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Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry.

Ludwig C, Claassen M, Schmidt A, Aebersold R - Mol. Cell Proteomics (2011)

Bottom Line: We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88.Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values.The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland.

ABSTRACT
For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

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Identification of significant protein abundance changes between control and antibiotic-treated samples. Logarithmic protein changes (log2) of the 12 h of ciprofloxacin treatment (A) and 24 h of ciprofloxacin treatment (B) relative to the control condition were correlated to their respective logarithmic (log10) p values, calculated by a t test analysis. Threshold settings of protein changes >2-fold and p values < 0.01 (black lines) were applied to identify significantly regulated proteins.
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Figure 5: Identification of significant protein abundance changes between control and antibiotic-treated samples. Logarithmic protein changes (log2) of the 12 h of ciprofloxacin treatment (A) and 24 h of ciprofloxacin treatment (B) relative to the control condition were correlated to their respective logarithmic (log10) p values, calculated by a t test analysis. Threshold settings of protein changes >2-fold and p values < 0.01 (black lines) were applied to identify significantly regulated proteins.

Mentions: Finally, we were interested in evaluating the obtained absolute quantitative results in the context of biologically relevant interrogations. Therefore, we first investigated protein abundance changes upon varying exposure times to the antibiotic ciprofloxacin (supplemental Table S3). For the identification of significant protein changes, thresholds were defined based on a protein ratio > 2 and a p value < 0.01 (calculated by a two-tailed and heteroscedastic t test) (Fig. 5). Herein, three proteins showed a significant up-regulation after 12 and 24 h of ciprofloxacin treatment (recA, LIC_12210 and hsp15). In all three cases, these results confirmed regulative trends previously reported (24, 27). Furthermore, according to the applied filter criteria, three proteins were identified as significantly down-regulated after 12 h (Mcp, LIC_11769, and FliG), whereas only Mcp remained down-regulated also after 24 h of ciprofloxacin treatment. The detected down-regulated ratios were generally smaller than the up-regulated ones. Notably, in our analysis the statistical t test analysis has been performed based on technical replicates, but typically biological variability exceeds the technical SRM measurement error. Hence biological replicates are required for confident identification and quantification of especially small protein expression differences. However, here our focus was not the reliable quantification of new protein changes upon antibiotic treatment, but the validation of our quantitative data by confirming previously reported regulations.


Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry.

Ludwig C, Claassen M, Schmidt A, Aebersold R - Mol. Cell Proteomics (2011)

Identification of significant protein abundance changes between control and antibiotic-treated samples. Logarithmic protein changes (log2) of the 12 h of ciprofloxacin treatment (A) and 24 h of ciprofloxacin treatment (B) relative to the control condition were correlated to their respective logarithmic (log10) p values, calculated by a t test analysis. Threshold settings of protein changes >2-fold and p values < 0.01 (black lines) were applied to identify significantly regulated proteins.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3316728&req=5

Figure 5: Identification of significant protein abundance changes between control and antibiotic-treated samples. Logarithmic protein changes (log2) of the 12 h of ciprofloxacin treatment (A) and 24 h of ciprofloxacin treatment (B) relative to the control condition were correlated to their respective logarithmic (log10) p values, calculated by a t test analysis. Threshold settings of protein changes >2-fold and p values < 0.01 (black lines) were applied to identify significantly regulated proteins.
Mentions: Finally, we were interested in evaluating the obtained absolute quantitative results in the context of biologically relevant interrogations. Therefore, we first investigated protein abundance changes upon varying exposure times to the antibiotic ciprofloxacin (supplemental Table S3). For the identification of significant protein changes, thresholds were defined based on a protein ratio > 2 and a p value < 0.01 (calculated by a two-tailed and heteroscedastic t test) (Fig. 5). Herein, three proteins showed a significant up-regulation after 12 and 24 h of ciprofloxacin treatment (recA, LIC_12210 and hsp15). In all three cases, these results confirmed regulative trends previously reported (24, 27). Furthermore, according to the applied filter criteria, three proteins were identified as significantly down-regulated after 12 h (Mcp, LIC_11769, and FliG), whereas only Mcp remained down-regulated also after 24 h of ciprofloxacin treatment. The detected down-regulated ratios were generally smaller than the up-regulated ones. Notably, in our analysis the statistical t test analysis has been performed based on technical replicates, but typically biological variability exceeds the technical SRM measurement error. Hence biological replicates are required for confident identification and quantification of especially small protein expression differences. However, here our focus was not the reliable quantification of new protein changes upon antibiotic treatment, but the validation of our quantitative data by confirming previously reported regulations.

Bottom Line: We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88.Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values.The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland.

ABSTRACT
For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

Show MeSH
Related in: MedlinePlus