Molecular & Cellular Proteomics最新文献

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 quantified 6,081 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.

Achieving high-resolution spatial tissue proteomes requires careful balancing and integration of optimized sample processing, chromatography, and MS acquisition. Here, we present an advanced cellenONE protocol for loss-reduced tissue processing and compare all Evosep ONE Whisper Zoom gradients (20, 40, 80, and 120 samples per day), along with three common DIA acquisition schemes on a timsUltra AIP mass spectrometer. We found that tissue type was as important as gradient length and sample amount in determining proteome coverage. Moreover, the benefit of increased tissue sampling was gradient- and dynamic range-dependent. Analyzing mouse liver, a high dynamic range tissue, over tenfold more tissue sampling led to only ∼30% gain in protein identification for short gradients (120 SPD and 80 SPD). However, even the lowest tested tissue amount (0.04 nL) yielded 3,200 reproducibly quantified proteins for the 120 SPD method. Longer gradients (40 SPD and 20 SPD) instead significantly benefited from more tissue sampling, quantifying over 7,500 proteins from 0.5 nL of tonsil T-cell niches. Finally, we applied our workflow to a rare squamous cell carcinoma of the oral cavity, uncovering disease-associated pathways and region-specific protein level changes. Our study demonstrates that more than 100 high-quality spatial tissue proteomes can be prepared and acquired daily, laying a strong foundation for cohort-size spatial tissue proteomics in translational research.

N-glycosylation is an abundant and essential co/post-translational modification that is preserved across all eukaryotes. N-glycans have important functions in protein stability and protein-protein interactions. N-glycans exhibit a high degree of heterogeneity, even within an individual site on the same protein, a phenomenon that is termed "microheterogeneity" that is the focus of this review. Traditional analytical approaches with released glycans are limited in their usefulness in studying microheterogeneity due to most glycoproteins having more than one site of N-glycosylation. Since specific N-glycans at specific sites can confer important functions to glycoproteins, this presents a significant gap between the information content of glycomics and glycoproteomics experiments. More recently, tandem mass spectrometry of intact glycopeptides has been used to obtain site-specific information on N-glycan microheterogeneity. The microheterogeneity of glycoproteins presents a significant analytical challenge not only during mass spectrometry analyses but also in downstream data processing. Use of specialized search engines followed by extensive manual validation are often required for accurate and in-depth glycoproteomics. Overall, recent advances in analytical technology and data processing present exciting new opportunities to analyze N-glycans in a site-specific manner. Being able to define, understand functional consequences of, and even predict and direct N-glycan microheterogeneity has implications across many fields, including the manipulation and production of glycoprotein biologics.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, however, their use is limited by heterogeneous and unpredictable immune-related adverse events (irAEs), which can progress to life-threatening conditions requiring intensive care unit (ICU) admission. Reliable biomarkers for predicting and stratifying ICU-level irAEs are urgently needed to improve immunotherapy safety and critical care management. Here, we performed comprehensive mass spectrometry-based proteomic profiling to identify plasma biomarkers for the prediction and monitoring of irAEs in 65 patients receiving ICI treatment. Our analysis identified 217 differentially abundant proteins and four co-expression modules related to humoral (antibody-mediated) and cellular (T cell-mediated) immunity spanning mild to severe irAEs. Through feature selection and cross-validation with proteomics and ELISA data, we identified two key proteins, IL1RL1 and FABP3, as potential biomarkers for irAE risk. In addition, we developed a plasma proteomic machine learning model (ProIRAE) that demonstrated high and robust predictive performance with area under the curve (AUROC) values of 0.929 and 0.766 for identifying patients at risk of developing irAEs, and AUROC values of 0.978 and 1.000 for predicting severe irAEs in the discovery and independent validation cohorts, respectively. Collectively, our study provides a valuable plasma proteomic atlas of ICI-related irAEs. The ProIRAE model offers a non-invasive tool for detection and severity stratification of irAEs, with great potential to improve precision monitoring and management of immunotherapy complications in critical care settings.

Mass spectrometry-based proteomics has been applied to many fields and has made major contributions in our understanding of biology and medicine. Translation of this technology and assays to patient testing has been limited despite grand expectations. The amyloid protein identification by LCMS test is one successful example of the adaptation of this technology by molecular and clinical pathology laboratories. Through the illustration of this assay, we will address some of these challenges and outline a process for validation and implementation of mass spectrometry-based proteomics in the molecular pathology laboratory.

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 ∼500x 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.

Data-independent acquisition (DIA) mass spectrometry is essential for comprehensive quantification of proteomes, enabling deeper insights into cellular processes and disease mechanisms. On the timsTOF platform, diagonal-PASEF acquisition methods have recently been introduced to directly and continuously cover the observed diagonal shape of the peptide precursor ion distributions. Diagonal-PASEF has already shown great promise and its adaptation as a routine workflow can be further pushed with improved method development as well as enhanced algorithmic solutions. Here, we conducted a systematic and comprehensive optimization of diagonal-PASEF for 17-minute gradients on the timsTOF HT in conjunction to Spectronaut. We demonstrate that Spectronaut fully supports all tested diagonal-PASEF methods independent of the number of slices or overlaps and with minimal user intervention required. We derive an optimized analysis strategy where we coupled diagonal-PASEF acquisitions to retention time down-sampling by summation (RTsum) and thereby exploit the fast-cycling nature of diagonal-PASEF methods. Through the combination of RTsum with diagonal-PASEF, we demonstrate that this strategy yields higher signal-to-noise ratios while retaining the peak shape for analytes of interest ultimately improving overall number of peptide and protein identifications of diagonal-PASEF. Importantly, combining RTsum with diagonal-PASEF improved overall identifications and quantitative precision when compared to dia-PASEF with variable quadrupole isolation widths and across different input amounts for cell line injections. We also tested the performance of diagonal-PASEF in controlled quantitative experiments where diagonal-PASEF outperformed dia-PASEF in the overall number of retained candidates below 1% or 5% error-rate, quantitative precision and identifications on peptide level and protein level. These data indicate that RTsum demonstrates a positive use case of the high sampling rate of diagonal-PASEF and might therefore be a valuable addition to existing analysis pipelines. Collectively, our findings imply that diagonal-PASEF is developing into a competitive alternative to dia-PASEF and that the data analysis options are making fast progress.

Large macromolecular assemblies are integral to most cellular processes, making their identification and structural characterization an important strategy for advancing our understanding of protein functions. In this pilot study, we investigated large multiprotein assemblies from the cytoplasm of the slime mold Dictyostelium discoideum using shotgun electron microscopy (shotgun EM), the combined application of mass spectrometry-based proteomics and cryo-electron microscopy (cryo-EM) to heterogenous mixtures of proteins. With its similarities in cell structure and behavior to mammalian cells, D. discoideum has long served as an invaluable model organism, particularly in the study of immune cell chemotaxis, phagocytosis, bacterial infection, and other processes. We subjected D. discoideum soluble protein complexes to two-step fractionation, performing size-exclusion chromatography followed by mixed-bed ion-exchange chromatography. Isolated fractions containing a subset of high molecular weight-scale protein assemblies were subsequently analyzed using mass spectrometry to identify the proteins and cryo-EM to characterize their structures. Mass spectrometry analysis revealed 179 unique proteins in the isolated fractions, then single-particle cryo-EM analysis generated distinct 2D projections of several visually distinctive protein assemblies, from which we successfully identified and reconstructed three major protein complexes: the 20S proteasome, the dihydrolipoyllysine-residue succinyltransferase (Odo2) of the mitochondrial 2-oxoglutarate dehydrogenase complex, and polyketide synthase 16 (Pks16), thought to be the primary fatty acid synthase of D. discoideum. Based on the Pks16 structure, the first of the 40 D. discoideum PKSs to be experimentally determined, models for the full set of D. discoideum PKSs were constructed with help from AlphaFold 3. Comparative analysis enabled structural characterization of their reaction chambers. Shotgun EM thus provides a view of proteins in their native or near-native biological conformations and scaling up this approach offers an effective route to characterize new structures of multi-protein assemblies directly from complex samples.

Hair follicle development is a complex, highly regulated process involving interactions between epithelial and mesenchymal cells. However, the specific molecular mechanisms and important biological processes of hair follicle development remain poorly understood. How the extracellular matrix are involved in the hair follicle formation from hair germs remains to be investigated. In this study, we applied spatially resolved proteomic mapping to investigate the process of hair follicle development in skin organoids at different stages: D55, D75, D90, D140, D150, and D170, which corresponds to that from hair germ formation to hair follicle aging. Our analysis identified dynamic changes in protein expression and active protein synthesis during hair follicle appearance. We observed stage-specific protein expression patterns, with hair germ and hair peg formation, enriched in proteins involved in RNA processing and lipid metabolism. Meanwhile, hair follicle initial and full maturation highlighted proteins related to keratinization and extracellular matrix (ECM) organization. Notably, trend proteins involved in keratinization and neuron-neuron synaptic transmission were upregulated from hair germ formation to the hair follicle appearance. We also found that CSNK1A1 and SFN exhibit abnormal expression in the hair follicles of patients with cicatricial alopecia, which further proves the role of CSNK1A1 and SFN in the normal development of hair follicles. The results provide a comprehensive spatial proteomic map of hair follicle development and offer new insights into the biological process driving hair follicle formation and maturation. These findings may guide future therapeutic strategies for hair regeneration and the treatment of hair disorders.

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 WGCNA 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 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 (AUROC > 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.