeded into the device at 107 cells/µl cell density to make final cell seeding population of 75% MG-63: 25% HUVEC. The HUVECs get absorbed into the growing tumor spheroid and form mixed tumor /endothelial cell spheroids. The developed, cocultured MG-63/ HUVEC spheroids is grown in the device for a number of days using growth media specific for each cell type, mixed in 1:1 ratio. Scale bar is 100 µm. (B) Timeline of the process of growing mixed coculture spheroids in device as described in (A)…………………………………………………….. 70
Figure 21. (A) HUVEC growth in in the spheroids tracked by GFP biomarker fluorescence imaging. (A)Image of HUVEC+ MG-63 cocultured spheroids over different days within culture period. GFP- tagged HUVEC cells added to MG-63 in two different proportions Group 1. 75% MG-63: 25% HUVEC and Group 2. 50% MG-63: 50% MG-63 (during seeding step), to form mixed cocultured spheroids. HUVEC cells are tracked in the spheroids and the GFP responsive fluorescence intensity is measured w.r.t to different days in the culture period. The GFP fluorescence gives an approximate measure of HUVEC proliferation in the spheroids. Scale bar is 100 µm. (B) Plot showing GFP intensity of the HUVECs in spheroids w.r.t to different days of culture. 4-fold increase in GFP intensity of is observed at day 10 and day 14, for the groups with 25% initial HUVEC population, indicating significant healthy endothelial proliferation during this time for the groups. For groups with 50% initial HUVEC population, GFP intensity increases on day 6 and remains steady for day 10. N=3. Data are presented as mean±s.e.m. One-way ANOVA is performed for statistical analysis, and the data with statistically significant difference are labelled with different letters (a,b,c,d= p <0.05). (C) High magnification imaging of the mixed cocultured spheroids from both groups (75% MG-63: 25% HUVEC and 2. 50% MG-63: 50% MG-63) on day 2, with Hoechst (blue) to stain live intact nuclei, Cell Tracker (red) to stain MG-63 cells and GFP positive transfected HUVECs. In both groups, only a small percentage of initially seeded number of HUVECs is absorbed into the growing spheroids. HUVECs are observed to translocate and confine themselves, towards the peripheral region of the spheroids forming a corona around MG-63 cells which forms the central lumen Scale bar is 30 µm. In the early days of growth, HUVECs show low proliferation and remains restricted to the corona (C), penetrating slightly deeper into the lumen in later days, to form very tightly packed spheroids as observed from (A)……………………………………….………….. 71
Figure 22. Measurement of spheroid growth indicated by size and cytokine VEGF-A secretion. (A) Plot showing diameter of spheroids normalized to initial measured diameter observed on day 2, for spheroid groups with 25% HUVECs and 50% HUVECs and groups with no HUVEC (monocultured MG-63 only) as control group. For groups with 25% HUVEC, spheroids grow in size quickly as increase in diameter is observed from day 6, while for groups with 50% HUVEC, spheroids grow comparatively slowly significant change in diameter observed only on day 10. Spheroid diameters of both experimental groups are relatively similar to control group. (B) Plot showing normalized VEGF-A secreted by the different groups of spheroids. Groups with 25% HUVECs show about 6-fold increase in VEGF-A secretion from day 6, decreases by 50% and remains steady for days 10 and 14. For groups with 50% HUVECs, VEGF-A secretion increases slowly from day 6, to day 10, and decreases on day 14. VEGF secretion of groups with 25% HUVECs is significantly high compared to control group, while VEGF secretion of 50% HUVECs is relatively close to that of control group and shows slight increase on day 10. The increase in VEGF secretion for groups with 25% HUVECs is manifested as increase in spheroid diameter and healthy proliferation of HUVECs in later days as seen in figure 20 B. Data are presented as mean±s.e.m. One-way ANOVA is performed for statistical analysis, and the data with statistically significant difference are labelled with different letters (a,b,c,d= p <0.05) for plot A and (a,b,c,d,e,f,g,h= p <0.05) for plot B. (C) Confocal images of a typical MG-63/HUVEC spheroid after 14 days of growth, from different viewpoints, where MG-63 is pre-stained with Hoechst for nuclei and Cell Tracker Red, separately identified from GFP positive HUVECs. (D) Confocal image highlighting the GFP positive HUVECs in the spheroid. Scale bar is 10 µm……………………………………… 74
Figure S1: Plots showing relative viability of the MG-63 cells treated with different concentrations of YC-1 for 48 hours using 96-well plates. Cell viability is estimated using alamarBlue cell viability reagent…………………………………………………………… 80



List of Tables

Table 1. Total population and viability of the MG-63 cells, analyzed from the samples cultured with the reduced serum (nutrient deficiency) conditions. Data are shown as mean±s.e.m. (n=3).……………………………………………………………………………………… 33
Table 2. Total population and viability of the MG-63 cells analyzed from the samples cultured with the HIF inhibitor, YC-1. Data are shown as mean±sem (n=3). …………………………………………………………………………………………….. 36

1. S. Yamagishi, Y. Koga, A. Sotokawauchi, N. Hashizume, S. Fukahori, T. Matsui and M. Yagi, Current Pharmaceutical Design, 2019, 25, 313-324.
2. H. Quentmeier, R. A. F. Macleod, M. Zaborski and H. G. Drexler, Leukemia, 2006, 20, 471-476.
3. D. Irimia and M. Toner, Integrative Biology, 2009, 1, 506-512.
4. G. E. Gortz, M. Horstmann, B. Aniol, B. Delos Reyes, J. Fandrey, A. Eckstein and U. Berchner-Pfannschmidt, Journal of Clinical Endocrinology & Metabolism, 2016, 101, 4834-4842.
5. R. Giavazzi, M. Foppolo, R. Dossi and A. Remuzzi, Journal of Clinical Investigation, 1993, 92, 3038-3044.
6. Y. Liu, R. M. Tamimi, L. C. Collins, S. J. Schnitt, H. L. Gilmore, J. L. Connolly and G. A. Colditz, Breast cancer research and treatment, 2011, 129, 175-184.
7. N. S. Taichman, S. Young, A. T. Cruchley, P. Taylor and E. Paleolog, Journal of Leukocyte Biology, 1997, 62, 397-400.
8. J. Pérez-Carreón and J. Meléndez-Zajgla, Molecular Oncology Principles and Recent Advances, 2012, DOI: 10.2174/978160805016111201010148, 148-162.
9. M. Kunz and S. Ibrahim, Molecular cancer, 2003, 2, 23.
10. L. Ostergaard, A. Tietze, T. Nielsen, K. R. Drasbek, K. Mouridsen, S. N. Jespersen and M. R. Horsman, Cancer Research, 2013, 73, 5618-5624.
11. L. Galluzzi, T. Yamazaki and G. Kroemer, Nature Reviews Molecular Cell Biology, 2018, 19, 731-745.
12. B. Poljšak and I. Milisav, Bosn J Basic Med Sci, 2012, 12, 122-126.
13. T. Y. Lee, T. G. Purdie and E. Stewart, Quarterly Journal of Nuclear Medicine, 2003, 47, 171-187.
14. Q. Zhang, Q. Yan, H. Yang and W. Wei, Genes Dis, 2019, 6, 328-332.
15. A. Carreau, B. El Hafny-Rahbi, A. Matejuk, C. Grillon and C. Kieda, J Cell Mol Med, 2011, 15, 1239-1253.
16. L. Holmquist-Mengelbier, E. Fredlund, T. Löfstedt, R. Noguera, S. Navarro, H. Nilsson, A. Pietras, J. Vallon-Christersson, Å. Borg, K. Gradin, L. Poellinger and S. Påhlman, Cancer Cell, 2006, 10, 413-423.
17. D. R. Senger, S. J. Galli, A. M. Dvorak, C. A. Perruzzi, V. S. Harvey and H. F. Dvorak, Science, 1983, 219, 983.
18. Y. Kubota, K. Takubo, T. Shimizu, H. Ohno, K. Kishi, M. Shibuya, H. Saya and T. Suda, Journal of Experimental Medicine, 2009, 206, 1089-1102.
19. J. S. Lewis, R. J. Landers, J. C. E. Underwood, A. L. Harris and C. E. Lewis, Journal of Pathology, 2000, 192, 150-158.
20. F. Heits, D. M. Katschinski, G. J. Wiedemann, C. Weiss and W. Jelkmann, International Journal of Oncology, 1997, 10, 333-337.
21. Z. G. Tian, Y. Zhuang, Z. Jin, F. Zhou, L. F. Zhu and P. C. Shen, European Review for Medical and Pharmacological Sciences, 2018, 22, 5460-5470.
22. M. Hockel and P. Vaupel, JNCI-J. Natl. Cancer Inst., 2001, 93, 266-276.
23. C. J. Schofield and P. J. Ratcliffe, Nat Rev Mol Cell Biol, 2004, 5, 343-354.
24. G. N. Song, D. B. Darr, C. M. Santos, M. Ross, A. Valdivia, J. L. Jordan, B. R. Midkiff, S. Cohen, N. Nikolaishvili-Feinberg, C. R. Miller, T. K. Tarrant, A. B. Rogers, A. C. Dudley, C. M. Perou and W. C. Zamboni, Clinical Cancer Research, 2014, 20, 6083-6095.
25. Li, Oncology Reports, 1994, DOI: 10.3892/or_00000167.
26. M. A. J. Chaplain, Mathematical and Computer Modelling, 1996, 23, 47-87.
27. H. Zhou, Y. H. Yang, N. O. Binmadi, P. Proia and J. R. Basile, Experimental Cell Research, 2012, 318, 1685-1698.
28. M. Wartenberg, P. Budde, M. de Marees, F. Grunheck, S. Y. Tsang, Y. Huang, Z. Y. Chen, J. Hescheler and H. Sauer, Laboratory Investigation, 2003, 83, 87-98.
29. P. J. Simpson-Haidaris and B. Rybarczyk, in Fibrinogen, eds. W. Nieuwenhuizen, M. W. Mosesson and M. P. M. DeMaat, 2001, vol. 936, pp. 406-425.
30. B. Shi, O. Andrukhov, S. Berner, A. Schedle and X. Rausch-Fan, Dent Mater, 2014, 30, 839-847.
31. Y.-C. Tung, A. Y. Hsiao, S. G. Allen, Y.-s. Torisawa, M. Ho and S. Takayama, Analyst, 2011, 136, 473-478.
32. M. V. Monteiro, V. M. Gaspar, L. P. Ferreira and J. F. Mano, Biomaterials Science, 2020, 8, 1855-1864.
33. A. Malayeri, C. Sherborne, T. Paterson, S. Mittar, I. O. Asencio, P. V. Hatton and F. Claeyssens, International Journal of Bioprinting, 2016, 2, 67-77.
34. K. Kyriakidou, G. Lucarini, A. Zizzi, E. Salvolini, M. Mattioli-Belmonte, F. Mollica, A. Gloria and L. Ambrosio, Journal of Bioactive and Compatible Polymers, 2008, 23.
35. R. Jarrahy, W. Huang, G. Rudkin, J. Lee, K. Ishida, M. Berry, M. Sukkarieh, B. Wu, D. Yamaguchi and T. Miller, American journal of physiology. Cell physiology, 2005, 289, C408-414.
36. A. De Luca, L. Raimondi, F. Salamanna, V. Carina, V. Costa, D. Bellavia, R. Alessandro, M. Fini and G. Giavaresi, Journal of experimental & clinical cancer research : CR, 2018, 37, 2.
37. H. Shimada, Y. Hashimoto, A. Nakada, K. Shigeno and T. Nakamura, Biochemical and biophysical research communications, 2012, 417, 659-664.
38. P. K. Chaudhuri, M. Ebrahimi Warkiani, T. Jing, Kenry and C. T. Lim, Analyst, 2016, 141, 504-524.
39. D. Wlodkowic and J. M. Cooper, Current Opinion in Chemical Biology, 2010, 14, 556-567.
40. A. Albanese, A. K. Lam, E. A. Sykes, J. V. Rocheleau and W. C. W. Chan, Nature Communications, 2013, 4.
41. C. C. Huang, Z. X. Liao, H. M. Lu, W. Y. Pan, W. L. Wan, C. C. Chen and H. W. Sung, Chemistry of Materials, 2016, 28, 9017-9025.
42. D. Khaitan and B. S. Dwarakanath, Expert Opinion on Drug Discovery, 2006, 1, 663-675.
43. Q. Z. Zhang, A. L. Nguyen, S. H. Shi, C. Hill, P. Wilder-Smith, T. B. Krasieva and A. D. Le, Stem Cells and Development, 2012, 21, 937-947.
44. F. Hirschhaeuser, H. Menne, C. Dittfeld, J. West, W. Mueller-Klieser and L. A. Kunz-Schughart, Journal of Biotechnology, 2010, 148, 3-15.
45. A. Brunet, L. B. Sweeney, J. F. Sturgill, K. F. Chua, P. L. Greer, Y. X. Lin, H. Tran, S. E. Ross, R. Mostoslavsky, H. Y. Cohen, L. S. Hu, H. L. Cheng, M. P. Jedrychowski, S. P. Gygi, D. A. Sinclair, F. W. Alt and M. E. Greenberg, Science, 2004, 303, 2011-2015.
46. C. Cernigliaro, A. D'Anneo, D. Carlisi, M. Giuliano, A. M. Gammazza, R. Barone, L. Longhitano, F. Cappello, S. Emanuele, A. Distefano, C. Campanella, G. Calvaruso and M. Lauricella, Cancers, 2019, 11.
47. T. Das, T. K. Maitia and S. Chakraborty, Integrative Biology, 2011, 3, 684-695.
48. J. L. Martindale and N. J. Holbrook, Journal of Cellular Physiology, 2002, 192, 1-15.
49. F. Mpekris, S. Angeli, A. P. Pirentis and T. Stylianopoulos, Biomech Model Mechanobiol, 2015, 14, 1391-1402.
50. S. Dai, A. Gocher, L. Euscher and A. Edelman, The FASEB Journal, 2016, 30, 714.719-714.719.
51. F. Wagner, B. M. Holzapfel, L. C. Martine, J. McGovern, C. A. Lahr, M. Boxberg, P. M. Prodinger, S. Grassel, D. Loessner and D. W. Hutmacher, Acta Biomaterialia, 2019, 89, 372-381.
52. A. K. Abdel-Aziz, S. Shouman, E. El-Demerdash, M. Elgendy and A. B. Abdel-Naim, Chemico-Biological Interactions, 2014, 217, 28-40.
53. J. X. Chen, H. Zeng, Q. H. Tuo, H. D. Yu, B. Meyrick and J. L. Aschner, American Journal of Physiology-Heart and Circulatory Physiology, 2007, 292, H1664-H1674.
54. S. Kliche and J. Waltenberger, Iubmb Life, 2001, 52, 61-66.
55. S. Doublier, D. C. Belisario, M. Polimeni, L. Annaratone, C. Riganti, E. Allia, D. Ghigo, A. Bosia and A. Sapino, BMC cancer, 2012, 12, 4-4.
56. N. Gomez-Roman, M. Y. Chong, S. K. Chahal, S. P. Caragher, M. R. Jackson, K. H. Stevenson, S. A. Dongre and A. J. Chalmers, Molecular Cancer Therapeutics, 2019, DOI: 10.1158/1535-7163.MCT-18-1320, molcanther.1320.2018.
57. S. S. Chen, C. H. Tang, M. J. Chie, C. H. Tsai, Y. C. Fong, Y. C. Lu, W. C. Chen, C. T. Lai, C. Y. Wei, H. C. Tai, W. Y. Chou and S. W. Wang, Cell Death & Disease, 2019, 10.
58. L. Guillaume, L. Rigal, J. Fehrenbach, C. Severac, B. Ducommun and V. Lobjois, Sci Rep, 2019, 9, 6597.
59. A. S. Nunes, A. S. Barros, E. C. Costa, A. F. Moreira and I. J. Correia, Biotechnology and Bioengineering, 2019, 116, 206-226.
60. H. Chaddad, S. Kuchler-Bopp, G. Fuhrmann, H. Gegout, G. Ubeaud-Sequier, P. Schwinte, F. Bornert, N. Benkirane-Jessel and Y. Idoux-Gillet, Exp Cell Res, 2017, 360, 138-145.
61. B. Patra, C. C. Peng, W. H. Liao, C. H. Lee and Y. C. Tung, Sci Rep, 2016, 6, 21061.
62. S. Sarkar, C. C. Peng, C. W. Kuo, D. Chueh, H. M. Wu, Y. H. Liu, P. L. Chen and Y. C. Tung, Rsc Advances, 2018, 8, 30320-30329.
63. B. Patra, Y. S. Peng, C. C. Peng, W. H. Liao, Y. A. Chen, K. H. Lin, Y. C. Tung and C. H. Lee, Biomicrofluidics, 2014, 8, 052109.
64. C. C. Peng, W. H. Liao, Y. H. Chen, C. Y. Wu and Y. C. Tung, Lab Chip, 2013, 13, 3239-3245.
65. K. Y. Hsieh, C. K. Wei and C. C. Wu, International Journal of Molecular Sciences, 2019, 20.
66. B.-H. D. Duffy AM, Harmey JH, Journal, 2000-2013.
67. P. G. Daft, Y. Yang, D. Napierala and M. Zayzafoon, PLOS ONE, 2015, 10, e0121568.
68. H. L. Goel and A. M. Mercurio, Nat Rev Cancer, 2013, 13, 871-882.
69. J. P. Piret, C. Lecocq, S. Toffoli, N. Ninane, M. Raes and C. Michiels, Experimental Cell Research, 2004, 295, 340-349.
70. L. C. Crowley, B. J. Marfell and N. J. Waterhouse, Cold Spring Harb Protoc, 2016, 2016.
71. Y. Liang and S. M. Hyder, Endocrinology, 2005, 146, 3632-3641.
72. J. E. Nör, J. Christensen, D. J. Mooney and P. J. Polverini, Am J Pathol, 1999, 154, 375-384.
73. A. Boudria, C. Abou Faycal, T. Jia, S. Gout, M. Keramidas, C. Didier, N. Lemaitre, S. Manet, J. L. Coll, A. C. Toffart, D. Moro-Sibilot, C. Albiges-Rizo, V. Josserand, E. Faurobert, C. Brambilla, E. Brambilla, S. Gazzeri and B. Eymin, Oncogene, 2019, 38, 1050-1066.
74. S. Hanyu, K. Sakuma and A. Tanaka, Journal of Hard Tissue Biology, 2019, 28, 281-288.
75. K. C. Murphy, S. Y. Fang and J. K. Leach, Cell and Tissue Research, 2014, 357, 91-99.
76. J. Adams, P. J. Carder, S. Downey, M. A. Forbes, K. MacLennan, V. Allgar, S. Kaufman, S. Hallam, R. Bicknell, J. J. Walker, F. Cairnduff, P. J. Selby, T. J. Perren, M. Lansdown and R. E. Banks, Cancer Research, 2000, 60, 2898.
77. E. M. Balzer, Z. Tong, C. D. Paul, W.-C. Hung, K. M. Stroka, A. E. Boggs, S. S. Martin and K. Konstantopoulos, FASEB J, 2012, 26, 4045-4056.
78. A. Y. Hsiao, Y.-s. Torisawa, Y.-C. Tung, S. Sud, R. S. Taichman, K. J. Pienta and S. Takayama, Biomaterials, 2009, 30, 3020-3027.
79. C. F. Buchanan, C. S. Szot, J. W. Freeman and M. N. Rylander, 2011.
80. Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer, 1984, 95, 1-183.
81. B. H. Y. Yeung, F. H. Shek, N. P. Lee and C. K. C. Wong, Plos One, 2015, 10, 17.
82. J. Kolenda, S. S. Jensen, C. Aaberg-Jessen, K. Christensen, C. Andersen, N. Brunner and B. W. Kristensen, J Neurooncol, 2011, 103, 43-58.
83. G. Marino, M. Niso-Santano, E. H. Baehrecke and G. Kroemer, Nat Rev Mol Cell Biol, 2014, 15, 81-94.
84. J. K. Chen, S. F. Peng, K. C. Lai, H. C. Liu, Y. P. Huang, C. C. Lin, A. C. Huang, F. S. Chueh and J. G. Chung, In Vivo, 2019, 33, 801-810.
85. S. Rastogi, R. Kumar, S. R. Sankineani, K. Marimuthu, L. Rijal, S. Prakash, D. Jalan, S. A. Khan and M. C. Sharma, International Orthopaedics, 2012, 36, 2315-2321.
86. D. Shweiki, M. Neeman, A. Itin and E. Keshet, Proceedings of the National Academy of Sciences of the United States of America, 1995, 92, 768-772.
87. A. M. Baptista, A. F. D. F. Camargo, R. Z. Filippi, C. R. G. C. M. D. Oliveira, R. S. D. Azevedo Neto and O. P. D. Camargo, Acta Ortopédica Brasileira, 2014, 22, 250-255.
88. J. Lammli, M. Fan, H. G. Rosenthal, M. Patni, E. Rinehart, G. Vergara, E. Ablah, P. H. Wooley, G. Lucas and S.-Y. Yang, International orthopaedics, 2012, 36, 2307-2313.
89. R. I. Dmitriev and D. B. Papkovsky, Methods Appl Fluoresc, 2015, 3, 034001.
90. T. Anada, J. Fukuda, Y. Sai and O. Suzuki, Biomaterials, 2012, 33, 8430-8441.
91. P. Indovina, M. Collini, G. Chirico and M. T. Santini, FEBS Lett, 2007, 581, 719-726.
92. S. J. Zhang, M. Hosaka, T. Yoshihara, K. Negishi, Y. Iida, S. Tobita and T. Takeuchi, Cancer Research, 2010, 70, 4490-4498.
93. G. Khanal, S. Hiemstra and D. Pappas, Analyst, 2014, 139, 3274-3280.
94. M. Marenzana and T. R. Arnett, Bone Research, 2013, 1, 203-215.
95. A. V. Aleksandrova, N. P. Pulkova, T. N. Gerasimenko, N. Y. Anisimov, S. A. Tonevitskaya and D. A. Sakharov, Bulletin of Experimental Biology and Medicine, 2016, 160, 857-860.
96. K. C. Murphy, B. Hung, S. Browne-Bourne, D. J. Zhou, J. Yeung, D. C. Genetos and J. K. Leach, Journal of the Royal Society Interface, 2017, 14, 10.
97. J. H. Lee, Y. S. Han and S. H. Lee, Biomolecules & Therapeutics, 2016, 24, 260-267.
98. S. H. Kim, H. Kim, H. J. Ku, J. H. Park, H. Cha, S. Lee, J. H. Lee and J. W. Park, Redox Biology, 2016, 10, 211-220.
99. B. Hobson and J. Denekamp, British Journal of Cancer, 1984, 49, 405-413.
100. I. Kocherova, A. Bryja, P. Mozdziak, A. Angelova Volponi, M. Dyszkiewicz-Konwińska, H. Piotrowska-Kempisty, P. Antosik, D. Bukowska, M. Bruska, D. Iżycki, M. Zabel, M. Nowicki and B. Kempisty, J Clin Med, 2019, 8, 1602.
101. J. Ou, D. Guan and Y. Yang, Cell Mol Biol Lett, 2019, 24, 39.
102. K. Inomata and M. Honda, Materials (Basel), 2019, 12, 2869.
103. M. R. Soboleski, J. Oaks and W. P. Halford, FASEB J, 2005, 19, 440-442.
104. C. Coralli, M. Cemazar, C. Kanthou, G. M. Tozer and G. U. Dachs, Cancer Research, 2001, 61, 4784-4790.
105. H. Shoval, A. Karsch-Bluman, Y. Brill-Karniely, T. Stern, G. Zamir, A. Hubert and O. Benny, Sci Rep, 2017, 7, 10428.
106. A. Franchi, Clin Cases Miner Bone Metab, 2012, 9, 92-95.
107. C. Eilenberger, S. R. A. Kratz, M. Rothbauer, E.-K. Ehmoser, P. Ertl and S. Küpcü, MethodsX, 2018, 5, 781-787.