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. 2013 Sep 24;110(39):15842-7.
doi: 10.1073/pnas.1308265110. Epub 2013 Sep 9.

A chemostat array enables the spatio-temporal analysis of the yeast proteome

Nicolas Dénervaud  1 , Johannes Becker, Ricard Delgado-Gonzalo, Pascal Damay, Arun S Rajkumar, Michael Unser, David Shore, Felix Naef, Sebastian J Maerkl
Affiliations

A chemostat array enables the spatio-temporal analysis of the yeast proteome

Nicolas Dénervaud et al. Proc Natl Acad Sci U S A. .

Abstract

Observing cellular responses to perturbations is central to generating and testing hypotheses in biology. We developed a massively parallel microchemostat array capable of growing and observing 1,152 yeast-GFP strains on the single-cell level with 20 min time resolution. We measured protein abundance and localization changes in 4,085 GFP-tagged strains in response to methyl methanesulfonate and analyzed 576 GFP strains in five additional conditions for a total of more than 10,000 unique experiments, providing a systematic view of the yeast proteome in flux. We observed that processing bodies formed rapidly and synchronously in response to UV irradiation, and in conjunction with 506 deletion-GFP strains, identified four gene disruptions leading to abnormal ribonucleotide-diphosphate reductase (Rnr4) localization. Our microchemostat platform enables the large-scale interrogation of proteomes in flux and permits the concurrent observation of protein abundance, localization, cell size, and growth parameters on the single-cell level for thousands of microbial cultures in one experiment.

Keywords: DNA damage response; cell arrays; high-content imaging; microfluidics; yeast proteomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
A massively parallel microchemostat array for culturing 1,152 yeast strains. (A) Design of the microchemostat array with flow and control layers in blue and red, respectively. (B) Detailed schematic of a pair of microchemostats. (C) Illustration of the chip programming process.
Fig. 2.
Fig. 2.
(A) Scatter plot of the median data points before MMS treatment for two repeat experiments (formula image, formula image for linear data; formula image, formula image for log-transformed data; formula image, Spearman correlation coefficient; n, number of data points). (B) Mean abundance for 2,534 strains. Abundance is normalized to the median of the pre-MMS data points. The orange line shows the global median. The red and blue lines represent a threefold increase/decrease threshold. (C) Abundance fold change vs. pre-MMS abundance. The gray scale shows the significance of the fold change for each protein. Dashed lines show the P = 0.01 significance threshold. A total of 124 proteins fell above both significance thresholds (P value <0.01 and fold change >3). (D) Protein accumulation rate plotted vs. induction time for the 124 up-regulated proteins. SDs are shown with gray lines (n varies from 1 to 16). (Inset) Correlation between protein timing and mRNA timing.
Fig. 3.
Fig. 3.
(A) Localization change for all proteins that relocated after MMS treatment. For each heatmap, proteins were ranked by their timing, as shown by the green bar. Micrographs show examples for each class. (B–D) Timing comparison of protein relocation in the various stresses. The protein labels indicate the mean time of change, and dashed circles show the error (±SD).
Fig. 4.
Fig. 4.
(A) Median of strain size and cell growth of deletion-GFP strains. (B) Punctate formation for six P-body strains in various deletion backgrounds. (C) Changes in abundance and nuclear localization of Rnr4 as a result of gene deletions.
See this image and copyright information in PMC

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