A high-throughput microfluidic real-time gene expression living cell array
King, Kevin R.; Wang, Sihong; Irimia, Daniel; Jayaraman, Arul; Toner, Mehmet; Yarmush, Martin L.; KingAuthors contributed equally. Kevin R.; Center for Engineering and Medicine and Department of Surgery, Massachusetts General Hospital; Massachusetts Institute of Technology, Division of Health Science and Technology; Shriners Hospitals for Children, and Harvard Medical School; Wang Sihong; Center for Engineering and Medicine and Department of Surgery, Massachusetts General Hospital; Shriners Hospitals for Children, and Harvard Medical School; Irimia Daniel; Center for Engineering and Medicine and Department of Surgery, Massachusetts General Hospital; Shriners Hospitals for Children, and Harvard Medical School; Jayaraman Arul; Department of Chemical Engineering, Texas A&M University; Toner Mehmet; Center for Engineering and Medicine and Department of Surgery, Massachusetts General Hospital; Massachusetts Institute of Technology, Division of Health Science and Technology; Shriners Hospitals for Children, and Harvard Medical School; Yarmush Martin L.; Center for Engineering and Medicine and Department of Surgery, Massachusetts General Hospital; Massachusetts Institute of Technology, Division of Health Science and Technology; Shriners Hospitals for Children, and Harvard Medical School
Журнал:
Lab on a Chip
Дата:
2007
Аннотация:
The dynamics of gene expression are fundamental to the coordination of cellular responses. Measurement of temporal gene expression patterns is currently limited to destructive low-throughput techniques such as northern blotting, reverse transcription polymerase chain reaction (RT-PCR), and DNA microarrays. We report a scalable experimental platform that combines microfluidic addressability with quantitative live cell imaging of fluorescent protein transcriptional reporters to achieve real-time characterization of gene expression programs in living cells. Integrated microvalve arrays control row-seeding and column-stimulation of 256 nanoliter-scale bioreactors to create a high density matrix of stimulusâ response experiments. We demonstrate the approach in the context of hepatic inflammation by acquiring â ¼5000 single-time-point measurements in each automated and unattended experiment. Experiments can be assembled in hours and perform the equivalent of months of conventional experiments. By enabling efficient investigation of dynamic gene expression programs, this technology has the potential to make significant impacts in basic science, drug development, and clinical medicine.
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