Project description:Dynamic tyrosine phosphorylation is fundamental to a myriad of cellular processes. However, the inherently low abundance of tyrosine phosphorylation in the proteome and the inefficient enrichment of phosphotyrosine(pTyr)-containing peptides has led to poor pTyr peptide identification and quantitation, critically hindering researchers’ ability to elucidate signaling pathways regulated by tyrosine phosphorylation in systems where cellular material is limited. The most popular approaches to wide-scale characterization of the tyrosine phosphoproteome use pTyr enrichment with pan-specific, anti-pTyr antibodies from a large amount of starting material. Methods that decrease the amount of starting material and increase the characterization depth of the tyrosine phosphoproteome while maintaining quantitative accuracy and precision would enable the discovery of tyrosine phosphorylation networks in rarer cell populations. To achieve these goals, a novel method leveraging the multiplexing capability of tandem mass tags (TMT) and the use of trigger channels – cells treated with the promiscuous tyrosine phosphatase inhibitor pervanadate (PV) – selectively increased the relative abundance of pTyr-containing peptides. After PV facilitated selective fragmentation of pTyr-containing peptides, TMT reporter ions delivered sensitive quantitation of each peptide for the experimental samples while the quantitation from PV trigger channels was ignored. This method yielded up to 6.3-fold boost in quantification depth of statistically significant data derived from contrived ratios, compared to TMT without trigger channels or intensity-based label-free (LF) quantitation while maintaining quantitative accuracy and precision, allowing quantitation of over 2300 unique pTyr peptides from only 1 mg of T cell receptor-stimulated Jurkat T cells. The trigger channel strategy can potentially be applied in analyses of other post-translational modifications where treatments that broadly boost the levels of those modifications across the proteome are available.

Project description:Signal propagation mediating numerous cellular processed in T cells is orchestrated by tyrosine phosphorylation with a high degree of regulation1, 2. Because aberrant tyrosine phosphorylation can result in many diseases3, a method to systematically quantify this dynamic process can be helpful in understanding the mechanistic underpinnings of T cell functions. However, accurate quantitation of tyrosine phosphorylation is challenging due to its low abundance4. Mass spectrometry(MS) based phosphotyrosine proteomics has emerged to be an attractive solution for wide scale profiling of the tyrosine phosphoproteome5, 6. Many advances have been made in this field to improve the enrichment efficiency of phosphotyrosine(pTyr)-containing peptides to improve the coverage depth7-11. Separately, computational methods of Fourier transform-(FT)MS such as an Orbitrap analyzer have also made significantly progress in the quantitation of TMT samples. For example, the phase-constrained spectrum deconvolution method (ΦSDM) have been shown to substantially improve the spectral quality and speed of FT-MS instruments12. ΦSDM deconvolves FT spectra into frequency distributions to allow more efficient extraction of the harmonic components of the oscillating ions12. As a result, higher mass resolution and accuracy can be achieved with shorter transient times12. We recently developed the BOOST approach by coupling PV boost channel in a multiplexed TMT approach to significantly increase the quantitative depth of the tyrosine phosphoproteome13. While it has been shown to work in Jurkat cell line, it has not been demonstrated to work in scarce mice primary T cells. Here, we demonstrate the first phosphotyrosine proteomics performed in mice primary T cells using BOOST that enabled the identification of more than 6000 pTyr peptides using only 1 mg of protein. We further demonstrated the importance of ΦSDM, in which when disabled, enabled significantly deeper quantitation depth in low-abundance samples. Using samples with contrived ratios, disabling ΦSDM improved the precision of low-abundance peptides and allowed the quantitation of up to 2-fold more increase in statistically significant ratios.

Project description:Quantitative proteomics in large cohorts is highly valuable for biomedical/pharmaceutical investigations but often suffers from suboptimal reliability, accuracy, and reproducibility. Here we describe a new ultra-high-resolution (UHR) IonStar method achieving precise/reproducible protein measurement in large-cohorts while eliminating accuracy-related problems such as ratio compression, by taking advantage of the exceptional selectivity of UHR-MS1 detection (240k_FWHM). Using mixed-proteome sets reflecting large-cohort analysis using technical or biological replicates (N=56), we comprehensively compared the quantitative performances of UHR-IonStar with a state-of-the-art SWATH-MS method. The SWATH-MS employs a cutting-edge TripleTOF and a SpectronautTM package and was meticulously optimized to maximize sensitivity, reproducibility and proteome-coverage, by developing a highly extensive, customized spectral-library, reproducible/robust capillary-LC separation and narrow, variable-window acquisition. Comparing quantitative performances of the two distinct methods (i.e. MS1-vs.MS2-based) affords interesting and valuable observations. While the performances for higher-abundance proteins (i.e. higher 75% proteins in abundance) are quite similar by the two methods, UHR-IonStar showed markedly better quantitative accuracy, precision and reproducibility (e.g. R2>0.9 by UHR-IonStar vs. R2<0.4 by SWATH-MS) for proteins of lower 25% in abundance, and much improved sensitivity/selectivity for discovering significantly-altered proteins, especially these with changes ≤2.5 folds. Furthermore, UHR-IonStar showed more accurate protein quantification in single analysis of each individual sample in a large set, which is an inadequately-investigated albeit highly critical parameter for large-cohort analysis. Finally, we compared the two methods in measuring time courses of altered proteins in cancer cells (N=36) treated by paclitaxel, where dysregulated biological processes have been well-established. UHR-IonStar discovered more altered-proteins in biological processes and pathways that are known to be induced by paclitaxel, with substantially better significance scores. Additionally, UHR-IonStar showed markedly superior ability in accurately depicting the time courses of tubulin-α/β isoforms which are well-known to be consistently induced by paclitaxel. In summary, UHR-IonStar represents a reliable, robust and cost-effective solution for large-cohort proteomics quantification with excellent accuracy and precision.

Dataset Information

Proteome-wide evaluation of two common protein quantification methods

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Proteome-Wide Evaluation of Two Common Protein Quantification Methods.

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