Regression modeling to inform cell incorporation into therapies for craniosynostosis.
Regression modeling to inform cell incorporation into therapies for craniosynostosis.
复制标题
DOI：
10.1097/scs.0b013e31826cfe09
复制
发表时间：
2013-01
期刊：
影响因子：
--
通讯作者：
Cooper GM
中科院分区：
文献类型：
--
作者：
Cray J Jr;Cooper GM
为颅缝早闭 (CS) 设计合适的组织工程解决方案需要确定 CS 衍生细胞是否与正常 (WT) 细胞不同，以及适合测试差异的测定方法。统计比较细胞行为的传统方法可能无法准确反映生物学相关的差异，因为它们不能很好地解决变异问题。在这里，逻辑回归用于确定哪些测定可以识别 WT 和 CS 祖细胞之间的生物学差异。对 WT 和 CS 兔的脂肪、肌肉和骨髓来源的细胞进行定量碱性磷酸酶 (ALP) 和 MTS 增殖测定。数据根据测定、细胞类型和培养天数进行分层。计算变异系数并将测定结果编码为预测变量。表型（WT 或 CS）被编码为因变量。为判别模型绘制了敏感性-特异性曲线、分类表和 ROC 曲线。使用两个数据集进行后续分析；一个用于开发逻辑回归模型进行预测，另一个独立数据集用于确定基于预测方程预测群体成员资格的能力。所有差异化测量结果的变异系数都很高。模型实施后，观察到骨髓检测对表型的预测率为 72-100%。我们发现我们的肌肉和骨髓来源的细胞存在预测差异，这表明存在生物学相关的差异。这种数据分析方法可以帮助识别病理个体和正常个体之间没有差异的同质细胞或成骨潜力不同的细胞，具体取决于正在开发的细胞疗法的类型。
Designing an appropriate tissue engineering solution for craniosynostosis (CS) necessitates determination of whether CS derived cells differ from normal (WT) cells and what assays are appropriate to test for differences. Traditional methodologies to statistically compare cellular behavior may not accurately reflect biologically relevant differences because they poorly address variation. Here, logistic regression was used to determine which assays could identify a biological difference between WT and CS progenitor cells. Quantitative alkaline phosphatase (ALP) and MTS proliferation assays were performed on adipose, muscle, and bone marrow-derived cells from WT and CS rabbits. Data was stratified by assay, cell type, and days in culture. Coefficients of Variation were calculated and assay results coded as predictive variables. Phenotype (WT or CS) was coded as the dependent variable. Sensitivity-specificity curves, classification tables, and ROC curves were plotted for discriminating models. Two data sets were utilized for subsequent analyses; one used to develop the logistic regression models for prediction, the other independent data set was used to determine the ability to predict group membership based on the predictive equation. The resulting coefficients of variation were high for all differentiation measures. Upon model implementation, bone marrow assays were observed to result in 72–100% predictability for phenotype. We found predictive differences in our muscle- and bone marrow-derived cells suggesting biologically relevant differences. This data analysis methodology could help identify homogenous cells that do not differ between pathologic and normal individuals or cells that differ in their osteogenic potential, depending on the type of cell-based therapy being developed.