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

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.

Fig. 1.

Schematic workflow of absolute label-free protein abundance estimation using SRM. The method comprises a two-step procedure. A, in the first step, a calibration curve is generated based on a defined number of anchor point proteins. For this, protein intensity values derived from label-free SRM measurements of best flyer peptides are correlated to independently determined absolute protein concentrations using the SID methodology. In this step, the accuracy of the method can be estimated, and the best model of peptide and transition combinations can be identified. B, in the second step, absolute protein abundances of a user-defined number of target proteins can be estimated by monitoring also for those proteins best flyer peptides and by applying the model and calibration curve derived from step 1. To ensure optimal comparability in MS signal responses between anchor point and target proteins, ideally both data sets should be measured simultaneously.

Fig. 2.

Best flyer peptide selection, transition selection, and FDR estimation. A, distribution of endogenously detectable best flyer peptides for the 16 proteins used as anchor points. B, 66 selected best flyer peptides were endogenously detectable by SRM with 4–14 transitions. C, the complete label-free SRM data set was scored using the algorithm mProphet, which takes into account information from measured target (gray) and decoy (black) peptide peak groups (31). Discrimination between target and decoy peptides led to an estimated FDR of ∼1%.

Fig. 3.

Model selection and accuracy estimation using Monte Carlo cross-validation. A, heat map visualization of the predictive measurement accuracy, represented by the cross-validated mean fold error, applying different models based on varying peptide and transition counts. Each square represents one particular linear model, which considers a specific number of summed best flyer peptides and most intense transitions, as annotated by the axes. Ranking of peptides and transitions was performed based on decreasing signal intensity. B and C, prediction error histograms for the linear models considering either the single best flying peptide per protein (TopPep1/TopTra6) or the summed intensity of the three best flying peptides (TopPep3/TopTra2). D and E, linear regression curves for the two models TopPep1/TopTra6 and TopPep3/TopTra2, respectively.

Fig. 4.

Biological reproducibility of model selection and calibration curve generation. A and B, to test the reproducibility of the determined mean fold error distributions based on varying peptide and transition combinations, we performed the Monte Carlo cross-validation analysis on three biological L. interrogans samples: a control sample (Fig. 3A), 12 h of treatment with ciprofloxacin (A) and 24 h of treatment with ciprofloxacin (B). C, overlay of linear calibration curves generated for the three different biological samples over a measurement period of 70 h. Each data point represents an averaged value of three technical replicates.

Fig. 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.