Genetic circuits for high-throughput, multi-sensory, live cell microRNA prof

描述（由申请人提供）：我们提案的长期目标是开发一种新的技术平台，用于生成癌细胞的有价值的活细胞microRNA表达数据。MicroRNA是一类进化上保守的非编码RNA，它调节靶mRNA的稳定性和翻译效率，在调节发育和疾病状态中起着关键作用。虽然当代平台，如微阵列和RT-qPCR能够测量聚合物miRNA水平，但只有有限的研究涉及单细胞分布，并且没有系统创建的数据集可用于癌症研究。最重要的是，需要分布和时间序列数据来鉴定多模式miRNA，表征表达变异性，发现显著的miRNA间相关性，并能够更准确地分析和分类癌细胞类型和状态。最后，没有功能数据集存在的特点的相互作用的miRNA与遗传电路元件，将是有用的癌症诊断和基因为基础的治疗。我们提出了微流体和合成生物学的创新组合来克服这一障碍，从而产生大量的新数据集，大型生物传感器库，并最终治愈癌症。 我们将利用高通量微流控平台组装基因电路库，作为传感器来测量靶细胞系中的microRNA表达水平。这些电路将具有单个或多个microRNA的输入。我们将为大量实验验证的人类microRNA（microRNA图谱中列出的412个）组装一个单输入microRNA传感器库，并使用这些传感器测量15个目标健康和癌细胞系的表达水平。我们将使用具有多个microRNA输入的传感器来生成以前无法获得的microRNA相关数据，从而为更深入地了解癌症等疾病的重要途径的运作提供途径。与来自微阵列和类似技术的数据相比，这些表达数据集将通过单独的微流体模块从大量单个活细胞中实时实验测量。我们将使用microRNA相关数据来提高癌细胞分类器的精度。

DESCRIPTION (provided by applicant): The long-term objective of our proposal is to develop a novel technological platform for generating valuable live cell microRNA expression data for cancer cells. MicroRNAs are a class of evolutionary conserved non-coding RNAs that regulate stability and translation efficiency of target mRNAs, playing a critical role in regulating development as well as disease states. While contemporary platforms such as microarrays and RT-qPCR are capable of measuring aggregate miRNA levels, only limited research has addressed single-cell distributions and no systematically created dataset is available for cancer research. Most significantly, distributions and time-series data are required to identify multimodal miRNAs, characterize expression variability, find significant inter-miRNA correlations, and enable more accurate analysis and classification of cancer cell types and states. Finally, no functional datasets exist that characterize the interaction of miRNA with genetic circuit elements that will be useful for both cancer diagnosis and gene-based therapy. We propose an innovative combination of microfluidics and synthetic biology to overcome this hurdle, leading to massive new datasets, large libraries of biosensors, and ultimately therapeutic cancer cures. We will utilize a high throughput microfluidic platform to assemble libraries of genetic circuits that act as sensors to measure microRNA expression levels in target cell lines. These circuits will feature inputs for single or multiple microRNAs. We will assemble a library of single-input microRNA sensors for a large set of experimentally-validated human microRNAs (412, presented in the microRNA atlas) and use these sensors to measure expression levels in 15 target healthy and cancer cell lines. We will use sensors featuring multiple microRNA inputs to generate previously unavailable microRNA correlation data, thus providing an avenue for gaining deeper insight into the operations of pathways important to diseases such as cancer. These expression data sets, in contrast to data derived from microarrays and similar techniques, will be experimentally measured in real-time from large numbers of individual live cells via a separate microfluidic module. We will use the microRNA correlation data to increase the precision of cancer cell classifiers.