系统生物学

Glioblastoma (GBM) is the most aggressive brain tumor, and different efforts have been employed in the search for new drugs and therapeutic protocols for GBM. A label-free, mass spectrometry–based quantitative proteomics has been developed to identify and characterize proteins that are differentially expressed in GBM to gain a better understanding of the interactions and functions that lead to the pathological state focusing on the extracellular matrix (ECM). The main challenge in GBM research has been to identify novel molecular therapeutic targets and accurate diagnostic/prognostic biomarkers. To better investigate the GBM secretome upon in vitro treatment with histone deacetylase inhibitor (iHDAC), we employed a high-throughput label-free methodology of protein identification and quantification based on mass spectrometry followed by in silico studies. Our analysis revealed significant changes in the ECM protein profile, particularly those associated with the angiogenic matrisome. Proteins such as decorin, ADAM10, ADAM12, and ADAM15 were differentially regulated upon in silico analysis. In contrast, key angiogenesis markers such as VEGF and ECM proteins like fibronectin and integrins did not display significant changes. These results suggest that iHDAC inhibitors may modulate or suppress tumor behavior growth by targeting ECM proteins’ secretion rather than directly inhibiting angiogenesis.

Proteomics analysis is crucial for understanding the molecular mechanisms underlying muscle adaptations to different types of exercise, such as concentric and eccentric training. Traditional methods like two-dimensional gel electrophoresis and standard mass spectrometry have been used to analyze muscle protein content and modifications. This protocol details the preparation of muscle samples for proteomics analysis using ultra-high-performance liquid chromatography (UHPLC). It includes steps for muscle biopsy collection, protein extraction, digestion, and UHPLC-based analysis. The UHPLC method offers high-resolution separation of complex protein mixtures, providing more detailed and accurate proteomic profiles compared to conventional techniques. This protocol significantly enhances sensitivity, reproducibility, and efficiency, making it ideal for comprehensive muscle proteomics studies.

The extracellular matrix (ECM) is a complex network of proteins that provides structural support and biochemical cues to cells within tissues. Characterizing ECM composition is critical for understanding this tissue component’s roles in development, homeostasis, and disease processes. This protocol describes an integrated pipeline for profiling both cellular and ECM proteins across varied tissue types using mass spectrometry–based proteomics. The workflow covers stepwise extraction of cellular and extracellular proteins, enzymatic digestion into peptides, peptide cleanup, mass spectrometry analysis, and bioinformatic data processing. The key advantages include unbiased coverage of cellular, ECM-associated, and core-ECM proteins, including the fraction of ECM that cannot be solubilized using strong chaotropic agents such as urea or guanidine hydrochloride. Additionally, the method has been optimized for reproducible ECM enrichment and quantification across diverse tissue samples. This protocol enables systematic mapping of the ECM at a proteome-wide scale.

Bottom-up proteomics utilizes sample preparation techniques to enzymatically digest proteins, thereby generating identifiable and quantifiable peptides. Proteomics integrates with other omics methodologies, such as genomics and transcriptomics, to elucidate biomarkers associated with diseases and responses to drug or biologics treatment. The methodologies employed for preparing proteomic samples for mass spectrometry analysis exhibit variability across several factors, including the composition of lysis buffer detergents, homogenization techniques, protein extraction and precipitation methodologies, alkylation strategies, and the selection of digestion enzymes. The general workflow for bottom-up proteomics consists of sample preparation, mass spectrometric data acquisition (LC-MS/MS analysis), and subsequent downstream data analysis including protein quantification and differential expression analysis. Sample preparation poses a persistent challenge due to issues such as low reproducibility and inherent procedure complexities. Herein, we have developed a validated chloroform/methanol sample preparation protocol to obtain reproducible peptide mixtures from both rodent tissue and human cell line samples for bottom-up proteomics analysis. The protocol we established may facilitate the standardization of bottom-up proteomics workflows, thereby enhancing the acquisition of reliable biologically and/or clinically relevant proteomic data.

Chemical proteomics focuses on the drug–target–phenotype relationship for target deconvolution and elucidation of the mechanism of action—key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of “protein melting” and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10–100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures.

Graphical abstract:

Cells sense and respond to mitogens by activating a cascade of signaling events, primarily mediated by tyrosine phosphorylation (pY). Because of its key roles in cellular homeostasis, deregulation of this signaling is often linked to oncogenesis. To understand the mechanisms underlying these signaling pathway aberrations, it is necessary to quantify tyrosine phosphorylation on a global scale in cancer cell models. However, the majority of the protein phosphorylation events occur on serine (86%) and threonine (12%) residues, whereas only 2% of phosphorylation events occur on tyrosine residues (Olsen et al., 2006). The low stoichiometry of tyrosine phosphorylation renders it difficult to quantify cellular pY events comprehensively with high mass accuracy and reproducibility. Here, we describe a detailed protocol for isolating and quantifying tyrosine phosphorylated peptides from drug-perturbed, growth factor-stimulated cancer cells, using immunoaffinity purification and tandem mass tags (TMT) coupled with mass spectrometry.

Protein N-glycosylation plays a vital role in diverse cellular processes, and dysregulated N-glycosylation is implicated in a variety of human diseases including neurodegenerative disorders and cancer. With recent advances in high-resolution mass spectrometry-based glycoproteomics technologies enabling large-scale N-glycoproteome profiling of disease and control samples, analysis of the large datasets has become a challenge. Here, we provide a protocol for the systems-level analysis of in vivo N-glycosylation sites on N-glycosylated proteins and their changes in human disease, such as Alzheimer's disease. The protocol includes quantitation and differential analysis of N-glycopeptide abundance, in addition to integrative N-glycoproteome and proteome data analyses, to determine disease-associated changes in N-glycosylation site occupancy and identify differentially N-glycosylated proteins in human disease versus control samples. This protocol can be modified and applied to study proteome-wide N-glycosylation alterations in response to different cellular stresses or pathophysiological states in other organisms or model systems.

Protein-ligand binding prediction is central to the drug-discovery process. This often follows an analysis of genomics data for protein targets and then protein structure discovery. However, the complexity of performing reproducible protein conformational analysis and ligand binding calculations, using vetted methods and protocols can be a challenge. Here we show how Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE), an open-source web-based compute and analytics platform for computational chemistry developed based on the Galaxy bioinformatics platform, makes protocol sharing seamless following genomics and proteomics. BRIDGE makes available tools and workflows to carry out protein molecular dynamics simulations and accurate free energy computations of protein-ligand binding. We illustrate the dynamics and simulation protocols for predicting protein-ligand binding affinities in silico on the T4 lysozyme system. This protocol is suitable for both novice and experienced practitioners. We show that with BRIDGE, protocols can be shared with collaborators or made publicly available, thus making simulation results and computations independently verifiable and reproducible.

The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Advanced mass spectrometry technology has pushed proteomic analyses to the forefront of biological and biomedical research. Limitations of proteomic approaches now often remain with sample preparations rather than with the sensitivity of protein detection. However, deciphering proteomes and their context-dependent dynamics in subgroups of tissue-embedded cells still poses a challenge, which we meet with a detailed version of our recently established protocol for cell-selective and temporally controllable metabolic labeling of proteins in Drosophila. This method is based on targeted expression of a mutated variant of methionyl-tRNA-synthetase, MetRS^L262G, which allows for charging methionine tRNAs with the non-canonical amino acid azidonorleucine (ANL) and, thus, for detectable ANL incorporation into nascent polypeptide chains.