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. 2012 Mar;11(3):M111.013722.
doi: 10.1074/mcp.M111.013722. Epub 2011 Oct 20.

System-wide Perturbation Analysis With Nearly Complete Coverage of the Yeast Proteome by Single-Shot Ultra HPLC Runs on a Bench Top Orbitrap

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System-wide Perturbation Analysis With Nearly Complete Coverage of the Yeast Proteome by Single-Shot Ultra HPLC Runs on a Bench Top Orbitrap

Nagarjuna Nagaraj et al. Mol Cell Proteomics. .
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Abstract

Yeast remains an important model for systems biology and for evaluating proteomics strategies. In-depth shotgun proteomics studies have reached nearly comprehensive coverage, and rapid, targeted approaches have been developed for this organism. Recently, we demonstrated that single LC-MS/MS analysis using long columns and gradients coupled to a linear ion trap Orbitrap instrument had an unexpectedly large dynamic range of protein identification (Thakur, S. S., Geiger, T., Chatterjee, B., Bandilla, P., Frohlich, F., Cox, J., and Mann, M. (2011) Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, 10.1074/mcp.M110.003699). Here we couple an ultra high pressure liquid chromatography system to a novel bench top Orbitrap mass spectrometer (Q Exactive) with the goal of nearly complete, rapid, and robust analysis of the yeast proteome. Single runs of filter-aided sample preparation (FASP)-prepared and LysC-digested yeast cell lysates identified an average of 3923 proteins. Combined analysis of six single runs improved these values to more than 4000 identified proteins/run, close to the total number of proteins expressed under standard conditions, with median sequence coverage of 23%. Because of the absence of fractionation steps, only minuscule amounts of sample are required. Thus the yeast model proteome can now largely be covered within a few hours of measurement time and at high sensitivity. Median coverage of proteins in Kyoto Encyclopedia of Genes and Genomes pathways with at least 10 members was 88%, and pathways not covered were not expected to be active under the conditions used. To study perturbations of the yeast proteome, we developed an external, heavy lysine-labeled SILAC yeast standard representing different proteome states. This spike-in standard was employed to measure the heat shock response of the yeast proteome. Bioinformatic analysis of the heat shock response revealed that translation-related functions were down-regulated prominently, including nucleolar processes. Conversely, stress-related pathways were up-regulated. The proteomic technology described here is straightforward, rapid, and robust, potentially enabling widespread use in the yeast and other biological research communities.

Figures

Fig. 1.
Fig. 1.
Minimalistic proteomics setup. Yeast samples were lysed and prepared by the FASP method. Peptides were purified on StageTips and placed in an autosampler, which loads them directly on to a relatively long column (50 cm). The binary gradient system is provided by an UHPLC system (EASY nLC 1000) system coupled to a bench top quadrupole Orbitrap mass spectrometer (Q Exactive) via a nanoelectrospray source. The data obtained were analyzed in the MaxQuant computational proteomics platform, and bioinformatics analyses were performed using the Perseus tool.
Fig. 2.
Fig. 2.
In-depth coverage of the yeast proteome. A, number of peptides identified in individual runs with and without matching between the runs. Peptides identified by matching are indicated in green. B, proteins identified in individual runs with the gain from matching between the runs indicated in green. Proteins identified with single peptide hits are shown in red. C, the median sequence coverage of individual runs after matching was ∼17%. The median sequence coverage from the combined run for 4099 proteins was 22.9% as shown. D, the conjoint circles represent the frequency of identification of proteins in the six runs. Proteins identified in all six runs were designated as core proteome in the innermost circle.
Fig. 3.
Fig. 3.
Dynamic range of the identified proteome. Expression levels of identified proteins were roughly estimated using their summed peptide intensities. The proteins were ranked into five quantiles based on their abundance. A Fisher exact test extracted enriched GO terms in each quantile (false discovery rate < 0.02 after Benjamini-Hochberg correction).
Fig. 4.
Fig. 4.
Quantification of the yeast proteome using spike-in SILAC labeling. From four single-shot runs, more than 3200 proteins were quantified with respect to the spike-in SILAC mix. A, box plot of the number of ratio counts contributing to quantification of each protein. B and C, distribution of protein ratios to the spike-in SILAC standard. D, the reproducibility of spike-in SILAC quantification of the biological replicates as illustrated by the protein ratio correlations as shown here.
Fig. 5.
Fig. 5.
Quantitation of heat shock response by spike in SILAC strategy. A, schematic representation of the heat shock experiment. Samples at t = 0 and 30 min after incubation at 37 °C were mixed 1:1 with spike-in SILAC standard grown at 30 °C. B, fold change represented in log2 ratios is shown for selected proteins.
Fig. 6.
Fig. 6.
Hierarchical clustering of significantly changing proteins. A, clustering of significantly up- and down-regulated proteins upon heat shock. Significance was determined by analysis of variance with correction for multiple hypothesis testing. B and C, expression patterns for clusters enriched for ribosome biogenesis (B) and response to stress (C) show the two major trends of protein regulation.

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