Molecular & Cellular Proteomics最新文献

Septic shock, the excessive immune response to pathogen infection, accounts globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific, and insensitive, hindering timely and effective treatment. Aiming to establish easy-to-measure glyco-signatures that may identify the most critically unwell patients, we applied comparative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n = 29) and nonsurvivors (n = 8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome measured upon intensive care unit (ICU) admission (day 1) indicated survivorship-specific glyco-signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (accuracy 94.6%) and correctly predicted 29 of 29 survivors (specificity 100%) and six of eight nonsurvivors (sensitivity 75%). Interrogation of the serum N-glycome data revealed that Lewis x (Le^x)-type N-glycans are elevated in nonsurvivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Among the 58 other Le^x-containing serum glycoproteins that were strongly associated with acute phase response and stress pathways, alpha-1-acid-glycoprotein (AGP-1) was identified as a principal carrier of Le^x glycoepitopes with a potential to stratify septic shock survivors from nonsurvivors (AUC 0.90). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay AGP-1-Le^x glycoforms that identify individuals experiencing poor survival outcomes already upon ICU admission, with the potential to translate to early individualized clinical care at the bedside.

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, the combined application of mass spectrometry-based proteomics and 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 multiprotein assemblies directly from complex samples.

Nitric oxide (NO) is a crucial signaling molecule involved in various developmental processes and stress responses through post-translational protein modification and modulation of gene expression. Despite significant advances in understanding the mechanism of NO-mediated protein modifications, how NO regulates gene expression remains largely unclear. Here, we show that the energy sensor KIN10, a catalytic α-subunit of sucrose non-fermenting 1-related kinase 1, plays a vital role in NO-mediated regulation of gene expression in Arabidopsis. NO-mediated S-nitrosylation at Cys-177 of KIN10 inhibits its degradation, leading to protein stabilization. A non-nitrosylatable mutation of Cys-177 to serine results in NO insensitivity and functional deficiencies. Quantitative phosphoproteomic analysis reveals that S-nitrosylation at Cys-177 of KIN10 modulates the phosphorylation of splicing factors within the spliceosome. We propose that NO regulates RNA splicing through the enhancement of KIN10 activity via S-nitrosylation, thereby establishing a molecular link between NO signaling and gene expression.

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 data-independent acquisition schemes on a timsUltra athena ion processor 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 samples per day (SPD) and 80 SPD). However, even the lowest tested tissue amount (0.04 nl) yielded 3200 reproducibly quantified proteins for the 120 SPD method. Longer gradients (40 SPD and 20 SPD) instead significantly benefited from more tissue sampling, quantifying over 7500 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.

Single-cell omics technologies, such as single-cell RNA-Seq and single-cell proteomics, offer unprecedented insights into cellular heterogeneity and dynamic regulatory processes. However, integrating these data types to construct comprehensive transcription-translation profiles remains challenging because of their distinct and complex behaviors. This study presents a novel approach using pseudotemporal cell ordering to integrate single-cell RNA-Seq and single-cell proteomics by mass spectrometry data, facilitating the analysis of transcription-translation expression dynamics. We collected longitudinal single-cell samples following hypoxia. By leveraging key markers, we constructed pseudotemporal trajectories for each data type, revealing transcriptional and translational responses to hypoxia. This profile of unified single-cell mRNA and protein expression uncovers distinct regulatory mechanisms, including an immediate transcriptomic response, followed by delayed proteomic expression. It illustrates the use of pseudotemporal integration to integrate single-cell transcriptomic and proteomic datasets to understand the cellular phenotypes under hypoxic stress and provides a framework for future investigations into transcription-translation dynamics.

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 is 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 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.

Neutrophils respond rapidly to inflammation and infection via defense mechanisms, including degranulation, reactive oxygen species production, and neutrophil extracellular trap formation (known as "NETosis"). As the most abundant neutrophil components, granule proteins constitute the major mediators of neutrophil effector functions and likely orchestrate their functional diversity. However, a systematic profile of these proteins, particularly their temporal release dynamics during inflammatory responses, remains uncharacterized. Here, we performed a "multistate" proteomic study to explore circulating neutrophils' dynamic responses to diverse infectious and inflammatory signals over time. Circulating neutrophils exhibited both conserved and stimulus-specific protein expression programs. Through integrated characterization of the cellular and secretory proteome landscapes, we delineated the release patterns of canonical granule proteins and identified inflammatory mediators, including soluble membrane receptors. Notably, granule membrane receptors were translocated to the cell surface and shed via proteolytic cleavage, highlighting their dynamic regulation and diversity. These findings revealed the complexity of the neutrophil degranulation program, demonstrating its stimulus-dependent and temporally layered nature. Our study provides a functional atlas of neutrophil degranulation upon inflammation, which would strengthen our understanding of neutrophil activation in inflammation and facilitate the exploration of inflammation management therapies.

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," which is the focus of this review. Traditional analytical approaches with released glycans are limited in their usefulness in studying microheterogeneity because of 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 is 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.