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• W1917995433 abstract "The ability to correlate single-cell genetic information with cellular phenotypes is of great importance to biology and medicine, as it holds the potential to gain insight into disease pathways that is unavailable from ensemble measurements. We present a microfluidic approach to parallelized, rapid, quantitative analysis of messenger RNA from single cells via RT-qPCR. The approach leverages an array of single-cell RT-qPCR analysis units formed by a set of parallel microchannels concurrently controlled by elastomeric pneumatic valves, thereby enabling parallelized handling and processing of single cells in a drastically simplified operation procedure using a relatively small number of microvalves. All steps for single-cell RT-qPCR, including cell isolation and immobilization, cell lysis, mRNA purification, reverse transcription and qPCR, are integrated on a single chip, eliminating the need for off-chip manual cell and reagent transfer and qPCR amplification as commonly used in existing approaches. Additionally, the approach incorporates optically transparent microfluidic components to allow monitoring of single-cell trapping without the need for molecular labeling that can potentially alter the targeted gene expression and utilizes a polycarbonate film as a barrier against evaporation to minimize the loss of reagents at elevated temperatures during the analysis. We demonstrate the utility of the approach by the transcriptional profiling for the induction of the cyclin-dependent kinase inhibitor 1a and the glyceraldehyde 3-phosphate dehydrogenase in single cells from the MCF-7 breast cancer cell line. Furthermore, the methyl methanesulfonate is employed to allow measurement of the expression of the genes in individual cells responding to a genotoxic stress." @default.
• W1917995433 created "2016-06-24" @default.
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• W1917995433 date "2015-10-14" @default.
• W1917995433 modified "2023-09-24" @default.
• W1917995433 title "A microfluidic approach to parallelized transcriptional profiling of single cells" @default.
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• W1917995433 doi "https://doi.org/10.1007/s10404-015-1657-2" @default.
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