Usprech Jenna, Romero David A, Amon Cristina H, Simmons Craig A
Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada; Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, 661 University Ave, Toronto, Ontario M5G 1M1, Canada.
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada.
Acta Biomater. 2017 Aug;58:34-43. doi: 10.1016/j.actbio.2017.05.044. Epub 2017 May 19.
The physical and chemical properties of a biomaterial integrate with soluble cues in the cell microenvironment to direct cell fate and function. Predictable biomaterial-based control of integrated cell responses has been investigated with two-dimensional (2D) screening platforms, but integrated responses in 3D have largely not been explored systematically. To address this need, we developed a screening platform using polyethylene glycol norbornene (PEG-NB) as a model biomaterial with which the polymer wt% (to control elastic modulus) and adhesion peptide types (RGD, DGEA, YIGSR) and densities could be controlled independently and combinatorially in arrays of 3D hydrogels. We applied this platform and regression modeling to identify combinations of biomaterial and soluble biochemical (TGF-β1) factors that best promoted myofibrogenesis of human mesenchymal stromal cells (hMSCs) in order to inform our understanding of regenerative processes for heart valve tissue engineering. In contrast to 2D culture, our screens revealed that soft hydrogels (low PEG-NB wt%) best promoted spread myofibroblastic cells that expressed high levels of α-smooth muscle actin (α-SMA) and collagen type I. High concentrations of RGD enhanced α-SMA expression in the presence of TGF-β1 and cell spreading regardless of whether TGF-β1 was in the culture medium. Strikingly, combinations of peptides that maximized collagen expression depended on the presence or absence of TGF-β1, indicating that biomaterial properties can modulate MSC response to soluble signals. This combination of a 3D biomaterial array screening platform with statistical modeling is broadly applicable to systematically identify combinations of biomaterial and microenvironmental conditions that optimally guide cell responses.
We present a novel screening platform and methodology to model and identify how combinations of biomaterial and microenvironmental conditions guide cell phenotypes in 3D. Our approach to systematically identify complex relationships between microenvironmental cues and cell responses enables greater predictive power over cell fate in conditions with interacting material design factors. We demonstrate that this approach not only predicts that mesenchymal stromal cell (MSC) myofibrogenesis is promoted by soft, porous 3D biomaterials, but also generated new insights which demonstrate how biomaterial properties can differentially modulate MSC response to soluble signals. An additional benefit of the process includes utilizing both parametric and non parametric analyses which can demonstrate dominant significant trends as well as subtle interactions between biochemical and biomaterial cues.
生物材料的物理和化学性质与细胞微环境中的可溶性信号整合,以指导细胞命运和功能。基于生物材料的可预测的整合细胞反应控制已通过二维(2D)筛选平台进行了研究,但三维(3D)中的整合反应在很大程度上尚未得到系统探索。为满足这一需求,我们开发了一个筛选平台,使用聚乙二醇降冰片烯(PEG-NB)作为模型生物材料,在该平台上,可以在3D水凝胶阵列中独立且组合地控制聚合物重量百分比(以控制弹性模量)、粘附肽类型(RGD、DGEA、YIGSR)及其密度。我们应用该平台和回归模型来确定生物材料和可溶性生化(TGF-β1)因子的组合,这些组合能最佳地促进人间充质基质细胞(hMSCs)的肌成纤维细胞生成,以增进我们对心脏瓣膜组织工程再生过程的理解。与2D培养不同,我们的筛选结果显示,柔软的水凝胶(低PEG-NB重量百分比)最能促进表达高水平α-平滑肌肌动蛋白(α-SMA)和I型胶原蛋白的伸展型肌成纤维细胞。无论培养基中是否存在TGF-β1,高浓度的RGD都会在TGF-β1存在的情况下增强α-SMA表达并促进细胞铺展。引人注目的是,使胶原蛋白表达最大化的肽组合取决于TGF-β1的存在与否,这表明生物材料特性可以调节间充质基质细胞对可溶性信号的反应。这种将3D生物材料阵列筛选平台与统计建模相结合的方法广泛适用于系统地确定能最佳引导细胞反应的生物材料和微环境条件的组合。
我们提出了一种新颖的筛选平台和方法,用于模拟和确定生物材料和微环境条件的组合如何在3D中引导细胞表型。我们系统地识别微环境信号与细胞反应之间复杂关系的方法,在存在相互作用的材料设计因素的条件下,对细胞命运具有更强的预测能力。我们证明,这种方法不仅预测间充质基质细胞(MSC)的肌成纤维细胞生成是由柔软、多孔的3D生物材料促进的,还产生了新的见解,展示了生物材料特性如何不同地调节间充质基质细胞对可溶性信号的反应。该过程的另一个好处包括利用参数分析和非参数分析,这可以证明主要的显著趋势以及生化和生物材料信号之间的微妙相互作用。