Molecular & Cellular Proteomics最新文献

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 is widely performed, proteomic analyses of early vertebrate embryos remain limited due to technical challenges in embryonic sample preparation. In this study, we present an "in-cell proteomics" approach, which bypasses cell lysis and yolk depletion, processes individual embryos directly in functionalized filter devices, and generates liquid chromatography-mass spectrometry (LC-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 (FAIMS) and single-shot data-independent acquisition (DIA) MS workflow, this approach enabled us to consistently quantify ∼6,200 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) 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, while dorsal-ventral differences were minimal. This study establishes a novel in-cell proteomics technology in conjunction with FAIMS and DIA-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.

Biophysical proteomics assays allow for proteome-wide, label-free monitoring of ligand-induced changes in protein structure and stability, offering insights into protein-ligand interactions and modulation of biophysical properties of cellular proteins. These assays exploit the principle that compound-induced alterations in structure or stability of proteins can be detected through changes in their susceptibility to denaturation. Here, we introduce solvent proteome profiling in cells (SPICE), which employs solvent-based denaturation of proteins under otherwise physiological conditions in intact cells. We characterized solvent-induced denaturation of proteins inside cells as distinct from that in cell extracts and validated SPICE by detecting known drug-target interactions for multiple compound classes. Our results indicate that SPICE, unlike experiments in cell extracts, also detects secondary compound-induced effects such as target profiles of drug metabolites, modulation of protein-protein interactions, and downstream signaling events. We further demonstrate complementarity of SPICE and cellular thermal shift assay, which both robustly detect the designated targets of well-characterized drugs and individually provide biologically meaningful and interpretable results. Finally, we show that SPICE can detect covalent drug-targets, compound-induced target-destabilization and stabilization of degrader drug targets despite their concurrent degradation.

Protein truncation is a common modification that can alter protein localization, interaction, activity, and function. Top-down proteomics targets the identification of all molecular forms in which a protein can exist (termed "proteoforms") and is thus well-suited for termini analysis. To examine the properties, origin, and consistency of truncated proteoforms, we performed a meta-analysis of 50 top-down proteomics datasets published over the past decade, covering 140,000 proteoforms derived from 14,500 proteins across various species. On average across all datasets, approximately 71% of proteoforms were truncated, with the vast majority not yet being documented in protein databases. Our analysis was able to distinguish between artificial truncations (e.g., sample preparation effects on labile peptide bonds) and endogenous truncations, enabling the identification of novel signal peptides and truncations between structured domains. This study highlights the importance of a common yet understudied mechanism for generating protein diversity and provides a valuable resource for future studies, targeting truncated proteoform functions or aiming to reduce artefacts in proteomics sample preparation.

Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially abundant proteins, and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in quantitative proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to tandem mass tag data from >1000 cancer patient samples spanning 10 cancer types from the Clinical Proteomics Tumor Atlas Consortium. Lupine outperforms the state of the art for proteomics imputation, uniquely identifies differentially abundant proteins and Gene Ontology terms, and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open-source Python package.