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Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise


A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.

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Figure 1: Quantitative analyses of protein abundance using flow cytometry.
Figure 2: Single-cell variation, gating and global trends in noise.
Figure 3: Biological structure of protein variation.
Figure 4: Overview of major factors contributing to biological noise.

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The authors acknowledge M. Bigos, P. Dezain and S. Elmes for their help with cytometry; K. Uffenheimer and A. Carroll (A.C.) for assistance with automation; W. Wickner for an anti-GFP antibody; R. Tsien for a construct encoding tdTomato; A.C., W.-K. Huh, M. Jonikas, V. Zapeda and E. Griffis for experimental assistance; A. H. DePace for graphical assistance; S. Collins, V. Denic, H. El Samad, V. L. Newman, E. K. O'Shea and members of the Weissman laboratory for insightful comments; and the Hertz Foundation, the NIH, DARPA and HHMI for funding.

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Correspondence to John R. S. Newman or Jonathan S. Weissman.

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Reprints and permissions information is available at The authors declare no competing financial interests.

Supplementary information

Supplementary Notes 1

Instruction manual for custom software described in the main text. Instructions on how to use ‘HTSPro’ to control the delivery of samples to a flow cytometer, as well as to control the software that runs the cytometer. As noted in the Supplementary Text, the software is available to academic users from the Authors. (PDF 8360 kb)

Supplementary Notes 2

Supplementary information about experimental design, execution and interpretation. Also provided is a supplementary discussion and supplementary figures that support the Main Text. (PDF 1125 kb)

Supplementary Table 1

Abundance and variation measurements for strains grown in YEPD and SD. (XLS 1672 kb)

Supplementary Table 2

Supplementary Table 2 nature04785-s04.xls Changes in protein and mRNA levels for strains grown in YEPD and SD. (XLS 195 kb)

Supplementary Table 3

Statistics for calculating the number of false positive and false negative strains. (PDF 46 kb)

Supplementary Table 4

Primers Used for Tagging and Deletion of ORFs (PDF 54 kb)

Supplementary Table 5

Organization of GO-Term, transcription factor and transcription module correlations associated with low or high variation (PDF 120 kb)

Supplementary Table 6

References for data used for P-value calculations (PDF 99 kb)

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Newman, J., Ghaemmaghami, S., Ihmels, J. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).

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