T-cell receptor (TCR) signaling plays a crucial role in various biological processes and is usually studied using global mass spectrometry-based phosphoproteomic studies. Despite advancements in targeted mass spectrometry-based assays for protein quantification, their application in studying signaling processes, for example, reproducible measurements of post-translational modifications (PTMs) such as phosphorylation, remains limited. Tyrosine phosphorylation is critical for many signaling pathways but presents challenges due to the low abundance of phosphotyrosine-containing peptides. Conventional untargeted methods often encounter data gaps when analyzing large sample sets, particularly for low-abundance peptides. To address this issue, a targeted proteomics method called “SureQuant” was employed, which relies on triggered data acquisition with heavy isotope-labeled peptides. This method has been shown to provide sensitive and reproducible quantification of low-abundance peptides. Here we describe the development of a SureQuant-based method to quantify phosphotyrosine peptides that are involved in the TCR signaling pathway. To monitor the change in phosphotyrosine signals upon activation, the T-cells were stimulated with anti-CD3/CD28 antibodies. We successfully quantified changes in important phosphotyrosine peptides in primary T-cells upon stimulation with anti-CD3/CD28 antibodies. This study showcases the ability of the SureQuant approach to accurately quantify low-abundance phosphotyrosine peptides, highlighting its broader potential to study a diverse set of PTMs in physiological or clinical settings.
T-cell receptor (TCR) signaling plays a fundamental role in immune responses, regulating T-cell activation, differentiation, and function. While tyrosine phosphorylation is a key regulatory mechanism in this pathway, the low abundance of phosphotyrosine peptides presents a major challenge for their detection and quantification in complex biological samples. By employing the SureQuant targeted mass spectrometry approach, we achieved highly sensitive and reproducible quantification of key phosphotyrosine sites involved in T-cell activation.
This study provides a systematic view of TCR signaling dynamics, revealing distinct phosphorylation patterns across different activation timepoints. Our findings demonstrate the effectiveness of SureQuant in quantifying low-abundance, post-translationally modified peptides, offering a valuable tool for studying signaling pathways with greater precision.
Extracellular vesicles (EVs) are secreted by cells and enclosed within lipid bilayers. These vesicles contain diverse biomolecular components, including proteins, nucleic acids, lipids, and metabolites. They serve critical roles in intercellular communication and regulate multiple physiological and pathological processes, such as immune modulation, angiogenesis, and tumorigenesis and metastasis. Notably, EVs exhibit marked heterogeneity in both physical characteristics and biomolecular composition. This article will systematically characterize the multidimensional heterogeneity of EVs at the population level through comprehensive analysis of their biogenesis origins, size distribution, surface protein, surface glycan chains, and surface lipid. Conventional population-level analyzes yield averaged molecular profiles that obscure subtype-specific functional correlations, thereby limiting mechanistic insights into EV subpopulation biology. To further understand EV heterogeneity, it is necessary to enhance our understanding about molecular characteristics of EVs from the population to the single particle level. Current single EVs analysis techniques mainly include super-resolution microscopy (SRM), atomic force microscopy (AFM), nanoparticle tracking Analysis (NTA), flow cytometry (FCM), surface enhanced Raman spectroscopy (SERS), mass spectrometry (MS), and proximity barcoding assay (PBA). In this review, we systematically examine population-level EV heterogeneity; evaluate single-particle detection methodologies; and discuss emerging technologies (e.g., click chemistry, Olink proteomics, and molecular imprinting) for resolving single EV heterogeneity.
Chemical proteomics probes serve as critical tools for investigating small molecule–protein interactions within complex biological systems. Traditionally, they are categorized into covalent probes and photoaffinity probes. They facilitate drug target discovery, targeted ligand screening, dynamic evaluation of enzyme activities in disease contexts, protein modification mapping, and bridging proteomics with other omics platforms. Despite their significant utility, challenges remain in probe design optimization, reduction of non-specific interactions, and expansion of targetable proteomic landscapes. Future research efforts are expected to focus on the development of novel probes, the integration of chemical proteomics with structural biology and artificial intelligence, and the advancement of clinical applications. These innovations will deepen our understanding of protein functions and support the advancement of precision medicine. In this review, we summarize the classification and fundamental principles of chemical proteomics probes and provide an in-depth discussion of their diverse applications.
Top-down mass spectrometry (TDMS) is the method of choice for analyzing intact proteoforms, as well as their posttranslational modifications and sequence variations. In top-down tandem mass spectrometry (TD-MS/MS) experiments, multiple proteoforms are often co-fragmented, resulting in multiplexed TD-MS/MS spectra. Due to their increased complexity, compared to spectra from single proteoforms, multiplexed TD-MS/MS spectra present significant challenges for proteoform identification and quantification. Here we present TopMPI, a new computational tool specifically designed for the identification of multiplexed TD-MS/MS spectra. Experimental results demonstrate that TopMPI substantially increases the sensitivity and accuracy of proteoform identification in multiplexed TD-MS/MS spectral analysis compared to existing tools.
Top-down mass spectrometry (TDMS) is a powerful technique for analyzing intact proteoforms; however, identifying multiple co-fragmented proteoforms from multiplexed tandem mass spectrometry (MS/MS) spectra remains a significant challenge.
In this paper, we introduce TopMPI, a new computational tool specifically designed to identify multiplexed TD-MS/MS spectra using a two-round database search strategy.
Compared to existing tools, TopMPI significantly improves the sensitivity and accuracy of proteoform identification from multiplexed MS/MS spectra. The development of TopMPI enhances the identification of low abundance proteoforms in complex biological samples and increases the potential of TDMS for discovering proteoform biomarkers in disease studies.
Guava (Psidium guajava), referred to as the “tropical apple,” is esteemed for its sweet flavor, nutritional density, and medicinal attributes, being rich in ascorbic acid, phenolics, carotenoids, fibers, and minerals. Despite its agricultural significance, guava cultivation faces considerable challenges from plant-parasitic nematodes, particularly root-knot nematodes from the Meloidogyne spp. In South America, Meloidogyne enterolobii causes severe root damage and economic losses to this crop. Plants fight nematodes through complex immune mechanisms involving pattern recognition receptors and signaling pathways, such as pattern-triggered immunity. The present research employed comparative shotgun proteomic analysis complemented by microscopic imaging and histochemical assays of roots from susceptible P. guajava and resistant P. guineense, inoculated or not with M. enterolobii. Psidium-M. enterolobii interactions revealed intricate plant cellular responses such as giant cells formation, hypersensitivity reactions, and biochemical pathway adjustments in sucrose transport and antioxidant enzyme activities. Synthesis and accumulation of secondary metabolites like terpenes, alkaloids, and phenolics in inoculated and resistant plants were positively correlated to plant resilience. Heat shock proteins and protein disulfide isomerases also emerged as pivotal in plant response, being upregulated during nematode infection.
The work addresses and unravels some of the puzzle pieces in the net of processes triggered in a plant prey (Psidium spp.), of either susceptible (P. guajava) or resistant (P. guineense) phenotypes, when confronted by its nematode predator (Meloidogyne enterolobii).
The main alterations detected in the roots of these plants ranged from giant cells formation, hypersensitivity reactions, biochemical adjustments in sucrose transport pathways and in antioxidant enzyme activities, to increases in secondary metabolites (terpenes, alkaloids, and phenolics) and in heat shock proteins and protein disulfide isomerases.
All these defensive mechanisms were triggered by the nematode attack on both species and were more prominent in P. guineense, which positively correlates them to the plant resistance against M. enterolobii.
Mass spectrometry–based top-down protein analysis requires efficient separation. In the context of proteoform analysis, capillary zone electrophoresis (CZE) is very valuable. The resolution of two peaks in CZE can be increased when the absolute mobility of the counter-directed electroosmotic flow (EOF) is close to the effective mobility of the analytes, resulting in a low apparent mobility of the analytes. The mobility of the EOF of highly efficient sulfobetaine-modified poly(α-L-lysine) (α-PLL) coatings changes depending on the number of modified side chains. Here, such coatings are used to selectively increase the peak resolution of proteoforms of model proteins and analytes in a complex protein sample (intact yeast protein extract). Whereas a high EOF system allows for the separation of proteins of a wide mobility range (complete proteome), lower EOF systems allow for a much better separation of proteins and proteoforms of low mobility, including those containing acidic post-translation modifications (PTMs). This leads to the identification of 2.5 times more proteoforms by MS/MS experiments in the lower mobility range of the yeast proteome. The sulfobetaine-modified α-PLL coatings presented here exhibit a toolbox for highly resolved separation of proteins and proteoforms in targeted or untargeted top-down protein analysis.
Sample complexity is one of the main challenges when analyzing a proteome on the proteoform level.
In the course of this, capillary electrophoresis–mass spectrometry turned out to be an excellent tool because of its high-performing separation, particularly for large molecules.
Here, we present a method enabling the best possible separation due to efficient and EOF-tunable coatings, allowing for flexible and dedicated selection of a range of proteins and proteoforms to be analyzed under ideal separation conditions.
The high performance is demonstrated by the separation of proteoforms of common PTM-rich model proteins as well as complex proteome samples.
The brain's extracellular matrix (ECM) is an intricate and dynamic network that plays essential roles in neurodevelopment, synaptic plasticity, and circuit stability. Despite its importance, the molecular composition and spatial organization of the developing brain ECM remain poorly characterized. In this commentary, we highlight the recent study by Vilicich et al., which employs a multi-modal approach, integrating single-cell transcriptomics, proteomics, and immunohistofluorescence, to construct a map of the ECM in the developing neocortex of humans and non-human primates. By curating a comprehensive list of extracellular proteins termed the “Exomatrix” and analyzing their distribution across cortical layers and developmental stages, the authors reveal layer-specific and evolutionarily conserved ECM features. These findings not only expand our understanding of ECM's role in shaping the brain during early development but also emphasize its potential involvement in the pathogenesis of neurodevelopmental disorders, urging further research into ECM biology as a frontier in neuroscience.
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