Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation - PubMed (original) (raw)

Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation

Suman S Thakur et al. Mol Cell Proteomics. 2011 Aug.

Abstract

In-depth MS-based proteomics has necessitated fractionation of either proteins or peptides or both, often requiring considerable analysis time. Here we employ long liquid chromatography runs with high resolution coupled to an instrument with fast sequencing speed to investigate how much of the proteome is directly accessible to liquid chromatography-tandem MS characterization without any prefractionation steps. Triplicate single-run analyses identified 2990 yeast proteins, 68% of the total measured in a comprehensive yeast proteome. Among them, we covered the enzymes of the glycolysis and gluconeogenesis pathway targeted in a recent multiple reaction monitoring study. In a mammalian cell line, we identified 5376 proteins in a triplicate run, including representatives of 173 out of 200 KEGG metabolic and signaling pathways. Remarkably, the majority of proteins could be detected in the samples at sub-femtomole amounts and many in the low attomole range, in agreement with absolute abundance estimation done in previous works (Picotti et al. Cell, 138, 795-806, 2009). Our results imply an unexpectedly large dynamic range of the MS signal and sensitivity for liquid chromatography-tandem MS alone. With further development, single-run analysis has the potential to radically simplify many proteomic studies while maintaining a systems-wide view of the proteome.

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Figures

Fig. 1.

Fig. 1.

Chromatographic performance at long gradient times. A, Yeast peptides separated using a 480 min gradient on a 50 cm column packed with 1.8 μm C18 beads. Contour plot from MaxQuant shows uniform peptide separation along the retention time and the m/z range. B, Same as (A), but using a 140 min gradient. C, Comparison of the number of peptides, number of isotope patterns and peak widths in two different gradient lengths. D, Comparison of the distribution of peak widths between the two gradients.

Fig. 2.

Fig. 2.

Single-run analysis of the yeast proteome by LC MS/MS. Schematic representation of proteins involved in glycolysis/gluconeogenesis, in the citric acid cycle (TCA cycle) and in the glyoxylate pathway in yeast. The scheme is based on ref (20) and extra proteins were added based on the KEGG pathway database. Proteins that were identified in the single-run analysis and were not targeted in ref (20) are in yellow and proteins that were found by both strategies are in blue. Gal10 was not targeted and not found in the single-run analysis (white).

Fig. 3.

Fig. 3.

Dynamic range of single-run analysis of a human cell line. Ranking of HEK293 proteins according to their absolute amounts. Quantification is based on added peptide intensities of the proteins as described in the EXPERIMENTAL PROCEDURES section.

Fig. 4.

Fig. 4.

KEGG pathways in HEK293 cells. Analysis of the representation of KEGG pathways in a triplicate of single-run analyses compared with the total number of proteins in the pathway according to the KEGG database.

Fig. 5.

Fig. 5.

SILAC-based quantification of HEK293 proteins. HEK293 cells were labeled with heavy or light amino acids and quantified using single-run analysis. A, A density plot shows the ratio distribution of proteins from a triplicate single-run analysis. B, Histogram of the coefficient of variation of each of the quantified proteins.

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