3D Cellular Morphotyping of Scaffold Niches

Stephen J. Florczyk, Mylene Simon, D. Juba, P. Pine, S. Sarkar, Desu Chen, Paula J. Baker, S. Bodhak, Antonio Cardone, M. Brady, P. Bajcsy, C. Simon
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引用次数: 1

Abstract

There is currently no method for assessing the nature of the cell niche provided by 3D biomaterial scaffolds. Analyzing human bone marrow stromal cell (hBMSC) 3D cell shape in response to different biomaterial scaffolds allowed the 3D cell niche promoted by biomaterial scaffolds to be evaluated. Primary hBMSCs (p5) were seeded (5,000 cells/cm2) in 10 different biomaterial scaffolds and cultured for 24 h. Samples were fixed and stained for actin and nucleus, imaged with confocal microscopy to obtain a 3D volume (z-stack), and 3D cell shape was analyzed with computational approaches. Over 100 cells were imaged per scaffold group (10 scaffold groups, ~1250 cells total), resulting in the largest known 3D stem cell dataset (~135,000 files, ~135 GB) and enabling a high degree of statistical rigor. The images were segmented using an automated algorithm and a final dataset of 969 well-segmented cells were analyzed with 79 shape metrics, which enabled 3D cellular morphotyping of scaffold niches. The variety of scaffolds studied promoted different cell morphologies during culture and there were significant differences in shape metrics, particularly for cell depth, surface area, and volume. This study demonstrated a quantitative approach to analyze 3D cell shape and morphotype and is the largest known study analyzing 3D cell shape in response to a variety of biomaterial scaffolds. The dataset is publically accessible with an online 3D viewer. These results could inform the selection of prospective scaffolds for applications based on 3D cell shape in the tissue of interest.
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支架壁龛的三维细胞形态分型
目前还没有方法来评估由3D生物材料支架提供的细胞生态位的性质。分析人骨髓基质细胞(hBMSC) 3D细胞形态对不同生物材料支架的响应,可以评估生物材料支架促进的3D细胞生态位。将原代hBMSCs (p5)(5000个细胞/cm2)播种于10种不同的生物材料支架中,培养24小时。将样品固定并染色肌动蛋白和细胞核,用共聚焦显微镜成像以获得3D体积(z-stack),并使用计算方法分析3D细胞形状。每个支架组超过100个细胞成像(10个支架组,总计约1250个细胞),从而形成已知最大的3D干细胞数据集(约135,000个文件,约135gb),并实现了高度的统计严谨性。使用自动算法对图像进行分割,并使用79个形状指标分析969个分割良好的细胞的最终数据集,从而实现支架龛的3D细胞形态分型。在培养过程中,所研究的支架的多样性促进了不同的细胞形态,并且在形状指标上存在显著差异,特别是在细胞深度、表面积和体积上。该研究展示了一种定量分析3D细胞形状和形态的方法,是目前已知的最大的分析3D细胞形状对各种生物材料支架响应的研究。该数据集可通过在线3D查看器公开访问。这些结果可以为基于感兴趣组织中的3D细胞形状的应用选择前瞻性支架提供信息。
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