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.
MicroRNAs are a class of small, non-coding RNA molecules that play an important role in regulating development and various diseases including cancer. There is an increasing need for comprehensive catalogs of microRNA expression levels, which would be valuable for almost any developmental or disease study as well as therapeutics. To obtain this data, we will use microfluidic technology to assemble libraries of novel genetic circuits featuring microRNA sensors to obtain, for the first time, experimentally derived sets of microRNA expression data from large numbers of live, single cells. These datasets will then allow us to correlate expression patterns between microRNAs in multiple cell types for applications such as classifying cancer cells.
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