Using Statistical Modeling to Understand and Predict Pediatric Stem Cell Function.
Using Statistical Modeling to Understand and Predict Pediatric Stem Cell Function.
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DOI：
10.1161/circgen.118.002403
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发表时间：
2019-05
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通讯作者：
Farnaz Shoja-Taheri;A. George;Udit Agarwal;M. Platt;G. Gibson;Michael E. Davis
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作者：
Farnaz Shoja-Taheri;A. George;Udit Agarwal;M. Platt;G. Gibson;Michael E. Davis
背景先天性心脏缺陷是儿童发病率和死亡率的主要原因，尽管有先进的外科治疗，许多患者仍进展为心力衰竭。目前，移植是唯一有效的治疗方法，并受到供体可用性和器官排斥的限制。最近，细胞疗法已成为治疗小儿心力衰竭的一种新方法，有几项正在进行的临床试验。然而，干细胞治疗的功效是可变的，选择具有最高修复效果的干细胞一直是一个挑战。方法：我们先前证明了人c-kit+祖细胞（hCPCs）在幼年心力衰竭大鼠模型中的年龄依赖性修复作用。使用患者样本的一小部分，计算建模分析显示，可以建立将测序数据与表型结果联系起来的回归模型。在目前的研究中，我们使用了类似的定量模型来确定是否可以在更大的患者人群中进行预测，并使用新生儿hCPC验证了该模型。我们对从32例患者（包括8例新生儿样本）分离的c-kit+祖细胞进行了RNA测序。我们测试了我们的模型的2个功能参数，细胞增殖和条件培养基的趋化潜力。结果有趣的是，在每个选定的新生hCPC系中观察到的增殖和迁移反应与其预测的对应物相匹配。然后，我们进行了典型的途径分析，以确定潜在的机制信号，调节hCPC的性能，并确定了几个免疫应答基因与性能。ELISA分析证实了在性能良好的hCPC中存在选择的细胞因子，并提供了更多的信号以进一步验证。这些数据表明，可以使用RNA测序等大型数据集来预测细胞行为，并且我们可能能够识别c-kit+祖细胞超过或低于预期的患者。通过系统生物学方法，可以定制干预措施以改善细胞治疗或模拟修复细胞的质量。
BACKGROUND Congenital heart defects are a leading cause of morbidity and mortality in children, and despite advanced surgical treatments, many patients progress to heart failure. Currently, transplantation is the only effective cure and is limited by donor availability and organ rejection. Recently, cell therapy has emerged as a novel method for treating pediatric heart failure with several ongoing clinical trials. However, efficacy of stem cell therapy is variable, and choosing stem cells with the highest reparative effects has been a challenge. METHODS We previously demonstrated the age-dependent reparative effects of human c-kit+ progenitor cells (hCPCs) in a rat model of juvenile heart failure. Using a small subset of patient samples, computational modeling analysis showed that regression models could be made linking sequencing data to phenotypic outcomes. In the current study, we used a similar quantitative model to determine whether predictions can be made in a larger population of patients and validated the model using neonatal hCPCs. We performed RNA sequencing from c-kit+ progenitor cells isolated from 32 patients, including 8 neonatal samples. We tested 2 functional parameters of our model, cellular proliferation and chemotactic potential of conditioned media. RESULTS Interestingly, the observed proliferation and migration responses in each of the selected neonatal hCPC lines matched their predicted counterparts. We then performed canonical pathway analysis to determine potential mechanistic signals that regulated hCPC performance and identified several immune response genes that correlated with performance. ELISA analysis confirmed the presence of selected cytokines in good performing hCPCs and provided many more signals to further validate. CONCLUSIONS These data show that cell behavior may be predicted using large datasets like RNA sequencing and that we may be able to identify patients whose c-kit+ progenitor cells exceed or underperform expectations. With systems biology approaches, interventions can be tailored to improve cell therapy or mimic the qualities of reparative cells.