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Comparative Study
. 2016 May;15(5):1622-41.
doi: 10.1074/mcp.M116.058354. Epub 2016 Feb 24.

Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes

Jennifer G Abelin  1 , Jinal Patel  1 , Xiaodong Lu  1 , Caitlin M Feeney  1 , Lola Fagbami  1 , Amanda L Creech  1 , Roger Hu  1 , Daniel Lam  1 , Desiree Davison  1 , Lindsay Pino  1 , Jana W Qiao  1 , Eric Kuhn  1 , Adam Officer  1 , Jianxue Li  2 , Susan Abbatiello  1 , Aravind Subramanian  1 , Richard Sidman  2 , Evan Snyder  3 , Steven A Carr  1 , Jacob D Jaffe  4
Affiliations
Comparative Study

Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes

Jennifer G Abelin et al. Mol Cell Proteomics. 2016 May.

Abstract

Profiling post-translational modifications represents an alternative dimension to gene expression data in characterizing cellular processes. Many cellular responses to drugs are mediated by changes in cellular phosphosignaling. We sought to develop a common platform on which phosphosignaling responses could be profiled across thousands of samples, and created a targeted MS assay that profiles a reduced-representation set of phosphopeptides that we show to be strong indicators of responses to chemical perturbagens.To develop the assay, we investigated the coordinate regulation of phosphosites in samples derived from three cell lines treated with 26 different bioactive small molecules. Phosphopeptide analytes were selected from these discovery studies by clustering and picking 1 to 2 proxy members from each cluster. A quantitative, targeted parallel reaction monitoring assay was developed to directly measure 96 reduced-representation probes. Sample processing for proteolytic digestion, protein quantification, peptide desalting, and phosphopeptide enrichment have been fully automated, making possible the simultaneous processing of 96 samples in only 3 days, with a plate phosphopeptide enrichment variance of 12%. This highly reproducible process allowed ∼95% of the reduced-representation phosphopeptide probes to be detected in ∼200 samples.The performance of the assay was evaluated by measuring the probes in new samples generated under treatment conditions from discovery experiments, recapitulating the observations of deeper experiments using a fraction of the analytical effort. We measured these probes in new experiments varying the treatments, cell types, and timepoints to demonstrate generalizability. We demonstrated that the assay is sensitive to disruptions in common signaling pathways (e.g. MAPK, PI3K/mTOR, and CDK). The high-throughput, reduced-representation phosphoproteomics assay provides a platform for the comparison of perturbations across a range of biological conditions, suitable for profiling thousands of samples. We believe the assay will prove highly useful for classification of known and novel drug and genetic mechanisms through comparison of phosphoproteomic signatures.

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Figures

Fig. 1.
Fig. 1.
Large-scale transcriptional and phosphoproteomic profiling data for identification reduced-representation phosphoproteome probes. A, A schematic depicting the development of the P100 assay. Drug treatments that modulated phosphorylation in pleiotropic ways were selected, and large-scale global phosphorylation data were collected from multiple cell types treated with these drugs. Representative phosphopeptide probes were identified from these data and used to configure the P100 assay. Confirmation and proof-of-principle of the P100 assay's functionality were demonstrated via classification and stratification of samples from multiple biological contexts. Associations among samples were then identified using P100 data. B, Affymetrix gene expression levels corresponding to genes annotated as kinases or phosphatases were extracted from the CMap database and clustered (dendrograms omitted for clarity). Combinations of cell/compound treatments with similar kinase/phosphatase gene expression profiles are illustrated and groups with similar profiles are summarized by the “Running Average” graphic above. Arrows indicate strongly coordinated groups of samples. C, A workflow for the large-scale discovery experiments is depicted. Cells were grown in SILAC medium, lysed, digested, fractionated, and analyzed using high resolution UPLC-MS. D, A clustered heatmap representing the 1,200 commonly observed phosphosites that were present in >75% of all MS experiments is displayed. Groups of phosphosites with coordinate activity are clustered together along the vertical axis, whereas samples are clustered along the horizontal axis (dendrograms omitted for clarity). Data underlying the figure are available in Supplemental Data Set 1.
Fig. 2.
Fig. 2.
Diverse compound treatments produce unique phosphosignatures in discovery data. Selected regions of the large-scale discovery heatmap (Fig. 1D) are shown. A, Phosphosignatures of paclitaxel-treated cells cluster together regardless of cell line (MCF7, PC3, or HL60), and an up-regulation of several phosphosites across a diverse set of proteins is shown. Both lineage-independent (B) and lineage-specific signatures (C) across a set of cardiac glycoside structural analogs (digoxin, digoxigenin, digitoxigenin, and lanatoside (C) are shown. D, A cluster of phosphosites that appear to be coordinately regulated across a diverse set of conditions. The parent genes of these sites have a strong bias toward chromatin function. E, A tight network of protein-protein interactions in which 7 of the 8 parent genes in the chromatin binding cluster participate.
Fig. 3.
Fig. 3.
P100 automated sample processing, pathway coverage, and data analysis pipeline. A, A schematic of automated P100 sample processing. On the first day, cells are lysed, subjected to protein quantification and diluted to a uniform concentration. Proteins are then reduced, alkylated, and digested. On the second day, samples are desalted using a 96-well device and dried overnight. On the third day, phosphopeptide enrichment by IMAC and desalting occurs. Finally, samples are analyzed using high resolution UPLC-HCD MS/MS. B, The top signaling pathways represented by the larger set of phosphopeptides used to identify the reduced-representation phosphopeptide probe set are shown. Each signaling pathway is depicted as a circle that is sized to indicate the number of source proteins involved in a specific pathway. A larger size indicates that a signaling pathway is represented by a larger number of phosphopeptides. The color of each pathway is only meant to show the diversity of the signaling pathways represented. C, The data analysis pipeline for the P100 assay is shown. Data are collected in a 96-well plate format, analyzed within Skyline, and exported for summarization. Phosphopeptide probes and sample outliers are removed and the light/heavy peptide ratios are normalized. Quality controlled data are hierarchically clustered and molecular signatures for different perturbations are revealed.
Fig. 4.
Fig. 4.
Reduced-representation P100 data recapitulates large-scale discovery data. P100 reduced representation validation data set. A, A heatmap of P100 phosphoprofiles produced from samples generated under the same conditions as the initial large-scale phosphoproteomic experiments. Many of the same sample associations observed in large-scale data can be seen using the reduced-representation P100 assay. Cyan box: Biological replicates and structurally related compounds clustered closely together, as shown for digoxigenin, digitoxigenin, digoxin, and lanatoside C treatments. Green box: Phosphoprofiles of GW8510 treatments cluster across cell lines, while retaining some cell line-specific responses. B, A summary of the pairwise correlations between non-self (a sample of one cell type treated with one compound compared with all other cell line and treatment combinations), the same drug treatment across cell lines (all biological replicates of the same drug compared in two different cell lines), and the same drug treatment within each cell line are shown (all biological replicates of the same drug compared in a single cell line).
Fig. 5.
Fig. 5.
Signature strength in the P100 assay is dose responsive. A, A heatmap illustrating a condensed signature derived from a subset of P100 probes as a result of treating cells (MCF7, PC3) with varying doses of the kinase inhibitor staurosporine (0.5–20 mm) is shown. Examples of phosphopeptide probes that decrease in response to increased staurosporine dose (cluster 1 and profile (B) and probes that increase in response to increasing dose (cluster 2 and profile (C) are observed.
Fig. 6.
Fig. 6.
P100 generates molecular signatures in embryonic stem cells (ESC) and neuronal precursor cells (NPC) in response to multiple compound treatments. The heatmap illustrates phosphosignaling signatures in ESC and NPC when treated with different classes of epigenetically active drugs including a Brd4 inhibitor, an EZH2 inhibitor, and HDAC inhibitors. As observed in the P100 validation experiments, replicates of the same drug treatment cluster together within each cell type. Additionally, molecular signatures of the same treatment cluster across NPC and ES, as seen with the Brd4 inhibitor JQ1. Neither cell type nor any drug treatment (with the exception of MS-275) depicted above was used in the development of the P100 assay.
Fig. 7.
Fig. 7.
Time-resolved signatures from P100 reveal modularity of biologically important signaling pathways. A, Pathway reconstruction showing the targets of a set of drugs known to inhibit important nodes in key signaling pathways. These drugs were administered for 3, 6, and 24 h to MCF7 cells. B, P100 molecular signatures from samples treated with the drugs. No clustering of samples has been performed, but P100 probes (rows) have been clustered (dendrogram omitted). In each major column corresponding to one drug, individual profiles from each sample are ordered according to time point from left to right. C, Correlation matrix of profiles from comparing profiles of samples at the same time point. The boundaries shown are meant to draw attention to the off-diagonal adjacent enrichment of correlation. These boundaries happen to correspond to the modules in the reconstructed pathway, and are colored by pathway module as in Fig. 7A.
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