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Expression analysis of mRNAs transcribed from certain genes can be used as important sources of biomarkers for in vitro diagnostics. While the use of reverse transcription quantitative PCR (RT-qPCR) can provide excellent analytical sensitivity for monitoring transcript numbers, more sensitive approaches for expression analysis that can report results in near real-time are needed for many critical applications. We report a novel assay that can provide exquisite limits-of-quantitation and consists of reverse transcription (RT) followed by a ligase detection reaction (LDR) with single-pair fluorescence resonance energy transfer (spFRET) to provide digital readout through molecular counting. For this assay, no PCR was employed, which enabled short assay turnaround times. To facilitate implementation of the assay, a cyclic olefin copolymer (COC) microchip, which was fabricated using hot embossing, was employed to carry out the LDR in a continuous flow format with online single-molecule detection following the LDR. As demonstrators of the assay's utility, MMP-7 mRNA was expression profiled from several colorectal cancer cell lines. It was found that the RT-LDR/spFRET assay produced highly linear calibration plots even in the low copy number regime. Comparison to RT-qPCR indicated a better linearity over the low copy number range investigated (10-10,000 copies) with an R(2) = 0.9995 for RT-LDR/spFRET and R(2) = 0.98 for RT-qPCR. In addition, differentiating between copy numbers of 10 and 50 could be performed with higher confidence using RT-LDR/spFRET. To demonstrate the short assay turnaround times obtainable using the RT-LDR/spFRET assay, a two thermal cycle LDR was carried out on amphiphysin gene transcripts that can serve as important diagnostic markers for ischemic stroke. The ability to supply diagnostic information on possible stroke events in short turnaround times using RT-LDR/spFRET will enable clinicians to treat patients effectively with appropriate time-sensitive therapeutics.

Single-Pair Fluorescence Resonance Energy Transfer Analysis of mRNA Transcripts for Highly Sensitive Gene Expression Profiling in Near Real Time