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颖的技术平台，用于生成有价值的癌细胞活细胞 microRNA 表达数据。 MicroRNA是一类进化保守的非编码RNA，可调节靶mRNA的稳定性和翻译效率，在调节发育和疾病状态中发挥关键作用。虽然微阵列和 RT-qPCR 等当代平台能够测量总 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识别多模式 miRNA、表征表达变异性、发现显着的 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，尚不存在能够表征 miRNA 与遗传电路元件相互作用的功能数据集，而这些数据集可用于癌症诊断和基于基因的治疗。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，从而产生大量新数据集、大型生物传感器库，并最终实现癌症治疗。 我们将利用高通量微流体平台来组装遗传电路库，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。我们将为大量经过实验验证的人类 microRNA（412 个，在 microRNA 图集中提供）组装一个单输入 microRNA 传感器库，并使用这些传感器测量 15 个目标健康细胞系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对癌症等疾病重要的通路的运作提供途径。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进行实验测量。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.