TitleHigh-throughput single-cell analysis for the proteomic dynamics study of the yeast osmotic stress response
AuthorsZhang, Rongfei
Yuan, Haiyu
Wang, Shujing
Ouyang, Qi
Chen, Yong
Hao, Nan
Luo, Chunxiong
AffiliationPeking Univ, Acad Adv Interdisciplinary Studies, Ctr Quantitat Biol, Beijing, Peoples R China.
Peking Univ, Sch Phys, State Key Lab Artificial Microstruct & Mesoscop P, Beijing, Peoples R China.
Peking Univ, Peking Tsinghua Ctr Life Sci, Beijing, Peoples R China.
Ecole Normale Super, 24 Rue Lhomond, F-75231 Paris, France.
Univ Calif San Diego, Div Biol Sci, Mol Biol Sect, La Jolla, CA 92093 USA.
Peking Univ, Acad Adv Interdisciplinary Studies, Ctr Quantitat Biol, Beijing, Peoples R China.
Luo, CX (reprint author), Peking Univ, Sch Phys, State Key Lab Artificial Microstruct & Mesoscop P, Beijing, Peoples R China.
KeywordsMAP KINASE PATHWAY
SACCHAROMYCES-CEREVISIAE
TRANSCRIPTIONAL RESPONSE
MICROFLUIDIC DEVICE
SIGNALING DYNAMICS
GENE-EXPRESSION
GLYCEROL
ADAPTATION
NUTRIENT
HOG1
Issue Date2017
PublisherSCIENTIFIC REPORTS
CitationSCIENTIFIC REPORTS.2017,7.
AbstractMotorized fluorescence microscopy combined with high-throughput microfluidic chips is a powerful method to obtain information about different biological processes in cell biology studies. Generally, to observe different strains under different environments, high-throughput microfluidic chips require complex preparatory work. In this study, we designed a novel and easily operated high-throughput microfluidic system to observe 96 different GFP-tagged yeast strains in one switchable culture condition or 24 different GFP-tagged yeast strains in four parallel switchable culture conditions. A multi-pipette is the only additional equipment required for high-throughput patterning of cells in the chip. Only eight connections are needed to control 96 conditions. Using these devices, the proteomic dynamics of the yeast stress response pathway were carefully studied based on single-cell data. A new method to characterize the proteomic dynamics using a single cell's data is proposed and compared to previous methods, and the new technique should be useful for studying underlying control networks. Our method provides an easy and systematic way to study signaling pathways at the single-cell level.
URIhttp://hdl.handle.net/20.500.11897/475191
ISSN2045-2322
DOI10.1038/srep42200
IndexedSCI(E)
Appears in Collections:前沿交叉学科研究院
物理学院
生命科学学院
人工微结构和介观物理国家重点实验室

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