Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Kumar, Hitendra
Doctor of Philosophy - PhD
University of British Columbia
The natural tissues are architecturally complex and heterogenous in nature and comprise of a dense vasculature for facilitating nutrient and chemokines transport. To mimic the functionality of natural tissues, bioprinting methods should be able to capture the tissue heterogeneity and create a vascularized scaffold. Stereolithography (SLA) bioprinting is a promising process to fabricate high-resolution anatomically accurate tissue analogues. However, non-optimized bioinks and poor design of the scaffolds limit the capability of SLA bioprinting. In view of these challenges, a method to obtain SLA bioprinting compatible gelatin methacryloyl (GelMA) bioinks which maintain a low-viscosity liquid state at room temperature is presented. The method considers GelMA synthesis parameters – solvent, pH and duration; to tune the end product. The obtained bioinks exhibit superior photopolymerization kinetics, biocompatibility and printability. Addition of a three-component photoinitiator further improved the sensitivity of the bioinks to visible light and resulted in an accelerated photopolymerization. The printing time was nearly halved and structurally stable structures could be formed with lower GelMA concentration. Next, a numerical model is developed to predict the hydrogel formation in mask photopolymerization. Considering a realistic Gaussian illumination and diffusive transport of chemical species within the bioink, the model was able to predict the printing artifacts like overcrosslinking and distortion in the hydrogel shape. In the last step, a vascular network generation method inspired from leaves and tree growth is presented. Biologically relevant vasculature was formed guided by medical image derived tissue structure and porosity. By studying nutrient transport, optimized networks with increased complexity were obtained. In combination, the described bioinks and the numerical models provide effective tools for designing the tissue mimics with embedded heterogeneity and an intrinsic vasculature to achieve the functionality of natural tissues.
LIGHT-BASED BIOPRINTING FOR FABRICATING VASCULARIZED TISSUES by Hitendra Kumar M. Tech., Indian Institute of Technology Kanpur, 2016 B. Tech., Indian Institute of Technology Kanpur, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2021 © Hitendra Kumar, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Light-based bioprinting for fabricating vascularized tissues submitted by Hitendra Kumar in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering. Examining Committee: Dr. Keekyoung Kim Department of Mechanical and Manufacturing Engineering University of Calgary Supervisor Dr. Kevin Golovin School of Engineering The University of British Columbia Co-supervisor Dr. Ian Foulds School of Engineering The University of British Columbia Supervisory Committee Member Dr. Frederic Menard Department of Chemistry The University of British Columbia Supervisory Committee Member Dr. Solomon Tesfamariam School of Engineering The University of British Columbia University Examiner Dr. Xiongbiao (Daniel) Chen Department of Mechanical Engineering University of Saskatchewan External Examiner iii Abstract The natural tissues are architecturally complex and heterogenous in nature and comprise of a dense vasculature for facilitating nutrient and chemokines transport. To mimic the functionality of natural tissues, bioprinting methods should be able to capture the tissue heterogeneity and create a vascularized scaffold. Stereolithography (SLA) bioprinting is a promising process to fabricate high-resolution anatomically accurate tissue analogues. However, non-optimized bioinks and poor design of the scaffolds limit the capability of SLA bioprinting. In view of these challenges, a method to obtain SLA bioprinting compatible gelatin methacryloyl (GelMA) bioinks which maintain a low-viscosity liquid state at room temperature is presented. The method considers GelMA synthesis parameters – solvent, pH and duration; to tune the end product. The obtained bioinks exhibit superior photopolymerization kinetics, biocompatibility and printability. Addition of a three-component photoinitiator further improved the sensitivity of the bioinks to visible light and resulted in an accelerated photopolymerization. The printing time was nearly halved and structurally stable structures could be formed with lower GelMA concentration. Next, a numerical model is developed to predict the hydrogel formation in mask photopolymerization. Considering a realistic Gaussian illumination and diffusive transport of chemical species within the bioink, the model was able to predict the printing artifacts like overcrosslinking and distortion in the hydrogel shape. In the last step, a vascular network generation method inspired from leaves and tree growth is presented. Biologically relevant vasculature was formed guided by medical image derived tissue structure and porosity. By studying nutrient transport, optimized networks with increased complexity were obtained. In combination, the described bioinks and the numerical models provide effective tools for designing the tissue mimics with embedded heterogeneity and an intrinsic vasculature to achieve the functionality of natural tissues. iv Lay Summary This thesis aims to address the challenge of recreating tissue mimics which are structurally and functionally like natural tissues in human body using bioprinting. First, a simple method to prepare gelatin biomaterials is presented. These materials solidify when exposed to visible light and can create complex 3D shapes of the tissues. Next, the chemical components harnessing the light energy to facilitate the solidification of these materials are improved to speed-up the bioprinting process. Then a mathematical model is developed which can predict the transformation of these biomaterials from liquid to gel on light exposure. This model is aimed to assist in designing the bioprinting process and the shapes of the tissues such that the resulting gel structure includes an underlying variation in stiffness that can guide the growth of cells within. Finally, a blood vessel network is designed for the tissue shapes to sustain the long-term growth of embedded cells. v Preface The research presented in this thesis was conducted in the Advanced Biofabrication Laboratory (ABL) within the School of Engineering at the University of British Columbia and Department of Mechanical and Manufacturing Engineering at the University of Calgary under the supervision of Dr. Keekyoung Kim and Dr. Kevin Golovin. Parts of this thesis have been published in peer-reviewed journals and conference proceedings. This work has been made possible through the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Foundation for Innovation John R. Evans Leaders Opportunity Fund, SERB-UBC Doctoral Fellowship and computational resources from Compute Canada. Details of publications with the author’s contributions are explained below: Peer-reviewed journal articles: 1. H. Kumar, K. Sakthivel, M. Gamaleldin, E. Boras, S. R. Shin and K. Kim, “Designing Gelatin Methacryloyl (GelMA)‐Based Bioinks for Visible Light Stereolithographic 3D Biofabrication”, Macromolecular Bioscience, 2000317, 2020. Contribution – I conceptualized the idea, designed and performed the experiments and wrote the manuscript. This work was also featured as front cover. Parts of Chapter 2 have been published in this article. 2. Z. Wang#, H. Kumar#, Z. Tian, X. Jin, J. F. Holzman, F. Menard, and K.Kim. “Visible light photoinitiation of cell-adhesive gelatin methacryloyl hydrogels for stereolithography 3D bioprinting.” ACS applied materials & interfaces 10, no. 32 (2018): 26859-26869. vi Contribution – As a co-first author (#), I contributed in biomaterial synthesis, performing SLA bioprinting experiments, characterizing cell viability and photocrosslinking kinetics. I also wrote sections of the manuscript and oversaw the manuscript revision. 3. H. Kumar, F. Menard and K. Kim, “Rapid and high-resolution bioprinting using a cyclic-pathway visible light photoinitiator”. (in preparation) Contribution – I conceptualized the idea and designed and performed all the experiments. I have also written the manuscript. Parts of Chapter 3 will be published in this article. 4. H. Kumar, P. Ambhorkar, I. Foulds, K. Golovin, and K. Kim, “Prediction and remediation of bioprinting artifacts in SLA using a numerical model”. (in preparation) Contribution – I conceptualized the idea, developed the numerical model and performed the simulations. I have also written the manuscript. Parts of Chapter 4 will be published in this article. 5. H. Kumar, K. Dixit, R. Sharma, E. MacDonald, S. Kamle, N. Sinha and K. Kim, “Closed-loop workflow for designing scaffold vasculature for bioprinting thick tissues”. (in preparation) Contribution – I conceptualized the idea, developed the numerical model and performed the simulations. I have also written the manuscript. Parts of Chapter 5 will be published in this article. Conference Proceedings: 1. H. Kumar, K. Sakthivel, M. Gamaleldin, E. Boras, S. R. Shin and K. Kim, “Mechanistic study of synthesizing tunable gelatin methacrylate (GelMA) bioinks for rapid and high-resolution stereolithography bioprinting”, Society for Biomaterials 2021 Annual Meeting and Exposition, April 2021. vii Contribution – I conceptualized the idea, designed and performed the experiments. I gave an oral presentation about this work. 2. H. Kumar, K. Sakthivel, M. Gamaleldin, E. Boras, S. R. Shin and K. Kim, “Fast photocrosslinkable gelatin bioinks optimized for rapid stereolithography bioprinting”, BCREGMED & ISCRM Cascadia Corridor Research Symposium, November 2020. Contribution – I conceptualized the idea, designed and performed the experiments. I gave an oral presentation about this work. 3. H. Kumar, K. Sakthivel, M. Gamaleldin, E. Boras, S. R. Shin and K. Kim, “Gelatin methacryloyl bioinks tailored for rapid visible light stereolithography bioprinting”, 21st Annual Alberta Biomedical Engineering Conference, October 2020. Contribution – I conceptualized the idea, designed and performed the experiments. I gave an oral presentation about this work. 4. H. Kumar, K. Sakthivel, M. Gamaleldin, E. Boras, S. R. Shin and K. Kim, “Thermally Insensitive Gelatin-based Bioinks for Visible Light Stereolithography 3D Bioprinting”, Biofabrication, Ohio, October 2019. Contribution – I conceptualized the idea, designed and performed the experiments. This work was presented as a poster. Book Chapters: 1. H. Kumar and K. Kim, “Stereolithography 3D bioprinting.” Methods in molecular biology (Clifton, NJ), 2140, 93. (Invited) Contribution – I wrote a review on stereolithography 3D bioprinting. Parts of Chapter 1 have been published in this article. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii Acknowledgements .................................................................................................................... xxi Chapter 1 Introduction..................................................................................................................1 1.1 Background ..................................................................................................................... 1 1.2 Stereolithography bioprinting ......................................................................................... 5 1.3 Biomaterials for tissue engineering ................................................................................ 7 1.4 Scaffold design.............................................................................................................. 10 1.5 Research objectives ....................................................................................................... 12 1.5.1 Development of a low-gelation point bioink ............................................................ 13 1.5.2 Development of a visible light photoinitiator for accelerated photopolymerization 13 1.5.3 Numerical modeling of the mask photopolymerization............................................ 13 1.5.4 Development of an optimized vasculature design scheme ....................................... 14 1.6 Thesis structure ............................................................................................................. 14 Chapter 2 Development of a low gelation temperature gelatin methacryloyl bioink ............16 2.1 Synopsis ........................................................................................................................ 16 2.2 Experimental section ..................................................................................................... 17 2.2.1 Effect of solvent on gelling behavior ........................................................................ 17 2.2.2 GelMA synthesis ....................................................................................................... 18 2.2.3 Quantification of the degree of substitution.............................................................. 19 2.2.4 Effect of solvent and catalyst on gelatin hydrolysis ................................................. 20 2.2.5 GelMA pre-polymer solution and hydrogel preparation .......................................... 20 2.2.6 Crosslinking time measurements .............................................................................. 21 2.2.7 Physical properties of GelMA hydrogels .................................................................. 22 2.2.8 Printability................................................................................................................. 23 ix 2.2.9 GelMA degradation test ............................................................................................ 24 2.2.10 Cell culture ............................................................................................................ 25 2.2.11 Bioprinting ............................................................................................................ 25 2.2.12 Statistical analysis ................................................................................................. 27 2.3 Results and Discussion ................................................................................................. 27 2.3.1 Solvent effect on the thermal sensitivity of GelMA ................................................. 27 2.3.2 GelMA degree of substitution characterization ........................................................ 31 2.3.3 GelMA photopolymerization kinetics....................................................................... 38 2.3.4 Physical properties of GelMA .................................................................................. 41 2.3.5 Printability of GelMA with stereolithography .......................................................... 48 2.3.6 Bioprinting ................................................................................................................ 52 2.4 Discussion ..................................................................................................................... 61 2.5 Summary ....................................................................................................................... 67 Chapter 3 Rapid and high-resolution bioprinting using a photocatalytic visible light photoinitiator ................................................................................................................................69 3.1 Synopsis ........................................................................................................................ 69 3.2 Experimental section ..................................................................................................... 70 3.2.1 GelMA prepolymer preparation ................................................................................ 70 3.2.2 Comparison of photoinitiator formulations .............................................................. 70 3.2.3 Crosslinking time measurements .............................................................................. 71 3.2.4 Physical characterization of GelMA hydrogels ........................................................ 71 3.2.5 Hydrogel deformation observation ........................................................................... 71 3.2.6 Bioprinting capability evaluation .............................................................................. 72 3.2.7 Biocompatibility assessment ..................................................................................... 72 3.3 Results and Discussion ................................................................................................. 73 3.3.1 Comparison of photoinitiator formulations .............................................................. 73 3.3.2 Photopolymerization kinetics.................................................................................... 77 3.3.3 Physical characteristics ............................................................................................. 81 3.3.4 Hydrogel deformation ............................................................................................... 84 3.3.5 Bioprinting capability evaluation .............................................................................. 86 x 3.3.6 Biocompatibility characterization ............................................................................. 89 3.4 Summary ....................................................................................................................... 91 Chapter 4 Prediction and remediation of bioprinting artifacts in SLA using a numerical model .............................................................................................................................................93 4.1 Synopsis ........................................................................................................................ 93 4.2 Theoretical model ......................................................................................................... 94 4.2.1 Photopolymerization model ...................................................................................... 94 4.2.2 Numerical model setup ............................................................................................. 98 4.2.3 Diffusion and mask projection ................................................................................ 105 4.2.4 Projection intensity ................................................................................................. 106 4.3 Results and Discussion ............................................................................................... 111 4.3.1 Effect of mask intensity distribution ....................................................................... 111 4.3.2 Effect of BAP concentration ................................................................................... 114 4.3.3 Effect of mask spacing ............................................................................................ 118 4.3.4 Effect of bioink layer thickness .............................................................................. 121 4.3.5 Effect of pulsed illumination .................................................................................. 124 4.4 Summary ..................................................................................................................... 132 Chapter 5 Closed-loop workflow for designing scaffold vasculature for bioprinting thick tissues ..........................................................................................................................................134 5.1 Synopsis ...................................................................................................................... 134 5.2 Theoretical model ....................................................................................................... 136 5.2.1 Medical image processing....................................................................................... 136 5.2.2 Vascular network generation .................................................................................. 141 5.2.3 Diffusion-based vasculature optimization .............................................................. 146 5.3 Results and Discussion ............................................................................................... 149 5.3.1 Liver 3D model generation ..................................................................................... 149 5.3.2 Vasculature generation............................................................................................ 154 5.3.3 Growth media transport .......................................................................................... 155 5.4 Summary ..................................................................................................................... 161 Chapter 6 Conclusion ................................................................................................................163 xi 6.1 Concluding Remarks ................................................................................................... 163 6.2 Significance of contributions ...................................................................................... 166 6.3 Future works ............................................................................................................... 168 Bibliography ...............................................................................................................................170 Appendices ..................................................................................................................................183 xii List of Tables Table 2.1 List of gelatin samples for solvent effect comparison. ................................................. 17 Table 2.2. List of synthesized GelMA combinations. .................................................................. 19 Table 2.3. Comparison of GelMA bioink photocrosslinkability with visible light photoinitiation. .................................................................................................................................... 64 Table 4.1. Constituent reactions in photopolymerization and their respective kinetic parameters. .................................................................................................................................... 95 Table 4.2. Diffusion coefficient of reactant and intermediate species used in the numerical model. ....................................................................................................................... 101 Table 4.3. Experiment specific parameters used in the numerical model setup. ........................ 102 Table 5.1. Liver MRI dataset specifications. .............................................................................. 137 xiii List of Figures Figure 1.1. Heterogeneity and complexity of natural tissues. (A) Types of blood-tissue interfaces in central nervous system. (B) Complex vasculature present in the liver visualized using contrast enhanced 3D MRI. Adapted from ([12],[13]) ......................................... 2 Figure 1.2. Medical image deried tissue models. (A) MRI structural data of brain tissue. (B) 3D reconstructed skull and brain model with tumor identified from MR images. (C) 3D printed prototype of the generated 3D model. Adapted from ([95]).......................... 12 Figure 2.1. Solvent dependent hydrolysis of gelatin. (A) Representative images showing gelation time for gelatin hydrolyzed in different solvents (DMSO, RO, PBS & CB) for different durations (3 hours, 12 hours & 24 hours). Black, blue and red markers are assigned for completely gelled, partially gelled and non-gelling samples respectively. (B) Comparison of gelation time at room temperature. (C) Viscosity measured for 5% (w/v) gelatin solutions at 37oC using Ubbelohde viscometer. (n = 5) (D) Average molecular weight calculated based on the Mark-Houwink equation. (n = 5) .......... 28 Figure 2.2. GelMA gelation characterization. Representative images showing gelation time for GelMA synthesized in different solvents (DMSO, RO, PBS & CB) and pH combinations for different durations (3 hours & 12 hours). Blue, black and red markers are assigned for completely gelled, partially gelled and non-gelling samples respectively. ............................................................................................................. 33 Figure 2.3. GelMA substitution characterization. (A) Quantified degree of substitution based on reduction in lysine signal. (n = 3; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). (B) Recorded 1H NMR spectra of various GelMA. Peaks (1) represents the presence of gelatin backbone (phenylalanine), (2) shows the presence of substituted methacrylate groups, (3) is signal corresponding to lysine and (4) also corresponds to substituted methacrylate groups. ..................................................... 34 Figure 2.4. GelMA photocrosslinking kinetics. (A) Schematic of curing kinetics measurement using a modified probe. (B) Modified probe for sinusoidal shearing of GelMA droplet. (C) Curing kinetics for all GelMA combinations represented by increasing shear force recorded for 1000 seconds with exposure beginning at 60 seconds. (n = 5) .............................................................................................................................. 40 xiv Figure 2.5. Physical properties of low sol-gel transition temperature GelMA hydrogels. (A) Compressive Young’s modulus measured using Hertz contact mechanics model (n = 5); * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). (B) Equilibrium mass swelling ratio (n = 5; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). .................................................................................................................... 42 Figure 2.6. Temperature dependence of properties of DMSO-12h GelMA. (A) Variation of compressive modulus (n = 5; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). (B) Variation of photopolymerization kinetics (n = 5). ............................ 45 Figure 2.7. Microstructure characterization of GelMA. (A) Representative SEM images showing the microstructure of crosslinked GelMA (scale bar = 200 µm). (B) Average pore size measured from SEM images for respective GelMA (n = 5; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). .............................................................. 47 Figure 2.8. Pore size distribution for the GelMA combinations obtained from porous structure observed in SEM images. ........................................................................................ 48 Figure 2.9. Printability comparison of various low sol-gel transition temperature GelMA. A small mesh pattern of outer edge dimensions of 4.5 mm x 4.5 mm and line width of 200 µm printed with different GelMA. The initial pre-polymer solution layer thickness was kept 300 µm. The pattern was projected for 60 seconds. (Scale bar = 1 mm .. 50 Figure 2.10. Printing resolution characterization with RO-12h-L GelMA. Lines printed with projection widths corresponding to 1, 2 and 5 pixels for 60, 120, 180 and 240 seconds. The initial pre-polymer solution layer thickness was kept at 300 µm. (scale bar = 500 µm). ......................................................................................................................... 51 Figure 2.11. Biocompatibility characterization of RO-GelMA. (A) Representative live/dead assay images for RO-12h-L and RO-12h-H GelMA at day 1, 5 and 7 respectively. (B) Quantitative comparison of cell viability at day 1, 4 and 7 respectively (n = 5; ns p > 0.05 and * p < 0.05). (Scale bar – 100 µm). ............................................................ 53 Figure 2.12. Characterization of GelMA (RO-12h-L) biocompatibility with cell viability. (A) Printed honeycomb structure in bright field and fluorescent imaging (scale bar = 500 µm). (B) Representative zoomed-in images of various portions of honeycomb showing live cells (green: Calcein AM) and dead cells (red: EthD-III) at day 1 (scale xv bar = 200 µm). (C) Comparison of cell viability for day 1 and day 5 samples (n = 5; ns p > 0.05). ............................................................................................................. 54 Figure 2.13. Characterization of GelMA (RO-12h-L) biocompatibility with cell morphology. (A) Printed honeycomb structure in bright field and fluorescent imaging at day 5 (scale bar = 500 µm). (B) Representative zoomed-in images of various portion of honeycomb showing cell morphology with F-actin (green: phalloidin) and nuclei staining (blue: DAPI) (scale bar = 50 µm). ............................................................. 55 Figure 2.14. Bioprinting miniature human ear with GelMA (RO-12h-L). (A) 3D model of human ear used for printing. (B) bioprinted multilayered structure forming miniature human ear (scale bar = 1 cm). (C) Angled view showing the feature details of various segments. (scale bar = 1 cm). .................................................................................. 57 Figure 2.15. Representative fluorescent zoomed-in images of various portions of bioprinted ear showing cell morphology after 14 days of culture with F-actin (green: phalloidin) and nuclei staining (blue: DAPI) (scale bar = 100 µm (i, ii), 50 µm(iii)). ..................... 58 Figure 2.16. Bioprinted miniature brain cross-section with astrocytes. (A) Cross-section pattern used for printing. (B) Bioprinted scaffold (scale bar: 1 cm). (C) Angled view showing the thickness. (scale bar = 1 cm). (D) (i-iv) Representative images of astrocyte morphology observed on day 30. F-actin/Nuclei staining done by Phalloidin (Green)/DAPI(Blue). (scale bar: (i) 100 µm, (ii)-(iv) 50 µm). ................................ 60 Figure 2.17. GelMA degradation test – 10% w/v GelMA crosslinked in 96-well plate (100 µL in each well) for 5 minutes. Incubated at 37oC in freshly prepared collagenase I solution (50 µg/mL in DPBS). (n = 5). .................................................................................. 67 Figure 3.1. Comparison of different photoinitiator compositions. (A) Compressive modulus and (B) mass swelling ratio for EY+TEOA, EY+TEOA+NVP and EY+TEOA+BAP. (n=5; ns p>0.05, * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). ..... 74 Figure 3.2. Recording photocrosslinking kinetics of GelMA. (A) Schematic of the coupled photopolymerization and UV-Vis spectroscopy setup for real time monitoring of hydrogel opacity. ..................................................................................................... 77 xvi Figure 3.3. Photocrosslinking kinetics of GelMA with three-component photoinitiator. (A) Recorded polymerization kinetics curves by measuring absorbance at 490 nm. (B) Comparison of kinetics curve slopes for different BAP concentration. .................. 79 Figure 3.4. (A) Different regimes of crosslinking kinetics (A – acceleration, D – deceleration, S – saturation). (B) First derivative of the kinetics curves representing the speed of polymerization. (C) Second derivative of the kinetics curve representing acceleration of the polymerization. Acceleration to deceleration transition for different bioinks are marked as (i) (0.1 mM BAP), (ii) (0.05 mM BAP), (iii) (0.02 mM BAP) and (iv) (0.0 mM BAP) respectively. ........................................................................................... 80 Figure 3.5. Microporous structure characterization of GelMA hydrogels with varying BAP concentration. (A) Representative SEM images showing internal pore structure. (B) Average pore size measured from SEM images. (n = 5; ns p>0.05, * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). .............................................................. 83 Figure 3.6. Pore size distribution for GelMA with different BAP concentrations obtained from porous structure observed in SEM images (green lines mark the average pore size). ................................................................................................................................. 84 Figure 3.7. Deformation in bilayer hydrogel flower structure. (A) Top view of the fabricated structure. (B) Side view showing the bent structure and the base line (red), top surface horizontal line (green) and the horizontal line corresponding to the topmost corner of bent structure (blue). ................................................................................................ 85 Figure 3.8. Bioprinting capability of GelMA with BAP photoinitiator. (A) Printing liver shaped vasculature with 5% GelMA and varying BAP and exposure times. (B) Printing dendrite pattern with 7.5% GelMA and varying BAP concentration. ..................... 87 Figure 3.9. Biocompatibility of GelMA hydrogels with BAP.(A) Representative live/dead assay images for hydrogels with EY+TEOA and EY+TEOA+BAP photoinitiators. (B) Calculated cell viability for EY+TEOA and EY+TEOA+BAP samples. (n = 5; ns p>0.05). (C) Elongated morphology observed in the cells encapsulated in GelMA hydrogels with 0.05 mM and 0.1 mM BAP after 3 days of culture. ....................... 90 Figure 4.1. Domain setup in numerical simulations. The dimensions of the bioink domain, illuminated regions and oxygen flux boundary condition on top boundary are shown. xvii The probes are located at 0 mm, 0.25 mm and 0.5 mm from the bottom of the bioink domain and are indicated as “bottom”, “middle” and “top” respectively. ............ 103 Figure 4.2. Light intensity decay within the bioink layer following the Beer-Lambert law. The intensity decay for a smaller bioink thickness is shown in the inset which corresponds to the thickness of the domain used for simulations. ............................................. 104 Figure 4.3. Light intensity distribution in photo-pattern. (A) Pixel wise Gaussian intensity plot for a photo-pattern encompassing 4 pixels. The combined intensity is also shown (red). The regions illuminated in ideal and Gaussian illumination are marked. The overexposed contributing and non-contributing regions of the Gaussian illumination are also shown. ...................................................................................................... 108 Figure 4.4. Comparison of photo-patterns formed by (A) ideal illumination and (B) Gaussian illumination. ........................................................................................................... 109 Figure 4.5. Topographical representation of light intensity. (A) Honeycomb photo-pattern – (i) ideal light intensity photo-pattern, (ii) ideal light intensity topographical representation, (iii) gaussian light intensity photo-pattern and (iv) Gaussian light intensity topographical representation. (B) Neuron photo-pattern – (i) ideal light intensity photo-pattern, (ii) ideal light intensity topographical representation, (iii) Gaussian light intensity photo-pattern and (iv) gaussian light intensity topographical representation. ........................................................................................................ 110 Figure 4.6. Comparison of ideal and Gaussian illumination. (A) Formed hydrogel profiles at different exposure time. (B) Oxygen concentration in the bioink. ........................ 112 Figure 4.7. Comparison of ideal and Gaussian illumination. (A) Oxygen probe plot for ideal illumination. (B) DOC probe plot for ideal illumination. (C) Oxygen probe plot for Gaussian illumination. (D) DOC probe plot for Gaussian illumination. ............... 113 Figure 4.8. Effect of BAP (RBr) concentration. Simulated hydrogel profiles for different BAP concentrations (A) 0 mM, (B) 0.02 mM, (C) 0.05 mM and (D) 0.1 mM. ............. 115 Figure 4.9. Effect of BAP concentration on oxygen in bioink layer. Oxygen probe plots at (A) bottom, (B) middle and (C) top of the bioink domain. .......................................... 116 Figure 4.10. Effect of BAP concentration on DOC within bioink layer. DOC probe plots at (A) bottom, (B) middle and (C) top of the bioink domain. .......................................... 117 xviii Figure 4.11. Effect of distance between illuminated regions. (A) Comparison of the photo-patterns in the two simulations. Simulated hydrogel profiles for (B) 0.5 mm spaced pattern and (B) 0.1 mm spaced pattern. ............................................................................. 120 Figure 4.12. Probe plots for (A) oxygen concentration and (B) DOC at the middle axis between the illuminated regions. ......................................................................................... 121 Figure 4.13. Effect of bioink layer thickness on hydrogel formation. Simulated hydrogel profiles for (A) 0.25 mm and (B) 0.5 mm thick bioink layers. ........................................... 123 Figure 4.14. Effect of bioink layer thickness on oxygen depletion. Simulated oxygen concentration profiles for (A) 0.25 mm and (B) 0.5 mm thick bioink layer. ............................... 123 Figure 4.15. Effect of bioink layer thickness. (A) Oxygen probe plots. (B) DOC probe plots. . 124 Figure 4.16. Comparison of hydrogel formation at same exposure time with (A) continuous projection and pulsed projection with pulse width of (B) 2s, (C) 5s and (D) 10s respectively. ........................................................................................................... 126 Figure 4.17. Oxygen probe plots for continuous and pulsed projection models at the bottom surface of the bioink layers at (A) exposed region and (B) midway between the exposed regions. .................................................................................................................. 127 Figure 4.18. DOC probe plots for continuous and pulsed projection models at the bottom surface of the bioink layers at (A) exposed region and (B) midway between the exposed regions. .................................................................................................................. 127 Figure 4.19. Comparison of hydrogel formation at same total light dose with (A) continuous projection and (B) pulsed projection with pulse width of 2s. ................................ 128 Figure 4.20. Effect of pulsed illumination on hydrogel formation in closely spaced exposed regions. Simulated hydrogel profiles for (A) continuous projection and (B) pulsed projection. .............................................................................................................. 129 Figure 4.21. Effect of pulsed illumination on oxygen depletion in closely spaced exposed regions. Simulated oxygen concentration profiles for (A) continuous projection and (B) pulsed projection. ................................................................................................... 130 Figure 4.22. Effect of pulsed illumination. Oxygen probe plots for continuous and pulsed models at (A) bottom, (B) middle and (C) top of the bioink domain. ............................... 131 xix Figure 4.23. Effect of pulsed illumination. DOC probe plots for continuous and pulsed models at (A) bottom, (B) middle and (C) top of the bioink domain. ................................... 132 Figure 5.1. Sequence of steps involved in iterative vasculature generation and optimization. .. 138 Figure 5.2. Representative images from MRI dataset showing liver tissue in different imaging modes – (A) T1-weighted, (B) T2-weighted, (C) T1-weighted with contrast agent (T3), (D) T2-weighted with contrast agent (T4). ................................................... 139 Figure 5.3. Detailed sequence of steps in MR image segmentation for liver 3D model generation, tissue equivalent porosity parameter estimation and source point generation. Input is marked in green; output is marked in orange and stored information of interest is enclosed in red boxes. ............................................................................................ 141 Figure 5.4. Detailed graphical representation of venation algorithm. (A) Initiation of the vascular network. (B) Propagation of the network by formation of new nodes. (C) Branching in the network. (D) Source termination by approached branches. ........................ 144 Figure 5.5. Steps in determining the nodes in the relative neighborhood of a source. ............... 145 Figure 5.6. Examples of vascular networks generated from described venation algorithm. (A) Closed network around a human brain (proportions are exaggerated for clear visualization). (B) Open networks enclosed in human liver showing arterial (red) and venous (blue) networks. ......................................................................................... 146 Figure 5.7. Model definitions for cell growth media transport simulation. (A) Description of domains properties, inlet and outlet. (B) (i) Perspective view of equivalent porosity parameter variation in xy and yz cross-sectional planes. (B) (ii) Top view of xy cross-sectional plane showing local variations in the equivalent porosity parameter within the liver body. ........................................................................................................ 148 Figure 5.8. MR image processing and 3D model generation. (A) Representative T3 mode slice image. (B) Slice image representing the cross-section of smoothed 3D liver model. (C) MR image segmented by applying cross-section slice image (B) as binary mask. (D) Rough 3D liver model generated from filtered point cloud. (E) 3D liver model after applying smoothing operations. (F) Smoothed 3D liver model after removing aorta. (G) Reconstruction error due to smoothing represented as error in slice area. ............................................................................................................................... 151 xx Figure 5.9. Volume intensity plots showing the intensity variations in (A) T1-weighted, (B) T2-weighted, (C) T1-weighted with contrast agent and (D) T2-weighted with contrast agent datasets. (E) Histogram representation of voxel distribution at various intensity. ............................................................................................................................... 153 Figure 5.10. Vascular network generation using venation algorithm. (A) 3D spatial distribution of source points for vein network generation. (B) Partially formed vein network. (D) 3D models of combined artery (red) and vein (blue) networks. (D) 3D rendering of the generated vascular networks enclosed in the liver body. ...................................... 155 Figure 5.11. Cell growth media distribution in liver by diffusive mass transport simulation. Concentration isosurface plots in the liver body at (A) 30 and (B) 60 minutes due to the growth media transport from vasculature obtained in first iteration. (C) Point cloud representation of nutrient scarce region. (D) Distribution of source points for iteration 2 of venation (old points – green; new points – red). .............................. 157 Figure 5.12. Cell growth media distribution in liver by diffusive mass transport simulation. Concentration isosurface plots in the liver body at (A) 30 and (B) 60 minutes due to the growth media transport from vasculature obtained in second iteration. (C) Point cloud representation of nutrient scarce region. (D) Distribution of source points for iteration 2 of venation (old points – green; new points – red). .............................. 158 Figure 5.13. Generated liver vascular networks from venation algorithm and guided by MR images. (A) Vasculature generated in iteration 1 – (i) perspective view, (ii) top view and (iii) 3D rendering image with liver body. (B) Vasculature generated in iteration 2 – (i) perspective view, (ii) top view and (iii) 3D rendering image with liver body. (C) Vasculature generated in iteration 3 – (i) perspective view, (ii) top view and (iii) 3D rendering image with liver body. ..................................................................... 160 Figure 5.14. Time series plot of volume covered with growth media concentration higher that 5% and 10% threshold values with vasculature from iteration 1 and 2. ...................... 161 xxi Acknowledgements First and foremost, I would like to express my enduring gratitude to my supervisors Dr. Keekyoung Kim and Dr. Kevin Golovin for their guidance and supervision during my Ph.D. They motivated and encouraged me throughout my research period and also provided me with invaluable advice and ideas in both research and life. They both have always been a great role model and source of inspiration to me. I would like to thank my committee members Dr. Ian Foulds and Dr. Frederic Menard for providing constructive inputs and many great ideas throughout this Ph.D. career. I am also grateful to my lab mates and friends from the Advanced Biofabrication Laboratory (Kabilan, Mahmoud, Pranav, Rafeal, Jenny and Yoonjung) at UBC Okanagan and University of Calgary who helped me with their greatest knowledge and provided me with their resources. I cannot thank my father, Balram, and mother, Sushila Devi, enough for their continued love and constant support. They are the pillars of my life who boosted my confidence whenever I felt low and without them, this would have not been possible. I also thank my sisters Neelam and Rupa who have always guided me and supported at every turn of life. I would also like to thank my friends Neha, Prem, Rohan Sharma, Rohan Bishnoi who have provided moral support throughout this journey. xxii Dedication To my parents and sisters who have made countless sacrifices, and, on whose shoulders, I stand and look into this world. 1 Chapter 1 Introduction 1.1 Background Tissue engineering emerged a few decades back with a promise to produce and repair complex tissues in human body.[1],[2] In conquest to fabricate highly complex tissue constructs, the theme of bioprinting has developed at an unprecedented rate. Drawing inspiration from several fabrication methodologies, researchers have made momentous advances in the field of bioprinting. Beginning from two-dimensional (2D) bioprinting, several studies have focused on the fabrication of engineered tissues and have been successful to an extent. Despite these advances, creating fully functional implantable tissues still remains a distant goal.[3]–[5] A tissue comprises of cells and extracellular matrix (ECM) that provides structural support to a diverse population of cells, hence, creates a complex heterogenous microenvironment.[6] This microenvironment is an amalgamation of several embedded biochemical and biophysical growth factors arising from the interaction of various constituent cells arranged in a three-dimensional (3D) domain.[7] The accurate spatial arrangement of cells is an indispensable factor to achieve the interaction between different cell types which makes a tissue functional. Some cell types, while proliferating, continue to secrete and remodel the ECM in a localized domain.[8] Such remodeling is essential to achieve the natural 3D tissue structure. Bioprinting aims to achieve the well-controlled 3D arrangement of constituent cell populations by fabricating scaffolds using suitable biomaterials. Once such structural arrangement is achieved on a macro/mesoscale, the cells begin to proliferate, interact, and remodel 2 their surroundings in microscale. Processes like inkjet, extrusion, direct laser writing, etc. have been used extensively in bioprinting to create scaffolds.[9] Extrusion bioprinting remains the most widely used methodology owing to the ease of controlling the printing process and its capability in handling multiple materials. Recently, other bioprinting methods have also become the center of attraction due to their advantages in terms of being rapid and non-contact.[10] Light-assisted stereolithography bioprinting is a class of such methods,[11] which is selected as the foundation of the proposed research. Figure 1.1. Heterogeneity and complexity of natural tissues. (A) Types of blood-tissue interfaces in central nervous system. (B) Complex vasculature present in the liver visualized using contrast enhanced 3D MRI. Adapted from ([12],[13]) Bioprinting, in a nutshell, is a process of fabricating scaffolds for the purpose of mimicking natural tissues. In heterogeneous tissue structures, multiple cell-types are organized and housed within a cell-specific ECM, which, in turn, has a unique composition dependent on tissue-type.[14] For example, Figure 1.1A shows the various types of blood-tissue interfaces in the central nervous 3 system and the constituent components and types of cells. Figure 1.1B shows the complexity of a hepatic vascular network visualized using contrast enhanced 3D MRI. For this reason, it is necessary for bioprinting systems to have the ability to print multiple bioinks and multiple-cell types precisely. Extrusion bioprinting is a simple method that uses pneumatic pressure to extrude a mixture of hydrogels and cells, which is called a bioink, to create 3D tissue structures.[15][16] While most bioprinting systems are only capable of printing single-bioinks, with extrusion systems it is possible to extrude multiple bioinks for creating heterogeneous structures.[17][18] It is also possible to dispense bioinks via multiple nozzles by installing additional printheads. Coordinating extrusion between multiple nozzles can lead to the fabrication of complex high-resolution patterns with diverse material compositions, setting the stage for the dispensing of multiple-bioinks for tissue engineering.[19][20][21][22] While extrusion bioprinting offers the capability to print multiple bioinks with high cell viabilities, it is a considerably slow process that administers large shearing forces on cells during the extrusion process, and in the process, harming them. On the other hand, stereolithography (SLA) bioprinting offers a non-contact, rapid, and high-resolution printing process with which high cell viabilities could be maintained. In this process, when a photocrosslinkable polymer is exposed to either a UV or visible light pattern, only the exposed area is crosslinked thus manufacturing a complex pattern.[23][24] However, the SLA bioprinting is held back by virtue of restricted material selection and the difficulty to print multiple materials. Photocrosslinkable bioinks are used in both types of bioprinting process and offer advantage by offering the ability to morph into desirable structure and tune the physical characteristics on application of light of suitable wavelength. Further, photocrosslinkability offers selectively altering portions of a fabricated tissue mimic and present a means of achieving heterogenous 4 artificial tissues. Therefore, photocrosslinkable bioinks and their characteristics are focus of this research with SLA bioprinting as the target process. Introducing a vascular network into bioprinted structures is a significant challenge that has hampered growth in tissue engineering. It is known that the maximum distance for oxygen diffusion from a blood vessel is about 100 μm,[25] therefore, to avoid cell death and ischemia, there must exist a blood vessel within that distance from a cell. Due to its geometric complexity, introducing a vascular network in tissue scaffolds is a significant challenge. However, by varying crosslinking parameters and modulating sol-gel transformations, it is possible to extrude vascular trees within another hydrogel-based structure.[26] A similar concept was demonstrated by Hinton et al., who successfully built a 3D structure embedded within another thermoresponsive hydrogel. [27] In the study, suspended hydrogel structures made of various materials including alginate, collagen, and fibrin were fabricated in various proof-of-concept geometries based on internal anatomical structures like the coronary artery, the heart, and the brain. The scaffold design is an often overlooked but crucial part of a bioprinting process. To fabricate and fully functional tissues and organs, precise tuning of scaffold parameters (e.g. degradation rate, porosity, strength, etc.) and design of the vascular network are required to control the tissue growth and morphology.[28]–[31] A well-defined 3D model is another primary requirement for bioprinting a well-resolved scaffold. Medical imaging modalities (CT, MRI, SPECT, ultrasound, etc.) provide the raw data in the form of tissue contrast images. Creating anatomically correct 3D models from such data has been the focus of research in the biomedical imaging field. Medical images have been used in some instances to determine bone mechanical properties and porosity of tissues.[32]–[34] Tissue electromagnetic properties can also be determined from medical imaging methods,[35] however, determining soft tissue mechanical properties is still a challenge which limits the designing of 5 scaffolds. The cellular growth in a scaffold is highly influenced by the physical properties and geometrical structure of the scaffold as well as the interaction among constituent cell population.[36],[37] These interactions should also be considered in designing a scaffold to achieve controlled tissue growth towards the projected morphology.[29],[30],[38]–[40] Therefore, in addition to designing the scaffold, it is crucial to study how the characteristics of the scaffold biomaterials evolve during the bioprinting process. The following sections present a brief discussion on SLA bioprinting, photocrosslinkable biomaterials, and the design of the scaffolds along with highlighting some of the principal challenges. 1.2 Stereolithography bioprinting Stereolithography (SLA) 3D bioprinting has emerged as a prominent bioprinting method addressing the requirements of complex scaffold fabrication. Compared to conventional bioprinting methods, the SLA 3D bioprinting offers several advantages in the form of multi-scale, high resolution, and rapid printing of highly complex scaffolds. In addition, the ability to fabricate implantable scaffolds with anatomically accurate geometry, precisely controlled surface characteristics, and tunable physical and chemical properties make it highly relevant for clinical applications.[41]–[43] Similar to the SLA fabrication method used in microfabrication or 3D printing, SLA bioprinting fabricates the 3D tissue constructs in a layer-by-layer manner. For this purpose, the designed 3D model is sliced into a stack of 2D images. Each 2D image in the stack represents the cross-section of the 3D solid model at a specified plane from the top. Hence, the input for the SLA 3D bioprinting process is in the form of sliced images.[41],[44] Since many biomedical imaging methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), generate 6 tissue images in the form of image stacks,[45] the biomedical imaging data can be directly utilized in the SLA bioprinting. Therefore, the SLA 3D bioprinting can be used for a number of applications based on the wide range of input dataset – from primitive user-defined geometries to complex tissue structures.[46]–[50] Due to rapid crosslinkability offered by UV based photoinitiators, most of the SLA bioprinting systems have been used with a UV illumination source. Soman et al. used a maskless SLA printing system to fabricate complex microscale free-form structures of flower, pyramid, spiral, and dome shapes.[51] Gauvin et al. also used projection SLA bioprinting system to fabricate 3D cell-encapsulated scaffolds using gelatin methacryloyl (GelMA) and demonstrated high cell viability and cell elongation.[43] Although SLA bioprinting is a rapid process, it can still take up to a few hours depending on the complexity and size of scaffolds. Extended duration of UV exposure has been found to have a negative impact on cell viability.[23],[52] Especially for sensitive cell types, high UV dose can induce mutations and adversely affect their growth rate. Advancements in visible light photopolymerization methods have drawn attention towards SLA bioprinting using visible light. Still in a nascent state, some visible light SLA systems have been developed demonstrating high biocompatibility even for extended light exposure.[23] In another study, the applicability of visible light crosslinkable GelMA for in vivo experiments was demonstrated without the use of SLA bioprinting process[53]. Sakai et al. recently reported a visible light bioprinting system using alginate derived biomaterial and an organometallic photoinitiator.[54] The illumination source used was in the far end of the visible spectrum (452nm).[54] The current limitations posed by the visible light based SLA bioprinting systems are primarily due to the low mechanical strength of photocrosslinked hydrogels when natural biomaterials are used.[23],[53] The second factor is the low efficiency of the visible light 7 photoinitiators and oxygen inhibition in the photopolymerization process.[55] Due to these factors, the bioprinting process is challenging to control and requires extensive research on the optimization of the bioink and the process. 1.3 Biomaterials for tissue engineering The success of a biofabrication process is centrally dependent on the appropriate choice of a bioink and its physical and biological properties. Bioinks primarily consist of cells and biomaterials which can also contain various types of biological reagents such as genes, nanovesicles, or growth factors, etc. General characteristics of a successful bioink are biocompatibility, high resolution printability, tunability of its physical properties, rapid polymerization, and suitable mechanical properties to support the printed 3D structures.[56]–[60] The choice of biomaterials, as well as cell types, is guided by the intended application. Furthermore, there are different sets of prerequisites for an ideal bioink, which are optimum for each bioprinting technique or intended application so as to attain a successful combination of high resolution and high fidelity 3D geometries with high cell viability.[61],[62] For instance, in the case of extrusion-based bioprinting, bioinks should be capable of undergoing physical gelation by temperature and ions or should be chemically crosslinked by light or crosslinkers before or during the extrusion of materials.[63] For the purpose of stereolithographic (SLA) bioprinting, the bioinks should be crosslinkable upon interaction with various type of lights. In this nozzle-free process, printing time is independent of the printed structure’s complexity.[64],[65] Due to the absence of any external shear force applied to the cells, this bioprinting method exhibits excellent cell viability and biocompatibility.[43],[66] However, the material-light interaction, in terms of the bioink’s physical properties in combination with the parameters for the photocrosslinking process, highly 8 governs the resolution and mechanical properties of the printed 3D structures.[67] UV or visible light was commonly used in the SLA bioprinting system as required by specific photoinitiators. To create covalent bonding in bioinks via photocrosslinking without significant cytotoxicity of encapsulated cells, two mechanisms as acryloyl-based crosslinking[68]–[70] and the thiol-ene click reaction[71]–[73], were commonly used. Hence, when using hydrogel macromers as bioinks, an acryloyl functional group or an alkenyl functional group should be used to modify the hydrogel. Although there have been some studies to directly use an assembly of cells to build 3D constructs, the most common way to build 3D tissues is to use hydrogels as scaffolding materials. There are several advantages associated with the use of hydrogels as they provide a 3D supporting structure along with attachment sites for cells to grow in a 3D microenvironment. Hydrogels are among the most prominent bioinks for biofabrication and tissue regeneration applications. Hydrogels can be rendered excellent mimics of the extracellular matrix due to their cytocompatibility, high water content, tunable mechanical properties and degradation kinetics, and modifiable chemical structures.[74],[75] Acryloyl or acrylate hydrogels are the most commonly used photocrosslinkable biomaterials in tissue engineering and biomedical applications. These hydrogels contain an electron deficient functional group with terminal carbon-carbon double bond. Methacryloyl-based photocrosslinkable hydrogels are synthesized from macromolecular hydrogels by reacting with a methacrylation agent (e.g., glycidyl methacrylate or methacrylic anhydride) to substitute an acryloyl group. The substitution degree of methacryloyl functional groups can be controlled by the concentration of the methacrylation agent, the duration of the reaction, and the pH of the medium during the synthesis.[76],[77] Some of the hydrogels falling into this category are poly (ethylene glycol) diacrylate (PEGDA), poly (ethylene glycol) dimethacrylate (PEGDMA), gelatin 9 methacryloyl (GelMA), and dextran methacrylate (DexMA). The acryloyl hydrogels undergo chain polymerization reaction in the presence of free radicals to form a crosslinked network of hydrogels.[78] Due to excellent biocompatibility, gelatin-based hydrogels have been widely used in STL 3D bioprinting.[79] By using different photoinitiators, complex GelMA scaffolds were fabricated by UV, visible light, and two-photon based SLA bioprinting systems. [41],[42],[53],[68],[77],[79]–[83] The hydrogels containing acryloyl and other acryloyl derivatives are popular but at the same time, they are highly influenced by the problem of oxygen inhibition. Acryloyl hydrogels undergo free-radical mediated polymerization which can be inhibited by the presence of oxygen.[55] Oxygen can quench the excited state of photoinitiators or react with initiating free radicals to form peroxyl radicals which do not favor the polymerization reaction.[84],[85] This phenomenon decelerates the polymerization process and forces to increase light exposure time. Consequently, longer illumination achieves a high degree of crosslinking on the desired areas but also results in partial crosslinking of the surrounding areas due to ambient and scattered light. This is termed as overcrosslinking of the pattern and adversely affects the resolution of the printing process. Photoinitiators cleave to form a free radical and initiate the photocrosslinking reaction of hydrogels upon excitation by light with suitable wavelengths. Photoinitiators are typically classified into Type-I and Type-II based on intermediate steps involved in the crosslinking. Type-I photoinitiators are often referred to as cleavable photoinitiators and do not require multiple components. When the light of suitable wavelength span is illuminated, Type-I photoinitiators form an excited triplet state. This state is short-lived and then undergoes cleavage to produce two free radicals which initiate the crosslinking of hydrogel.[86] Irgacure-2959 is the most widely used 10 Type-I photoinitiator with good cytocompatibility.[43],[52],[77] LAP is another Type-I photoinitiator which can be used with both UV-A (365 nm) and visible light (405 nm) wavelengths. With high water solubility, high polymerization rate, and better cytocompatibility, LAP became more preferable than commonly used Igacure-2959.[87] Another class of organometallic photoinitiators has also emerged operating in the far end of the visible spectrum (400-450 nm).[88]–[91] On the other hand, Type-II photoinitiators are referred to as bimolecular photoinitiating systems (e.g., benzophenone/tertiary amine). The free radical formation by Type-II systems begins with the excitation of benzophenone which initiates a fast electron transfer from the tertiary amine. This step is followed by a slow proton transfer process resulting in the formation of H-donor radical for initiating the crosslinking of hydrogels.[86] Involvement of additional steps increase the complexity of the crosslinking mechanism and, on the other hand, decrease the efficiency of Type-II systems which make slower than Type-I systems.[92] Several studies have utilized Eosin-Y based visible light photocrosslinking and demonstrated high cytocompatibility for 3D bioprinting applications.[42],[71],[72],[93],[94] Nonetheless, in view of the advances and challenges stated above, it is required to develop bioinks with fast photocrosslinking ability with visible light. Further, the photopolymerization process involving Type-II photoinitiator is highly complex and a better understanding of this process is needed to predict the outcome of bioprinting process and guide the scaffold design. 1.4 Scaffold design Developing successful heterogeneous tissue structures demands that tissue scaffolds be carefully designed, incorporating vasculature that is reminiscent of native tissues while also demonstrating the structural, populational and functional characteristics. A scaffold must be 11 designed that features a precise tuning of parameters (e.g. degradation rate, porosity, strength, etc.), and a well-connected vascular network. Medical imaging modalities such as CT, MRI, SPECT, and ultrasound provide a starting point for scaffold designing. In order to yield anatomically correct 3D models that eliminate noise from the medical images, segmentation methods are used. Gargiulo et al. combined images retrieved from CT and MRI scans with those retrieved from DTI (diffusion tensor imaging), a tool that makes it possible to reconstruct nerve fibers in the brain.[95] This model was then used to create a 3D model of a patient’s skull base, tumor and fiber tracts that was 3D printed (Figure 1.2A-C). This anatomically realistic model of a patient’s head was intended to aid surgeons in planning complicated surgeries; however, such technology is also making inroads into personalized tissue engineering. Recently, Lee et al. described the production of collagen-based cardiac structures that were based on models reconstructed from MRI scans.[96] Additionally, vascular structures were integrated into the model via a separate computational model that used heat-map data based on the distance of blood vessels from the coronary artery. The Voronoi space-filling algorithm was then extended to the rest of the structure in order to generate 3D microvasculature. Such integration of vascular networks within models for bioprinting could aid in creating functional heterogeneous tissues. This blend of the latest imaging technologies, in combination with bioprinting technologies will make it possible to advance the state of tissue-engineered constructs and enable a personalized therapeutic approach that is tailor-made, considering the patient’s physiology and their clinical condition. 12 Figure 1.2. Medical image deried tissue models. (A) MRI structural data of brain tissue. (B) 3D reconstructed skull and brain model with tumor identified from MR images. (C) 3D printed prototype of the generated 3D model. Adapted from ([95]). 1.5 Research objectives Goal of this research is to optimize photopolymerizable bioinks to address the limitations of light-based bioprinting methods in achieving fabrication of high-resolution scaffolds. Further, develop a framework for tuning and predicting the properties of the bioinks to achieve heterogenous scaffolds. To achieve this goal, following objectives are proposed in this research – 13 1.5.1 Development of a low-gelation point bioink Gelatin from porcine skin is chemically modified to obtain photocrosslinkable gelatin methacryloyl (GelMA). Various reaction parameters involved during the synthesis affect the resulting properties of GelMA and yield bioinks which can maintain a low viscosity liquid state at room temperature. This allows for easier handling and manipulation of bioinks. Detailed characterization of the properties of the obtained bioinks are performed and the influence of various factors on the bioink characteristics is identified. 1.5.2 Development of a visible light photoinitiator for accelerated photopolymerization A three-component photoinitiator is developed to initiate free-radical polymerization using visible light as input. It is coupled with the GelMA developed in Objective 1 to achieve faster bioprinting at higher resolution. Detailed analysis of the photocrosslinking kinetics of these bioinks is performed to identify how the bioinks transform to the hydrogel. The effect of the photoinitiator composition on the printability of bioinks is also evaluated. 1.5.3 Numerical modeling of the mask photopolymerization Using the characteristics of the biomaterial developed in the Objective 1 and the photoinitiator developed in Objective 2, a detailed numerical model is developed for the photopolymerization process. The model is used as a predictive tool to identify the defects introduced in the bioprinted scaffolds as a result of the photo-pattern and illumination characteristics. The evolution of physical properties of the hydrogel during polymerization and the bioink composition are also predicted using the developed model. 14 1.5.4 Development of an optimized vasculature design scheme A mathematical model is developed which is inspired from the vein networks in leaves and the growth patterns in the trees. This model is used to design blood vessel networks within an organ. The guiding biological information for these networks is derived from the medical images of organs. Further, the capability of these networks in sustaining the organ is assessed using which these networks are evolved to reach an optimized design. 1.6 Thesis structure This thesis comprises of six chapters which are organized as follows – Chapter 1 begins by introducing the theme of tissue engineering. The structure and composition of tissues within the human body are discussed and emphasis is laid on the inherent heterogeneity of the tissues and organs. Further, the role of 3D bioprinting is discussed along with some of the challenges posed by present bioprinting methods. A brief background of SLA bioprinting is presented with a discussion on biomaterials and bioinks used for fabricating scaffolds. The importance of scaffold designing is also presented. This chapter also lists the shortcoming of the bioinks which hinder the performance of the bioprinting methods and how these can be addressed through the objectives of this research work. Chapter 2 describes the synthesis process of GelMA bioinks in detail. A detailed account of the chemical modification of gelatin in different conditions is presented. The resulting bioink and hydrogels are characterized in-depth. These characterizations include the photocrosslinking kinetics, viscosity, physical strength and biocompatibility. Bioprinting studies involving fabrication of miniature version of human tissues is also presented along with the characterization of cell growth within the bioprinted tissues. 15 Chapter 3 delves into improving the printability of the developed bioinks by introducing a three-component photoinitiator. The mechanism of action of the developed photoinitiator is described in detail. The characterization of photopolymerization kinetics of the resulting bioinks is presented and the different phases of the polymerization are discussed. Further, the printing speed and resolution are compared followed by a brief biocompatibility assessment. An interesting deformation behavior of the fabricated hydrogels is also presented in brief. Chapter 4 describes a numerical model for the photopolymerization of the GelMA bioink and the three-component photoinitiator developed in Chapter 2 and 3. A detailed explanation of the participating reactions is presented and the influence of governing parameters like oxygen inhibition is discussed. The photo-patterns are manipulated both spatially and temporally and their effect on the photopolymerized hydrogel formation are simulated. Chapter 5 embarks into the designing of the scaffolds. Chapters 2, 3 and 4 focus on developing bioinks for SLA bioprinting and a predictive model for tuning the hydrogel formation with these bioinks. This chapter complements the previous research objectives by providing a design framework for vascular scaffolds. In combination, all the objectives enable the fabrication of heterogenous and vascular artificial tissues. The chapter begins with describing the strategy behind medical image processing to derive tissue structure and estimate tissue porosity. Next, a venation algorithm is described which generates closed-type and open-type vascular networks guided by the tissue structure. Finally, a diffusive mass transport study is presented which evaluates the performance of the generated vasculature and optimizes it iteratively. Chapter 6 is the final chapter in this thesis and presents concluding remarks and summary of this research work. The novelty and contribution of the research presented in this thesis are stated. Finally, brief outlook on the future work is also presented. 16 Chapter 2 Development of a low gelation temperature gelatin methacryloyl bioink 2.1 Synopsis GelMA is one of the most extensively used biomaterials for biofabrication applications due to its high biocompatibility, allowing for cell adhesion and proliferation, biodegradability, photocrosslinkability, relative ease of synthesis, and cost-effectiveness.[69],[97] This chapter focuses on the mechanistic study of GelMA synthesis and influence of the reaction parameters on the resulting GelMA properties. GelMA synthesis includes several parameters such as the gelatin source, the solvent used as the reaction medium, the pH of the reaction, the methacrylate group donor, the catalyst, and the duration of the reaction. This multifactorial preparation method offers the advantage of tunability of the final product’s properties by manipulating the methacrylation reaction. A sequential investigation of individual parameters – solvent, pH, reaction duration is presented. The obtained GelMA pre-polymers were characterized according to their photocrosslinking kinetics, gelation behavior, mechanical properties, swelling ratio, and printability. Later parts of this chapter present a biofabrication application in which SLA bioprinting setup was used with the tuned GelMA bioinks to fabricate tissues with two different cell types: 3T3 fibroblasts and U118 astrocytes. The growth of cells within these tissues for a span on 30 days is also presented. 17 2.2 Experimental section 2.2.1 Effect of solvent on gelling behavior In order to study the effect of the solvent exclusively on the gelation behavior of the bioink, GelMA synthesis conditions as described in the previous section were emulated in the absence of the methacrylation agent. The gelatin was hydrolyzed in various solvents (Table 2.1.) and then freeze dried after dialyzing against RO water. Post lyophilization, the processed gelatin samples were dissolved in PBS to obtain 10% (w/v) solutions and their gelation behavior was observed at room temperature. The viscosity of the modified gelatin solutions was measured by Ubbelohde viscometer at 37oC and at a concentration of 5% (w/v) to avoid clogging of the viscometer due to gelation. The viscosity average molecular weight of the various gelatin solutions was calculated using the equation proposed by Deb and Chatterjee (Eq. 2.1)[98]–[100] [𝜂] =1𝐶 [3 ln(𝜂) +32𝜂𝑠𝑝2 − 3𝜂𝑠𝑝]13 (2.1) , where 𝜂, 𝜂𝑠𝑝 and [𝜂] are the measured viscosity, specific viscosity and intrinsic viscosity of the gelatin solution respectively. The gelation behavior of the synthesized GelMA combinations was also observed at room temperature. Table 2.1 List of gelatin samples for solvent effect comparison. Solvent for hydrolysis Hydrolysis time (hours) Nomenclature DMSO 3, 12, 24 DMSO-3h, DMSO-12h, DMSO-24h RO 3, 12, 24 RO-3h, RO-12h, RO-24h PBS 3, 12, 24 PBS-3h, PBS-12h, PBS-24h 18 2.2.2 GelMA synthesis Gelatin methacryloyl (GelMA) was synthesized with different combinations of reagents and reaction conditions as listed in Table 2.2. The various solvents for GelMA synthesis reported earlier were selected in this study.[67],[68],[75],[97],[101],[102] The synthesis was done following the protocol reported previously[67] with some modifications. In brief, 5 g of powdered gelatin from porcine skin (Type A, Bloom strength 300, Sigma-Aldrich, St. Louis, MO, USA) was dissolved in 50 mL of a suitable solvent. The solvents selected for this study were – dimethyl sulfoxide (DMSO) (Fisher bioreagents, Waltham, MA, USA), reverse osmosis purified water (RO), phosphate-buffered saline (PBS) (Sigma-Aldrich) and 0.25 M sodium bicarbonate buffer (CB) in RO water. After complete dissolution, the pH of the resulting solution was adjusted to either of two levels – 3.5 (L) or 9 (H), where L and H indicate low and high pH respectively. For pH adjustment, 1 M NaOH and 1 M HCl were added dropwise to the gelatin solution. In the case of DMSO being used as a solvent, 0.3 g of 4-Dimethylaminopyridine (DMAP) (Sigma-Aldrich, St. Louis, MO) was added as a catalyst. After the pH adjustment, 9 mL of glycidyl methacrylate (GMA) (Sigma-Aldrich) was added to the gelatin solution dropwise. The solution was then maintained at 50 oC with constant stirring at 500 RPM for either 3 hours or 12 hours. Upon completion of the reaction, the solution was diluted with 50 mL of RO water and transferred into dialysis tubing (12-14 kDa) (Fisher scientific, Waltham, MA, USA) to be dialyzed against deionized water for 3 days. Water was changed twice a day. After 3 days, the remaining solution was filtered through a porous paper filter followed by freezing and then lyophilization at -54 oC to obtain a foamy solid of GelMA. The lyophilized samples were stored at -20 oC for further use. Schematic 1 shows the schematic of the substitution reaction with its possible outcomes. Hydrolysis of GMA generates methacrylate functional groups which can be substituted at (a) 19 primary amine (-NH2) and (b) hydroxyl groups (-OH). In another reaction pathway, the GMA undergoes an epoxide-ring opening mechanism and results in substitution at (d) primary amine and (c) hydroxyl groups. Table 2.2. List of synthesized GelMA combinations. Solvent Reaction time (hours) pH/catalyst Nomenclature DMSO 3, 12 DMAP DMSO-3h, DMSO-12h RO 3, 12 3.5, 9 RO-3h-L, RO-3h-H, RO-12h-L, RO-12h-H PBS 3, 12 3.5, 9 PBS-3h-L, PBS-3h-H, PBS-12h-L, PBS-12h-H CB 3, 12 3.5, 9 CB-3h-L, CB-3h-H, CB-12h-L, CB-12h-H 2.2.3 Quantification of the degree of substitution The degree of substitution of the synthesized GelMA was measured using proton nuclear magnetic resonance (1H NMR) as reported previously.[23],[68],[103] A concentrated solution of GelMA was prepared by dissolving 0.8 g of GelMA in 0.6 mL of deuterium oxide (Acros Organics, Fisher scientific). A 400 MHz spectrometer (Bruker, Billerica, MA, USA) was used to record the 1H NMR spectra for the synthesized GelMA samples. The recorded data was preprocessed by performing phase correction, suitable baseline fitting and scaling in the MestReNova software (Mestrelab Research, Santiago de Compostela, Spain). The internal reference was set to phenylalanine signals (7.2-7.5 ppm). The peaks of primary amine groups (2.8–2.95 ppm) were integrated to compute the degree of substitution. 20 2.2.4 Effect of solvent and catalyst on gelatin hydrolysis The effect of catalyst DMAP on the gelatin hydrolysis during the synthesis of DMSO-12h GelMA was assessed using the procedure described in Section 2.2.1. In brief, the gelatin was dissolved in DMSO to obtain 10% w/v solutions. One solution was heated at 50oC with constant stirring of 500 rpm for 12 hours. Another was supplemented with 0.6% w/v DMAP and heated in similar conditions. Next, the solutions were transferred to dialysis tubes (12-14 kDa molecular weight cut-off) and dialyzed against deionized water for 3 days with water change twice a day. After dialysis, the solutions were frozen for 1 day at -20 oC and then lyophilized for 3 days at -53 oC to obtain a foam of hydrolyzed gelatin. To observe the extent of hydrolysis, the hydrolyzed gelatin samples were dissolved in PBS to obtain 10% w/v solutions and maintained at 37 oC. Next, the solutions were placed at room temperature and allowed to cool down. The gelation time was recorded by visually inspecting the solutions after intervals of 5 minutes. 2.2.5 GelMA pre-polymer solution and hydrogel preparation The lyophilized form of GelMA synthesized in different conditions – solvent, pH and reaction duration as described earlier (Section 2.2.2) were dissolved in PBS along with the addition of photoinitiator to obtain a solution with 10% (w/v) GelMA macromer. The photoinitiator was comprised of photosensitizer Eosin Y (Sigma-Aldrich) and co-initiators triethanolamine (TEOA) (Sigma-Aldrich) and N-vinylpyrrolidone (NVP) (Sigma-Aldrich) with final concentrations of 0.02 mM, 0.2% and 74 nM respectively in the prepared GelMA pre-polymer solution. The nomenclature used in the manuscript indicates the synthesis conditions used for their respective GelMA combinations, for example, “PBS-3h-L GelMA” implies the GelMA was synthesized with PBS as the reaction solvent, 3 hours as reaction duration and with the pH adjusted to 3.5 (low) at 21 the beginning of the reaction. Furthermore, all the GelMA pre-polymer solutions and bioinks were prepared with PBS as the solvent. The pre-polymer solutions of various GelMA combinations were photocrosslinked to form the hydrogels for characterization and bioprinting using the digital light processing (DLP) based projection system described in detail previously.[23] The hydrogel preparation conditions for different characterizations are described in the following sections. The bioprinting was performed in a layer-by-layer manner by sequentially depositing and photocrosslinking the layers of the pre-polymer solution in a pattern representing the cross-section of the 3D construct as described in detail in an earlier work.[23] 2.2.6 Crosslinking time measurements Photocrosslinking kinetics of the synthesized GelMA were recorded by applying sinusoidal shear loading on the bioink undergoing photopolymerization. GelMA bioink was prepared with 10% (w/v) macromer. A vertical axis micromechanical testing machine (Mach-1 v500c, Biomomentum Inc., Montreal, QC, Canada) was used to apply sinusoidal deformation and record the force response of the curing hydrogel. The probe and platform were modified by attaching 3-(Trimethoxy silyl) propyl methacrylate (TMSPMA) (Sigma-Aldrich) coated glass plates. GelMA samples were loaded by sandwiching 100 µL of the pre-polymer solution between the probe and the platform glass plates maintained at a separation of 2 mm for all measurements. A sinusoidal displacement was applied on the probe with an amplitude of 0.5 mm and a frequency of 1 Hz. All measurements were recorded for a duration of 1000 seconds. A DLP projector was used as the illumination source placed at a distance of 20 cm with an intensity of 48.6 mW/cm2, emulating the bioprinting setup described earlier.[67] 22 2.2.7 Physical properties of GelMA hydrogels The mechanical stiffnesses of the hydrogels obtained from photocrosslinking synthesized GelMA were assessed using the Hertz contact mechanics method. The contact model used was of a flat-ended rigid cylinder and an elastic half-space. First, 2 mL of the GelMA pre-polymer solution was poured in a small Petri dish (36 mm in diameter) to form a 2 mm thick layer. The solution was then exposed to visible light (wavelength – 400-700 nm) in the SLA bioprinting setup to undergo photocrosslinking and form GelMA hydrogels as described earlier. Considering the large volume of the pre-polymer solution, the projection duration was kept at 30 minutes to achieve saturation in crosslinking. Post crosslinking, the hydrogels were submerged in PBS for 24 hours to ensure the hydrogels had reached their swollen states. A vertical axis micromechanical testing machine was used to record the force-displacement plots for the hydrogels using a flat-ended cylindrical probe (5 mm in diameter). The contacting end of the probe was submerged in PBS and applied a 10-20% strain on the hydrogel. The linear part of the recorded force-displacement curve was selected to calculate the slope. The young’s modulus was obtained using the following relation: 𝐸 =𝑚×(1−𝜗2)2𝑟 (2.2) , where 𝜗 is Poisson's ratio (0.5 for GelMA hydrogel),[104] 𝑟 is the radius of the cylindrical probe, and 𝑚 is the slope of the force-displacement curve. The swelling ratios of various GelMA samples were measured based on the method reported earlier.[67] In brief, the pre-polymer solution was poured into the wells of a 96-well plate, with well diameters of 6.4 mm and heights of 10 mm, and illuminated with visible light in the bioprinting system. To achieve saturated crosslinking, the exposure time was set to 30 minutes. 23 Upon crosslinking, the samples were transferred to a 24-well plate and were submerged in PBS to achieve the fully swollen state, then they were weighed after 24 hours. Next, the samples were frozen for 24 hours and lyophilized at -50 oC for 2 days. The freeze-dried samples were weighed, and the mass swelling ratios were calculated as follows: Swelling ratio =𝑠𝑤𝑜𝑙𝑙𝑒𝑛 𝑤𝑒𝑖𝑔ℎ𝑡𝑑𝑟𝑖𝑒𝑑 𝑤𝑒𝑖𝑔ℎ𝑡 (2.3) A scanning electron microscope (SEM) was used to compare of the microstructures of the crosslinked GelMA hydrogels. The samples were prepared in a similar manner as for the compressive modulus characterization. Upon crosslinking, the samples were frozen for 2 days at -20 oC followed by lyophilization at -50 oC for 3 days to obtain foamy fragments. The fragments were sputter-coated with Pt/Pd alloy (80% Pt and 20% Pd). The images were recorded using a SEM (Mira3 XMU, TESCAN, Brno, Czech Republic). 2.2.8 Printability To assess the SLA printing capability with the current bioprinting system,[67] the eight GelMA combinations (PBS-3h-H, CB-3h-L, CB-3h-H, DMSO-12h, RO-12h-L, RO-12h-H, PBS-12h-L, and CB-12h-L) with low sol-gel transition temperature were compared to identify the combination yielding the best printed hydrogel structure with the least crosslinking time. The hydrogel patterns were printed using the DLP based SLA bioprinting system as described earlier. First, a vat was formed with a glass coverslip (Thickness 0.17 mm, VWR micro cover glass No. 1, VWR, PA, USA). Next, a pre-polymer layer with a thickness of 300 µm was deposited. A simple mesh pattern with dimensions of 4.5 mm x 4.5 mm was projected on the GelMA prepolymer solution with a photocrosslinking time of 60 seconds. The GelMA pre-polymer solution 24 composition was the same as described in earlier sections. The printed mesh features were washed with PBS and stained with red food dye to aid in visualization. Images were recorded using a digital camera augmented with a 15X macro lens. After determining the GelMA bioink combination which yielded the best printability, the overall bioprinting resolution was characterized by printing line features. Lines with widths of 1, 2, and 5 pixels were projected on the GelMA pre-polymer solution, forming a layer with a thickness of 500 µm. Each line width was projected for the durations of 60, 120, 180, and 240 seconds respectively. The printed lines were washed with PBS to remove the uncrosslinked pre-polymer solution and then they were imaged using an inverted microscope (Axio Observer 7, Carl Zeiss Canada Ltd., ON, Canada) in bright field mode with phase contrast. 2.2.9 GelMA degradation test The enzymatic degradation was evaluated for RO-12h-L, RO-12h-H and DMSO-12h GelMA considering physical properties of RO-GelMA and use of DMSO-12h GelMA in our previous works. First, a pre-polymer solution was prepared with 10% w/v GelMA and Eosin Y based photoinitiator (0.02 mM Eosin Y, 0.2% w/v TEA and 74 nM NVP) in PBS. The wells of a 96-well plate were filled with 100 µL of pre-polymer solution and crosslinked using the SLA bioprinting system for a duration of 15 minutes to achieve high degree of crosslinking. The crosslinked hydrogel disks were incubated at 37oC and 5% CO2 in a freshly prepared collagenase I solution (50 µg/mL in DPBS). After intervals of 1 hour, a group of 5 disks was removed from the solution to record the remaining mass of the hydrogel with the first measurement being done before incubating in collagenase I solution. The hydrogels disks were gently blotted with wipe to 25 remove excess solution and weighed using a table-top precision balance. A total of 10 measurements were recorded over a span of 9 hours. 2.2.10 Cell culture The 3T3 fibroblasts and U118 cell lines were cultured in glass cell culture plates filled with the cell culture media and incubated at 37 oC and 5% CO2. The cell culture media was comprised of Dulbecco’s modified Eagle’s medium (Lonza, Basel, Switzerland) supplemented with 10% (v/v) heat-inactivated fetal bovine serum (Thermo Fisher Scientific, Waltham, MA) and 1% (v/v) penicillin-streptomycin (Sigma-Aldrich). 2.2.11 Bioprinting In continuation with the previous characterizations, 10% (w/v) GelMA pre-polymer solution was prepared under sterilized conditions. Cultured 3T3 fibroblasts were collected by trypsinization followed by centrifuging as described previously,[67] and then dispersed homogeneously in the pre-polymer solution to obtain a bioink with 8x106 cells/mL. Two structures with different dimensional scales were bioprinted – a honeycomb pattern and a miniaturized human ear. Cultured U118 astrocytes were also collected in a similar manner and dispersed in the pre-polymer solution to yield a bioink with 8x106 cells/mL. This bioink was used to bioprint a thick, miniaturized cross-sectional slice of the human brain. After printing, the uncrosslinked bioink was removed and samples were washed twice with PBS. The samples were cultured at 37 oC with 5% CO2 in cell culture media. The bioprinted honeycomb samples were cultured and characterized for cell viability on day 1 and day 5. The samples were washed twice with PBS to remove the media prior to incubating 26 in live/dead assay (Biotium, Hayward, CA, USA) for 30 minutes. The assayed samples were imaged using an automated fluorescent microscope. The samples were kept hydrated to maintain consistency in the measurements. An objective lens of 5X was used with enhanced green fluorescence protein (EGFP) and mCherry channels to acquire fluorescent images. The Z-stack function was used to capture cross-sectional images at various heights. Cell morphologies in the various bioprinted samples were characterized at different times by cytoskeleton and nuclei staining using Phalloidin (Cytoskeleton, Denver, CO, USA) and 4’,6-diamidino-2-phenylindole (DAPI) (FluoroshieldTM with DAPI, Sigma-Aldrich). The honeycomb sample was characterized on day 5, the miniature human ear sample was characterized on days 14 and 24, and the miniaturized brain slice sample was characterized on day 30. After removing the cell culture media, the samples were washed 3 times with PBS. The cells were fixed by incubating them in 3.7% (v/v) paraformaldehyde (Sigma-Aldrich) for 30 minutes at room temperature, then they were washed with PBS. Next, the fixed samples were treated with 0.5% (v/v) Triton X-100 (Sigma-Aldrich), diluted in PBS for 10 minutes and then washed with PBS. To stain the cytoskeletons of the cells, the samples were incubated in 100 nM stock solution of phalloidin 488 for 60 minutes at room temperature, then they were washed once with PBS. Finally, the PBS solution was removed, and the samples were mounted by using mounting media with DAPI. The stained samples were examined under a fluorescent microscope. Objective lenses of 5X, 10X and 20X were used with DAPI and EGFP fluorescent channels to image the samples in tile and Z-stack modes. 27 2.2.12 Statistical analysis All data are represented as mean ± standard deviation. Statistical analysis was performed using MATLABTM (Mathworks, Natick, MA, USA). Statistical significance was determined by one-way analysis of variance using Tukey’s multiple comparison test. Differences were considered to be statistically significant at p < 0.05 with the significance levels –ns p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001. 2.3 Results and Discussion 2.3.1 Solvent effect on the thermal sensitivity of GelMA To exclusively observe the effect of the solvent used during the GelMA synthesis, we simulated the solvent-gelatin interaction by heating the gelatin in various solvents for durations of 3, 12, and 24 hours. The solutions were maintained at 50 oC by continuous monitoring and stirred constantly at 500 rpm. During this process, the gelatin undergoes hydrolysis which results in the shortening the molecular chains. We compared the gelation behavior of various gelatin samples by preparing 10% (w/v) solutions in PBS solvents and by measuring the time required for complete gelation at room temperature. A complete list of compared gelatin samples is presented in Table 2.1 along with the respective nomenclature. 28 Figure 2.1. Solvent dependent hydrolysis of gelatin. (A) Representative images showing gelation time for gelatin hydrolyzed in different solvents (DMSO, RO, PBS & CB) for different durations (3 hours, 12 hours & 24 hours). Black, blue and red markers are assigned for completely gelled, partially gelled and non-gelling samples respectively. (B) Comparison of gelation time at room temperature. (C) Viscosity measured for 5% (w/v) gelatin solutions at 37oC using Ubbelohde viscometer. (n = 5) (D) Average molecular weight calculated based on the Mark-Houwink equation. (n = 5) Gelatin samples hydrolyzed in solvents DMSO, RO and PBS for 3, 12, and 24 hours were found to undergo gelation at room temperature. Gelatin hydrolyzed in solvent CB for 3 and 12 29 hours gelled at room temperature, whereas the 24-hour hydrolyzed sample remained liquid indicating a high degree of hydrolysis (Figure 2.1A). The differences in gelation times as shown in Figure 2.1B, also provide an indication of the extent of the hydrolysis of gelatin. Based on the gelation time, it can be inferred that DMSO resulted in the least degree of hydrolysis, followed by RO with a slightly higher degree of hydrolysis. PBS hydrolyzed the gelatin more than RO and was followed by CB which resulted in the highest degree of hydrolysis among all four solvents. The hydrolyzed gelatin samples were further analyzed for viscosity. The viscosities of the 5% (w/v) gelatin solutions were measured using an Ubbelohde viscometer at 37 oC to avoid clogging the viscometer capillary. Figure 2.1C shows the measured viscosity of gelatin hydrolyzed for 3 hours, 12 hours and 24 hours, respectively, in various solvents. An increase in the heating duration directly resulted in a decrease in the viscosity for all gelatin solutions. In DMSO, however, the change in viscosity was statistically insignificant. When heated in RO, the solutions with 3 hours and 12 hours of heating exhibited viscosities within a similar range whereas the solution heated for 24 hours demonstrated a significant decrease in viscosity. Heating in PBS solvent resulted in viscosities similar to the corresponding gelatin solutions in RO solvent. When comparing samples hydrolyzed in PBS solvent, the viscosities after 24 hours and 12 hours of heating in PBS were significantly lower than the viscosity after 3 hours of heating in PBS. On the other hand, the CB solvent had a strong influence on the viscosities of the samples, which were found to be lower than the viscosities of the corresponding gelatin solutions in all of the other solvents. Similar to other aqueous-based solvents, with an increase in the heating duration in CB, the viscosities of the resulting CB solvent gelatin solutions decreased significantly. The measured viscosity was then used to compute the intrinsic viscosity using single point measurements.[98],[99],[105],[106] Intrinsic viscosity was further used to compute viscosity average 30 molecular weights using the Mark-Houwink equation with the respective parameters for gelatin at 37 oC.[106],[107] Longer heating durations in all the solvents increased the extent of hydrolysis and, consequently, lower average molecular weights were obtained (Figure 2.1D). The gelatin heated in DMSO demonstrated low hydrolysis and had an average molecular weight close to pristine gelatin (3.75 x 105 Da ± 0.86). RO and PBS solvents exhibited a higher degree of hydrolysis than DMSO, and the CB solvent presented the highest extent of hydrolysis among all solvents for samples heated for 3 hours, 12 hours and 24 hours. The observed trend is expected as the gelatin molecules are comprised of several peptide bonds which undergo hydrolysis in aqueous solvents. The rate of hydrolysis of the peptide bonds is highly influenced by the pH of the solvent.[108] The hydrolysis at low pH requires a strong acid and a high temperature whereas, in the pH range of 8-10, hydrolysis is comparatively faster and does not require a high temperature. Gelatin has also been observed to function as a weak pH buffer.[109] When RO solvent is used, the pH remains within a range of 6.5-7, which lessens the extent of gelatin hydrolysis as shown by the calculated average molecular weight. In the PBS solvent, the pH is maintained by the buffer at 7.4 which also contributes to a similar hydrolysis rate as the RO solvent. Hence, the observed viscosity and calculated average molecular weights of hydrolyzed gelatin in PBS were in the same range as the RO solvent without any statistically significant variations. On the other hand, the CB solvent maintained the solution pH close to 9 which resulted in a significantly higher rate of hydrolysis for the gelatin. Consequently, the observed viscosities and calculated average molecular weights of CB hydrolyzed gelatin solutions were significantly lower. Hydrolyzing for 24 hours in CB solvent results in gelatin molecular chains too small to undergo gelation. Hence, if the GelMA is synthesized in a similar condition, it 31 is expected to have a slow sol-gel transition at room temperature but would not be able to form a stable polymerized network. 2.3.2 GelMA degree of substitution characterization The GelMA was synthesized by the substitution of methacrylate groups on the primary amines present in the gelatin molecule (Scheme 2.1) in the presence of GMA. To determine the suitable reaction conditions for synthesizing GelMA with a slow sol-gel transition at room temperature, combinations of DMSO, RO, PBS and CB solvents with reaction durations of 3 hours and 12 hours were explored sequentially (Table 2.1). The chemistry of GMA is also dependent on the pH of the solution; hence, two pH conditions are included as an additional parameter in the GelMA synthesis. GelMA with low sol-gel transition temperature were confirmed by observing the thermal gelation behavior at room temperature (23 oC) (Figure 2.2) which showed some combinations to retain liquid state. Among the GelMA with 3-hour synthesis durations, DMSO-3h, RO-3h-L, RO-3h-H, and PBS-3h-L underwent gelation at room temperature while the PBS-3h-H, CB-3h-L, and CB-3h-H remained liquid. PBS-3h-H exhibited a slightly higher gelation temperature, and partial gelation was observed after 13 hours of storage at room temperature. Among GelMA with 12-hour synthesis durations, all the combinations were found to maintain a liquid state at room temperature. As discussed in the previous section, heating gelatin in the respective solvents for 24 hours resulted in a high degree of hydrolysis. GelMA synthesis for 24 hours was not performed as all the synthesis combinations had slow sol-gel transition at room temperature after 12 hours. 32 Scheme 2.1. Gelatin methacryloyl (GelMA) synthesis in using glycidyl methacrylate (GMA). Possible outcomes of substitution reactions are annotated by (a), (b), (c) and (d) respectively. 33 Figure 2.2. GelMA gelation characterization. Representative images showing gelation time for GelMA synthesized in different solvents (DMSO, RO, PBS & CB) and pH combinations for different durations (3 hours & 12 hours). Blue, black and red markers are assigned for completely gelled, partially gelled and non-gelling samples respectively. 34 Figure 2.3. GelMA substitution characterization. (A) Quantified degree of substitution based on reduction in lysine signal. (n = 3; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). (B) Recorded 1H NMR spectra of various GelMA. Peaks (1) represents the presence of gelatin backbone (phenylalanine), (2) shows the presence of substituted methacrylate groups, (3) is signal corresponding to lysine and (4) also corresponds to substituted methacrylate groups. 35 The degrees of substitution for various GelMA were determined by analyzing 1H NMR spectroscopy data (Figure 2.3A). The presence of a methacrylate vinyl signal (6.0-6.2 ppm and 5.6-5.8 ppm) and the presence of a signal from the methyl group of GMA at 1.8 ppm (Figure 2.3B) both indicated the substitution of gelatin. Measurements of the reduction in the signal of primary amines on lysine (2.80-2.95 ppm), normalized against an internal reference of the phenylalanine signal (7.0-7.5 ppm) present in the gelatin backbone were used to compute the degree of substitution (Figure 2.3B). Among the 3-hour syntheses, the degree of substitution was found lowest in PBS-3h-L and RO-3h-L combinations at 15.6 ± 3.5% and 24.3 ± 6.2% respectively. The CB-3h-L GelMA underwent higher substitution with a degree of functionalization of 62 ± 2.5%. On the other hand, GelMA synthesis in DMSO catalyzed by DMAP exhibited a high substitution rate of 74.6 ± 3%. Characterization of GelMA with 12-hour synthesis durations revealed high substitution rates for nearly all solvents with the exception of RO. Synthesizing GelMA in DMSO catalyzed by DMAP resulted in a degree of substitution of 91.6 ± 2.9%. The solvents PBS and CB also exhibited high substitution rates as shown in Figure 2.3A. On the other hand, using RO solvent at a low pH resulted in a lower substitution rate of 56.3 ± 2%. The reaction between GMA and gelatin has attracted attention, owing to gaps in the understanding of the reaction mechanism.[110] Methacrylic anhydride (MA) is a more widely used reagent for the synthesis of GelMA with a well-understood mechanism. The clarity in the reaction mechanism allows for accurate quantification of the degree of substitution. In GelMA synthesis using MA, an aqueous solvent (PBS or carbonate-bicarbonate buffer) is often used. Since the by-product of the reaction, methacrylic acid, can significantly lower the pH of the solution and protonate the NH2 groups, making them unavailable for the reaction, the substitution reaction is facilitated by maintaining a high pH. The primary amine in the lysine group is a preferred location 36 for grafting methacrylate moieties on gelatin. Hence, predominantly, the substitution occurs on primary amine groups and is quantified by observing the reduction of the lysine peak in the 1H NMR spectra. On the other hand, the reaction between GMA and gelatin can follow different mechanisms based on the pH of the solution.[110] At a higher pH, the GMA molecule cleaves to substitute methacrylate groups on the available primary amine groups in the gelatin and produces glycidol as the by-product. Glycidol, being slightly corrosive in nature, may result in degradation of the gelatin, however, no studies have been conducted in this regard. Furthermore, at a lower pH, the GMA molecule undergoes an epoxide ring-opening mechanism and results in the grafting of a larger group without any by-products. At the same time, a lower pH also blocks the primary amine groups and significantly reduces the rate of reaction. The carboxylic groups present in gelatin also offer a grafting site for the GMA molecule, but those are less preferred in comparison to primary amine groups at a higher pH.[111] The fraction of substitution for the carboxylic group increases at a lower pH but is expected to be lower than the substitution at the primary amine due to slower reaction kinetics. Hence, among the GelMAs synthesized in this work, all the combinations at a lower pH exhibit a lower substitution rate at the primary amine when compared to the corresponding combinations at a high pH. Although a more accurate representation of data will involve the quantification of substitution at the primary amine and the carboxylic groups, in this work, we have reported the degree of substitution based on the reduction in the signal corresponding to the primary amine in 1H NMR (2.80-2.95 ppm). Substitution of GMA at the carboxylic group cannot be directly quantified from the 1H NMR spectra and requires additional investigation.[111] The solvent also plays a decisive role in the reaction kinetics. The various aqueous (RO, PBS, and CB) and organic (DMSO) solvents were tested to investigate their effects on the degree 37 of substitution of GelMA. The comparison shown in Figure 2.3A revealed that the substitution reaction was highly favored in DMSO and CB. This is expected, as an organic base, DMAP, was used with DMSO to catalyze the reaction. CB is a strong buffer and maintains the pH around 9, which is favored for substitution at the primary amine; hence, a higher substitution rate is observed. Compared to CB, PBS is a weaker buffer and maintains the pH around 7.4. This also favors the substitution reaction at the primary amine but is comparatively slower than CB. When starting with low pH in CB, the pH re-adjusts close to 9 within a short interval of time which is evident from comparable substitution rates for 3-hour synthesis in CB at both low and high pH. In the case of 12-hour synthesis, the solution remains close to pH 9 for a longer duration and both CB-12h-L and CB-12h-H exhibit high substitution rates in the same range. In PBS, when starting with acidic conditions, the solution remains at low pH for a longer duration compared to CB, and, consequently, a lower substitution rate is observed for PBS-3h-L compared to PBS-3h-H. For 12-hour synthesis, both PBS-12h-L and PBS-12h-H exhibit high methacrylation rates due to longer reaction times, but PBS-12h-H shows a slightly higher value than PBS-12h-L. On the other hand, when RO is used, the pH is maintained closer to the initially adjusted value. In this case, the gelatin solution can act as a weak buffer and maintain the pH around 6.5-7. For a duration of 3 hours, starting with low pH results in a very low methacrylation rates. When the synthesis is initiated with high pH, the reaction proceeds rapidly, but due to the buffering properties of the gelatin solution and the formation of glycidol as a by-product, the pH is decreased to the range of 6.5-7 and the reaction is impeded significantly. Synthesizing for 12 hours improves the substitution rate and results in GelMA with low sol-gel transition temperature. 38 2.3.3 GelMA photopolymerization kinetics Along with a slow sol-gel transition at room temperature, for SLA bioprinting, the bioink should be highly responsive to visible light which is evaluated from photopolymerization kinetics. Based on their non-gelling behavior at room temperature as observed in the previous section, the following ten GelMA combinations: PBS-3h-H, CB-3h-L, CB-3h-H, DMSO-12h, RO-12h-L, RO-12h-H, PBS-12h-L, PBS-12h-H, CB-12h-L, and CB-12h-H were tested for their photocrosslinking characteristics. The photopolymerization/curing kinetics were measured by applying cyclic sinusoidal shearing strain on the GelMA pre-polymer droplet as shown in Figure 2.4A. Figure 2.4B shows the modified probe on the micromechanical testing machine used for recording the force response. The glass slides used in the modified probe were coated with TMSPMA. As the GelMA pre-polymer solution underwent photocrosslinking, the terminal acrylate functional groups on the TMSPMA coated glass surface also covalently bonded with the GelMA hydrogel. This bonding immobilized the GelMA hydrogel and the shear force experienced by the hydrogel was recorded by the probe. Figure 2.4C shows the photopolymerization progression in various GelMA for a span of 1000 seconds, which includes 60 seconds of idle measurement without illumination and 940 seconds of measurement with illumination. No increment was observed in shear stress during the idle measurement time for all the selected GelMA combinations, confirming that they did not undergo physical gelation at room temperature. Figure 2.4C also shows that with prolonged exposure to visible light, the GelMA hydrogels become stiffer, which implies an increment in the degree of crosslinking with time. The shear response curves show a similar trend with a region of rapid increment followed by a region of slow increment or stagnation. Different rates of crosslinking can be inferred by comparing the curvatures of the photocrosslinking curves. The upward concave shapes of the curves for durations 39 less than 200 seconds implies an acceleration in the crosslinking (i.e. the increasing rate of crosslinking). The downward concave of the curves past 200 seconds implies a deceleration in the crosslinking (i.e. decreasing rate of crosslinking). This trend is expected as the activation of Eosin Y initially results in a rapid generation of free-radicals.[112] These free-radicals participate in the polymerization of GelMA, consuming the available methacrylate functional units. Although Eosin Y can regenerate due to its photoredox properties,[113],[114] the co-initiator TEOA is consumed with time by the polymerization and scavenging of free-radicals by oxygen. Furthermore, NVP also results in a complete reduction of Eosin Y, causing the depletion of Eosin Y. Owing to these processes, the photopolymerization process slows down and asymptotically approaches a stagnant state. Upon comparing the curing kinetics, the GelMA combinations with RO solvent (RO-12h-L and RO-12h-H) were observed to have the highest shear stress response, which increased rapidly with illumination time. This indicates the fastest curing kinetics and shortest photocrosslinking times among the compared GelMA combinations. Among the GelMA synthesized in 3 hours, the photocrosslinking was fastest for PBS-3h-H, followed by CB-3h-L and CB-3h-H. Despite the higher degree of methacrylation seen in CB-3h-H, a slower photocrosslinking was observed compared to PBS-3h-H, which brings out the effect of the degree of hydrolysis. If a comparison is drawn among PBS-3h-H, PBS-12h-H, CB-3h-H, and CB-12h-H, the photocrosslinking behavior agrees well with the trend observed in the degree of hydrolysis among PBS-3h, PBS-12h, CB-3h and CB-12h. 40 Figure 2.4. GelMA photocrosslinking kinetics. (A) Schematic of curing kinetics measurement using a modified probe. (B) Modified probe for sinusoidal shearing of GelMA droplet. (C) Curing kinetics for all GelMA combinations represented by increasing shear force recorded for 1000 seconds with exposure beginning at 60 seconds. (n = 5) The pH in GelMA synthesis had interesting consequences in photocrosslinking behavior. Upon comparing the combinations of CB-3h-H, RO-12h-H, PBS-12h-H, and CB-12h-H against CB-3h-L, RO-12h-L, PBS-12h-L, and CB-12h-L respectively, it was observed that GelMA with 41 low pH demonstrated superior photocrosslinking behavior than the corresponding combinations with high pH. As discussed in earlier sections, the GMA reacts via an epoxide ring-opening mechanism in low pH which results in the substitution of a longer carbon chain molecule with a terminal methacrylate group. Being away from the bulky gelatin backbone, this terminal methacrylate group experiences less steric hindrance and can participate readily in the polymerization. On the other hand, synthesis in high pH results in the hydrolysis of GMA and the substitution of the methacrylate group directly on the gelatin backbone. Structurally, in this configuration, the bulky gelatin backbone can sterically hinder the methacrylate groups and lower their participation in the polymerization. Therefore, GelMA synthesized with low pH was found to exhibit better photocrosslinking kinetics. 2.3.4 Physical properties of GelMA The samples for mechanical characterization were prepared in the form of a disk of 36 mm in diameter and 2 mm in thickness. The Hertz contact mechanics model was used to estimate the compressive modulus of the hydrogel samples. Among the various GelMA with low sol-gel transition temperatures, synthesizing in RO solvent resulted in the highest compressive modulus while CB solvent resulted in the lowest compressive modulus (Figure 2.5A). Considering the syntheses with 3-hour reaction times, PBS-3h-H exhibited the highest stiffness followed by CB-3h-L and CB-3h-H respectively. Among the 12-hour syntheses, the hydrogel stiffness was the highest for GelMA synthesized in RO solvent and decreased in the order of PBS, DMSO, and CB solvents. The combinations PBS-12h-H and CB-12h-H were observed to have poor crosslinking kinetics as shown in Figure 2.4C, and hence, did not fully crosslink to form a stable hydrogel. The stiffness 42 of the hydrogel is dominantly governed by the size of the monomer molecular chains, i.e. gelatin backbone chain length in GelMA. The degree of photocrosslinking is another factor which significantly affects the stiffness of the gel, which is, in turn, governed by the degree of substitution. The trend in the modulus observed in Figure 2.5A is expected and can be explained based on the average molecular weight after hydrolyzing in various solvents (Figure 2.1D) and the degree of substitution (Figure 2.3A). Among the 3-hour synthesis combinations, hydrolysis in PBS was lower compared to CB which should result in a higher modulus of PBS-3h-H hydrogel. On the other hand, PBS-3h-H had a slightly lower degree of substitution compared to CB-3h-H, which should result in a comparatively higher degree of crosslinking in CB-3h-H and, consequently, a higher modulus of the CB-3h-H hydrogel. With PBS-3h-H exhibiting a higher stiffness overall, it can be concluded that the influence of the degree of hydrolysis in determining the modulus of the hydrogel supersedes the effect of the degree of substitution. This conclusion is also reinforced by a comparison of RO-12h-L, PBS-12h-L, and CB-12h-L. Figure 2.5. Physical properties of low sol-gel transition temperature GelMA hydrogels. (A) Compressive Young’s modulus measured using Hertz contact mechanics model (n = 5); * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). (B) Equilibrium mass swelling ratio (n = 5; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). 43 The pH plays an interesting role in influencing the mechanical properties of the hydrogels. All the GelMA combinations with low pH were found to have either an equal or higher modulus than their corresponding combinations with high pH. For example, a comparison of the 3-hour synthesis in CB solvent reveals a higher stiffness in CB-3h-L than in CB-3h-H. The combinations CB-12h-L and CB-12h-H had an equal degrees of substitution (Figure 2.3A), however, CB-12h-L exhibited better photocrosslinking kinetics than CB-12h-H and could form a stable hydrogel for mechanical testing whereas CB-12h-H could not form a stable photocrosslinked hydrogel. This difference in the mechanical strength can be attributed to the different reaction pathways followed by GMA in low pH, as shown in Scheme 2.1. Low pH results in substitution via the epoxide ring-opening mechanism at the primary amine groups and the hydroxyl groups. The presence of such substitution can also be affirmed by the observation that, despite a lower degree of substitution at the primary amine in CB-3h-L than CB-3h-H, CB-3h-L can form a stronger photocrosslinked hydrogel. This conclusion can be further supported by a comparison of the moduli of RO-12h-L and RO-12h-H. RO-12h-L has lower substitution at the primary amine than RO-12h-H but has a comparable modulus. As stated earlier, the exact quantification of substitution at the hydroxyl groups requires an in-depth characterization and will be studied in a future work. Another interesting observation is exhibited by DMSO-12h GelMA. DMSO has been used in several previous works for the synthesis of GelMA using GMA. The compressive modulus obtained from the mechanical testing experiments agrees with the trend of values reported in the literature. Examining the effect of the solvent on the hydrolysis of gelatin has shown that DMSO has a minimal hydrolyzing effect (Figure 2.1D). Also, the degree of substitution was found to be high (~91%), as shown in Figure 2.3A. Owing to these observations, the mechanical strength of the DMSO-12h GelMA hydrogel is expected to be high and comparable to RO solvent GelMA. 44 On the contrary, the compressive modulus for the DMSO-12h GelMA obtained from the mechanical testing experiments is lower and suggests a higher extent of hydrolysis, similar to CB solvent, in 3 hours of synthesis. The effect of DMSO on the hydrolysis of esters is well documented in the literature, in which DMSO in the presence of KOH is found to cause extensive hydrolysis of ester bonds. However, the effect of DMSO on the hydrolysis of peptide bonds has not been investigated extensively. To identify the component which causes high hydrolysis of gelatin in DMSO, we compared the gelling behavior of gelatin hydrolyzed for 12 hours in DMSO and DMSO with DMAP (DMSO-DMAP). Gelatin hydrolyzed in DMSO gelled after 10 minutes at room temperature, whereas gelatin hydrolyzed in DMSO with DMAP gelled after 30 minutes, suggesting a slightly greater extent of hydrolysis (Figure 2.1B). The gelation time for the DMSO-DMAP hydrolyzed gelatin sample was also similar to the gelation time for the CB hydrolyzed gelatin sample indicating a similar extent of hydrolysis, which provides an explanation for the similar compressive moduli for CB-3h-H and DMSO-12h. We would also like to point out that through several syntheses of DMSO-12h GelMA, we have observed a strong dependence of hydrolysis on the synthesis temperature. A slight increment in the reaction temperature beyond 50oC resulted in GelMA with very low mechanical strength and poor photocrosslinking behavior (Figure 2.6A-B). Hence, precise control over reaction temperature should be maintained for GelMA synthesis. 45 Figure 2.6. Temperature dependence of properties of DMSO-12h GelMA. (A) Variation of compressive modulus (n = 5; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). (B) Variation of photopolymerization kinetics (n = 5). Followed by the mechanical strength testing, the water uptake capability of various GelMA samples was characterized. Mass swelling ratio was measured for various GelMA combinations, which is defined as the ratio of hydrated mass to dried mass of the hydrogel (Figure 2.5B). Prior to weighing, the samples were immersed in PBS for 24 hours to ensure complete hydration. Among the hydrogels, CB-3h-H, DMSO-12h, and CB-12h-L had the highest water uptakes. In comparison, RO-12h-L and RO-12h-H hydrogels exhibited slightly lower water uptakes with mass swelling ratios of 9.04 and 9.41 respectively, which are comparable to values reported in previous studies using UV and visible light-based photocrosslinking.[67]–[69] The ability of a biomaterial to absorb water highly influences the biocompatibility of the material and its potential to integrate with the native tissue in vivo.[115] A converse trend is observed between the mass swelling ratios and the mechanical strengths of various GelMA. Such a trend is expected as the higher stiffness of the hydrogel makes its structure rigid and results in a lower water uptake. 46 The microstructures of the photocrosslinked GelMA hydrogels were examined with a scanning electron microscope (SEM). The representative microstructure images are shown in Figure 2.7A. All the GelMA combinations were observed to form highly porous structures. The average pore diameter was measured from the SEM images using a custom script prepared in MATLABTM. All the GelMA hydrogels had an average pore size greater than 200 µm (Figure 2.7B). CB-3h-L, CB-3h-H, PBS-12h-L, and CB-12h-L exhibited slightly smaller pore sizes. However, due to the large variation in the pore size, no statistically significant difference was observed among most of the hydrogels. A representative pore size distribution within various GelMA combinations is also shown in Figure 2.8. Porosity is an important aspect of the hydrogels which affects the diffusion of the solutes and other proteins. Porosity has also been found to influence cellular growth and behavior.[116],[117] Hydrogels with large pore sizes have been found to demonstrate higher cell migration and proliferation as compared to hydrogels with smaller pores.[118] Different cell types and processes in tissue engineering require optimum pore sizes. Hydrogel porosity can be directly controlled by changing the macromer concentration,[67] but at the same time, a trade-off is made on the stiffness of the hydrogels. Moreover, in earlier studies using visible light photocrosslinked GelMA hydrogels, the porosity has been observed to be in the range of 100 µm-150 µm.[53],[67] On the contrary, all the GelMA combinations presented in this work resulted in hydrogels with pore for sizes larger than 200 µm. This observation will be further examined in future work. Additionally, a significant variation in stiffness can also be achieved among various hydrogels without any significant decrease in porosity. This provides an opportunity to tune the contrasting hydrogel properties suitable different target tissue types. 47 Figure 2.7. Microstructure characterization of GelMA. (A) Representative SEM images showing the microstructure of crosslinked GelMA (scale bar = 200 µm). (B) Average pore size measured from SEM images for respective GelMA (n = 5; * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). 48 Figure 2.8. Pore size distribution for the GelMA combinations obtained from porous structure observed in SEM images. 2.3.5 Printability of GelMA with stereolithography We characterized the suitability of the selected GelMA combinations with the SLA bioprinting setup. Eight combinations which were able to form stable photocrosslinked hydrogels for the characterization of mechanical properties were selected for the printability tests. In the test, a simple 2x2 mesh pattern with dimensions of 4.5 mm x 4.5 mm was printed (Figure 2.9). The RO-12h-L and RO-12h-H combinations underwent rapid photocrosslinking and could form a well-resolved and stable mesh pattern with an exposure time of 60 seconds. The combinations PBS-3h-H, CB-3h-L, and DMSO-12h could form the clearly visible mesh pattern but were structurally unstable. CB-3h-H could initiate the photocrosslinking with a short exposure time but did not form 49 any clear pattern. Owing to the slow photocrosslinking kinetics of PBS-12h-L and CB-12h-L, these hydrogels did not undergo photopolymerization with 60 seconds of exposure time. A quantitative comparison of the printability of various GelMA types is described in the Appendix (Figure A1). The mesh pattern selected for the printability comparison is geometrically simple but offers complexities in the forms of resolution (line width = 250 µm) and the printing of features with an aspect ratio (height: width) greater than 1 in a single layer. In the SLA bioprinting, maintaining a high resolution is crucial in fabricating well-resolved features of a macroscale tissue or in fabricating microscale tissue models. When printing mesoscale or microscale features, often times a high aspect ratio printing is required. High aspect ratio printing can also translate to faster biofabrication. To overcome these challenges, the photopolymerizable biomaterial should offer a short crosslinking time for maintaining a high resolution, high mechanical strength, and the three-dimensional shape of the printed scaffold. As seen in mesh patterns formed by PBS-3h-H, CB-3h-L, and DMSO-12h, the lines took a wavy appearance which could have either resulted from the high aspect ratio of the lines or from the low stiffness of the hydrogel. Since these lines were uniform in the mesh patterns printed with RO-12h-L and RO-12h-H GelMA, it can be concluded that the waviness of the printed lines was caused by the mechanically weak hydrogel. With PBS-3h-H, CB-3h-L and DMSO-12h GelMA, increasing the pattern projection time beyond 60 seconds could result in a higher degree of crosslinking to form a more stable and stronger hydrogel, at the expense of a lower resolution and over-crosslinking. 50 Figure 2.9. Printability comparison of various low sol-gel transition temperature GelMA. A small mesh pattern of outer edge dimensions of 4.5 mm x 4.5 mm and line width of 200 µm printed with different GelMA. The initial pre-polymer solution layer thickness was kept 300 µm. The pattern was projected for 60 seconds. (Scale bar = 1 mm Based on the printability test and characterizations conducted in previous sections, among the eight GelMA combinations with low sol-gel transition temperatures, RO-12h-L and RO-12h-H were selected as the best combinations for stereolithography bioprinting. Both combinations offer rapid crosslinkability for high resolution, high mechanical strength to maintain 3D shape and repeatability in their properties. Due to the comparable properties of RO-12h-L and RO-12h-H combinations and the slightly better photocrosslinking kinetics of RO-12h-L, for further characterizations, RO-12h-L GelMA was used. 51 Figure 2.10. Printing resolution characterization with RO-12h-L GelMA. Lines printed with projection widths corresponding to 1, 2 and 5 pixels for 60, 120, 180 and 240 seconds. The initial pre-polymer solution layer thickness was kept at 300 µm. (scale bar = 500 µm). Followed by the printability test, RO-12h-L GelMA was examined for printing the smallest feature sizes with the current SLA bioprinting system. Line patterns with widths of 1, 2 and 5 pixels were projected on a 500 µm layer of RO-12h-L pre-polymer solution. The projection duration was varied from 60 seconds to 240 seconds with increments of 60 seconds. Under all the conditions, the pre-polymer solution underwent photopolymerization except for the projection of the 1-pixel line for 60 seconds as shown in Figure 2.10. The line widths obtained for 1, 2 and 5 pixels were approximately 50 µm, 100 µm and 250 µm, respectively. Lines that were projected for 60 seconds with 5 pixels did not achieve a high degree of crosslinking and buckled to take on 52 a wavy appearance. Despite a low degree of photocrosslinking, the hydrogel lines could retain their structural integrity. As the projection time was increased, the 5-pixel lines appeared more rigid and uniform and eventually began to increase in width due to over-crosslinking (exposure time 240 seconds). Similarly, for 2-pixel line projections, the printed lines were soft with 60 seconds of exposure time and buckled with large deformations. As the crosslinking time increased, the deformations decreased and eventually disappeared (exposure time 240 seconds). In the case of 1-pixel lines, even with longer exposure times, the printed lines underwent buckling because of the high aspect ratio of the features (approximately 10:1; height: width). Despite buckling, the base of the lines remained uniform, and overall, the hydrogel maintained its structural integrity. From the preceding characterization, it is shown that by using the RO-12h-L GelMA with the current bioprinting system, high-resolution 3D scaffolds with microscale feature sizes can be printed rapidly. 2.3.6 Bioprinting Both RO-12h-L and RO-12h-H combinations offer rapid crosslinking abilities and can be used for a spectrum of applications. For a comprehensive comparison, the cell viabilities after 1, 4 and 7 days of culture in RO-12h-L and RO-12h-H GelMA were compared. The cell viability was found to be higher than 90% for both hydrogel combinations and no significant difference was observed (Figure 2.11A-B). Hence, both RO-12h-L and RO-12h-H GelMA are suitable biomaterials for SLA bioprinting. In this work, RO-12h-L GelMA was used to demonstrate the application in bioprinting. First, we demonstrated bioprinting a honeycomb structure with microscale features as shown in Figure 2.12A-B. The biocompatibility of the combination of materials and the printing process was assessed via a Live/Dead assay. Among samples with 1 and 53 5 days of culture, cell viability was observed to be 90% and 92% respectively without any statistically significant difference (Figure 2.12C). Figure 2.11. Biocompatibility characterization of RO-GelMA. (A) Representative live/dead assay images for RO-12h-L and RO-12h-H GelMA at day 1, 5 and 7 respectively. (B) Quantitative comparison of cell viability at day 1, 4 and 7 respectively (n = 5; ns p > 0.05 and * p < 0.05). (Scale bar – 100 µm). 54 Figure 2.12. Characterization of GelMA (RO-12h-L) biocompatibility with cell viability. (A) Printed honeycomb structure in bright field and fluorescent imaging (scale bar = 500 µm). (B) Representative zoomed-in images of various portions of honeycomb showing live cells (green: Calcein AM) and dead cells (red: EthD-III) at day 1 (scale bar = 200 µm). (C) Comparison of cell viability for day 1 and day 5 samples (n = 5; ns p > 0.05). 55 After 5 days of culturing, we also examined the cellular morphology within the bioprinted honeycomb structure. Figure 2.13A-B shows the cell morphology visualized with F-actin/Nuclei staining. A majority of the cells were found to elongate and formed interconnecting networks (Figure 2.13B). Figure 2.13. Characterization of GelMA (RO-12h-L) biocompatibility with cell morphology. (A) Printed honeycomb structure in bright field and fluorescent imaging at day 5 (scale bar = 500 µm). (B) Representative zoomed-in images of various portion of honeycomb showing cell morphology with F-actin (green: phalloidin) and nuclei staining (blue: DAPI) (scale bar = 50 µm). 56 Furthermore, we bioprinted a miniaturized human ear to demonstrate the accuracy of the printing for free-form organs in the meso/macro scale. Figure 2.14A shows the 3D model of a human ear used for printing and Figure 2.14B shows the printed scaffold. The scaffold had a maximum thickness of 3 mm and measured 21 mm and 15 mm along the longitudinal and lateral axes respectively. The scaffold was fabricated by sequentially bioprinting 10 stacked layers with 300 µm thicknesses and with different cross-sections. Total fabrication time was 12 minutes with each layer photocrosslinked for 60 seconds and a total of 2 minutes spent of addition of bioink between projection steps. As further shown in Figure 2.14C, we were able to bioprint the ear with various features, like helix, fossa, concha, antitragus, lobule and external auditory meatus. We characterized the cellular growth by observing the cell morphology of encapsulated 3T3 fibroblasts at the marked locations (i) and (ii) shown in Figure 2.14B. After 14 days of culture, we observed a high population of elongated and interconnected cells on the areas close to the boundaries and the areas with lower hydrogel thickness (Figure 2.15). However, the cells encapsulated deep within the hydrogel exhibited significantly lower elongation. The bioprinted ear after 24 days of culture exhibited a comparatively higher population of elongated cells in all the regions (Figure 2.15). All the cells were observed to elongate and form densely interconnected networks. Such a difference is expected, as the absence of a vascular network in the thick hydrogel hinders the growth of cells deep within the hydrogel, due to inadequate availability of nutrients and oxygen. 57 Figure 2.14. Bioprinting miniature human ear with GelMA (RO-12h-L). (A) 3D model of human ear used for printing. (B) bioprinted multilayered structure forming miniature human ear (scale bar = 1 cm). (C) Angled view showing the feature details of various segments. (scale bar = 1 cm). 58 Figure 2.15. Representative fluorescent zoomed-in images of various portions of bioprinted ear showing cell morphology after 14 days of culture with F-actin (green: phalloidin) and nuclei staining (blue: DAPI) (scale bar = 100 µm (i, ii), 50 µm(iii)). In the third bioprinting experiment, we demonstrated the potential application of synthesized low sol-gel transition temperature GelMA in brain tissue engineering with the culturing of fabricated tissues over an extended period of time. We printed a thick miniaturized human brain slice with encapsulated U118 astrocytes, as shown in Figure 2.16. The cross-section 59 pattern used for printing the miniaturized human brain slice is shown in Figure 2.16A. The scaffold thickness was 1.4 mm and the longitudinal and lateral axes measured 22 mm and 20 mm respectively. This scaffold was comprised of a single layer which was photocrosslinked for 120 seconds. The bioprinted scaffold was able to capture the intended external geometry with good resolution as shown in Figures 2.16B-C. Additionally, the internal details within the designed scaffold were also included in the bioprinted scaffold. Similar to previous bioprinted samples, we observed the cell morphology by staining for F-actin and nuclei. After 30 days of culture, we observed a high astrocyte population throughout the scaffold (Figure 2.16D). The cell morphology was elongated, suggesting good integration with the hydrogel scaffold. The astrocytes were also observed to form an interconnected network, which is a characteristic exhibited by astrocytes in their native microenvironment.[119] Most importantly, several astrocytes were observed to have a large number of processes (marked by red arrows in Figure 2.16D), which indicates the activated state of the astrocytes and also mimics their behavior in their native 3D microenvironment.[120],[121] 3D culture of astrocytes has been an area of focus for numerous studies.[121]–[123] Long term culture of encapsulated astrocytes in a highly biocompatible and patterned hydrogel scaffold also presents the possibility to study astrocytes and their interaction with other cells in detail. 60 Figure 2.16. Bioprinted miniature brain cross-section with astrocytes. (A) Cross-section pattern used for printing. (B) Bioprinted scaffold (scale bar: 1 cm). (C) Angled view showing the thickness. (scale bar = 1 cm). (D) (i-iv) Representative images of astrocyte morphology observed on day 30. F-actin/Nuclei staining done by Phalloidin (Green)/DAPI(Blue). (scale bar: (i) 100 µm, (ii)-(iv) 50 µm). We demonstrated excellent biocompatibility of the RO-12h-L GelMA in the bioprinted structures with microscale and mesoscale features with 3T3 fibroblasts. The elongation of fibroblasts within the hydrogel matrix suggests the presence of cell-adhesion moieties in the 61 hydrogel. Pristine gelatin contains cell-adhesion moieties, and from these observations, it can also be concluded that while synthesizing GelMA in RO solvent at a low or high pH, these cell-adhesion sites are preserved. 2.4 Discussion In the present study, we have delineated the effect of three reaction parameters – solvent, pH, and reaction duration on the mechanisms and the resulting properties of the obtained GelMA. This work also emphasizes the recent advancements in bioink development and offers an advantage in the form of tunability of a popular semi-synthetic biomaterial GelMA. We primarily focus on the rheological and photocrosslinking behavior of the bioinks with respect to the SLA bioprinting process. The resulting bioinks exhibit low sol-gel transition temperatures and remain liquid with low viscosity at room temperature. This characteristic helps in quickly spreading the bioink in the vat to form a thin and uniform layer, which is advantageous in maintaining consistent bioprinting and high resolution. Fast photocrosslinking is also an important characteristic to maintain the high spatial resolution of the bioprinted scaffold, the uniform distribution of encapsulated cells and high cell viability. Moreover, a bioink with such characteristics can be used to fabricate morphologically accurate scaffolds with anatomically relevant dimensions. Gelatin is a naturally occurring and popular biomaterial for tissue engineering applications due to its high biocompatibility and its ability to closely mimic the extra-cellular matrix (ECM)[69]. Another attractive feature of gelatin is the ease of chemical functionalization to synthesize photocrosslinkable GelMA.[67]–[69],[72],[103],[124],[125] Many studies have used photocrosslinkable GelMA as major components of bioinks for creating various tissue constructs using bioprinting technique.[53],[79],[126]–[128] Among those studies, the tunability of the physical properties of GelMA-62 based bioinks is another focus area which has been targeted by composite or blend bioinks made from GelMA and foreign additives such as nanomaterials or other biomaterials such as alginate, collagen, etc.[69],[129] However, a significantly small number of published studies have highlighted the challenges faced in bioprinting due to the fast sol-gel transition of GelMA bioinks at room temperature.[124],[130] In earlier works, Hoch and colleagues reported highly substituted GelMA bioinks optimized for inkjet bioprinting which maintained a liquid state at room temperature.[124] Their findings emphasized the tuning of the bioink’s rheological properties by varying the ratios of gelatin and methacrylation agents in the synthesis process which translated to different degrees of substitution. Additionally, substitution of acetyl groups also affected the gelling behavior by hindering the hydrogen bond formation.[101] Later, Li et al. utilized a similar approach and reported GelMA hydrogels with high crosslinking densities by synthesizing GelMA through sequential substitutions of MA and GMA.[131] However, these studies focused on a single parameter, the of degree of substitution, while the GelMA synthesis process involves a combination of several parameters which results in a batch-to-batch variation in its properties, as has been highlighted in other studies.[132] The investigation of the effect of various GelMA synthesis parameters presented in this work is of high importance, as it addresses the research void left in the mechanistic understanding of the GelMA synthesis process and allows the GelMA’s rheological or physical properties to be tuned without the use of any foreign additives. The recorded photocrosslinking kinetics also highlights the fact that different illumination durations can create significant differences in the stress response of the hydrogel, which can be used to lay an underlying stiffness and porosity gradient in the fabricated scaffolds. Our research group has conducted a series of studies on the SLA bioprinting of GelMA and has demonstrated its potential towards the fabrication of complex structures in a nascent 63 state.[23],[42] Despite well resolved bioprinted structures, the bioink has been a point of concern due to inconsistencies in its physical and chemical properties.[132] If a comparison is drawn, this work presents a three-fold improvement in the photocrosslinking ability and mechanical stiffness compared to our earlier studies.[64],[67] A brief comparison of the GelMA bioinks synthesized in this work with GelMA bioinks used in other studies involving visible light photocrosslinking is also presented in Table 2.3. The comparison, which considers various factors that could influence photocrosslinking, clearly highlights the fast photopolymerization ability of RO-12h-L and RO-12h-H GelMA bioinks with visible light illumination. The fast photocrosslinkability and high stiffness of the resulting hydrogels allowed for the fabrication of high resolution and structurally stable scaffolds. 64 Table 2.3. Comparison of GelMA bioink photocrosslinkability with visible light photoinitiation. Photoinitiator components Illumination source Pre-polymer solution volume Photocrosslinking time in bioprinting per layer Ref. Macromer Photoinitiator 10% (w/v) GelMA 0.1 mM Eosin Y, 1.5% (w/v) TEOA, 1% (w/v) N-vinylcaprolactum 100 mWcm-2 xenon source, 450-550 nm, 1 cm 10 µL 20 s [53] 10% PEG, 5% PEG+ 5% GelMA, 2.5% PEG+7.5% GelMA 0.01 mM Eosin Y, 0.1% (w/v) TEOA, 37 nM NVP 2500 lumen projector lamp, 400-750 nm, 10cm 287 µL 120 s [64] 10% (w/v) GelMA 0.05 mM Eosin Y, 0.225 M TEOA, 74 mM NVP 500 mWcm-2 argon ion laser, 514 nm NA 300 s [133] 10% and 20% (w/v) GelMA 0.1 mM Eosin Y, 1.5% (w/v) TEOA, 1% (w/v) N-vinylcaprolactum 100 mWcm-2 xenon source, 450-550 nm, 1 cm 70 µL 240 s [134] 10% (w/v) GelMA 0.02 mM Eosin Y, 0.2% (w/v) TEOA, 74 nM NVP 48.6 mWcm-2 projector lamp, 400-750 nm, 10cm 200 µL 120 s [67] 10% (w/v) GelMA 0.02 mM Eosin Y, 0.2% (w/v) TEOA, 74 nM NVP 48.6 mWcm-2 projector lamp, 400-750 nm, 10cm 300 µL 60 s Present work By changing the synthesis parameters, the reaction mechanism and degree of hydrolysis were altered, resulting in eight GelMA combinations with diverse properties. The different levels of mechanical stiffness in the GelMA combinations also demonstrates their use in applications involving stiff and soft tissues. Generally, a trade-off has been observed between the stiffness and porosity of the hydrogels.[67]–[69] Interestingly, most GelMA combinations presented here did not show any steep variation in the pore size (Figure 2.7B). Such behavior can be attributed to a 65 combination of three factors – the extent of hydrolysis, the degree of substitution and the size of the substituted acryloyl functional group. In PBS-12h-H GelMA, while the high pH results in a high degree of substitution of small methacrylate functional groups, the reaction duration of 3 hours results in low extent of hydrolysis and a large average macromer molecule length. The long macromer molecules show a dominating effect by sterically hindering the closely spaced methacrylate functional groups and consequently, exhibit large pores. However, when the reaction duration was increased to 12 hours, there was a slight increase in the degree of substitution for low pH synthesis (Figure 2.3A) along with an increase in the extent of hydrolysis (Figure 2.1D). This translates to a lower average pore size, since the overall steric hindrance on the closely positioned methacrylate groups is reduced. In case of GelMA synthesized in CB solvent, for the 3-hour reaction duration, the degree of hydrolysis was higher than GelMA synthesized in PBS solvent, which was further increased for the 12-hour reaction duration (Figure 2.1D). Hence, the GelMA synthesized in CB for 12 hours exhibited a smaller average pore size than the GelMA synthesized in CB for 3 hours. The extent of hydrolysis in the GelMA synthesized in RO for 12 hours was greater than the extent of hydrolysis in the GelMA synthesized in PBS for 3 hours. Hence, the average pore size observed for RO-12h-L and RO-12h-H GelMA were smaller than PBS-3h-H. The variation of pore sizes observed between the GelMAs synthesized in the RO and PBS solvents for 12 hours could be a result of the presence of a higher fraction of large methacrylate functional groups in RO-12h-L and RO-12h-H GelMA than in PBS-12h-H, due to their initial and solvent native pH differences. As shown in additional experiments (Figure 2.6), the GelMA synthesized in DMSO for 12 hours in the presence of DMAP as a catalyst underwent a high extent of hydrolysis. Additionally, only small methacrylate functional groups are substituted on gelatin due to the absence of an epoxide ring opening pathway for GMA in DMSO with DMAP. Both of these 66 factors result in the relatively smallest average pore size among all the compared GelMA combinations. Furthermore, these properties could be of importance as stiffer scaffolds can be fabricated without significantly sacrificing the average pore size and the ease of mass transport through the pores. Although, the porosity observed in this study is comparatively higher than that reported in some previous works,[53],[69],[135] the unevenness of the cellular growth in the bioprinted miniature human ear (Figure 2.15) at day 14 points to an important matter of concern. Such unevenness is caused by the inadequate availability of nutrients in the bulk of large scaffolds, and thus emphasizes the fabrication of vascularized scaffolds. Being a naturally derived hydrogel, GelMA usually suffers from high degradation rates. However, the RO-GelMA combinations, when compared against the DMSO-12h GelMA, exhibited significantly lower degradation rates (Figure 2.17). As a comparison, GelMA synthesized using another commonly used protocol degraded three times faster in a lower collagenase concentration than the RO-GelMA.[132],[134] Additionally, the bioprinted scaffolds were also cultured beyond 3 weeks and did not show high degradation or distortions in their shapes. This presents opportunities for culturing cell-laden scaffolds over an extended period of time and for studying the maturation of the scaffold which has not been adequately focused on. Here we also demonstrate the culture and activation of astrocytes in a 3D microenvironment. The astrocytes are a slow-growing cell type and the low degradation rate of RO-GelMA allows for a sufficient culture duration to observe their proliferation and behavior in 3D. The presented bioinks along with the SLA bioprinting system could provide an excellent platform for the study of biological processes involving the 3D interaction of cells. 67 Figure 2.17. GelMA degradation test – 10% w/v GelMA crosslinked in 96-well plate (100 µL in each well) for 5 minutes. Incubated at 37oC in freshly prepared collagenase I solution (50 µg/mL in DPBS). (n = 5). 2.5 Summary GelMA synthesis under various conditions was studied in this chapter and eight GelMA combinations were presented with a spectrum of properties. With a focus on SLA bioprinting, the roles of the solvent, the reaction duration, and the pH were elaborated to obtain GelMA which exhibited slow sol-gel transition at room temperature and could rapidly photopolymerize. Through the characterization of physical properties like gelation behavior, mechanical strength, water uptake capacity and photocrosslinking kinetics, we obtained eight GelMA combinations suitable for SLA bioprinting. After comparing the printing ability of various combinations, it was revealed that RO-GelMA was the most suitable for the rapid fabrication of high resolution and mechanically stable structures. RO-GelMA was also found to exhibit excellent biocompatibility, evident by the high cell viability and cell-matrix integration after several days in culture. The data accumulated 68 through these observations suggest that the identified GelMA synthesis parameters can be used to reliably synthesize GelMA highly suited for SLA bioprinting. The obtained RO-GelMA demonstrated significantly faster photopolymerization kinetics as compared to other combinations and also exhibited slower degradation rates. These bioinks are capable of rapid fabrication of large scaffolds with high resolution and can provide structural support for encapsulated cells for a longer duration. 69 Chapter 3 Rapid and high-resolution bioprinting using a photocatalytic visible light photoinitiator 3.1 Synopsis The synthesis of GelMA bioinks and their physical and biological characteristics were well established in the previous chapter. Although the printability was improved, the photocrosslinking duration is longer as compared to commonly used UV based bioinks. Longer photopolymerization can result in longer exposure of cells to the free-radical species which can cause cell damage. Therefore, further speed-up in the photopolymerization kinetics is required. In this chapter, a three-component photoinitiator using phenacylbromide (BAP) is presented to achieve faster photocrosslinking. A detailed characterization of the photocrosslinking behavior of the GelMA with the BAP photoinitiator is presented with insights into the intermediate processes and reactions taking place concurrently. The printing capability of these bioinks with lower concentration of GelMA is also discussed by fabricating thin structures in shorter duration. A brief analysis of biocompatibility of the hydrogels formed by these bioinks is presented in later sections of the chapter. Further, a strange behavior induced by BAP is described in which the internal structure of the hydrogels after crosslinking was altered over time. The results presented in the chapter show that the proposed three-component BAP photoinitiator resulted in a significant speed-up in the photopolymerization and nearly halving the crosslinking and bioprinting time. 70 3.2 Experimental section 3.2.1 GelMA prepolymer preparation Gelatin methacryloyl (GelMA) optimized for SLA bioprinting, was prepared using the method described in Chapter 2. Type B gelatin (5 g) was used with 50 mL RO water as solvent and 9 mL glycidyl methacrylate as methacrylation agent. The pH was adjusted to 9 at the beginning of the 12-hour synthesis followed by dialysis and lyophilization to obtain RO-12h-H GelMA. Pre-polymer solutions were prepared from the lyophilized GelMA by dissolving in PBS or RO water and adding the photoinitiator. 3.2.2 Comparison of photoinitiator formulations Three photoinitiator compositions were prepared for observing the influence on photocrosslinking. First composition was prepared by adding 0.02 mM Eosin Y (EY) photosensitizer and 0.2% triethanolamine (TEOA) co-initiator. Second composition included 0.02 mM EY, 0.2% TEOA and 74 nM N-Vinylpyrrolidone (NVP) as catalyst. In the third composition, 0.02 mM EY, 0.2% TEOA and a secondary co-initiator 0.1 mM 2-Bromoacetophenone (2-BAP, BAP or phenacylbromide) a were added. Since BAP is insoluble in aqueous solvents like PBS and RO water, it was first dissolved in ethanol to obtain a concentrated stock solution and then the BAP stock solution was added to the GelMA prepolymer solution to obtain final concentrations of 0.1 mM. The hydrogels of 10% GelMA pre-polymer solutions with these photoinitiators were formed by irradiating with visible light for 600 seconds in the SLA bioprinting system described earlier.[67] Compressive modulus and mass swelling ratio of the prepared hydrogels were measured as described in Chapter 2 and used for comparison. 71 3.2.3 Crosslinking time measurements Photocrosslinking kinetics of the GelMA prepolymer solutions were obtained by real-time UV-Vis spectroscopy measurement of prepolymer solutions undergoing photopolymerization. GelMA prepolymer solution was prepared with 5% (w/v) macromer and BAP concentration varied to 0.02 mM, 0.05 mM and 0.1 mM. A transparent cuvette was filled with 700 µL of the pre-polymer solution and placed in a real-time UV-Vis spectroscope (FLAME-T-UV-VIS, Ocean Optics, FL, USA). The spectroscopy illumination source was placed in-line (horizontally) with the spectroscopy sensor and a DLP projector was placed perpendicular (vertically) to the UV-Vis spectroscopy axis as shown in Figure 3.2A. The spectrum was recorded with a boxcar width of 20 and integration time of 5 ms. All measurements were recorded for a duration of 1000 seconds. The DLP projector used as the photopolymerization illumination source was placed at a distance of 20 cm with an intensity of 48.6 mW/cm2, emulating the bioprinting setup described earlier.[67] 3.2.4 Physical characterization of GelMA hydrogels Microstructure was characterized as described in Chapter 2 for compositions with 0 mM, 0.02 mM, 0.5 mM and 0.1 mM BAP compositions for samples immediately after photocrosslinking. The effect of overnight immersion on microstructure was examined by SEM imaging of hydrogels after overnight immersion in RO water. Hydrogels with 0 mM, 0.02 mM, 0.05 mM and 0.1 mM BAP were used in this experiment. 3.2.5 Hydrogel deformation observation Two batches of 7.5% w/v GelMA prepolymer solution were prepared with RO water as solvent. Two-component photoinitiator (EY and TEOA) and three-component photoinitiator (EY, 72 TEOA and BAP) were added to the first and second batches of the GelMA prepolymer solution respectively. The BAP concentration was set to 0.1 mM. A flower pattern (20 mm x 20 mm) was printed using prepolymer solution without BAP with the hydrogel thickness of 1 mm. Next, the uncrosslinked GelMA solution was removed and prepolymer solution with BAP was added to form a 1 mm thick layer on top of the crosslinked flower pattern. The same flower pattern was projected to polymerize the second layer aligned with and on top of the first crosslinked flower pattern to achieve a bilayer structure with combined thickness of 2 mm. Next, the uncrosslinked prepolymer solution was removed, samples were washed thrice with RO water and then immersed overnight in RO water at room temperature. The extent of deformation was qualitatively examined by observing the distance between the top corner of the petal in the flower pattern and the base of the petri dish. 3.2.6 Bioprinting capability evaluation To assess the printing capability with the three-component photoinitiator, two miniaturized test patterns – (tree and liver vasculature) were printed with different projection durations. The prepolymer solutions were dispensed in a custom-made vat to form a layer thickness of 300 µm on top of a transparent glass substrate. The printed structures were images using a smartphone camera and the visual structural integrity was used to qualitatively compare the printing time. 3.2.7 Biocompatibility assessment The biocompatibility of the GelMA bioinks and hydrogels with BAP as photoinitiator component was assessed using live/dead assay as described in Chapter 2. 3T3 mouse fibroblasts were used as model cells and 7.5% GelMA with 0.02 mM EY, 0.2% TEOA and 0.1 mM BAP was 73 used for cell viability characterization. The cell adhesion to the 7.5% GelMA hydrogels with 0.05 mM and 0.1 mM BAP was evaluated by comparing the cell morphology in bright field images captured in an inverted microscope. 3.3 Results and Discussion 3.3.1 Comparison of photoinitiator formulations In the previous chapter and in some earlier studies, a three-component photoinitiator composition including 0.02 mM EY, 0.2% TEOA and 74 nM NVP. Therefore, in the first step, we compared three different photoinitiator compositions to examine the influence of different components. Bioinks with 10% GelMA and photoinitiator formulations comprising EY + TEOA, EY + TEOA + NVP and EY + TEOA + BAP were crosslinked to form hydrogels with 600 seconds to achieve nearly saturated crosslinking. The compressive modulus of the formed hydrogels was compared as it is proportional to the degree of crosslinking within the hydrogel. The hydrogels with EY + TEOA achieved 14.45 kPa modulus and addition of NVP resulted in a slightly higher modulus of 18.98 kPa as shown in Figure 3.1A. Further, when NVP was replaced by BAP, a steep increase in modulus was observed with a final value of 33.57 kPa which was higher than two times the modulus of EY + TEOA hydrogels. 74 Figure 3.1. Comparison of different photoinitiator compositions. (A) Compressive modulus and (B) mass swelling ratio for EY+TEOA, EY+TEOA+NVP and EY+TEOA+BAP. (n=5; ns p>0.05, * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). This comparison implies that the EY+TEOA+BAP formed the densest crosslinked hydrogel followed by EY+TEOA+NVP. The EY+TEOA photoinitiator composition resulted in least dense crosslinking among the three hydrogels which is expected as only the EY and TEOA interactions resulted in formation of free radical species which initiate the polymer chain formation. This is further supported by the mass swelling ratio of the hydrogels which is the ratio of hydrated and dried weight of the hydrogels. Decrease in mass swelling ratio implies reduction in water uptake capacity of the hydrogels due to presence of a dense crosslinked internal structure. Scheme 3.1 shows the various interactions and pathways involved when each of the photoinitiator components are considered.[136]–[138] In case of the photoinitiation with EY and TEOA only, the EY photosensitizer absorbs visible light (𝜆𝑒𝑥𝑚𝑎𝑥 = 500 𝑛𝑚) to transition to an excited singlet state. This is followed by transition to an excited triplet state which, further, form a triplet complex with TEOA. This complex breaks with single electron transfer from TEOA to 75 EY and as a result, 𝑇𝐸𝑂𝐴+ ∙ and 𝐸𝑌3− ∙ radical species are generated. The 𝑇𝐸𝑂𝐴+ ∙ interacts with methacrylate grouls (𝐶 = 𝐶) in GelMA to initiate the polymer chain formation. The 𝐸𝑌3− ∙ radical species are quenched by 𝐻+ and 𝑂2 in the prepolymer/bioink solution to regenerate EY and reduce to 𝐸𝑌𝐻2− ∙ respectively. Additionally, all the steps involve a competing quenching reaction with O2. The activation of EY using visible light also encompasses a complex sequence of transition steps which has not been explored completely.[137] In this photoinitiator formulation, only TEOA is significantly consumed and limits the extent of polymerization. Scheme 3.1. Reaction pathways and intermediate species involved in EY+TEOA, EY+TEOA+NVP and EY+TEOA+BAP photoinitiator formulations. 76 In the second photoinitiator formulation comprising EY, TEOA and NVP as the catalyst, the same sequence of events as discussed above follows. When the 𝐸𝑌3− ∙ radical species are formed, they can also interact with vinyl functional group of NVP to result in hydrogen abstraction and formation of 𝐸𝑌𝐻22− species. These completely reduced EY species are called leuco EY and appear pale green after the loss of characteristic pink color of EY. Leuco EY are completely inactivated species and do not participate in any further reactions. On the other hand, an 𝑁𝑉𝑃 ∙ radical is formed during this step. This radical contributes in polymerization by interacting with the GelMA methacrylate functional groups. Additionally, this radical is quenched by O2 and causes faster depletion of O2 in the bioink layer which, in turn, speeds up the polymerization.[139] In this formulation, TEOA, NVP and EY are significantly consumed and limit the extent of polymerization. In the third photoinitiation composition, BAP is used as a second co-initiator in addition to EY and TEOA components.[138],[140] The steps involved up to 𝑇𝐸𝑂𝐴+ ∙ and 𝐸𝑌3− ∙ radical species generation remain the same. Further, the 𝐸𝑌3− ∙ acts as a photosensitizer with absorption shifted towards blue light (𝜆𝑒𝑥𝑚𝑎𝑥 = 400 𝑛𝑚) and reduced molar absorptivity as compared to EY at 500 nm.[137] The excited 𝐸𝑌3− ∙∗ radical reacts with BAP to undergo a second electron transfer and generated 𝐵𝐴𝑃 ∙ radical. This is also a regenerative step for EY as the 𝐸𝑌3− ∙∗ is quenched to ground state EY which can be again sensitized by visible light. The 𝐵𝐴𝑃 ∙ serves to initiate the polymer chain formation as well as rapidly deplete the O2 in the bioink layer. Consequently, the polymerization is significantly sped up. In this photoinitiator formulation, TEOA and BAP are significantly consumed whereas EY consumption is considerably low. 77 3.3.2 Photopolymerization kinetics To characterize the influence of BAP on the photocrosslinking behavior of GelMA, four different compositions were tested. The bioink compositions were varied by changing BAP concentration to 0.02, 0.05 and 0.1 mM which correspond to 1X, 2.5X and 5X of EY concentration respectively and compared against a control composition with 0 mM BAP. Other components in the bioink (GelMA, EY and TEOA) were kept consistent. Figure 3.2B shows the color changes that occur during the photopolymerization. Figure 3.2B(i) shows the color of the prepolymer solution before visible light irradiation, Figure 3.2B(ii) shows the color of the photoinitiator (EY+TEOA+BAP) observed after continuous visible light irradiation and Figure 3.2B(iii) shows the translucent appearance of the photocrosslinked hydrogel. Figure 3.2. Recording photocrosslinking kinetics of GelMA. (A) Schematic of the coupled photopolymerization and UV-Vis spectroscopy setup for real time monitoring of hydrogel opacity. 78 The real-time UV-Vis spectroscopy coupled with in situ photopolymerization highlights clear differences in the photopolymerization rates among the various bioink compositions. The photocrosslinking rate increased with increasing BAP concentration. The initial slope of the crosslinking kinetic curves was also evaluated as representative of rate of photopolymerization. An averaged slope was used due to the presence of concave and convex regions in the kinetics curves. The observed slopes were 0.60 (31.13º), 1.21 (50.54º), 1.90 (62.20º) and 2.42 (67.61º) for 0 mM, 0.02 mM, 0.05 mM and 0.1 mM BAP concentration respectively (Figure 3.3A). As the BAP concentration is increased from 0 mM, the polymerization kinetics slope increases rapidly and approaches a plateau as the BAP concentration approaches 0.1 mM. The rate of increase of polymerization speed is not proportional to the BAP concentration and saturates at higher BAP concentration (Figure 3.3B). This could be a result of rapid consumption of TEOA making it the rate limiting species. 79 Figure 3.3. Photocrosslinking kinetics of GelMA with three-component photoinitiator. (A) Recorded polymerization kinetics curves by measuring absorbance at 490 nm. (B) Comparison of kinetics curve slopes for different BAP concentration. 80 Figure 3.4. (A) Different regimes of crosslinking kinetics (A – acceleration, D – deceleration, S – saturation). (B) First derivative of the kinetics curves representing the speed of polymerization. (C) Second derivative of the kinetics curve representing acceleration of the polymerization. Acceleration to deceleration transition for different bioinks are marked as (i) (0.1 mM BAP), (ii) (0.05 mM BAP), (iii) (0.02 mM BAP) and (iv) (0.0 mM BAP) respectively. 81 Figure 3.4A-C shows the comparison of different regions of the photopolymerization kinetics curves. Regions marked by A, D and S represent acceleration, deceleration and saturation of the photopolymerization respectively. Figure 3.4B shows the first derivative of the recorder polymerization kinetics curve. The overall magnitude was highest for 0.1 mM BAP implying fastest instantaneous polymerization rate among all the compositions. The second derivative of the kinetics curve is shown in Figure 3.4C and represents the acceleration and deceleration of the polymerization. As discussed earlier, quenching by oxygen competes with various reactions which can result in polymerization and causes significant slowdown of the polymerization. The magnitude of the second derivative in the acceleration region indicates the rate of depletion of oxygen in the bioink layer. Bioinks with higher concentration of BAP deplete the oxygen rapidly and the various reactions begin contributing to polymer formation sooner. Closer examination of the transition of the second derivative curve from +ve to -ve values reveals the exact time when the polymerization kinetics of the respective BAP concentration composition transitions from acceleration to deceleration states. This also provides an indication that beyond this transition point, the polymerization might be controlled by other physical factors such as diffusion of chemical species within the bioink. Additionally, these transition points can point to depletion of TEOA and BAP which slows down the polymerization. A comparison of the contrast of the GelMA bioinks with different BAP concentrations is presented in the Appendix (section A3, Figure A3). 3.3.3 Physical characteristics Chapter 2 presented a detailed physical characterization of GelMA bioinks. Here we examined the internal structure of the photocrosslinked GelMA hydrogels obtained from bioinks 82 with different BAP concentration. Figure 3.5A shows the representative SEM images of the internal porous structure of the respective hydrogels. Upon measuring the pore size from the SEM images and comparing the average values, we observed an increasing trend in the pore size with increasing BAP concentration (Figure 3.5B). However, the clear reason behind this trend remains unknown currently and required further investigation. Further, a shrinking in the photocrosslinked hydrogels was observed over time in some pilot experiments. To investigate this observation, the photocrosslinked GelMA hydrogels with different BAP concentrations were prepared and a set of samples was incubated in RO water at room temperature for 24 hours. RO water was as solvent in preparing and incubation of these samples to exclude influence of ionic interactions. The internal porous structure of the hydrogels with 24 hours hydration is shown in Figure 3.5A. The average pore size measured from the SEM images for these samples revealed a significant reduction from the pore size of samples lyophilized post-photopolymerization (Figure 3.5B). In the samples without BAP a small decrease in the average pore size was observed in the hydrogels immersed in RO water, however, the size distribution of pores in 24 hour hydration samples had complete overlap with post-pohotocrosslinking samples. As the BAP concentration was increased, the reduction in pore size of 24 hour hydration samples increased as compared to post-photocrosslinking samples. Moreover, as the BAP concentration was increased, a comparatively uniform porous internal structure was observed in 24 hour hydration samples. This was also evident from the narrower pore size distribution in Figure 3.6. Among the 24 hour hydration samples, these was a reduction in the average pore size with increasing BAP concentration. This implies that the presence of BAP influences the internal pore structure of the hydrogels over time when they are kept hydrated. 83 However, the exact mechanism behind this observation remains unclear and needs to be investigated along with the impact on bulk properties of the resulting hydrogels. Figure 3.5. Microporous structure characterization of GelMA hydrogels with varying BAP concentration. (A) Representative SEM images showing internal pore structure. (B) Average pore size measured from SEM images. (n = 5; ns p>0.05, * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001). 84 Figure 3.6. Pore size distribution for GelMA with different BAP concentrations obtained from porous structure observed in SEM images (green lines mark the average pore size). 3.3.4 Hydrogel deformation After observing the change in internal porous structure and shrinkage of GelMA hydrogels with BAP, a bilayer structure was fabricated. This was inspired from bilayer microscale structures in which each layer is fabricated by materials of different thermal expansion coefficients and the 85 overall structure bends on application of heat. The bottom layer of the flower pattern was fabricated with 7.5% GelMA using EY+TEOA photoinitiators (Figure 3.7A). The top layer was fabricated using 7.5% GelMA with EY+TEOA+ 0.1mM BAP as the photoinitiator. Upon hydration of the bilayer structure in RO water post-fabrication, the structure appeared to bend upwards (Figure 3.7B). The base line, horizontal line along the top surface and the horizontal line corresponding to the topmost corner of bent structure are marked with red, green and blue colors respectively. Closer examination of the structure revealed that the extremities of the flower petals elevated by 0.8 mm from their initial height after the fabrication. As described in the previous section, the addition of BAP resulted in reduction of average pore size of the hydrogels over time in hydration. From Figure 3.5B, we observe that the extent of pore size reduction was highest for 0.1 mM BAP hydrogels and least for 0 mM BAP hydrogels. Therefore, these two compositions were selected for the bilayer structure. When the bilayer structure was immersed in RO water, over time the top layer underwent higher extend of shrinkage as compared to the bottom layer and the differential shrinkage resulted in bending of the structure. This observation further highlights the potential application of these hydrogels in origami driven deforming structure. Figure 3.7. Deformation in bilayer hydrogel flower structure. (A) Top view of the fabricated structure. (B) Side view showing the bent structure and the base line (red), top surface horizontal line (green) and the horizontal line corresponding to the topmost corner of bent structure (blue). 86 3.3.5 Bioprinting capability evaluation The faster photopolymerization kinetics of bioinks are desirable for rapid SLA bioprinting and it plays a pivotal role in determining the SLA bioprinting resolution. First, a vasculature pattern in the form of a liver was printed using 5% GelMA bioinks with different BAP concentrations. The hydrogels formed with 5% GelMA bioinks are very soft and structurally weak. With the bioink without BAP, no printing was observed for 60- and 120-seconds photo-pattern exposure (Figure 3.8A). After 180 second exposure, a structurally unstable pattern was observed in which some of the branches were broken or disconnected. With 240 second exposure, a comparatively stable structure was observed with one broken branch. A red colored food dye was used for visualization of these otherwise transparent structures. After adding 0.02 mM BAP in the photoinitiator, 60 second and 120 second exposure did not yield and printed structure. The 180 second exposure resulted in a fairly stable structure which did not break and comprised of clearly distinguishable branches. Increasing the exposure time to 240 seconds resulted in overcrosslinking of the void and surrounding regions. With 0.05 mM BAP in photoinitiator, the partially formed structure appeared after 120 second exposure. However, it lacked structural strength to maintain the intended form and instead appeared distorted with buckled wavy branches. A more stable structure was obtained with 180 second exposure, however, certain regions were overcrosslinked. The 240 second exposure resulted in extensive overcrosslinking and polymerized a larger surrounding region. With 0.1 mM BAP in the photoinitiator, a partially photocrosslinkined portion of the pattern was visible with 60 seconds of irradiation. A better well-resolved structure with clearly distinguishable branches was obtained with 120 second exposure duration whereas 180 second and 240 second exposure caused overcrosslinking. Weak physical strength of 5% GelMA hydrogels makes is 87 challenging for visible light SLA bioprinting due to which the previous studies have used a minimum of 10% GelMA bioinks.[23],[53],[134],[141],[142] Figure 3.8. Bioprinting capability of GelMA with BAP photoinitiator. (A) Printing liver shaped vasculature with 5% GelMA and varying BAP and exposure times. (B) Printing dendrite pattern with 7.5% GelMA and varying BAP concentration. 88 Next, we characterized the printing capability using 7.5% GelMA bioink with different BAP concentrations in the photoinitiator. A free-form dendrite pattern was bioprinted with different exposure times as shown in Figure 3.8B. In contrast to 5% GelMA bioink, all formulations of 7.5% GelMA bioinks formed a stable and complete structure with 60 second exposure time. In absence of BAP, the structure with 60 second exposure was formed with clearly distinguishable branches of all widths; however, one branch was broken. With 120 second exposure, a clear and stable structure was obtained. When bioink with 0.02 mM BAP was used, 60 second exposure generated a structurally stable structure with all the branches clearly formed. Increasing the exposure to 120 seconds resulted in partial overcrosslinking of the void regions between the branches. Similarly, 0.05 mM BAP in the photoinitiator generated a clear dendrite network without any under or overcrosslinking with 60 second exposure but resulted in high extend of overcrosslinking on increasing the exposure time to 120 seconds. The 0.1 mM BAP contributed to accelerated photocrosslinking and 60 second exposure generated an overcrosslinked pattern. A quantitative comparison of the printability of various GelMA bioinks with different BAP concentration is described in the Appendix (Figure A1). As observed in the photocrosslinking kinetics characterization, increasing the BAP concentration in the photoinitiator speeds-up the rate of polymerization. This is also corroborated from the reduction in exposure time to obtain a stable structure when BAP concentration is increased (Figure 3.8A). During the photocrosslinking of GelMA bioink using a three-component photoinitiator, a sequence of numerous intermediate reactions take place. These are highly influenced by the presence of oxygen which quenches several species and slows down the polymerization and by diffusion of generated free radical species.[55] When bioprinting small structures, slow photopolymerization can cause higher species diffusion and result in poor 89 bioprinting resolution whereas fast photopolymerization will not give enough time for species to diffuse farther. Faster photopolymerization also depletes the oxygen in the bioink layer to reduce the quenching of species. Another advantage of rapid bioprinting process is minimizing the exposure of cells to the harmful free radical species. 3.3.6 Biocompatibility characterization A preliminary assessment of biocompatibility of the hydrogels prepared with BAP was performed using 7.5% GelMA and encapsulated 3T3 fibroblasts. In the first investigation, cell laden hydrogel disks were prepared using two different photoinitiator formulations – 0.02 mM EY + 0.2% TEOA and 0.02 mM EY + 0.2% TEOA + 0.1 mM BAP respectively. Live-dead assay was performed 24 hours after encapsulating the cells. Figure 3.9A shows the representative EGFP (green) and mCherry (red) channels showing live cells in green and dead cells in red color respectively. The cell viability calculated from the live-dead assay images was 85% and 84% for EY+TEOA and EY+TEOA+BAP samples respectively (Figure 3.9B). This indicated the biocompatibility of the hydrogels formed using BAP. As described earlier, BAP is insoluble in PBS or RO water and requires dissolution in ethanol for bioink preparation. High cell viability after 24 hours shows that the small amount of ethanol carrier for BAP was well below the toxicity limit for the cells. However, detailed characterization of cell metabolic activity and function are needed to assess the long-term effects on the cells. Further, the adhesion of cells with the hydrogel was characterized by encapsulating 3T3 fibroblasts in GelMA hydrogels with 0.05 mM and 0.1 mM BAP respectively. After 3 days of culture, the cells adhered to the hydrogel matrix and achieved their characteristic spindle shaped morphology as shown in Figure 3.9C. Although this observation indicates cell adhesion properties of the GelMA hydrogels with BAP included in the 90 photoinitiator, cytoskeleton and nuclei staining are required to visualize the cell morphology in detail. Overall, these preliminary tests do point towards the biocompatibility and cell adhesion characteristics of the fabricated hydrogels and makes them an attractive candidate for rapid and high-resolution bioprinting applications. Figure 3.9. Biocompatibility of GelMA hydrogels with BAP.(A) Representative live/dead assay images for hydrogels with EY+TEOA and EY+TEOA+BAP photoinitiators. (B) Calculated cell viability for EY+TEOA and EY+TEOA+BAP samples. (n = 5; ns p>0.05). (C) Elongated 91 morphology observed in the cells encapsulated in GelMA hydrogels with 0.05 mM and 0.1 mM BAP after 3 days of culture. 3.4 Summary This chapter presented the development of a three component BAP photoinitiator for visible light photopolymerization of GelMA. Three different photoinitiation compositions were compared to establish the advantage offered by BAP photoinitiator. A detailed description of the underlying chemical reaction steps was discussed when NVP or BAP are used. The addition of BAP resulted in faster photopolymerization. The rate increased with increasing BAP concentration, however, was not proportional indicating a saturation limit caused by consumption of other chemical species. Along with faster photocrosslinking, addition of BAP also hinted towards increase in compressive modulus of resulting hydrogels and their ability to form stable scaffolds. The printing tests revealed that with BAP photoinitiator, low concentration of 5% GelMA could be printed with visible light which was not feasible in several previous studies. With BAP, well-resolved structures could be fabricated rapidly, and the printing time was comparable to commonly used UV polymerization of GelMA. Further, the cells were found to maintain high survival rate 24 hours after exposure to BAP. After three days of culture, the cells embedded in the hydrogels exhibited their characteristic spindle shaped morphology indicating adhesion to the hydrogel and implying cellular growth. Interestingly, the addition of BAP introduced some strange effects in the hydrogels as their internal structure transformed with time after crosslinking. The SEM images of samples revealed a more regular distribution of pores in the hydrogels which were hydrated for 24 hours after crosslinking. Although the exact mechanism behind this observation 92 remains unclear. Overall, the addition of BAP resulted in a bioink which can achieve rapid high-resolution bioprinting and potentially reduce the fabrication time by half. 93 Chapter 4 Prediction and remediation of bioprinting artifacts in SLA using a numerical model 4.1 Synopsis In Chapters 2 and 3, the GelMA preparation and BAP photoinitiator were described. The resulting bioink from their combination was found to achieve rapid photopolymerization and formed stiff hydrogels. These hydrogels also exhibited good biocompatibility for potential use in bioprinting. Faster photopolymerization kinetics implied increased sensitivity of the bioinks towards visible light and therefore, the window to tune the resulting hydrogel stiffness was also expanded. With lower light dose, incremental changes in the hydrogel stiffness could be achieved which, in turn, affect the swelling ratio and internal porous structure. Therefore, a predictive model is required to capture the behavior of these bioinks and predict how the photopolymerization will advance. Such model can also provide insights into how the hydrogel structure changes with crosslinking. Such prediction can aid in improving the accuracy of the bioprinting process. Moreover, the prediction of hydrogel properties can inform the composition of the microenvironment which the cells will experience in such hydrogel scaffolds and therefore, can help in predicting their growth or differentiation in case of stem cells. Towards this purpose, a detailed numerical model of the mask photopolymerization is presented in this chapter. The constituent reaction and transition steps are outlined which are representative of the GelMA monomer and EY+TEOA+BAP photoinitiation system. The boundary conditions are defined to 94 mimic the SLA bioprinting setup used earlier.[23] A detailed description of the illumination model in a DLP projection system is presented. Further, the effect of illumination, photo-pattern size and bioink layer thickness on the hydrogel formation and oxygen depletion in the bioink layer are discussed. Figure A4 (Appendix) shows the SLA printing system simulated and the various illumination and bioink photocrosslinking factors considered in the numerical model presented in this chapter. The results presented in this chapter provide insights into the factors which can contribute to low fidelity bioprinting and can be used to develop remediation strategies or introduce heterogeneity in the scaffolds. 4.2 Theoretical model 4.2.1 Photopolymerization model In order to simulate the stereolithography bioprinting, we simulated the underlying photopolymerization process by combining the elementary reactions involved. The model is derived from a previously established model using PEGDA monomer, Eosin Y (EY) photosensitizer and TEOA and VP as co-initiators.[139],[143],[144] The improved model presented here simulates the photopolymerization of GelMA with EY photoinitiator and comprises of four major parts – initiation, propagation, termination, and regeneration. All the constituent reactions in the presented model are listed in Table 4.1. A comparison of experimentally measured double-bond conversion reported earlier with the degree of conversion estimated with the improved model is presented in the Appendix (section A2, Figure A2). 95 Table 4.1. Constituent reactions in photopolymerization and their respective kinetic parameters. Eq. # Reaction Rate Constant Rate Constant Value Ref. (4.1) 𝐸𝑌 + ℎ𝑣1 → 𝐸𝑌𝑠 𝑘𝑎𝑐𝑡𝐸𝑌 𝐼𝑜𝜀500𝐸500′ 10−𝜀500[𝐸𝑌](𝑧0−𝑧) Beer-Lambert (4.2) 𝐸𝑌𝑠 → 𝐸𝑌 𝑘𝑖𝑐 1.2 × 108 𝑠−1 [145] (4.3) 𝐸𝑌𝑠 → 𝐸𝑌𝑡 𝑘𝑖𝑠𝑐 5.0 × 108 𝑠−1 [145] (4.4) 𝐸𝑌𝑠 → 𝐸𝑌 + ℎ𝑣 𝑘𝑓 2.0 × 108 𝑠−1 [145] (4.5) 𝐸𝑌𝑠 + 𝑇𝐸𝑂𝐴 → 𝐸𝑌 + 𝑇𝐸𝑂𝐴 𝑘𝑞 3.5 × 109 𝑠−1 [145] (4.6) 𝐸𝑌𝑡 → 𝐸𝑌 𝑘𝑑𝑒𝑎𝑐𝑡 5.4 × 102 𝑠−1 [146] (4.7) 𝐸𝑌𝑡 + 𝐸𝑌 → 2𝐸𝑌 𝑘𝑡𝑒 3.0 × 108 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [146] (4.8) 𝐸𝑌 ∙ +𝐻+ → 𝐸𝑌𝐻 ∙ 𝑘𝑎𝑏 4.0 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [147] (4.9) 𝐸𝑌𝐻 ∙ +𝑂𝐻− → 𝐸𝑌 ∙ +𝐻2𝑂 𝑘𝑎𝑓 2.0 × 1010 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [147] (4.10) 𝐸𝑌𝐻 ∙ +𝐸𝑌𝐻 ∙ → 𝐸𝑌 + 𝐸𝑌𝐻2 𝑘𝑙𝑒𝑢𝑐𝑜 4.2 × 106 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [148] (4.11) 𝐸𝑌 ∙ +𝑂2 → 𝐸𝑌 + 𝑂2− ∙ 𝑘𝑟𝑒𝑔𝑒𝑛 1.0 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [149] (4.12) 𝐸𝑌 ∙ +ℎ𝑣2 → 𝐸𝑌 ∙∗ 𝑘𝑎𝑐𝑡𝐸𝑌∙ 𝐼𝑜𝜀400𝐸400′ 10−𝜀400[𝐸𝑌](𝑧0−𝑧) Beer-Lambert (4.13) 𝐸𝑌 ∙∗ +𝑂2 → 𝐸𝑌 + 𝑂2− ∙ 𝑘𝑟𝑒𝑔𝑒𝑛 1.0 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [149] (4.14) 𝐸𝑌 ∙∗ +𝑅𝐵𝑟 → 𝐸𝑌 + 𝑅 ∙ +𝐵𝑟− 𝑘𝑟𝑒𝑔𝑒𝑛 1.0 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [138] (4.15) 𝑅 ∙ +𝑂2 → 𝑅𝑂𝑂− ∙ 𝑘𝑖𝑛ℎ 4.9 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [150] (4.16) 𝐸𝑌𝑡 + 𝑇𝐸𝑂𝐴 → 𝐸𝑌 ∙ +𝑇𝐸𝑂𝐴+ ∙ 𝑘𝑖 4.2 × 107 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [145] (4.17) 𝑇𝐸𝑂𝐴+ ∙ +𝑂𝐻− → 𝑇𝐸𝑂𝐴 ∙ +𝐻2𝑂 𝑘𝑇𝐸𝑂𝐴𝑟𝑎𝑑 1.0 × 1010 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [151] (4.18) 𝑇𝐸𝑂𝐴 ∙ +𝑂2 → 𝑇𝐸𝑂𝐴+ + 𝑂2− ∙ 𝑘𝑇𝐸𝑂𝐴−𝑂2 3.5 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [151] (4.19) 𝑇𝐸𝑂𝐴 ∙ +𝐺𝑀 → 𝐺𝑀 ∙ 𝑘𝑝 2.17 × 104 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [152] (4.20) 𝑅 ∙ +𝐺𝑀 → 𝐺𝑀 ∙ 𝑘𝑝 2.17 × 104 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [152] (4.21) 𝑇𝐸𝑂𝐴 ∙ +𝐸𝑌𝐻 ∙ → 𝐸𝑌 + 𝑇𝐸𝑂𝐴 𝑘𝑟𝑒𝑔𝑒𝑛2 1.0 × 108 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [143],[144] (4.22) 𝐺𝑀 ∙ +𝐺𝑀 → 𝐺𝑀 ∙ 𝑘𝑝 2.17 × 104 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [152] (4.23) 𝐺𝑀 ∙ +𝑇𝐸𝑂𝐴 ∙ → 𝑃𝑜𝑙𝑦𝑚𝑒𝑟 𝑘𝑡 7.55 × 105 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [153] (4.24) 𝐺𝑀 ∙ +𝐺𝑀 ∙ → 𝑃𝑜𝑙𝑦𝑚𝑒𝑟 𝑘𝑡 7.55 × 105 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [153] (4.25) 𝐺𝑀 ∙ +𝑅 ∙ → 𝑃𝑜𝑙𝑦𝑚𝑒𝑟 𝑘𝑡 7.55 × 105 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [153] (4.26) 𝐺𝑀 ∙ + 𝑂2 → 𝐺𝑀𝑂𝑂 ∙ 𝑘𝑖𝑛ℎ 4.9 × 109 (𝐿𝑚𝑜𝑙 ∙ 𝑠) [154] 96 The photopolymerization begins by harnessing energy from projection illumination by 𝐸𝑌 photosensitizer which drives a cascade of transition, quenching and inactivation steps. The 𝐸𝑌 interacts with the irradiated light to generate excited singlet 𝐸𝑌 species 𝐸𝑌𝑠 (Eq. 4.1). The rate constant for this step is denoted by 𝑘𝑎𝑐𝑡𝐸𝑌 (Eq. 4.1) and is dependent on light intensity (𝐼), molar absorptivity of 𝐸𝑌 (𝜀) and the energy of the photons at the incident light wavelength (𝐸′). The light intensity is further governed by the Beer-Lambert’s law and is attenuated as the light travels deeper into the bioink layer from the surface. For simplicity, single wavelength of the incident light is considered at this step. With continuous exposure to visible light, the color of 𝐸𝑌 changes to a pale solution resulting in photobleaching effect. The inclusion of this effect is important in bioinks with high concentration of 𝐸𝑌, however, in the current model photobleaching effect is not included as the bioinks are formulated with 0.02 mM 𝐸𝑌. Therefore, the initial concentration of 𝐸𝑌 is used determining the transmitted light intensity within the bioink layer. Next, the 𝐸𝑌𝑠 species can undergo three different paths – revert to 𝐸𝑌 ground state by giving out the fluorescence emission (Eq. 4.2), revert to 𝐸𝑌 ground state upon quenching by 𝑇𝐸𝑂𝐴 (Eq. 4.5) or transition to a triplet excited state 𝐸𝑌𝑡 (Eq. 4.3). The 𝐸𝑌𝑡 species can further undergo three different pathways – deactivation to 𝐸𝑌 ground state (Eq. 4.6), termination by interacting with ground state 𝐸𝑌 to result in another ground state 𝐸𝑌 species (E. 4.7) or form a triplet complex with 𝑇𝐸𝑂𝐴 (Eq. 4.16). Equation 4.16 is also regarded as the initiation step as the triplet complex breaks and results into proton abstraction by 𝑇𝐸𝑂𝐴 to generate 𝑇𝐸𝑂𝐴+ ∙ and 𝐸𝑌 ∙ radical species. The 𝐸𝑌 ∙ formed in this step does not participate in the polymer chain initiation, propagation and termination steps due to being sterically hindered. Instead, the 𝐸𝑌 ∙ takes up proton from the 97 solution to form a reduced 𝐸𝑌𝐻 ∙ radical which, in turn, can either revert to 𝐸𝑌 ∙ by reacting with 𝑂𝐻 species in the solution or combine with another 𝐸𝑌𝐻 ∙ species to result in a ground state 𝐸𝑌 ∙ molecule and a completely reduced 𝐸𝑌𝐻2 molecule. The 𝐸𝑌𝐻2 is also known as leuco 𝐸𝑌 species and does not participate in any other reaction.[137] Addition of catalysts like 𝑉𝑃 or 𝑉𝐶 accelerate this step[53],[139] and generate additional 𝑉𝑃 ∙ or 𝑉𝐶 ∙ radicals to participate in polymer chain initiation and rapid depletion of 𝑂2. In another pathway, 𝐸𝑌 ∙ radical is also quenched by 𝑂2 to generate ground state 𝐸𝑌 and superoxide 𝑂2− ∙ species (Eq. 4.11). In a lesser explored but crucial pathway, the 𝐸𝑌 ∙ radical harnesses energy from visible light (𝜆𝑒𝑥𝑚𝑎𝑥 = 400 𝑛𝑚) and transitions to an exited 𝐸𝑌 ∙∗ radical species.[137] In commonly used 𝐸𝑌 photoinitiation systems, these species do not contribute and are eventually quenched by 𝑂2 (Eq. 4.13) , however, in the current model these species react with BAP (denoted as 𝑅𝐵𝑟 for simplicity) to undergo a regenerative pathway to form ground state 𝐸𝑌, 𝑅 ∙ radical and 𝐵𝑟− species (Eq. 4.14). The 𝑇𝐸𝑂𝐴+ ∙ species formed during the initiation reaction (Eq. 4.16) further interact with 𝑂𝐻− species in the bioink solution to generate 𝑇𝐸𝑂𝐴 ∙ radicals (Eq. 4.17). Similar to other radical species, the 𝑇𝐸𝑂𝐴 ∙ radicals can be quenched by 𝑂2 (Eq. 4.18) to form superoxide and inactivated 𝑇𝐸𝑂𝐴+ species which does not participates in other steps. The 𝑇𝐸𝑂𝐴 ∙ radical can also combine with 𝐸𝑌𝐻 ∙ radical in a regenerative step to yield ground state 𝐸𝑌 and 𝑇𝐸𝑂𝐴 species (Eq. 4.21). In addition to quenching and regenerative steps, the 𝑇𝐸𝑂𝐴 ∙ radical species interact with monomer species (𝐺𝑀) to form growing polymer chains (𝐺𝑀 ∙) in the propagation step (Eq. 4.19) and also combine with 𝐺𝑀 ∙ chains to form the polymer in a termination step (Eq. 4.23). In this model, BAP (𝑅𝐵𝑟) is introduced which acts in parallel to 𝑇𝐸𝑂𝐴 to accelerate various steps in the photopolymerization. When the 𝐸𝑌 ∙∗ species are formed by blue light irradiation, 𝑅𝐵𝑟 98 competes with 𝑂2 to quench these species and, as a result, forms the 𝑅 ∙ radicals (Eq. 4.14). The 𝑅 ∙ radicals work in parallel with 𝑇𝐸𝑂𝐴 ∙ radicals to participate in oxygen quenching (Eq. 4.15), polymer chain initiation in propagation step (Eq .4.20) and polymer formation in termination step (Eq. 4.25) respectively. This contributes to a significant speed-up in the polymerization process while also leading to regeneration of 𝐸𝑌 photoinitiator. Upon initiation of polymer chains (𝐺𝑀 ∙) by 𝑇𝐸𝑂𝐴 ∙ and 𝑅 ∙ species, the chains grow by reacting with other monomer species (𝐺𝑀) and ultimately are terminated by combining with other growing chains, 𝑇𝐸𝑂𝐴 ∙ or 𝑅 ∙ radicals (Eqs. 4.22 – 4.25). Being radical species, 𝐺𝑀 ∙ are also quenched by 𝑂2 to form polymer chains with peroxide terminals (𝐺𝑀𝑂𝑂 ∙) which cannot grow further (Eq. 4.26). 4.2.2 Numerical model setup The polymerization model described in the previous section is implemented in COMSOL™ Multiphysics by combining diffusive mass transport and reaction chemistry. Utilizing the reaction equations of the polymerization model (Eqs. 4.1 – 4.26), the rate equations of all the chemical species were formulated in Eqs. 4.27 – 4.48. The kinetic parameters for various reactions have been adapted from previous studies involving same reactants.[138],[143],[144],[150] Some parameters have also been approximated from analogous reactions and are described in a previous study.[144] The diffusion coefficients listed in Table 4.2. are also adapted from previous studies.[143] The 𝐸𝑌 regeneration cycle has not been explored in detail and very little information is available on various intermediate processes. Further, the short-lived intermediate species and overlapping absorption/emission spectrum of transition state 𝐸𝑌 species makes the experimental investigation of this process extremely difficult. The findings by Aguirre-Soto et al. have been used to estimate 99 the molar absorptivity of excited 𝐸𝑌 ∙ radical species at 400 nm.[137] The 𝐸𝑌 − 𝑇𝐸𝑂𝐴 − 𝑅𝐵𝑟 system has not been studied earlier although some studies have focused on 𝐸𝑌 − 𝑅𝐵𝑟 systems for dehalogenation applications[155]–[157] but do not provide the kinetic parameters required for modeling the photopolymerization. The rate constants for 𝑅𝐵𝑟 involving steps were approximated based on the di-substituted aminoanthraquinone 𝐷𝐴𝐴𝑄 − 𝑅𝐵𝑟 model reported recently.[138],[140] Other parameters are based on experimental conditions used in SLA bioprinting system (Chapter 3) and are listed in Table 4.3. Due to high computation requirement and emphasizing the observation of microscale effects in high-resolution bioprinting, a small 2D domain 2.5 mm length and 0.5 mm bioink layer thickness was considered for the simulations. Reactant species rate equations Eosin Y (𝑬𝒀) species 𝑅𝐸𝑌 = −𝑘𝑎𝑐𝑡𝐸𝑌[𝐸𝑌] + (𝑘𝑖𝑐 + 𝑘𝑓)[𝐸𝑌𝑠] + 𝑘𝑞[𝑇𝐸𝑂𝐴][𝐸𝑌𝑠] + 𝑘𝑑𝑒𝑎𝑐𝑡[𝐸𝑌𝑡] + 𝑘𝑙𝑒𝑢𝑐𝑜[𝐸𝑌𝐻 ∙]2 +𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙][𝑂2] + 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗]([𝑂2] + [𝑅𝐵𝑟]) + 𝑘𝑟𝑒𝑔𝑒𝑛2[𝐸𝑌𝐻 ∙][𝑇𝐸𝑂𝐴 ∙] (4.27) 𝑅𝐸𝑌𝑠 = −(𝑘𝑖𝑐 + 𝑘𝑖𝑠𝑐 + 𝑘𝑓)[𝐸𝑌𝑠] − 𝑘𝑞[𝑇𝐸𝑂𝐴][𝐸𝑌𝑠] + 𝑘𝑎𝑐𝑡𝐸𝑌[𝐸𝑌] (4.28) 𝑅𝐸𝑌𝑡 = −𝑘𝑡𝑒[𝐸𝑌][𝐸𝑌𝑡] − 𝑘𝑑𝑒𝑎𝑐𝑡[𝐸𝑌𝑡] − 𝑘𝑖[𝐸𝑌𝑡][𝑇𝐸𝑂𝐴] + 𝑘𝑖𝑠𝑐[𝐸𝑌𝑠] (4.29) 𝑅𝐸𝑌∙ = −𝑘𝑎𝑏[𝐸𝑌 ∙][𝐻+] − 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙][𝑂2] − 𝑘𝑎𝑐𝑡𝐸𝑌∙[𝐸𝑌 ∙] + 𝑘𝑎𝑓[𝐸𝑌𝐻 ∙][𝑂𝐻−] +𝑘𝑖[𝑇𝐸𝑂𝐴][𝐸𝑌𝑡] (4.30) 𝑅𝐸𝑌𝐻∙ = −𝑘𝑎𝑓[𝐸𝑌𝐻 ∙][𝑂𝐻−] − 𝑘𝑙𝑒𝑢𝑐𝑜[𝐸𝑌𝐻 ∙]2 − 𝑘𝑟𝑒𝑔𝑒𝑛2[𝑇𝐸𝑂𝐴 ∙][𝐸𝑌𝐻 ∙] +𝑘𝑎𝑏[𝐸𝑌 ∙][𝐻+] (4.31) 𝑅𝐸𝑌𝐻2 = 𝑘𝑙𝑒𝑢𝑐𝑜[𝐸𝑌𝐻 ∙]2 (4.32) 𝑅𝐸𝑌∙∗ = −𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗]([𝑂2] + [𝑅𝐵𝑟]) + 𝑘𝑎𝑐𝑡𝐸𝑌∙[𝐸𝑌 ∙] (4.33) 100 Triethanolamine (𝑻𝑬𝑶𝑨) species 𝑅𝑇𝐸𝑂𝐴 = −𝑘𝑖[𝐸𝑌𝑡][𝑇𝐸𝑂𝐴] + 𝑘𝑟𝑒𝑔𝑒𝑛2[𝐸𝑌𝐻 ∙][𝑇𝐸𝑂𝐴 ∙] (4.34) 𝑅𝑇𝐸𝑂𝐴∙ = −𝑘𝑇𝐸𝑂𝐴−𝑂2[𝑇𝐸𝑂𝐴 ∙][𝑂2] − 𝑘𝑝[𝑇𝐸𝑂𝐴 ∙][𝐺𝑀] − 𝑘𝑟𝑒𝑔𝑒𝑛2[𝐸𝑌𝐻 ∙][𝑇𝐸𝑂𝐴 ∙] −𝑘𝑡[𝑇𝐸𝑂𝐴 ∙][𝐺𝑀 ∙] + 𝑘𝑇𝐸𝑂𝐴𝑟𝑎𝑑[𝑇𝐸𝑂𝐴+ ∙][𝑂𝐻−] (4.35) 𝑅𝑇𝐸𝑂𝐴+∙ = −𝑘𝑇𝐸𝑂𝐴𝑟𝑎𝑑[𝑇𝐸𝑂𝐴+ ∙][𝑂𝐻−] + 𝑘𝑖[𝐸𝑌𝑡][𝑇𝐸𝑂𝐴] (4.36) 𝑅𝑇𝐸𝑂𝐴+ = 𝑘𝑇𝐸𝑂𝐴−𝑂2[𝑇𝐸𝑂𝐴 ∙][𝑂2] (4.37) 2-Bromoacetophenone (𝑹𝑩𝒓) species 𝑅𝑅𝐵𝑟 = −𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗][𝑅𝐵𝑟] (4.38) 𝑅𝑅∙ = −𝑘𝑝[𝑅 ∙][𝐺𝑀] − 𝑘𝑡[𝑅 ∙][𝐺𝑀 ∙] − 𝑘𝑖𝑛ℎ[𝑅 ∙][𝑂2] + 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗][𝑅𝐵𝑟] (4.39) 𝑅𝐵𝑟− = +𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗][𝑅𝐵𝑟] (4.40) 𝑅𝑅𝑂𝑂−∙ = +𝑘𝑖𝑛ℎ[𝑅 ∙][𝑂2] (4.41) Oxygen (𝑶𝟐) species 𝑅𝑂2 = −𝑘𝑇𝐸𝑂𝐴−𝑂2[𝑇𝐸𝑂𝐴 ∙][𝑂2] − 𝑘𝑖𝑛ℎ[𝐺𝑀 ∙][𝑂2] − 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙][𝑂2] − 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗][𝑂2] −𝑘𝑖𝑛ℎ[𝑅 ∙][𝑂2] (4.42) 𝑅𝑂2−∙ = +𝑘𝑇𝐸𝑂𝐴−𝑂2[𝑇𝐸𝑂𝐴 ∙][𝑂2] + 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙][𝑂2] + 𝑘𝑟𝑒𝑔𝑒𝑛[𝐸𝑌 ∙∗][𝑂2] (4.43) Gelatin methacrylate (𝑮𝑴) species 𝑅𝐺𝑀 = −𝑘𝑝[𝐺𝑀]([𝑇𝐸𝑂𝐴 ∙] + [𝑅 ∙] + [𝐺𝑀 ∙]) (4.45) 101 𝑅𝐺𝑀∙ = −𝑘𝑡[𝐺𝑀 ∙]([𝑇𝐸𝑂𝐴 ∙] + [𝐺𝑀 ∙] + [𝑅 ∙]) − 𝑘𝑖𝑛ℎ[𝐺𝑀 ∙][𝑂2] + 𝑘𝑝[𝐺𝑀]([𝑇𝐸𝑂𝐴 ∙] + [𝑅 ∙]) (4.46) 𝑅𝐺𝑀𝑂𝑂∙ = 𝑘𝑖𝑛ℎ[𝐺𝑀 ∙][𝑂2] (4.47) 𝑅𝑃𝑜𝑙𝑦𝑚𝑒𝑟 = 𝑘𝑡[𝐺𝑀 ∙]([𝑇𝐸𝑂𝐴 ∙] + [𝐺𝑀 ∙] + [𝑅 ∙]) (4.48) Table 4.2. Diffusion coefficient of reactant and intermediate species used in the numerical model. Diffusion Constant Value (m2/s) Diffusion Species 𝐷𝐸𝑌 5.5 × 10−10 Eosin Y 𝐷𝐸𝑌𝑠 5.5 × 10−10 Eosin Y (singlet) 𝐷𝐸𝑌𝑡 5.5 × 10−10 Eosin Y (triplet) 𝐷𝐸𝑌∙ 5.5 × 10−10 Eosin Y Radical 𝐷𝐸𝑌𝐻∗ 5.5 × 10−10 Protonated Eosin Y Radical 𝐷𝐸𝑌𝐻2 5.5 × 10−10 Leuco Eosin Y (deactivated) 𝐷𝐸𝑌∙∗ 5.5 × 10−10 Excited Eosin Y radical 𝐷𝑇𝐸𝑂𝐴 7.043 × 10−10 Treithanolamine (TEOA) 𝐷𝑇𝐸𝑂𝐴+∙ 7.043 × 10−10 TEOA radical cation 𝐷𝑇𝐸𝑂𝐴∙ 7.043 × 10−10 TEOA radical 𝐷𝑇𝐸𝑂𝐴+ 7.043 × 10−10 TEOA cation 𝐷𝑅𝐵𝑟 7.043 × 10−10 2-Bromoacetophenone (BAP) 𝐷𝑅∙ 7.043 × 10−10 BAP radical 𝐷𝐵𝑟− 1.530 × 10−9 Bromine ion 𝐷𝑅𝑂𝑂−∙ 7.043 × 10−10 BAP radical 𝐷𝑂2 1.960 × 10−9 Oxygen 𝐷𝑂𝑂−∙ 1.960 × 10−9 Superoxide 𝐷𝐺𝑀 9.013 × 10−10 GelMA 𝐷𝐺𝑀∙ 9.013 × 10−10 Polymer chain radical (n-units long) 𝐷𝐺𝑀𝑂𝑂∙ 9.013 × 10−10 Peroxy radical 𝐷𝑃𝑜𝑙𝑦𝑚𝑒𝑟 9.013 × 10−10 Polymer chain 102 Table 4.3. Experiment specific parameters used in the numerical model setup. Variable Initial Value Description 𝐶𝐸𝑌 0.02 𝑚𝑀 Eosin Y concentration 𝐶𝑇𝐸𝑂𝐴 15 𝑚𝑀 Treithanolamine concentration 𝐶𝑂2 0.58 𝑚𝑀 Oxygen concentration 𝐶𝐺𝑀 15 𝑚𝑀 GelMA concentration 𝐶𝑅𝐵𝑟 0.1 𝑚𝑀 2-Bromoacetophenone concentration 𝑝𝐻 7.8 pH of the bioink 𝐶𝐻+ 10−𝑝𝐻𝑀 Proton concentration 𝐶𝑂𝐻− 1014−𝑝𝐻𝑀 Hydroxide ion concentration 𝐼0 45 𝑚𝑊/𝑐𝑚2 Light Intensity 𝜀500 85800 (𝐿𝑚𝑜𝑙 ∙ 𝑐𝑚) Extinction coefficient eosin Y (0-1μM) 𝐸500′ 2.39 × 105 (𝐽𝑚𝑜𝑙) Energy of photons at 500 nm 𝜀400 35800 (𝐿𝑚𝑜𝑙 ∙ 𝑐𝑚) Extinction coefficient eosin Y radical (0-1μM) 𝐸400′ 2.99 × 105 (𝐽𝑚𝑜𝑙) Energy of photons at 400 nm 𝛿 0.01 𝑚𝑚 Oxygen boundary layer in water 𝜇𝑟𝑒𝑙 6 Relative viscosity of the monomer 𝑏𝑖𝑜𝑖𝑛𝑘𝑥 2.5 𝑚𝑚 Modeled bioink width 𝑏𝑖𝑜𝑖𝑛𝑘𝑦 0.5 𝑚𝑚 Modeled bioink layer thickness 103 Figure 4.1. Domain setup in numerical simulations. The dimensions of the bioink domain, illuminated regions and oxygen flux boundary condition on top boundary are shown. The probes are located at 0 mm, 0.25 mm and 0.5 mm from the bottom of the bioink domain and are indicated as “bottom”, “middle” and “top” respectively. The domain setup along with the projected area are shown in Figure 4.1. The points probes are located at 0 mm, 0.25 mm and 0.5 mm from the bottom surface and referred to as “Bottom”, “Middle” and “Top” respectively. In contrast to conventional SLA printing systems with bottom projection and build plate, the modeled system in this study represents a bioink containing vat with open top surface exposed to ambient air and the photo-pattern was projected from the top along the vertical direction. This closely mimics the top projection SLA bioprinting system previously developed by our group.[23] A flux boundary condition was applied on the top boundary of the model domain to enforce the open vat setup. The flux is calculated by Eq. 4.49 𝐽𝑂2 =𝐷𝑂2(𝐶𝑂2−[𝑂2]𝑡)𝛿 (4.49) 104 , where 𝐽𝑂2 is the oxygen flux, 𝐷𝑂2is the diffusivity of oxygen in bioink, 𝐶𝑂2is initial oxygen concentration in the bioink, [𝑂2]𝑡 is current oxygen concentration at the boundary and 𝛿 is the thickness of diffusion boundary layer. The bioink top boundary was incident with light of intensity 46.8 mW/cm2 which decays as it travels within the bioink layer. Figure 4.2 shows the variation of intensity of projection light at 500 nm and 400 nm wavelengths for a bioink layer of 5 mm (main) and 0.5 mm (inset) respectively. As described earlier, the photobleaching effect and broad wavelength range were not considered in these models. Figure 4.2. Light intensity decay within the bioink layer following the Beer-Lambert law. The intensity decay for a smaller bioink thickness is shown in the inset which corresponds to the thickness of the domain used for simulations. 105 4.2.3 Diffusion and mask projection The photo-pattern was applied using a mathematical expression to form patterns with varying width and number of exposed regions. The total projection time is kept 300 seconds with continuous projection. Subsequently, the simulations were performed with pulsed illumination in which the total projection time was increased to maintain equivalent light dose. The pulse width was also varied between 0, 2, 5 and 10 seconds respectively, however, the duty cycle was fixed at 50%. The pulsed illumination was implemented using mathematical expression of a rectangular wave. A transition zone of 10% of the wave time period was defined to maintain 1st derivative of the wave and circumvent solution convergence problem. The photo-pattern projection resulted in formation of concentration gradients as the species were generated and consumed at various locations within the bioink domain. These concentration gradients were utilized with diffusive mass transport to include migration of species within the bioink. This also impacted the criteria used for defining degree of conversion (DOC) and, in turn, the gel formation. The DOC definition (Eq. 4.50) in earlier models was based on bulk polymerization and did not differentiate between species concentration arising from reaction and diffusive mass transport. A weight function (Eq. 4.51) was introduced to prioritize the contribution of reaction over the diffusive mass transport and the DOC was refined was shown in Eq. 4.52. The gel formation definition from earlier studies was used and sol-gel transition was defined at DOC = 0.2.[143],[158] This DOC threshold was defined based on combining experimental measurements with theoretical sol-gel transition limit (critical conversion, 𝛼𝑐) for multifunctional monomers (Eq. 4.53).[158] GelMA is approximated as monomer with three functional groups on an average and capability to form six arms during polymerization and cyclic polymerization is neglected due to steric hindrance provided by the large GelMA backbone. As the GelMA solution polymerizes, the 106 viscosity increases which restricts the diffusion of various species in the solution. This is dependent on the DOC and is implemented in the model using Eq. 4.54 in which 𝜇𝑟𝑒𝑙 is the relative viscosity of the GelMA solution and is considered to be 6 based on the findings reported in Chapter 2. 𝐷𝑂𝐶 =[𝐺𝑀]0−[𝐺𝑀][𝐺𝑀]0 (4.50) 𝑤 = ([𝑆][𝑆𝑚𝑎𝑥])2, ϶ [𝑆] = [𝑃] + [𝐺𝑀 ∙] + [𝐺𝑀𝑂𝑂 ∙] (4.51) 𝐷𝑂𝐶 = 𝑤 ×[𝐺𝑀]0−[𝐺𝑀][𝐺𝑀]0 (4.52) 𝛼𝑐 =1𝑓−1 ϶ 𝑓 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑔𝑟𝑜𝑢𝑝𝑠 𝑜𝑛 𝑚𝑜𝑛𝑜𝑚𝑒𝑟 (4.53) 𝐷 = 𝐷0𝑒−(𝜇𝑟𝑒𝑙×𝐷𝑂𝐶) (4.54) 4.2.4 Projection intensity In previous bulk-polymerization studies, ideal illumination has been considered with uniform spatial light intensity. A few studies have made the model more realistic by introducing the spatial variation of light intensity in the illumination in the form of a Gaussian distribution.[159]–[161] This is indeed expected as the light is reflected from a microscale mirror and comprises of three components – specular, glossy and diffuse.[162] In the presented model, a Gaussian light intensity distribution is defined to represent the spatial variation of light intensity at each pixel in the photo-pattern. Equations 4.55 and 4.56 represent the intensity distribution in 1D and 2D respectively. The pixel center is represented by 𝑥𝑝𝑖𝑥𝑒𝑙 and 𝑦𝑝𝑖𝑥𝑒𝑙 respectively. The pixels are considered square of edge 50 µm represented by 𝑤𝑝𝑖𝑥𝑒𝑙. 107 𝐼𝑥 = 𝐼0𝑒 (−(𝑥−𝑥𝑝𝑖𝑥𝑒𝑙)22𝑤𝑝𝑖𝑥𝑒𝑙2 ) (4.55) 𝐼𝑥,𝑦 = 𝐼0𝑒 [− ((𝑥−𝑥𝑝𝑖𝑥𝑒𝑙)22𝑤𝑝𝑖𝑥𝑒𝑙2 +(𝑦−𝑦𝑝𝑖𝑥𝑒𝑙)22𝑤𝑝𝑖𝑥𝑒𝑙2 )] (4.56) A comparison of designed photo-pattern, individual pixel intensity and overall photo-pattern intensity is shown in Figure 4.3. The intended projection region, pixels contributing to the photo-pattern and the actual projection region with overexposed parts are also shown (Figure 4.3A). The overexposed part is further divided into contributing and non-contributing zones due to presence of a threshold intensity for initiating photopolymerization. This threshold intensity varies with experimental conditions and the bioink composition, therefore, it is not included in the current model for simplicity. The ideal and Gaussian intensity profiles for a wider mask are shown in Figure 4.3B. As the illuminated region crosses a threshold width, the overall intensity achieves a saturation which is 2.5 times the peak intensity contributed by individual pixels. The intensity measured for the experimental SLA bioprinting system was 48.6 mW/cm2 with the full area projection. Therefore, the recorded intensity corresponds to the peak achievable value and the peak intensity at a single pixel is set as 19.44 mW/cm2. This intensity will be used for simulation with ideal illumination. Figure 4.4A-B shows a comparison of the photo-patterns formed using ideal and Gaussian illuminations respectively. The overexposed regions introduced in case of Gaussian illumination are also indicated in Figure 4.4B. Further, Figure 4.5 shows the topographical representation of the photo-pattern intensity (scaled for better visualization). It should be noted that the photo-pattern width significantly changes the local illumination intensity and, consequently, introduces variation in local photopolymerization rates which translates to poor bioprinting resolution. 108 Figure 4.3. Light intensity distribution in photo-pattern. (A) Pixel wise Gaussian intensity plot for a photo-pattern encompassing 4 pixels. The combined intensity is also shown (red). The regions illuminated in ideal and Gaussian illumination are marked. The overexposed contributing and non-contributing regions of the Gaussian illumination are also shown. 109 Figure 4.4. Comparison of photo-patterns formed by (A) ideal illumination and (B) Gaussian illumination. 110 Figure 4.5. Topographical representation of light intensity. (A) Honeycomb photo-pattern – (i) ideal light intensity photo-pattern, (ii) ideal light intensity topographical representation, (iii) gaussian light intensity photo-pattern and (iv) Gaussian light intensity topographical representation. (B) Neuron photo-pattern – (i) ideal light intensity photo-pattern, (ii) ideal light intensity topographical representation, (iii) Gaussian light intensity photo-pattern and (iv) gaussian light intensity topographical representation. 111 4.3 Results and Discussion 4.3.1 Effect of mask intensity distribution In the first study, two types of photo-patterns were compared by simulating the photopolymerization. Photo-patterns comprising of two illuminated regions were used with an ideal pixel light intensity distribution and a Gaussian pixel light intensity distribution respectively (Figure 4.4). The resulting hydrogels formed during the simulation are shown in Figure 4.6A for ideal and Gaussian illumination. Larger shape of hydrogels was achieved in case of Gaussian illumination as the overall intensity can reach 2.5 times the intensity of ideal illumination (Figure 4.3). Figure 4.6B shows the oxygen concentration within the bioink layer. With higher intensity of Gaussian illumination, the rate of EY activation and photoinitiation increases and various radical species are formed. This resulted in quicker depletion of oxygen as observed in the probe plots (Figure 4.7). On the other hand, the oxygen depletion is slower in ideal illumination case. This also contributes to a thicker oxygen layer formation near the top surface as the depletion rate is matched with the oxygen influx (Figure 4.6B). In contrast to the earlier bulk polymerization models, the presence of selective illumination results in formation of concentration gradients along the x-axis (horizontal). Within the illuminated region, the oxygen from top surface as well as the adjacent left and right-side regions flows in. This creates a slightly depleted region between the masks and, therefore, a shift in the formed hydrogels towards the center is observed. 112 Figure 4.6. Comparison of ideal and Gaussian illumination. (A) Formed hydrogel profiles at different exposure time. (B) Oxygen concentration in the bioink. 113 Figure 4.7. Comparison of ideal and Gaussian illumination. (A) Oxygen probe plot for ideal illumination. (B) DOC probe plot for ideal illumination. (C) Oxygen probe plot for Gaussian illumination. (D) DOC probe plot for Gaussian illumination. Figure 4.7B and D highlight the clear differences in the degree of conversion (DOC) and rate of polymerization in the two cases. When we look closely at the foremost region of DOC plots (Figure 4.7B inset), the concave up (i) and concave down shapes (ii) of the graph can be identified. These regions were also observed in experimental characterization of photopolymerization kinetics of GelMA with BAP in Chapter 3 and represent the acceleration and deceleration of polymerization respectively. 114 4.3.2 Effect of BAP concentration In the next study, the BAP concentration was increased which activated the regenerative pathway of EY. Figure 4.8A-D shows the profile of hydrogels formed at 0 mM, 0.02 mM, 0.05 mM and 0.1 mM BAP concentrations respectively. Compositions with 0 mM BAP and 0.02 mM BAP had very minute differences and resulted in similar output. This can be attributed to their comparable photocrosslinking kinetics (Figure 4.10A). Whereas the 0.05 mM and 0.1 mM BAP bioinks underwent rapid polymerization. Similar photocrosslinking kinetics were experimentally observed in Chapter 3. Addition of BAP also contributes to rapid oxygen depletion due to oxygen quenching reactions. However, since all of these compositions had very fast oxygen depletion in the exposed region, this difference was noticeable only at the probe located at the top surface (Figure 4.9C). 115 Figure 4.8. Effect of BAP (RBr) concentration. Simulated hydrogel profiles for different BAP concentrations (A) 0 mM, (B) 0.02 mM, (C) 0.05 mM and (D) 0.1 mM. 116 Figure 4.9. Effect of BAP concentration on oxygen in bioink layer. Oxygen probe plots at (A) bottom, (B) middle and (C) top of the bioink domain. 117 Figure 4.10. Effect of BAP concentration on DOC within bioink layer. DOC probe plots at (A) bottom, (B) middle and (C) top of the bioink domain. 118 When deciding the bioprinting fidelity, deviation from the intended width is considered as the governing parameter as the desired height of the hydrogel structure can be achieved through polymerizing multiple layers. The bioinks with 0 mM and 0.02 mM BAP exhibited good fidelity with the projected region at 150 seconds of photopolymerization. However, the achieved height of the hydrogels was nearly half of the bioink layer. On the other hand, bioink with 0.05 mM BAP showed good bioprinting fidelity after 90 seconds of exposure. The formed hydrogel was also of high aspect ratio, but the upper region was poorly formed and significantly thinner. The bioink with 0.1 mM BAP underwent rapid photopolymerization and achieved good bioprinting fidelity at a very short exposure time of 60 seconds. This was also evident by the bioprinting experiments presented in Chapter 3 in which 0.1 mM BAP could form clear patterns in 60 second exposure. The formed hydrogels in case of 0.1 mM BAP were of high aspect ratio and had uniform thickness along the height. These hydrogels were also able to reach extremely close to the intended height. For 0 mM and 0.02 mM BAP bioinks, in order to form hydrogels close to intended height, the spatial resolution was compromised whereas with 0.05 mM and 0.1 mM BAP bioinks, this was achieved without sacrificing the resolution and at a shorter exposure time. This highlights the capability of BAP containing bioinks to achieve rapid high-fidelity bioprinting. 4.3.3 Effect of mask spacing The separation between the illuminated regions can introduce bioprinting artifacts like overcrosslinking. Two photo-patterns were used in this study as shown in Figure 4.11A (i)-(ii). The separation between the illuminated regions was varied at 1 mm and 0.5 mm and the resulting hydrogel profiles were compared. Figure 4.11B-C shows the formed hydrogel profiles are different exposure times for 0.5 mm and 1 mm mask spacing respectively. At 90 second exposure, the 119 hydrogel formed with 0.5 mm mask spacing began to overcrosslink towards the center. This was a clear result of the oxygen depleted zone formed between the illuminated regions. Figure 4.12A shows a significantly reduced oxygen concentration at all the probe locations (Top, Middle and Bottom). The polymerization kinetics at the region between the illuminated zones is shown in Figure 4.12B. In case of 0.5 mm spaced mask, the polymerization between the illuminated regions onsets as early as 50 seconds whereas in case of 1 mm spaced mask, the polymerization begins after 70 second exposure. Eventually the region between the photo-pattern undergoes complete polymerization. These observations also highlight that there is a trade-off between bioprinting closely spaced features and the bioprinting fidelity. 120 Figure 4.11. Effect of distance between illuminated regions. (A) Comparison of the photo-patterns in the two simulations. Simulated hydrogel profiles for (B) 0.5 mm spaced pattern and (B) 0.1 mm spaced pattern. 121 Figure 4.12. Probe plots for (A) oxygen concentration and (B) DOC at the middle axis between the illuminated regions. 4.3.4 Effect of bioink layer thickness One of the printing parameters in the conventional bottom-projection SLA printing systems is the resin layer thickness which governs the resolution of the printed part. In case of top projection photopolymerization, the bioink layer thickness also influences the bioprinting process but in a slightly different way. In the initial stages of the polymerization, the height of the formed hydrogels 122 is very low due to the continuous oxygen influx from the top surface (Figure 4.13A). The width of the formed hydrogels is also less than the intended dimensions because the adjacent regions, being close to the open top surface, are replenished by the ambient oxygen (Figure 4.14A). Therefore, to achieve hydrogels closer to intended height, longer exposure duration will be required and the bioprinting process will be slowed down. On the other hand, thicker bioink layers can result in formation of high aspect ratio hydrogels in lesser time (Figure 4.13B). Although, the intended height will not be achieved, this behavior could be used to manipulate the bioprinting process. The bioink layer thickness can be intentionally increased to achieve the target hydrogel height predicted by the numerical model. The polymerization kinetics and oxygen depletion kinetics for different bioink layer thickness is shown in Figure 4.15A-B. Regardless of the bioink layer thickness, the top surface of the bioink behaves similarly with minute differences. Moreover, the kinetics at the bottom of thin bioink layer and the bottom and middle of the thick bioink layers were also comparable indicating similar progression of polymerization. 123 Figure 4.13. Effect of bioink layer thickness on hydrogel formation. Simulated hydrogel profiles for (A) 0.25 mm and (B) 0.5 mm thick bioink layers. Figure 4.14. Effect of bioink layer thickness on oxygen depletion. Simulated oxygen concentration profiles for (A) 0.25 mm and (B) 0.5 mm thick bioink layer. 124 Figure 4.15. Effect of bioink layer thickness. (A) Oxygen probe plots. (B) DOC probe plots. 4.3.5 Effect of pulsed illumination In this study, we introduced an additional photo-pattern parameter by converting it to a dynamic photo-pattern which is exposed in a pulsed manner i.e. the projection is turned off for a time interval (pulse time) and turned on again for the same time interval. The pulse width was varied to 2s, 5s and 10s respectively with the exposed regions separated by 1 mm. Figure 4.16A-D shows the hydrogel profiles formed at various exposure times. The polymerization proceeded 125 rapidly in continuous photo-pattern exposure as compared to pulsed exposure as evident by the size of the crosslinked hydrogels formed as well as the DOC probe plot (Figure 4.18). The onset of polymerization was first for the continuous projection and was delayed as the pulse width is shortened (Figure 4.18). This can also be attributed to the depletion of oxygen in the bioink layer. The continuous projection caused faster depletion of oxygen followed by pulse widths of 10s, 5s and 2s respectively (Figure 4.17). However, due to extended unexposed duration of 5s and 10s, the oxygen was replenished to some extend in the bioink layer which needed to be depleted during the next pulse. Among the pulsed projection photopolymerization, there was negligible difference in the formed hydrogel shape. However, the pulsed projection leads to an indirect advantage in the hydrogel formation. In order to from stiff hydrogels during bioprinting, a longer exposure time is needed but with longer exposure duration, the hydrogel begins to overcrosslink the nearby regions and compromises the printing fidelity. This is illustrated well in the case of continuous exposure hydrogel formation (Figure 4.16A). On the other hand, the pulsed exposure led to formation of comparatively stiffer hydrogels while preserving the printing fidelity across the width. Although, the height of the hydrogels was significantly lower than the bioink layer, this can be overcome by sequentially polymerizing multiple layers. The fidelity along the width is crucial to maintain and cannot be remedied with additional steps. Careful examination of the DOC probe plots (Figure 4.18A) also revealed that the lower pulse width exposure was able to achieve slightly higher stiffness of the hydrogels. However, the difference observed was very low and needs further investigation for a conclusive argument. Figure 4.17B and Figure 4.18B show the probe plots of oxygen concentration and DOC for the probe located at the bottom surface of the bioink layer and midway between the exposed regions. The continuous projection resulted in more depletion of 126 oxygen contributing to higher DOC which will eventually lead to overcrosslinking. Whereas, with pulsed exposure, this was mitigated, and significantly lower DOC was achieved. Figure 4.16. Comparison of hydrogel formation at same exposure time with (A) continuous projection and pulsed projection with pulse width of (B) 2s, (C) 5s and (D) 10s respectively. 127 Figure 4.17. Oxygen probe plots for continuous and pulsed projection models at the bottom surface of the bioink layers at (A) exposed region and (B) midway between the exposed regions. Figure 4.18. DOC probe plots for continuous and pulsed projection models at the bottom surface of the bioink layers at (A) exposed region and (B) midway between the exposed regions. Next, a comparison between the hydrogels formed with continuous projection and pulsed projection with 2s pulse width was performed (Figure 4.19) keeping the total light dose constant e.g. total light dose in 10s of pulsed projection corresponded to total light dose in 5s of continuous projection. In both cases, the printing fidelity along the width was maintained. Continuous projection formed hydrogels closer to the bioink layer whereas with pulsed projection a smaller 128 height of the hydrogels was achieved. However, the pulsed projection led to formation of hydrogels with comparatively dense crosslinked network than the continuous projection. Figure 4.19. Comparison of hydrogel formation at same total light dose with (A) continuous projection and (B) pulsed projection with pulse width of 2s. In order to highlight the influence of the pulsed projection on circumventing the overcrosslinking of the hydrogels, another simulation was performed by setting the separation between the exposed regions to 0.5mm and the pulse width was set to 5s. The resulting hydrogel profiles were compared to a similar configuration model with continuous photo-pattern projection. Figure 4.20 shows the hydrogel profiles formed in both cases. The continuous projection rapidly polymerized and exposed regions of the bioink and then the region between the mask began to polymerize due to oxygen depleted zone (Figure 4.20A). On the other hand, use of a pulsed illumination seemed to slow down the polymerization and the polymerization of the region 129 between the mask was delayed. This could be attributed to the reduced light dose in case of pulsed projection. However, the diffusion of oxygen in the bioink layer and oxygen quenching are also at play here. The continuous projection resulted in a severely oxygen depleted zone as shown in Figure 4.21A. On the other hand, the pulsed illumination resulted in a comparatively less severe oxygen depletion (Figure 4.21B). This was a result of pulsating generation of radical and other activated species and pulsating oxygen quenching process evident by the oxygen probe plots (Figure 4.22A-C). As a result, the polymerization in pulsed projection model progressed as a slower and steady rate (Figure 4.23A-C). Figure 4.20. Effect of pulsed illumination on hydrogel formation in closely spaced exposed regions. Simulated hydrogel profiles for (A) continuous projection and (B) pulsed projection. 130 Figure 4.21. Effect of pulsed illumination on oxygen depletion in closely spaced exposed regions. Simulated oxygen concentration profiles for (A) continuous projection and (B) pulsed projection. Although the overall bioprinting was slowed down, the pulsed illumination offered certain advantages. Comparing the DOC probe plot at “bottom” location, we find that the continuous and pulsed projections achieved similar extent of DOC, however, the polymerization in the pulse projection model was delayed (Figure 4.23A). This delay corresponded to the extended time required for oxygen depletion (Figure 4.22A). Considering the DOC and oxygen probe plots in the ‘mid’ regions (between the illuminated areas), we observe that the polymerization was consistently lower as compared to the continuous projection model (Figure 4.23). This implied that using the pulse projection suppressed the stray polymerization which leads to the overcrosslinking. When the projection is turned on in the pulsed model, there is a rapid rise in the oxygen concentration in the bioink which quenches the short polymer chains. Thereby only the dominant growing polymer chains sustain and result in formation of a hydrogel with relatively uniform DOC. This further 131 highlights the advantage of using pulsed projection in manipulating the hydrogels stiffness without sacrificing the printing accuracy. Further, a combination of pulsed and continuous projection photo-patterns can be explored to introduce the local variation in the hydrogel DOC and physical properties. Figure 4.22. Effect of pulsed illumination. Oxygen probe plots for continuous and pulsed models at (A) bottom, (B) middle and (C) top of the bioink domain. 132 Figure 4.23. Effect of pulsed illumination. DOC probe plots for continuous and pulsed models at (A) bottom, (B) middle and (C) top of the bioink domain. 4.4 Summary In this chapter, a versatile numerical model was presented for simulating the photopolymerization of GelMA. This model extended the biomaterial and photoinitiator described in previous chapters and used a three-component EY, TEOA and BAP photoinitiator. Upon providing relevant experimental conditions, the model was able to predict the polymerization kinetics of the bioink compositions. Further, the model was also able to simulate the expected hydrogel shape that could be achieved in given photocrosslinking conditions. These simulations also highlighted the bioprinting artifacts that are introduced due to diffusive transport of various 133 chemical species within the bioink domain. The role of oxygen in inhibiting the polymerization was also evident. Based on these observations, some variations in the photo-pattern were tested to identify the influence on the hydrogel formation. A pulsed photo-pattern projection revealed that by sacrificing the bioprinting speed, a hydrogel with uniform degree of crosslinking could be obtained. On the other hand, changing the bioink layer thickness revealed that thicker bioink layer can speed up the bioprinting process. Overall, the developed model presents a valuable tool in predicting as well as planning out the SLA bioprinting process. 134 Chapter 5 Closed-loop workflow for designing scaffold vasculature for bioprinting thick tissues 5.1 Synopsis In the previous chapters, the bioink design was discussed which included the detailed study of GelMA synthesis parameters and their effect on the resulting hydrogel properties. Further, the three-component BAP photoinitiator was added to the bioink to achieve faster photopolymerization. This was followed by the development of a numerical model to predict the hydrogel formation in given photopolymerization conditions. Overall, these three studies contribute towards bioinks using which hydrogels with tuned physical characteristics can be formed. The selective tuning of hydrogel can aid in fabricating scaffolds with embedded heterogenous microenvironment to guide the cell growth and tissue maturation. As mentioned in Chapter 1, vascularization is an indispensable requirement of fabricating large solid tissues. This was further reinforced by observations of cell growth in bioprinted miniature human ear (Chapter 2). To address this, a few studies have attempted to fabricate perfusable scaffolds using sacrificial materials.[21],[27],[163]–[165] In most of these works, simple hollow channels have been used as the vasculature. Kinstlinger et al. attempted to create a dendritic network, however, it was also modelled as a geometrically simple closed network.[165] These networks pose a problem of blood flow shunting and require more complexity in their architecture.[166] Therefore, a more complex and biomimetic vasculature needs to be designed. 135 Further, the performance of the designed vasculature should be examined for ensuring the nutrients are transported to the entire tissue. The vascular networks in the human body differ among the organs and tissues. In a typical network, the blood flows into the tissues through arteries which sub-divide into arterioles. The arterioles sub-divide to form capillaries which participate in exchange of nutrients and waste through diffusive mass transport. Further, the capillaries merge to form venules which, in turn, merge to form the veins for outflow of the blood from the tissue. When a scaffold with embedded vasculature is fabricated, the bioprinter capability limits the fabrication of small capillaries. Since, porous biocompatible hydrogels are used for fabricating these vascular networks, the diffusive mass transport from the blood vessel walls takes place until the endothelial lumens are formed completely. The surrounding cells rely on this diffusive mass transport until the capillaries are formed by angiogenesis. Therefore, an initially fabricated vasculature should be bioinspired, simplified to meet the bioprinting capabilities and sufficiently complex to provide diffusive nutrient transport to the entire scaffold during tissue/organ maturation period. This chapter presents a numerical model of designing the vasculature network which is inspired from the vein network present in the leaves. In previous studies, a leaf venation algorithm was presented which could mimic the 2D vein networks in the leaves.[167] This was further extended to mimic the 3D network of the tree branches [168]. First, the MR image processing is described to obtain tissue specific cues for guiding the vasculature. Next, the vasculature generation model is described in detail followed by the diffusive nutrient transport model. An iterative way of optimizing the vasculature is presented. The resulting vascular structures show an increment in the fraction of scaffold volume receiving required nutrition for the cells. 136 5.2 Theoretical model 5.2.1 Medical image processing The proposed model comprises of four main components – medical image processing, vascular network generation, nutrient transport modeling and source points generation as shown in Figure 5.1. The process begins by segmenting the medical images to obtain the shape of the target tissue or organ. The medical images were obtained in the form of slice images which show the tissue/organ cross-section structure at different depths. The region representing the target tissue/organ were segmented in each slice image and a point cloud representation of the tissue was generated from the segmented slice images. This point cloud was transitioned into a 3D structure of the tissue. In this study a an abdomen magnetic resonance imaging (MRI) open dataset retrieved from The Cancer Imaging Archive (TCIA, https://www.cancerimagingarchive.net/) was used with liver as the target organ. Some characteristics of the dataset are listed in the Table 5.1. The MR image dataset was used as it could provide tissue properties related to tissue water content as well as different MR modes allow potential for easier segmentation of the target tissue/organs. Further, this method can be extended to make use of MR elastography data to include tissue viscoelastic properties in vasculature guidance. Figure 5.2 shows different modes of the MRI dataset images in which T1 and T2 are without any contrast agent and T3 and T4 are with gadolinium contrast agent. 137 Table 5.1. Liver MRI dataset specifications. Property Value Procedure MRI ABDOMEN W+WO CONTRAST Format DICOM Device Philips Imaging DD 001 Patient information (sex, age, weight) M, 61Y, 127 kg Patient position HFS Magnetic field strength 1.5 T Echo numbers 1 Echo time 1.954 s Slice thickness 4 mm Spacing between slices 2 mm Pixel spacing 0.98 mm Imaged nucleus 1H 138 Figure 5.1. Sequence of steps involved in iterative vasculature generation and optimization. 139 Figure 5.2. Representative images from MRI dataset showing liver tissue in different imaging modes – (A) T1-weighted, (B) T2-weighted, (C) T1-weighted with contrast agent (T3), (D) T2-weighted with contrast agent (T4). First, an intensity-based segmentation was performed to isolate the liver region of higher intensity (gray level value). The slice images after the segmentation were converted to a point cloud dataset. This dataset was filtered by applying a point clustering operation using Euclidean distance between the points as the clustering criteria. Points away from the clusters or smaller sub-clusters were removed to obtain an initial 3D model of the liver. It should be noted that the MRI images comprise of several regions of similar intensity which hinders the intensity-based segmentation approach and introduces a lot of noise in the obtained point cloud data. Therefore, 140 manually specifying the liver boundaries for segmentation can be used as an alternative approach. The point cloud generated 3D model was represented as a 3D solid mesh and further improved by applying mesh smoothing and repairing steps. Next, the final liver 3D model was sliced into cross-section binary images using the slice spacing specified in the MRI dataset properties. The obtained slice images were superimposed with the corresponding input MR images and the superimposed regions from MR images were separated. Next, the T2 mode images (with superimposed liver cross-section) were selected for estimating a porosity equivalent parameter 𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦. The T2 mode images were used as the intensity in T2 mode is proportional to the water content in the tissue. Since these scaffolds are designed intended for fabrication with porous hydrogels, for this study, the approximate porosity of GelMA were used. Equation 5.1 was used to define 𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦 by considering the typical minimum (0.80) and maximum (0.90) porosity of GelMA hydrogels where 𝐼𝑥,𝑦,𝑧 represents the intensity of the voxel at (x,y,z) coordinates from the T2 mode images and 𝐼𝑥,𝑦,𝑧 𝑚𝑎𝑥 represents the maximum intensity. 𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦 = 0.8 + [(0.9 − 0.8) × (𝐼𝑥,𝑦,𝑧𝐼𝑥,𝑦,𝑧 𝑚𝑎𝑥 )] (5.1) Next, the histogram of the voxel intensity of the liver model was examined and a threshold was applied to identify regions of higher water content implying higher porosity equivalent (𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦). The point cloud representing high 𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦 regions was processed through a Euclidean point clustering step to form multiple sub-clusters. The centroids of these sub-clusters were selected as initial source (attraction) points for the vascular network generation. Additionally, a set 141 of initiation node points was selected on the liver 3D model surface for initiating the vascular network formation. The sequence of steps in MR image processing is shown in Figure 5.3. Figure 5.3. Detailed sequence of steps in MR image segmentation for liver 3D model generation, tissue equivalent porosity parameter estimation and source point generation. Input is marked in green; output is marked in orange and stored information of interest is enclosed in red boxes. 5.2.2 Vascular network generation The obtained source points and initiation points for the vascular network generation were, then, passed to a venation program prepared in MATLAB™. The various steps involved in the venation algorithm are represented graphically in Figure 5.4. In the first step, the distance between the first node i.e. the initiation node points (𝐼1 or 𝐼2) and the sources was calculated (Figure 5.4A). 142 A domain crossing restriction was applied such that if the line joining the node and source points crossed the domain at any point, the source was considered not to attract the node otherwise the source was considered to attract the node towards it. Next, all the source points attracting the node were listed as source-node pairs and unit vectors pointing towards each attracting source were placed at the node. A resultant vector was computed by combining all the respective unit vectors to determine the stepping direction. Following the stepping direction, a new node 𝑛1 or 𝑛2 was placed and the line joining the previous node (𝐼1 or 𝐼2) with new node formed a part of the vascular network (Figure 5.4B). Further, the attracting source and node pairs are determined again using the method described above. With n1 being closer to several nodes as compared to 𝐼1, those sources were omitted when determining attracting sources for node 𝐼1. This exclusion was performed by determining if a node is in the relative neighborhood of a source. In further steps when the nodes 𝑛1, 𝑛2, 𝑛3 and 𝑛4 have been formed, only source 𝑠𝑝 was eligible attracting source for node 𝑛1. This resulted in the formation of a new branch from node n1towards the source 𝑠𝑝 and created a bifurcation in the vasculature network (Figure 5.4C). A source termination criterion was implemented such that if a node lies within a defined kill distance from a source, it was terminated and not used for further computation. The sources 𝑠𝑝 and 𝑠𝑞 were terminated when the branches from nodes 𝑛1 and 𝑛3 approached the sources within the kill distance (Figure 5.4D). In case of closed vasculature networks, all the branches approaching a source were tracked and the source was terminated only if all the approaching branches reached within the kill distance of the source or the approaching branch nodes were excluded due to fall out of relative neighborhood of the source. Figure 5.5 shows the computations involved in determining the relative neighborhood of the source S. In this example, four branches approached the source S. Leading nodes of these 143 branches were designated as 𝑛𝐴, 𝑛𝐵, 𝑛𝐶 and 𝑛𝐷 respectively. The blue circle marks the locus of point at a distance of 𝑑𝑆𝐷 (distance between S and 𝑛𝐷). A node 𝑛𝑖 was considered in the relative neighborhood of source S if any node 𝑛𝑗 was closer to S than 𝑛𝑖 but the distance between 𝑛𝑖 and S was less than distance between 𝑛𝑖 and 𝑛𝑗 (Eq. 5.2).[167] (∀𝑛𝑗 ∈ 𝑁)‖𝑛𝑖 − 𝑆‖ < max {‖𝑛𝑗 − 𝑆‖, ‖𝑛𝑖 − 𝑛𝑗‖} (5.2) With this definition of relative neighborhood, the nodes 𝑛𝐴, 𝑛𝐵 and 𝑛𝐷 were in the relative neighborhood of the source S whereas the node 𝑛𝐶 was excluded because 𝑑𝑆𝐶 > max {𝑑𝑆𝐷 , 𝑑𝐷𝐶}. Inherently, this criterion imposed a geometric constraint that multiple vasculature branches can only approach a source if the lines joining the leading node of these branches and the source are at obtuse angles with respect to each other i.e. minimum angle between two approaching branches to a source will be 90o. In order to generate denser vascular network, this constraint was tuned, and Eq. 5.2 was modified to include a parameter 𝜑𝑎𝑝𝑝𝑟𝑜𝑎𝑐ℎ (Eq. 5.3). For 𝜃 = 60𝑜, the branches could approach the source with minimum angle of 60o between them. Increasing 𝜃 beyond 90o resulted in formation of a less dense vasculature. (∀𝑛𝑗 ∈ 𝑁)‖𝑛𝑖 − 𝑆‖ < max{‖𝑛𝑗 − 𝑆‖, (𝜑𝑎𝑝𝑝𝑟𝑜𝑎𝑐ℎ × ‖𝑛𝑖 − 𝑛𝑗‖)} , 𝑤ℎ𝑒𝑟𝑒 𝜑𝑎𝑝𝑝𝑟𝑜𝑎𝑐ℎ = (90𝜃) (5.3) 144 Figure 5.4. Detailed graphical representation of venation algorithm. (A) Initiation of the vascular network. (B) Propagation of the network by formation of new nodes. (C) Branching in the network. (D) Source termination by approached branches. 145 Figure 5.5. Steps in determining the nodes in the relative neighborhood of a source. The generated vascular network was further cleaned to remove short branches with less than two nodes. In order to obtain closed vascular networks, all the open branches in the network were identified and removed. The thickness of the parent and bifurcated child branches was determined by applying Murray’s Law with 𝑛 = 3 (Eq. 5.4). 𝑟𝑝𝑎𝑟𝑒𝑛𝑡𝑛 = ∑ 𝑟𝑐ℎ𝑖𝑙𝑑−𝑖𝑛 (5.4) The thickness of the first branch at the initiation nodes was defined manually. In case of closed networks, Murray’s Law poses a poorly defined problem with no solution. Therefore, another constraint was applied by limiting the minimum branch thickness to the bioprinting system capability. After applying the respective thickness to the entire network, a point cloud was obtained which was converted to a 3D mesh object to be used for fabrication or further steps. Figure 5.6 146 shows the example of a closed vascular network enclosing human brain and an open vascular network within a human liver generated from the described venation method. The initial point of these vascular networks coincided with the liver surface due to the selection of initiation nodes. Therefore, the main initiation branches were extended to protrude out of the liver 3D model and aid applying necessary boundary conditions. Figure 5.6. Examples of vascular networks generated from described venation algorithm. (A) Closed network around a human brain (proportions are exaggerated for clear visualization). (B) Open networks enclosed in human liver showing arterial (red) and venous (blue) networks. 5.2.3 Diffusion-based vasculature optimization After generating the 3D mesh objects of the vascular networks, they were imported in COMSOL™ along with the 3D model of the liver generated by MR image processing to simulate the mass transport of cell growth media (nutrient). Since the vascular network and the liver 3D structure would be fabricated using porous hydrogels, the surface of the vasculature within the liver was defined as an internal permeable wall. The vascular networks were defined as hollow domains filled with cell growth medium. The protruded parts of the arterial and venous vascular 147 networks were designated as inlet and outlet for cell growth media respectively (Figure 5.7A). Next, the liver body was defined as a porous medium filled with water and no initial cell growth media concentration. The external wall of the liver body was considered to be surrounded with mucous with a no flux boundary condition. Further, the liver body was modelled as a porous domain. The porosity was defined using the porosity equivalent parameter 𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦 determined from MR images. Figure 5.7B(i) & (ii) show the porosity variation in an x-y cross-sectional plane. The permeable boundary condition on the vasculature walls allowed diffusive mass transport with nutrient diffusion flux 𝐽𝑤𝑎𝑙𝑙 given by Eq. 5.5. 𝐽𝑤𝑎𝑙𝑙 = 𝐷 × ([𝑁]𝑙𝑖𝑣𝑒𝑟−[𝑁]𝑣𝑎𝑠𝑐𝑢𝑙𝑎𝑡𝑢𝑟𝑒𝛿𝑤𝑎𝑙𝑙) (5.5) , where 𝐷 is the diffusivity of the cell growth media in the aqueous medium, [𝑁]𝑙𝑖𝑣𝑒𝑟 and [𝑁]𝑣𝑎𝑠𝑐𝑢𝑙𝑎𝑡𝑢𝑟𝑒 are the concentration of cell growth medium in the liver outside the vascular network and concentration of cell growth medium in the vascular network respectively and 𝛿𝑤𝑎𝑙𝑙 is the thickness of the permeable wall of the vasculature. The influence of porosity of the medium on the mass transport was included through the following expression (Eq. 5.6). {(𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦)(𝑥,𝑦,𝑧)𝜕[𝑁]𝜕𝑡} − ∇. {(𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦)(𝑥,𝑦,𝑧)𝐷∇[𝑁]} = 0 (5.6) , where (𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦)(𝑥,𝑦,𝑧) is the medium porosity equivalent parameter at the (x,y,z) coordinates. The convective mass transport was not included. Although the cell growth medium comprises of numerous components, for simplicity, the cell growth media was modeled as a single species with 148 a uniform diffusive coefficient. The maximum concentration of the growth media was set to 1 mM implying 100% of the concentration. Figure 5.7. Model definitions for cell growth media transport simulation. (A) Description of domains properties, inlet and outlet. (B) (i) Perspective view of equivalent porosity parameter variation in xy and yz cross-sectional planes. (B) (ii) Top view of xy cross-sectional plane showing local variations in the equivalent porosity parameter within the liver body. 149 The diffusive mass transport simulations were run to compute the nutrient concentration profiles up to a duration of 60 minutes. This time threshold could be adjusted based on the target tissue/organ and the metabolic activity of the constituent cells. For the cells which could sustain for longer duration in absence of the growth medium, the threshold duration could be extended up to 120 minutes. Further, a threshold of 5% of the growth media sufficient for cell survival was applied. Therefore, regions in the liver domain with less than 0.05 mM (5%) were designated as ‘scarce’ regions. A point cloud dataset was obtained from COMSOL with a list of coordinates representing the 3D volume of the liver and corresponding nutrient concentration. This point cloud was filtered to retain scarce regions only followed by a point clustering operation with Euclidean distance between the points as the clustering criteria. The centroids of formed clustered were designated as the new sources for venation. The final list of sources included the sources from previous steps and the new sources. New sources which were within a threshold distance from the old sources were removed. This list of updated sources was used as input for next iteration of the venation to obtain a denser vascular network. 5.3 Results and Discussion 5.3.1 Liver 3D model generation Using the MR images dataset and the methodology described in Section 5.2.1, the image segmentation was performed by first applying an intensity threshold on T3 images (Figure 5.8A) to remove dark surrounding regions. Then, the filtered images were converted to a point cloud dataset and a point clustering operation was applied to filter the point cloud data. A 3D model was generated from the filtered point cloud data by applying the voxel scaling parameters from Table 5.1 as shown in Figure 5.8D. The surface of the generated 3D model was rough due to the low 150 resolution across the slices and error in segmentation to exact liver boundary. A mesh smoothing operation was performed to obtain a smooth surface 3D model as shown in Figure 5.8E. The generated model also included aorta in the structure which was removed manually to obtain final structure of the liver model (Figure 5.8F). Smoothing operation resulted in slight deformation of the 3D model and introduced a 6% volume shrinkage as compared to the 3D model shown in Figure 5.8D. The smoothed 3D model was again sliced to obtain the cross-section images with same slice spacing as the MRI dataset. Figure 5.8B shows a representative slice image. The reconstruction error introduced in each slice due to various smoothing operations was also computed by comparing the slice images for 3D models before (Figure 5.8D) and after smoothing (Figure 5.8E) and is shown in Figure 5.8G. In most slices, the cross-section area reconstruction error was 10% and below, however, initial slices suffered larger deformation. 151 Figure 5.8. MR image processing and 3D model generation. (A) Representative T3 mode slice image. (B) Slice image representing the cross-section of smoothed 3D liver model. (C) MR image segmented by applying cross-section slice image (B) as binary mask. (D) Rough 3D liver model generated from filtered point cloud. (E) 3D liver model after applying smoothing operations. (F) Smoothed 3D liver model after removing aorta. (G) Reconstruction error due to smoothing represented as error in slice area. 152 The slice images from smooth 3D model without aorta were further used to segment T1-T4 mode images from the MRI dataset as shown in Figure 5.8C. These images contained the intensity variation which was further used to estimate tissue properties. A 3D volume plot showing the intensity variation in the liver volume corresponding to different MRI modes (T1-T4) is shown in Figure 5.9A-D. The maximum (red) and minimum (blue) intensity values corresponded to 0 and 255 respectively. Next, histogram for these point clouds were generated representing the faction of voxels at different intensities (Figure 5.9E). The T3 and T4 mode images with contrast agent provided a nearly overlapping intensity distribution, however, they have subtle differences. This can also be seen when comparing the T3 and T4 slice images (Figure 5.2) where the intensity of the liver tissue is similar, however, a clear difference in the intensity of the vasculature can be observed. The T1 and T2 modes show a clearly distinguishable intensity distribution. The narrow distribution of all the modes indicated relatively uniform tissue characteristics in the liver except minor variations. It should be noted that some tissues/organs have highly heterogenous architecture and tissue properties and need to be approached with a different strategy. The T2 mode point cloud data was used to compute porosity equivalent parameter 𝜂𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦 as well as to identify the initial source points for venation algorithm. 153 Figure 5.9. Volume intensity plots showing the intensity variations in (A) T1-weighted, (B) T2-weighted, (C) T1-weighted with contrast agent and (D) T2-weighted with contrast agent datasets. (E) Histogram representation of voxel distribution at various intensity. 154 5.3.2 Vasculature generation Since the liver vasculature falls into the class of open vasculature, two separate networks were needed to represent the vasculature. Using the T3 volume data, two distinct set of source points were obtained for generating the artery and vein networks respectively. The initiation points for artery and vein networks were also defined separately. Figure 5.10A shows the initial set of source points for the vein network generation and lower half of the liver body for reference. Figure 5.10B shows a partially formed vein network following the steps described in Section 5.2.2. Once the artery and vein networks were generated, they were processed to remove very short branches and apply thickness. Final networks were converted to 3D mesh objects and are collectively shown in Figure 5.10C. The arterial network is shown in red color and the venous network is shown in blue color. Figure 5.10D shows a rendered image of the liver body combined with the vascular networks. 155 Figure 5.10. Vascular network generation using venation algorithm. (A) 3D spatial distribution of source points for vein network generation. (B) Partially formed vein network. (D) 3D models of combined artery (red) and vein (blue) networks. (D) 3D rendering of the generated vascular networks enclosed in the liver body. 5.3.3 Growth media transport The 3D models of the liver, artery and vein network were imported in COMSOL and the diffusive mass transport was simulated using the methodology described in the Section 5.2.3. Figure 5.11A-B show the isosurface plots of growth media concentration within the liver body at 30 and 60 minutes respectively. In this study, a lower value of the growth media diffusivity (𝐷 =1.9 × 10−9 𝑚2/𝑠) was selected for applying a more stringent criterion. However, the diffusivity 156 of various components in the growth medium vary and are higher than the selected value. For future analysis, the concentration distribution of various components of the growth media will vary throughout the liver volume and the proportionality between the different components will also be altered. In such case, either the growth media components could be considered separately or the lowest diffusivity value among all the components should be considered. The concentration distribution at 60 minutes was selected in form of the volume coordinates and corresponding concentration magnitude. A 10% concentration threshold was applied to obtain the point cloud representing scarce regions in the liver body as shown in Figure 5.11C. Next, a point clustering operation was applied on the scarce regions to generate an updated list of source points for the second iteration of vasculature generation. The complete set of source points (including artery and vein) are shown in Figure 5.11D in which old source points are marked in green and new source points are marked in red. The second iteration of the venation algorithm generated a denser network which, in turn, led to larger nutrient coverage in the liver body (Figure 5.12A-B). Similar to the first iteration, the scarce regions were obtained from the nutrient concentration distribution (Figure 5.12C). The new set of source points (old – green; new – red) were generated from the obtained scarce regions for further iterations (Figure 5.12D). 157 Figure 5.11. Cell growth media distribution in liver by diffusive mass transport simulation. Concentration isosurface plots in the liver body at (A) 30 and (B) 60 minutes due to the growth media transport from vasculature obtained in first iteration. (C) Point cloud representation of nutrient scarce region. (D) Distribution of source points for iteration 2 of venation (old points – green; new points – red). 158 Figure 5.12. Cell growth media distribution in liver by diffusive mass transport simulation. Concentration isosurface plots in the liver body at (A) 30 and (B) 60 minutes due to the growth media transport from vasculature obtained in second iteration. (C) Point cloud representation of nutrient scarce region. (D) Distribution of source points for iteration 2 of venation (old points – green; new points – red). The vascular networks generated from the three iterations are shown in Figure 5.13. The perspective views of the network 3D models are shown in Figures 5.13A(i), B(i) and C(i), top views are shown in Figures 5.13A(ii), B(ii) and C(ii) and the rendered 3D models are shown in Figures 5.13A(iii), B(iii) and C(iii) respectively. The volume coverage of the cell growth media within the liver with time is also shown in Figure 5.14 for iterations 1 and 2 by applying 5% and 159 10% threshold criteria. A maximum volume coverage of 20% was achieved by the vascular network formed after the second iteration. Typically, 4-5 iterations are expected to achieve a volume coverage greater than 80%. Further, the model lacks in capturing the consumption of growth media and waste generation by the cells. This could be introduced by considering a uniform distribution of the cells throughout the scaffold and assigning a uniform consumption rate. Moreover, the growth of cells and migration using agent-based models[169],[170] can also be implemented for a more realistic perspective on maturation of the fabricated vascularized tissues. 160 Figure 5.13. Generated liver vascular networks from venation algorithm and guided by MR images. (A) Vasculature generated in iteration 1 – (i) perspective view, (ii) top view and (iii) 3D rendering image with liver body. (B) Vasculature generated in iteration 2 – (i) perspective view, (ii) top view and (iii) 3D rendering image with liver body. (C) Vasculature generated in iteration 3 – (i) perspective view, (ii) top view and (iii) 3D rendering image with liver body. 161 Figure 5.14. Time series plot of volume covered with growth media concentration higher that 5% and 10% threshold values with vasculature from iteration 1 and 2. 5.4 Summary In this chapter, a closed-loop method of vasculature design is presented. Liver was selected as the target organ due to being a solid organ with high metabolic activity of the constituent cells and therefore, requirement of a highly complex vasculature. First, a methodology to process MR images was presented. The MR images were segmented to form point clouds representing the liver volume. The liver 3D model was created from these point clouds by applying various refining and smoothing operations. The generated liver model was again coupled with the MR images to derive volumetric tissue specifications. These derived properties were used with a venation algorithm to guide the vascular network formation. Several modifications in the venation were made to arrive at bioinspired vasculature design. The resulting vasculature from the first iteration was quite simplified and could not achieve nutrient transport to the entire liver volume. The outcome of the diffusive mass transport simulation was then used to identify the nutrient deficit regions which was used as feedback for the vasculature generation. Through the iterations, an increased complexity in the vascular network was observed along with increased nutrient covered liver 162 volume. The framework presented in this chapter also presents a potential tool for predicting the cell growth within the scaffold volume. The volumetric distribution of chemokines can also be predicted and controlled in the scaffold to guide cell growth. Moreover, if coupled with the predictive hydrogel formation model from previous chapter, this framework can be used to customize the scaffolds to guide the maturation of fabricated tissue over time. 163 Chapter 6 Conclusion 6.1 Concluding Remarks Bioprinting aims to replicate the natural tissues in human body and then engineer them to function better. The natural tissues and organs have evolved over time to achieve a complex architecture and comprise of several cell types. The complex architecture and underlying inhomogeneity provide niche for different cell types to grow and function properly. It is the interplay of the structural complexity and the heterogenous tissue physical properties that allows the tissues to achieve their function. Therefore, to mimic these tissues artificially, it is crucial that these heterogeneities be replicated. The artificial tissues which can mimic the function of the natural tissues offer excellent opportunity to repair the damaged tissues in the body. Moreover, these are crucial tools to understand how the cells interact with their surroundings and what happens when a foreign organism or substance is introduced in their microenvironment. The bioprinting methods have evolved to achieve bioprinting of complex tissue architecture. For example, popular extrusion bioprinting have now evolved to gel embedded extrusion processes capable of printing freestanding structures.[20],[96] The SLA bioprinting has evolved to a single step volumetric bioprinting capable of fabricating entire scaffolds within minutes.[171],[172] However, the performance of the bioprinting methods have limitations imposed by the bioinks. Earlier works were heavily dependent on use of synthetic polymers for scaffold fabrication like – PCL, PEGDA, etc.[42],[173] The synthetic polymers provided good strength and 164 stretching capability, however, lacked biocompatibility. The natural biopolymers like silk fibroin, GelMA, alginate, etc. have become popular since and are extensively explored to create bioactive scaffolds.[68],[115],[174]–[176] In order to achieve heterogenous scaffolds, these biomaterials need to be well-understood and tunable. Beyond heterogenous architecture, one of the prominent challenges in bioprinting nowadays is the vascularization of the scaffolds. Recent advancement towards the gel embedded extrusion bioprinting and SLA bioprinting have opened promising pathways to fabricate vascular scaffolds.[96],[177] However, the current studies are still limited to simple vascular networks. This is due to the bioprinting resolution limitations and the creation of vascular network models from medical images. The vascular networks derived from medical images are highly complex and noisy and cannot be easily fabricated.[13] Some studies have also presented mathematical models for designing the vascular networks which are guided by the tissue shape.[178],[179] Towards bioprinting functional complex tissues, a feasible and optimized design of vasculature is needed to ensure survival of cells encapsulated within thick tissues. In this thesis, the scaffold heterogeneity and vasculature design are explored. First, GelMA synthesis from gelatin is studied in depth owing to high biocompatibility and availability of gelatin. Three synthesis parameters – solvent, pH and reaction duration were studied and their contribution to the resulting GelMA bioink was characterized. As a result, eight different GelMA compositions were achieved which could maintain liquid state at room temperature for easier material handling. The bioink viscosity and photopolymerization kinetics were also tuned by combination of the synthesis parameters and the hydrogel formed exhibited variations in strength and internal pore structure. Later experiments also revealed high-resolution bioprinting capability and high biocompatibility of the GelMA bioinks. Through the combination of reaction conditions, an 165 optimized bioink was obtained which provided favorable microenvironment for a long duration (~1 month) for the cells to grow. This was the first step in improving the sensitivity of the GelMA bioink to the visible light so that the resulting hydrogels can be functionally graded by non-contact manipulation by light. In the next step, the optimized bioink was coupled with BAP photoinitiator. This resulted in further increasing the photopolymerization speed by making the bioink more sensitive towards visible light. As a result, the bioprinting time was reduced while the lower concentration of GelMA could be printed into stable patterns. This again serves to expand the window for manipulating hydrogel strength and porosity as softer hydrogels could be cast into the desired structure as well as stiffer hydrogels could be made. The biocompatibility of these bioinks was retained but long-term effects need to be characterized. Interestingly, the addition of BAP also introduced pore reorganization in the crosslinked hydrogels over time as they were kept hydrated. This was explored to create a shape morphing scaffold with limited success. After optimizing the macromer and photoinitiator components, the polymerization process to form the hydrogels was focused on. A detailed numerical model was developed to capture the complexity of the photopolymerization. Interestingly, the photopolymerization kinetics obtained from the numerical model captured acceleration and deceleration regimes which were observed in the in-situ UV-Vis spectroscopic characterization of the photopolymerization. Simulating variations in the photo-pattern configuration revealed possible imperfections in the printed hydrogel patterns but, at the same time, it also indicated the remediation techniques to improve the bioprinting fidelity. Overall, the developed numerical model combined with the optimized GelMA and BAP photoinitiator provides a tool to predict the scaffold properties and tune them for fabricating heterogenous scaffolds. 166 Next, the challenge of tissue vasculature was addressed using a venation algorithm. A methodology for MR image processing to reconstruct liver 3D model and evaluate equivalent tissue porosity parameter was developed to extract tissue specific data for guiding the vasculature design. The vasculature generated from venation algorithm was constrained to adhere to bioprinting capability limits as well as provide nutrient transport to the entire liver scaffold. These constraints drove the vasculature optimization and increased vasculature complexity was obtained with each iteration of optimization. The vasculature was also adaptive in nature and capable of addressing tissue specific requirements of open-type or closed-type vascular networks. The two frameworks developed in this work can be combined to design a wide range of tissue/organ scaffold by drawing inspiration from the natural tissues. The heterogeneity can be introduced guided by the cell responses observed in contemporary studies to engineer the growth of cells within these scaffolds. The developed bioinks and their design frameworks extend to guiding the stem cells behavior and differentiation. 6.2 Significance of contributions Bioprinting tissue mimics is not only directed towards in vivo tissue repair but is also crucial for developing drug screening platforms and studying the diseases in detail. Design of tissue scaffolds synergistic to the bioprinting method is crucial in recreating the natural tissue microenvironment for the cells. Contemporary methods lack bioprinting process specific optimized bioinks and prediction of how these bioinks transform. The vasculature is often designed to be highly simplified and distant from natural vasculature. These issues are addressed in this research by presenting an optimized photocrosslinkable bioink with numerical models to design 167 heterogenous scaffolds with bioinspired vasculature. The contributions of this thesis to research and the novelty of the presented research work are as follows – 1. Compared to previous works on GelMA preparation, a detailed description of the mechanism behind glycidyl methacrylate (GMA) substitution on gelatin is presented. This also elucidates the GMA substitution on other biomolecules. 2. A highly simplified and inexpensive GelMA synthesis protocol is developed with consistent end product and minimal batch-to-batch variation. 3. The RO-GelMA developed offer exceptional photopolymerization capability with visible light polymerization as compared to previous studies. 4. This is a first instance of use of BAP with EY and GelMA in a regenerative visible light photopolymerization for bioprinting. 5. The hydrogels formed using BAP photoinitiator exhibit shape morphing potential for 4D bioprinting. 6. This work introduces two simplified and inexpensive qualitative photopolymerization kinetics characterization methods by using a mechanical testing apparatus and a UV-Vis spectroscope. 7. The numerical model developed for photopolymerization captures the complexity of the Type-II photoinitiator system with regeneration which was not present in previous models. 8. The model is also able to predict crosslinked hydrogel width with higher accuracy which the previous models failed to predict. 9. The presented vasculature design methodology is a first instance of a closed-loop venation method which is guided by MR images and nutrient mass transport. 168 10. The improved venation algorithm is able to quickly form closed vascular networks in 3D which was regarded as an open problem in previous venation model. 11. The developed photopolymerization and vasculature generation models are scalable to large as well as small scaffolds and can be modified to include other biomimetic guidance. 6.3 Future works The bioinks and the corresponding predictive models developed in this work can be used in planning the SLA bioprinting process and designing the scaffolds. The vascularization design adds by improving the scaffold design to introduce embedded vascular network for sustaining the scaffold. Overall, these components provide an initial proof of concept of bioprinting design and can be improved further. One of the limitations of the developed GelMA bioink is its generic nature. The capability of these bioinks for tissue specific bioprinting can be improved by functionalizing the macromers with bioactive molecules. The described GMA grafting mechanism can be applied to graft biomolecules on GelMA to interact with the encapsulated cells. The BAP photoinitiator has shown promising results but this work can be extended to examine long term effect on the cells. The hydrogel deformation due to BAP needs to be explored further towards fabricating shape morphing scaffolds which can potentially be used to stimulate the cells. The extremities of the physical strength attainable by hydrogels using BAP should be determined to identify the tissue types which can be fabricated. Although the numerical model for mask photopolymerization is highly complex and detailed, it is still based on the approximated rate kinetics from various sources. Improvements are 169 required in identifying more accurate rate constants. The EY generative cycle is elusive and the intermediate steps are quite challenging to measure. In future these could be explored in detail. Moreover, the presented model needs to be experimentally validated to fine tune it. The model can also be extended to predict the feasible photo-pattern and illumination settings given the hydrogel structure. Finally, this model should be modified to predict the quantitative strength of the hydrogels formed. The data generated from numerical model and photopolymerization experiments can be further combined for use in machine learning for adaptive tuning of the numerical model to predict the hydrogel properties with higher accuracy. The vasculature model is successful in generative complex networks but can be improved to design more realistic and biomimetic networks. As mentioned earlier, individual components of the cell growth media can be examined separately along with modeling consumption by the cells. The model can also be extended to account for cell growth and scaffold degradation. These improvements will allow prediction of cell-scaffold interaction and scaffold maturation over time. Machine learning can also play an important role in this application. The predicted hydrogel characteristics can be combined with cell growth patterns (both observed and simulated) to include remodeling of the scaffold by cells and degradation. 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Lorente, S., Hautefeuille, M. & Sanchez-Cedillo, A. The liver, a functionalized vascular structure. Sci. Rep. 10, 1–10 (2020). 183 Appendices A1. Quantification of printing capability Printing tests were performed in Chapter 2 to assess the printability of various GelMA types synthesized under different reaction conditions. A small mesh pattern was fabricated with branch width in microscale to compare various GelMA types as shown in Figure 2.9. Further, in Chapter 3, the influence of BAP concentration on the printability of RO-GelMA was examined by fabricating a dendritic root pattern (Figure 3.8B). Sharpness of the printed patterns is utilized as the criteria for determining the conditions for best resolution among the respective GelMA or photoinitiator groups. The sharpness factor (𝑆) is defined as the root mean square of the gradient magnitude (Eq. A1.1) 𝑆 = √(|∇𝐼|2𝑁𝑝𝑖𝑥𝑒𝑙𝑠) (A1.1) where |∇𝐼| and 𝑁𝑝𝑖𝑥𝑒𝑙𝑠 represent the magnitude of the local intensity gradient and the number of pixels in the image of the fabricated hydrogel pattern, respectively. Calculated sharpness factor is shown in Figure A1(A-B) for comparing printability of different GelMA types and different BAP concentration with RO-GelMA, respectively. Higher values of sharpness factor imply hydrogel patterns with well-resolved clearly distinguishable features. Highest values were obtained for RO-GelMA when comparing different types of GelMA which was also determined with visual examination in Chapter 2. Comparison of different BAP concentrations in photoinitiator with RO-GelMA revealed that composition with 0.02 mM BAP achieved best printed hydrogel structure with 1 minute and 2 minute exposure. Higher BAP concentrations resulted in overcrosslinked patterns. With shorted exposure time, compositions with higher BAP concentration are expected to achieve better printing results with higher sharpness factor values. 184 Figure A1. Quantitative comparison of printability. (A) Sharpness factor calculated from images of the mesh pattern fabricated by different types of hydrogels. (B) Sharpness factor comparison of hydrogel patterns fabricated with different BAP concentrations. Data points corresponding to highest sharpness factors are marked with green box indicating best printability among the respective experiment groups. A2. Comparison of the proposed masked photopolymerization model The masked photopolymerization model described in Chapter 4 simulates the hydrogel formation with patterned exposure in a GelMA bioink with EosinY, TEOA and BAP photoinitiator. A preliminary validation of this model is presented here to compare against a similar monomer (PEGDA) and photoinitiator system (EosinY, TEOA and VP) reported earlier.[144] The proposed numerical model does not include VP in the photoinitiator. A bulk photopolymerization simulation was performed with the same initial concentrations of PEGDA, EosinY, TEOA and Oxygen as reported earlier along with same illumination intensity. [144] It was reported that the EosinY 185 concentrations used in the earlier model were 6-7 times lower than the concentrations required to match the double-bond conversion measured experimentally, therefore, simulations were performed with the presented model using 5 times and 10 times excess concentration of EosinY. Figure A2 shows the comparison of the predicted double-bond conversion (degree of conversion) by the model proposed in Chapter 4 with the experimentally measured double-bond conversion reported earlier.[144] The present model was able to closely predict the double-bond conversion at higher concentrations of EosinY with slight underestimation due to absence of VP in the reaction kinetic equations. Figure A2. Comparison of the masked photopolymerization model with the bulk photopolymerization model reported earlier.[144] (A) Experimentally measured double bond conversion (Adapted with permission from [144]). Predicted degree of conversion using the masked photopolymerization model proposed in the current work using (B) 5x EosinY concentration and (C) 10x EosinY concentration (simulated for 500s), respectively. 186 A3. Comparison of contrast of GelMA bioinks with BAP As described in Chapter 3 and 4, introduction of BAP in the photoinitiator increased the sensitivity the photocrosslinking rate of the GelMA bioinks. A comparison of the contrast of the GelMA bioinks with different BAP concentration is presented in this section. The masked photopolymerization numerical model presented in Chapter 4 was used to simulate bulk-photopolymerization in a 0.25 mm thick bioink layer. Auxiliary sweep of exposure intensity and BAP concentration were performed ranging from 10 𝑚𝑊/𝑐𝑚2 to 30 𝑚𝑊/𝑐𝑚2 for intensity and 0 mM to 0.1 mM or BAP. All the comparisons were made for hydrogel profiles obtained with exposure time of 30s. Figure A3 shows the variation of uncured hydrogel layer thickness with logarithmic exposure intensity. The slop of the trendlines represent the numerical value of the contrast for various bioinks. As observed in previous photocrosslinking and printing experiments presented in Chapter 3, higher BAP concentration increased the sensitivity and photocrosslinking kinetics of the GelMA bioinks which allow rapid fabrication of clear and distinguishable hydrogel features. The contrast increased steadily as BAP concentration increased from 0 mM to 0.05 mM. However, there was less increase in the contrast between bioinks with 0.05 mM and 0.1 mM BAP concentrations. This also reinforces the observed saturation in the photocrosslinking speed as shown in Figure 3.3B. 187 Figure A3. Comparison of contrast of GelMA bioinks with different BAP concentrations estimated from the masked photopolymerization numerical model. 188 Figure A4. 3D rendered model of the stereolithography system used in Chapter 2 and 3 (Adapted with permission from [44]). The projection illumination and bioink photocrosslinking factors which influence the photopolymerization process and hydrogel formation are shown along with their origin. These factors have been included in the masked photopolymerization numerical model presented in Chapter 4.
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Kim, Keekyoung
Golovin, Kevin
Light-based bioprinting for fabricating vascularized tissues
Text