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• W2018086110 abstract "Extracting single-cell information during cellular responses to external signals in a high-throughput manner is an essential step for quantitative single-cell analyses. Here, we have developed a simple yet robust microfluidic platform for measuring time-course single-cell response on a large scale. Our method combines a simple microwell-based cell docking process inside a patterned microfluidic channel, with programmable time-course live-cell imaging and software-aided fluorescent image processing. The budding yeast, Saccharomyces cerevisiae(S. cerevisiae), cells were individually captured in microwells by multiple sweeping processes, in which a cell-containing solution plug was actively migrating back and forth several times by a finger-pressure induced receding meniscus. To optimize cell docking efficiency while minimizing unnecessary flooding in subsequent steps, circular microwells of various channel dimensions (4–24 µm diameter, 8 µm depth) along with different densities of cell solution (1.5–6.0 × 109cells per mL) were tested. It was found that the microwells of 8 µm diameter and 8 µm depth allowed for an optimal docking efficiency (>90%) without notable flooding issues. For quantitative single-cell analysis, time-course (time interval 15 minute, for 2 hours) fluorescent images of the cells stimulated by mating pheromone were captured using computerized fluorescence microscope and the captured images were processed using a commercially available image processing software. Here, real-time cellular responses of the mating MAPK pathway were monitored at various concentrations (1 nM–100 µM) of mating pheromone at single-cell resolution, revealing that individual cells in the population showed non-uniform signaling response kinetics." @default.
• W2018086110 created "2016-06-24" @default.
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• W2018086110 date "2011-01-01" @default.
• W2018086110 modified "2023-10-17" @default.
• W2018086110 title "High-throughput single-cell quantification using simple microwell-based cell docking and programmable time-course live-cell imaging" @default.
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• W2018086110 doi "https://doi.org/10.1039/c0lc00114g" @default.
• W2018086110 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20957290" @default.
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