Peptides bound to class I major histocompatibility complexes (MHC) play a critical role in immune cell recognition and can trigger an antitumor immune response in cancer. Surface MHC levels can be ...modulated by anticancer agents, altering immunity. However, understanding the peptide repertoire's response to treatment remains challenging and is limited by quantitative mass spectrometry-based strategies lacking normalization controls. We describe an experimental platform that leverages recombinant heavy isotope-coded peptide MHCs (hipMHCs) and multiplex isotope tagging to quantify peptide repertoire alterations using low sample input. HipMHCs improve quantitative accuracy of peptide repertoire changes by normalizing for variation across analyses and enable absolute quantification using internal calibrants to determine copies per cell of MHC antigens, which can inform immunotherapy design. Applying this platform in melanoma cell lines to profile the immunopeptidome response to CDK4/6 inhibition and interferon-γ - known modulators of antigen presentation - uncovers treatment-specific alterations, connecting the intracellular response to extracellular immune presentation.
Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global ...metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite ...many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
The epidermal growth factor receptor (EGFR) is a primary contributor to glioblastoma (GBM) initiation and progression. Here, we examine how EGFR and key downstream signaling networks contribute to ...the hallmark characteristics of GBM such as rapid cancer cell proliferation and diffused invasion. Additionally, we discuss current therapeutic options for GBM patients and elaborate on the mechanisms through which EGFR promotes chemoresistance. We conclude by offering a perspective on how the potential of integrative systems biology may be harnessed to develop safe and effective treatment strategies for this disease.
Central to successful cancer immunotherapy is effective T cell antitumor immunity. Multiple targeted immunotherapies engineered to invigorate T cell-driven antitumor immunity rely on identifying the ...repertoire of T cell antigens expressed on the tumor cell surface. Mass spectrometry-based survey of such antigens (“immunopeptidomics”) combined with other omics platforms and computational algorithms has been instrumental in identifying and quantifying tumor-derived T cell antigens. In this review, we discuss the types of tumor antigens that have emerged for targeted cancer immunotherapy and the immunopeptidomics methods that are central in MHC peptide identification and quantification. We provide an overview of the strength and limitations of mass spectrometry-driven approaches and how they have been integrated with other technologies to discover targetable T cell antigens for cancer immunotherapy. We highlight some of the emerging cancer immunotherapies that successfully capitalized on immunopeptidomics, their challenges, and mass spectrometry-based strategies that can support their development.
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Although recent developments in MS have enabled the identification and quantification of hundreds of phosphorylation sites from a given biological sample, phosphoproteome analysis by MS has been ...plagued by inconsistent reproducibility arising from automated selection of precursor ions for fragmentation, identification, and quantification. To address this challenge, we have developed a new MS-based strategy, based on multiple reaction monitoring of stable isotope-labeled peptides, that enables highly reproducible quantification of hundreds of nodes (phosphorylation sites) within a signaling network and across multiple conditions simultaneously. We have applied this strategy to quantify temporal phosphorylation profiles of 222 tyrosine phosphorylated peptides across seven time points following EGF treatment, including 31 tyrosine phosphorylation sites not previously known to be regulated by EGF stimulation. With this approach, 88% of the signaling nodes were reproducibly quantified in four analyses, as compared with only 34% by typical information-dependent analysis. As a result of the improved reproducibility, full temporal phosphorylation profiles were generated for an additional 104 signaling nodes with the multiple reaction monitoring strategy, an 88% increase in our coverage of the signaling network. This method is broadly applicable to multiple signaling networks and to a variety of samples, including quantitative analysis of signaling networks in clinical samples. Using this approach, it should now be possible to routinely monitor the phosphorylation status of hundreds of nodes across multiple biological conditions.
Tyrosine phosphorylation regulates signaling network activity downstream of receptor tyrosine kinase (RTK) activation. Receptor protein tyrosine phosphatases (RPTPs) serve to dephosphorylate RTKs and ...their proximal adaptor proteins, thus serving to modulate RTK activity. While the general function of RPTPs is well understood, the direct and indirect substrates for each RPTP are poorly characterized. Here we describe a method, quantitative phosphotyrosine phosphoproteomics, that enables the identification of specific phosphorylation sites whose phosphorylation levels are altered by the expression and activity of a given RPTP. In a proof-of-concept application, we use this method to highlight several direct or indirect substrate phosphorylation sites for PTPRJ, also known as DEP1, and show their quantitative phosphorylation in the context of wild-type PTPRJ compared to a mutant form of PTPRJ with increased activity, in EGF-stimulated cells. This method is generally applicable to define the signaling network effects of each RPTP in cells or tissues under different physiological conditions.
CD8+ T cell recognition of
(
)-specific peptides presented on major histocompatibility complex class I (MHC-I) contributes to immunity to tuberculosis (TB), but the principles that govern ...presentation of
antigens on MHC-I are incompletely understood. In this study, mass spectrometry (MS) analysis of the MHC-I repertoire of
-infected primary human macrophages reveals that substrates of
's type VII secretion systems (T7SS) are overrepresented among
-derived peptides presented on MHC-I. Quantitative, targeted MS shows that ESX-1 activity is required for presentation of
peptides derived from both ESX-1 substrates and ESX-5 substrates on MHC-I, consistent with a model in which proteins secreted by multiple T7SSs access a cytosolic antigen processing pathway via ESX-1-mediated phagosome permeabilization. Chemical inhibition of proteasome activity, lysosomal acidification, or cysteine cathepsin activity did not block presentation of
antigens on MHC-I, suggesting involvement of other proteolytic pathways or redundancy among multiple pathways. Our study identifies
antigens presented on MHC-I that could serve as targets for TB vaccines, and reveals how the activity of multiple T7SSs interacts to contribute to presentation of
antigens on MHC-I.
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