Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues

Roland Bruderer, Oliver M Bernhardt, Tejas Gandhi, Saša M Miladinović, Lin-Yang Cheng, Simon Messner, Tobias Ehrenberger, Vito Zanotelli, Yulia Butscheid, Claudia Escher, Olga Vitek, Oliver Rinner, Lukas Reiter, Roland Bruderer, Oliver M Bernhardt, Tejas Gandhi, Saša M Miladinović, Lin-Yang Cheng, Simon Messner, Tobias Ehrenberger, Vito Zanotelli, Yulia Butscheid, Claudia Escher, Olga Vitek, Oliver Rinner, Lukas Reiter

Abstract

The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics. We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics. Utilizing HRM, we profiled acetaminophen (APAP)(1)-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD). Our findings imply that DIA should be the preferred method for quantitative protein profiling.

© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Figures

Fig. 1.
Fig. 1.
Novel DIA method for HRM and analysis using Spectronaut. (A) The novel SWATH-MS type DIA method consists of one survey scan and 19 DIA windows with adapted swath size to the precursor density implemented on a Thermo Scientific Q Exactive mass spectrometer. (B) iRT used in postacquisition prediction of peptide elution time in an HRM run. Using a static extraction window of 20min, 200 sampled, identified peptides were visualized. (C) SRM-like, direct visualization of extracted ion currents. Example of a fragment ion with an interfering signal (dotted line) as automatically detected by Spectronaut shown. Vertical line indicates iRT value of peptide assay (D). The histogram shows the normalized discriminant score used for the FDR (Spectronaut's Cscore) of targets, decoys and truly absent peptides (sample 3 profiling standard sample set). The use of scrambled peptide sequences as decoys (red) statistically mimics truly absent yeast peptides (blue). This is verified using a spectral library for yeast peptides in the analysis of acquisitions derived from the human cell line (gray). Note the clear separation of decoys and targets.
Fig. 2.
Fig. 2.
Design of the HRM and shotgun proteomics comparison experiment. (A) The profiling standard sample set consisted of eight samples with a constant background (HEK-293 cells) and 12 non-human proteins spiked in three master mix groups. The lowest concentration was set to the value 1. (B) Spectral acquisition of the profiling standard sample set was performed on a Q Exactive mass spectrometer in shotgun proteomics and HRM mode in a block-randomized manner. (C) The spectral library for the targeted search was generated from shotgun proteomics MS runs of the profiling standard sample set. The peptide and protein counts of the spectral libraries with increasing shotgun proteomics MS runs are shown in the bar plots.
Fig. 3.
Fig. 3.
Comparison of the quantitative measurements generated by HRM and shotgun proteomics for the background (HEK-293). (A) Number of peptide precursors that were identified in all the indicated MS runs without missing values. (B) Coefficients of variation of 4,360 peptides from the background, which were identified in both shotgun proteomics and HRM, and quantified in all the 24 runs. The box plots show significant difference for HRM and shotgun proteomics (**** t test, p value < .0001). (C) 200 peptides were randomly selected from all the peptides that were identified by both HRM and shotgun proteomics. The selected peptides were ordered in a heat map vertically by intensity and horizontally by sample. White spots indicate missing intensities among the selected peptides.
Fig. 4.
Fig. 4.
Model-based detection of differentially abundant proteins in the 28 possible pairwise comparisons of the profiling standard sample set. (A) Profile of the peptides of the spike in protein RNAS1 P61823 of master mix 2. The error bars show the standard deviation. (B) The top 200 candidate proteins ranked by adjusted p value. (C) The cumulative detection of spike in proteins of the 28 pairwise comparisons after setting the cutoff of the actual FDR to 0.05 (the number of false positives divided by the number of candidates, from the ground truth). (D) Receiver operating characteristic (ROC) curve of detecting differentially abundant proteins, obtained with respect to the known changes in protein concentrations. Each point on the curve corresponds to a different FDR cutoff, and relates the sensitivity (equivalently, true positive rate TPR) and specificity (equivalently, true negative rate TNR). The ROC curve uses all the proteins in the profiling standard sample set” (p value ≤ 2.22 10^-16) (34).
Fig. 5.
Fig. 5.
HRM profiling of APAP treated three-dimensional human liver microtissues. (A) Hematoxylin and eosin (H&E) stained three-dimensional microtissue shows liver-like morphology. CD68 positive Kupffer cells are present and distributed throughout the microtissue. Below, the viability of the microtissues after 72 h APAP treatment was analyzed using a luminescent cell viability assay. Three subtoxic concentrations (4.6, 13.7, 370.4 μm) and one toxic APAP concentration (3,333.3 μm) were analyzed by HRM (black) (12,000 cells per sample). (B) Number of peptides identified in all the indicated MS runs without missing value. (C) 200 peptides were randomly selected from all the peptides that were identified by HRM. The selected peptides were ordered in a heat map vertically by intensity and horizontally by sample. (D) CVs of peptides identified and quantified by HRM are visualized in a box plot. The CVs were calculated from the subset of peptides identified in the technical triplicates of the samples without missing values.
Fig. 6.
Fig. 6.
Biostatistical analysis of three-dimensional liver microtissues after APAP treatment. (A) The candidate's lists of the treatments compared with control. (B) Selected pathways, significantly enriched in the candidate lists of the APAP treatment. The p values indicate statistically significant overrepresentation of the genes in a given process. (C) Induction of the biotransformation phase I, II and III enzymes upon APAP exposure. The dashed line indicates unchanged expression compared with control. The “n.s.” marks not significantly detected changes. (D) NAPQI modification of PARK7 was detected at the cysteine 106. A reduction of the unmodified peptide was observed. HRM XICs shown of the NAPQI-modified, unmodified and a control peptide. The arrows indicate the peptide signals.

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