Molecular & Cellular Proteomics最新文献

Cell-cell communications are widely explored to understand tissue homeostasis and diseases. Numerous computational tools have been developed to infer cellular interactions from transcriptomic or proteomic expression data. However, proteins often carry post-translational modifications (PTMs) that can induce conformational switches and alter their functional properties. A key challenge remains to incorporate PTM data in the inference and analysis of cellular interactions. Here, we propose an extension of our previously published tool BulkSignalR to integrate PTM information in ligand-receptor interactions and downstream pathway predictions. This new functionality is compatible with bulk and single-cell data, and it supports all types of PTMs. Based on two illustrative datasets, we show that this new feature provides deeper insights into biological pathway regulation and that PTM integration helps reduce false-positive results occasionally produced by standard approaches.

Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition (DDA)-based software platforms for immunopeptidomics: MaxQuant, FragPipe, PEAKS and MHCquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-DDA-PASEF method. We assessed each software's ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate (FDR) control, achieving medium to high identification confidence correlations. The largest number of immunopeptides were identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.

Alzheimer's disease (AD) is an age-associated neurodegenerative disorder characterized by amyloid plaques, tau hyperphosphorylation, and synaptic dysfunction. Most available cellular AD models lack aging features, limiting their ability to recapitulate key pathological mechanisms. Here we applied high-resolution mass spectrometry-based multiplexed proteomics and phosphoproteomics in a discovery setting to characterize an accelerated AD (acAD) model that combines amyloid precursor protein (APP) and presenilin (PSEN) mutations with progerin, an aging-associated Lamin A mutant that accelerates aging. Across four phenotypes (control, progerin, classic AD, and acAD), we identified 8279 proteins, quantified 6081 proteins, and detected phosphorylation dynamics. Relative to the classic model, acAD exhibited broader proteome remodeling, including amplified downregulation of synaptic and cytoskeletal proteins, upregulation of transcription and translation machinery, and pathway-level changes in neuronal signaling, mitochondrial dynamics, and neuroinflammation. Phosphoproteome analysis revealed widespread changes in RNA-binding and cytoskeletal proteins, aligning with recent data from two murine AD models. These findings show that acAD captures canonical AD phenotypes while uniquely modeling age-related inflammation and phosphorylation, providing a resource to accelerate studies of proteome-level mechanisms of AD progression and to inform strategies targeting cytoskeletal and inflammatory pathways.

Protein denaturation-based assays, such as thermal proximity coaggregation (TPCA) and ion-based proteome-integrated solubility alteration (I-PISA), are powerful tools for characterizing global protein-protein interaction (PPI) networks. These workflows utilize different denaturation methods to probe PPIs, i.e., thermal- or ion-based. How denaturation differences influence PPI network mapping remained to be better understood. Here, we provide an experimental and computational characterization of the effect of the denaturation-based PPI assay on the observed PPI networks. We establish the value of both soluble and insoluble fractions in PPI prediction, determine the ability to minimize sample amount requirement, and assess different relative quantification methods during virus infection. Generating paired TPCA and I-PISA datasets, we define both overlapping sets of proteins and distinct PPI networks specifically captured by these methods. Assessing protein physical properties and subcellar localizations, we show that size, structural complexity, hydrophobicity, and localization influence PPI detection in a workflow-specific manner. We show that the insoluble fractions expand the detectable PPI landscape, underscoring their value in these workflows. Focusing on selected PPI networks (cytoskeletal and DNA repair), we observe the detection of distinct functional populations. Using influenza A infection as a model for cellular perturbation, we demonstrate that the integration of PPI predictions from soluble and insoluble workflows enhances the ability to build biologically informative and interconnected networks. Examining the effects of reducing starting material for TPCA assays, we find that PPI prediction quality remains robust when using a single well of a 96-well plate, a ∼500× reduction in sample input from usual workflows. Introducing simple workflow modifications, we show that label-free data-independent acquisition (DIA) TPCA yields performance comparable to the traditional tandem mass tag (TMT) data-dependent acquisition (DDA) TPCA workflow. This work provides insights into denaturation-based assays, highlights the value of insoluble fractions, and offers practical improvements for enhancing global PPI network mapping.

Adolescent idiopathic scoliosis (AIS) is the most common spinal deformity encountered in adolescents. Here we portray the plasma proteomic landscape of 235 AIS samples. Enrichment analysis demonstrate that proteins with the increased level in AIS are significantly enriched in pathways including muscle weakness, disorder of hormone, whereas proteins showed decreased level in healthy controls are mainly involved in pathways related to immune response. The weighted gene correlation network analysis analysis indicates unbalanced lipid and glucose metabolism due to the insulin signaling activation could affect the AIS progression. Molecular subtyping classifies AIS patients into three subtypes that connected with significantly different Cobb angle (the standard radiographic measure of spinal curvature) with the estrogen and glucocorticoid disorder and have effects on the muscle weakness and bone remodeling, respectively. Additional, non-linear associations between Cobb and plasma proteome data reveals that the plasma proteome of 26 degrees and 51 degrees is dramatically differed across these two Cobb ranges. Finally, we construct two proteomics classifiers for the AIS screening and progression state prediction that have the good performance on both discovery and validation cohort (area under the receiver operating characteristic >0.90). This study generates a high-quality data resource that may benefit basic research and provides additional biological insights underlying clinical features of AIS.

Early embryonic development requires tightly regulated molecular programs to coordinate cell division, fate specification, and spatial patterning. While transcriptomic profiling has been widely performed, proteomic analyses of early vertebrate embryos remain limited owing to technical challenges in embryonic sample preparation. Here, we present an "in-cell proteomics" strategy, which bypasses cell lysis and yolk depletion, processes individual embryos directly in functionalized filter devices, and generates mass spectrometry (MS)-friendly samples in an extremely robust and streamlined manner. This single-vessel approach minimizes sample loss and technical variation, offering a highly sensitive and accurate alternative to low-input and low-cell quantitative proteomics. Coupled with field asymmetric ion mobility spectrometry and single-shot data-independent acquisition MS workflow, this approach enabled us to consistently quantify ∼6200 proteins from a single Xenopus tropicalis embryo, representing the deepest proteomic coverage of early X. tropicalis developmental stages reported to date. Investigation of the temporal proteomes across five cleavage stages (from 1- to 16-cell stages) revealed a drastic proteomic shift between 2- and 4-cell stages, followed by more gradual transitions thereafter. Spatial analysis of dissected 8-cell blastomeres uncovered pronounced molecular asymmetry along the animal-vegetal axis, whereas dorsal-ventral differences were minimal. This study establishes a novel in-cell proteomics technology in conjunction with field asymmetric ion mobility spectrometry and data-independent acquisition MS as a robust platform for high-resolution, low-input developmental proteomics analysis and provides a comprehensive spatiotemporal protein atlas for early X. tropicalis embryos.

The involvement of the oral microbiome (OM) in the pathophysiology of Alzheimer's disease and vascular dementia has been recognized epidemiologically, but the molecular mechanisms remain elusive. In this study, we uncovered the presence of OM-derived proteins (OMdPs) in brain extracellular vesicles (bEVs) from post-mortem Alzheimer's disease and vascular dementia subjects using unbiased metaproteomics. OMdP circulation in blood EVs was also confirmed in an independent cohort. Our findings also reveal that specific OMdPs are present in bEVs, with their levels varying with disease progression. Peptidome-wide correlation analyses further explored their exchange dynamics and composition within bEVs. In addition, we validated the ability of OM-derived EVs to cross the blood-brain barrier using a blood-brain barrier-on-a-chip model, confirming a potential route for bacterial-derived molecules to reach the central nervous system. Bioinformatics-driven interaction analyses indicated that OMdPs engage with key neuropathological proteins, including amyloid-beta and tau, suggesting a novel mechanism linking dysbiotic OM to dementia. These results provide new insights into the role of the OM in neurodegeneration and highlight OMdPs as potential biomarkers and therapeutic targets.

Antibody fragment crystallizable region (Fc) glycosylation critically modulates immune signaling, yet characterization of glycosylation beyond the immunoglobulin G (IgG) isotype remains limited. Here, we present the first site-specific glycoprofiling of immunoglobulin A (IgA) and immunoglobulin M (IgM) in elderly individuals with tuberculosis (TB), a population particularly susceptible to disease reactivation. Using dual-enzyme digestion and targeted LC-MS/MS analysis, we quantified Fc glycosylation of IgG, IgA, and IgM in plasma from 20 patients with active TB (ATB), 18 with latent TB infection (LTBI), and 20 controls. Consistent with previous studies, IgG1 and IgG2 in ATB displayed reduced galactosylation and elevated fucosylation compared with LTBI. Extending the analysis to other isotypes, we identified analogous alterations in IgA and IgM. ATB samples showed reduced digalactosylation and increased monogalactosylation at IgA1/2-N144/131, indicating a shift toward agalactosylation. In IgM, decreased galactosylation at N171, N332, and N395, increased agalactosylation at N563, and increased fucosylation and sialylation at N71 were observed in ATB relative to LTBI and controls. Integrating 18 significantly altered glycosylation traits across all three Ig isotypes revealed coordinated humoral remodeling associated with active disease. Collectively, these findings indicate that IgA and IgM, like IgG, undergo infection-associated proinflammatory glycan remodeling, underscoring their overlooked roles in antibody-mediated immune modulation and providing a broader framework for understanding humoral responses in aging and chronic infection.

Essential tremor (ET) stands as one of the most prevalent movement disorders originating from cerebellar dysfunction. However, effective treatment remains limited, largely due to a poor understanding of its molecular pathology. The harmaline-induced tremor in mice is a well-established model for ET research, though its mechanisms remain unclear. This study aimed to get insight into the molecular intricacies underlying cerebellar dysfunction in this model. Combining LC-MS/MS and RNA-Seq approach, we delved into the cerebellar alterations in harmaline-induced tremor in mouse. Multi-omics profiling identified 5194 correlated coding molecules, among which 19 were significantly dysregulated. Further KEGG enrichment analysis identified cerebellar serotonin transporter (SERT) as the key molecule in harmaline-induced tremor. We validated the upregulation of SERT in the cerebellar cortex following harmaline induction, particularly within Purkinje cells, and demonstrated that pharmacological inhibition or genetical knockdown of SERT significantly attenuated tremor severity and neuronal hyperexcitability. Further mechanistic studies revealed that harmaline-induced SERT upregulation leads to depleted serotonin levels in the cerebellum, contributing to tremor pathogenesis. In general, our study unveils crucial insights that could pave the way for molecular target identification and effective therapeutic interventions for ET.

Epithelial-mesenchymal transition (EMT) is a key biological process in physiological and pathological conditions, spanning development, wound healing, and cancer. Vimentin, a key cytoskeletal intermediate filament (IF) protein, is an established intracellular determinant of EMT. Recently, extracellular vimentin has also emerged with important functions, and we demonstrated that vimentin from fibroblast-derived extracellular vesicles (EVs) promotes wound healing. Building on these findings, we explored whether extracellular vimentin regulates EMT. We employed fibroblast-derived EVs to assess their EMT-driving capacity. Using coculture models and EV treatments from WT and vimentin-KO fibroblasts, we observed that fibroblasts induce an EMT phenotype in epithelial cells, marked by elevated mesenchymal markers and reduced epithelial markers. EVs from vimentin-deficient fibroblasts showed a decreased EMT-inducing capacity and failed to stimulate cell cover closure, underscoring vimentin's critical role in orchestrating these processes. Coculturing epithelial cells with WT fibroblasts mirrored these outcomes, while vimentin-deficient fibroblasts produced similarly poor EMT induction. Proteomic profiling revealed that WT EVs contained an enriched set of EMT-associated proteins, including those involved in cytoskeletal organization, cell adhesion, and EMT-regulating signaling pathways. Notably, these proteins, such as fibronectin and N-cadherin, were significantly diminished in vimentin-deficient EVs. Moreover, we identified over 600 additional proteins uniquely present in WT-derived EVs, with enrichment in key biological processes like wound healing and cell migration. These findings demonstrate that vimentin-positive EVs drive EMT by transmitting a specific protein cargo that supports EMT-related cellular changes. The vimentin-positive EV proteome will help understand EMT mechanisms and develop targeted therapies for pathological conditions related to abnormal EMT.