A High Throughput, Human, In Vitro Model of Neuronal Stretch Injury

目前已有100多项临床试验用于治疗创伤性脑损伤。不过，这个条件， 它是美国年轻人最常见的杀手，目前还没有得到证实的治疗方法。TBI是 很难治疗，因为它是不同的：每个患者都有不同的病理组合和 遗传优势和劣势的不同组合。可能没有一种药物可以治疗所有的脑损伤 所有患者的病理检查。然而，有可能开发一套治疗重要疾病的方法 分离研究脑损伤的病理机制。本着同样的精神，如果是这样的话，治疗可能会更有效 为常见的、有影响力的基因类型量身定做。在患者亚群中靶向TBI病理亚群可能 实现对看似不可能一下子解决的问题的逐项解决。然而，它需要 一种新型的临床前模式。在这项建议中，神经元牵张损伤(NSI)与其他脑损伤是分开的 并首次应用于人类诱导多能干细胞衍生神经元(HiPSCN)。 HiPSCN可以被设计成包含特定患者的基因组，或者通过一个单独的 遗传变异(这些被称为等基因细胞系)。同质的人类神经元可以在 大量的细胞，使这些细胞成为高通量药物发现的理想选择。该模型应用了一种生物保真 第一次以96井的形式进行拉伸侮辱。然而，直到非常高的速度才能进行筛选 达到了一致性水平，并验证了检测治疗益处的能力。高 吞吐量筛查进行多次比较，重复次数很少，因此它们需要极其严格的分析。 标准差应该比阳性对照和阴性对照之间的差值小6倍 (对应于z&gt；0，其中z是现场的标准验证参数)。长期目标是发现 NSI的新治疗方法，并了解驱动病理的特定患者的细胞自主因素。整体而言 此应用程序的目标是将我们现有的NSI模型发展为 严格的高通量药物筛选试验。我们的中心假设是，在优化后的 模特。HPSCN将被培养在硅胶膜上，并用定制的设备拉伸以诱导 NSI。NSI病理将通过荧光定量分析细胞活性和形态来衡量 显微图像。模型的优化分为3个步骤：机械损伤优化、优化 了解损伤表型，优化阳性对照化合物的治疗效果。其基本原理是 为这项工作建立一个平台，为未来发现新的NSI疗法的实验，测量 遗传变异的影响和解决原位条件的其他方面(例如，氧气和葡萄糖 剥夺、星形胶质细胞激活、炎性细胞因子等)。这项工作将使第一个高吞吐量 进行NSI治疗的筛查。它还将使神经创伤的第一个等基因实验成为可能。这些工具保存着 承诺逐步取得临床成功，以取代临床完全失败的现状。

More than 100 clinical trials have been conducted in traumatic brain injury (TBI). Nevertheless, this condition, which is the most common killer of young people in the United States, remains without a proven therapy. TBI is hard to treat because it is heterogeneous: every patient has a different combination of pathologies and a different combination of genetic strengths and weaknesses. There may not be a single drug that treats all TBI pathologies in all patients. However, it may be possible to develop a suite of treatments for important pathologies of TBI by studying each in isolation. In the same spirit, treatment may be more effective if it is tailored to common, influential genotypes. Targeting subsets of TBI pathology in subsets of patients may enable piece-wise solution of a problem that seems impossible to solve at a single stroke. However, it requires a new type of pre-clinical model. In this proposal, neuronal stretch injury (NSI) is isolated from other TBI pathologies and applied to human induced pluripotent stem cell-derived neurons (hiPSCNs) for the first time. hiPSCNs can be engineered to contain the genomes of specific patients, or to differ from controls by a single genetic variant (these are known as isogenic cell lines). Homogeneous human neurons can be generated in large numbers, making these cells ideal for high throughput drug discovery. The model applies a biofidelic stretch insult in a 96 well format for the first time. However, a screen cannot be conducted until a very high level of consistency has been achieved and the capacity to detect therapeutic benefit has been verified. High throughput screens make many comparisons with few replicates so they require an extremely rigorous assay. Standard deviations should be 6 times smaller than the difference between positive and negative controls (corresponds to z>0 where z is the standard validation parameter in the field). The long term goal is to discover new treatments for NSI and understand patient-specific, cell autonomous factors driving pathology. The overall objective of this application, which is a vital step towards this goal, is to develop our existing NSI model into a rigorous high throughput drug screening assay. Our central hypothesis is that z will be >0 in the optimized model. hiPSCNs will be cultured on silicone membranes and stretched with a custom-built device to induce NSI. NSI pathology will be measured by quantitative analysis of cell viability and morphology in fluorescent microscopic images. The model will be optimized in 3 steps: optimization of the mechanical insult, optimization of the injury phenotype and optimization of the therapeutic effect of positive control compounds. The rationale for this work is to build a platform for future experiments that discover novel NSI therapies, measure the influence of genetic variants and address other aspects of the in situ condition (e.g. oxygen glucose deprivation, astrocyte activation, inflammatory cytokines etc.). This work will enable the first high throughput screen for an NSI therapy. It will also enable the first isogenic experiment in neurotrauma. These tools hold the promise of incremental clinical success in place of the status quo of total clinical failure.