Mass spectrometry has enabled the study of cellular signaling on a systems-wide scale, through the quantification of post-translational modifications, such as protein phosphorylation1. Here we describe EasyPhos, a scalable phosphoproteomics platform that now allows rapid quantification of hundreds of phosphoproteomes in diverse cells and tissues at a depth of >10,000 sites. We apply this technology to generate time-resolved maps of insulin signaling in the mouse liver. Our results reveal that insulin affects ~10% of the liver phosphoproteome and that many known functional phosphorylation sites, and an even larger number of unknown sites, are modified at very early time points (<15 s after insulin delivery). Our kinetic data suggest that the flow of signaling information from the cell surface to the nucleus can occur on very rapid timescales of less than 1 min in vivo. EasyPhos facilitates high-throughput phosphoproteomics studies, which should improve our understanding of dynamic cell signaling networks and how they are regulated and dysregulated in disease.
At a glance
Proteomics Identifications Database
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- Supplementary Figure 1: Precision of the phosphoproteomics workflow. (485 KB)
Two identical SILAC-labelled HeLa cell populations were mixed a immediately after cell scraping or b after the phosphorylation enrichment workflow and immediately before LC-MS analysis. c-d Distribution and standard deviations of SILAC ratios of phosphopeptides from the experiments shown in the panel above are shown. e Sources of variability in phosphoproteome platform (1) biological, (2) workflow, or (3) LC-MS, investigated by replicates at each level as indicated. f Box-plot of Coefficient of Variation (%) of phosphopeptide intensities calculated from replicate sample data in e. g Average Pearson correlation coefficients (in box inset) for the replicate experiments, and density plots depicting the log2 transformed intensities of two representative replicates. Representative samples shown in plots were chosen based on the closest match between pairwise correlation coefficients and average correlation coefficients for all replicates. h Performance of match between runs (MBR) in transferring identification of phosphopeptides and quantification between replicate workflow samples. Percent valid values are calculated by dividing the number of unique phosphopeptides quantified in all replicates by the total number of possible quantification points, i.e. (∑ phosphopeptides quantified / (total number of phosphopeptides identified x number of replicates)) x 100). Error bars denote SD.
- Supplementary Figure 2: Phosphoproteome analysis of mouse cell lines and tissues. (574 KB)
a Experimental design for phosphoproteome analysis of control (PBS) and insulin-treated mouse liver cell lines (100 nM insulin, 15 minutes) and unstimulated (fed animals) liver, kidney and brain mouse tissues with six biological replicates each. Samples were enriched using the described phosphoproteomics pipeline, and the analysis was performed with single-run measurements lasting 0.5-1.5 day for all replicates of the respective cell or tissue typeper cell or tissue type. b Summary of the identified phosphorylation sites, including enrichment specificity (number of unique phosphopeptides identified divided by the total number of unique peptides identified). c Rate of phosphopeptide identification over the LC gradient in a single sample (Hepa 1-6), by sequencing (MS/MS) or match between runs (MBR) in MaxQuant. d Comparison of phosphosites (Class 1) quantified in this dataset with those quantified in the data of our previous liver and Hepa 1-6 cell line study (Monetti et al., 2011) analyzed with the same version of MaxQuant (18.104.22.168).
- Supplementary Figure 3: Coverage and overlap compared with published tissue phosphoproteome datasets. (394 KB)
a Quantified localized phosphosites reported in deep rodent tissue phosphoproteome studies. b Comparison of the tissue-specific components of mouse tissue phosphoproteomes in this dataset with data published in a large study by Huttlin et al., reveals high overlap of tissue phosphoproteomes with the single-run approach.
- Supplementary Figure 4: Quantitative analysis of tissue phosphoproteomes. (465 KB)
a Heat map of Pearson correlation coefficients, and multi-scatter plots showing reproducibility between cell line and tissue phosphoproteomes. b Principal component analysis (PCA) of mouse cell lines and tissue phosphoproteomes, revealing liver and kidney are similar, while brain is highly distinct. c Quantitative enrichment analysis of mouse brain, kidney and liver phosphoproteomes. Number of significantly enriched (t-test with Permutation-based FDR < 0.01) phosphorylation sites in each tissue compartment, and percentage of the phosphoproteome this represents.
- Supplementary Figure 5: Deep, time-resolved atlas of insulin stimulation in the liver. (650 KB)
a-b Heat map of Pearson correlation coefficients showing reproducibility between phosphoproteomes of mouse livers stimulated with insulin or PBS (‘Fasted’) for a four ‘early’ and b seven ‘intermediate’ time points. c-d PCA of insulin stimulated mouse liver. e Summary of quantified unique phosphoproteins, phosphopeptides and phosphorylation sites (Class 1) from the complete liver insulin time-series study. f Overlap between the phosphorylation sites quantified in this study, and the combined data from 9 mouse tissues from a large tissue-specific phosphorylation atlas (Huttlin et al., 2010). Data were kindly provided by the authors, and analyzed using MaxQuant using identical settings to enable likewise comparison. g Quantification coverage of unique phosphopeptide species (singly, doubly or greater phosphopeptides) containing only localized (Class 1) phosphosites. Inner circle denotes phosphopeptides quantified in ≥ 3 biological replicates across all insulin stimulated time points for the respective study, and average quantification coverage is shown for this core phosphoproteome (77% and 85% respectively). h Distribution of phosphosite localization probabilities for all phosphosites in the liver time series study, and for Class 1 sites the proportion of phosphoserine (pS), phosphothreonine (pT) and phosphotyrosine (pY) sites. i Dynamic range of MS-signals of phosphopeptides from the liver insulin time-series study span 7 orders of magnitude. Enrichment of protein GO terms (biological process and cellular component) in each intensity abundance range was assessed by Fisher’s exact test (Benjamini-Hochberg FDR < 0.02). The position of GO terms along the horizontal axis represents enrichment of these terms within the respective abundance quartile.
- Supplementary Figure 6: A time-resolved map of liver insulin signaling in vivo. (814 KB)
a Assembly of insulin signaling proteins from multiple database sources. The combined list contains the majority of known insulin signaling molecules, however not all proteins in the database are known to be regulated by phosphorylation. b The canonical insulin signaling network, comprising the PI3K-Akt and MAPK signaling branches. Proteins were curated from multiple database sources in a, as well as from literature. Shown on these proteins are known key insulin-regulated phosphorylation sites. Circles denote phosphorylated residue types (purple = Serine, Blue = Threonine, yellow = Tyrosine, grey = Not quantified) and phosphorylation site position (mouse). Below these are the average response of the phosphosite (fold change compared with PBS control, log2), at 0.5 and 6 min. in our time-resolved liver phosphoproteome data.
- Supplementary Text and Figures (2,214 KB)
Supplementary Figures 1–6;