Journal of Proteome Research最新文献

The Proteograph Product Suite, a multiplexed nanoparticle (NP) protein corona-based workflow, substantially improves the depth of detection of proteins by mass spectrometry (MS) by compressing the dynamic range of protein abundances. Here, we evaluate its quantitative performance and suitability for large-scale studies. Using multispecies spike-in experiments, we assessed fold change accuracy, linearity, precision, and the lower limit of quantification (LLOQ) across multiple MS platforms. Combined with the Orbitrap Astral MS, the Proteograph XT assay enabled identification of more than 7,000 plasma proteins. In mixed-species dilution experiments, fold change accuracy was preserved, with Proteograph quantifying 3.5 times more proteins than the Neat plasma workflow at the same fold change error threshold. Similar accuracy was observed with the Orbitrap Exploris 480 MS, and we also demonstrate that different proteome backgrounds do not impact the accuracy. Data produced with NPs from the four distinct NP batches (each supporting >100,000 assays) showed only a 4% increase in protein intensity CV across batches. Together, these results demonstrate that the Proteograph Product Suite provides depth as well as quantitative accuracy and precision to power new biomarker discovery and biological understanding in population-scale plasma proteomics cohorts.

Mass spectrometry imaging has emerged as a pivotal tool in spatial metabolomics, yet its reliance on the imzML format poses critical challenges in data storage, transmission, and computational efficiency. While imzML ensures cross-platform compatibility, its lower compressed binary architecture results in large file sizes and high parsing overhead, hindering cloud-based analysis and real-time visualization. This study introduces an enhanced Aird compression format optimized for spatial metabolomics through two innovations: (1) a dynamic combinatorial compression algorithm for integer-based encoding of m/z and intensity data; (2) a coordinate-separation storage strategy for rapid spatial indexing. Experimental validation on 47 public data sets demonstrated significant performance gains. Compared to imzML, Aird achieved a 70% reduction in storage footprint (mean compression ratio: 30.89%) while maintaining near-lossless data precision (F1-score = 99.75% at 0.1 ppm m/z tolerance). For high-precision-controlled data sets, Aird accelerated loading speeds by 13-fold in MZmine. The Aird format overcomes crucial bottlenecks in spatial metabolomics by harmonizing storage efficiency, computational speed, and analytical precision, reducing I/O latency for large cohorts. By achieving near-native feature detection accuracy, Aird establishes a robust infrastructure for translational applications, including disease biomarker discovery and pharmacokinetic imaging.

Post-translational modifications (PTMs) dynamically regulate cellular processes by modifying protein function. Among these, lactylation, a modification derived from lactate, functions through direct or indirect modification of histones or nonhistone proteins. While glycosylation and phosphorylation have established roles in bone metabolism and joint disorders, the biological significance of lactylation in musculoskeletal diseases remains underexplored. This study synthesizes current evidence investigating lactylation in four major orthopedic diseases: intervertebral disc degeneration (IVDD), osteoporosis (OP), osteoarthritis (OA), and spinal cord injury (SCI). The evidence indicates that lactylation modulates disease progression through dual mechanisms: coordinating cellular metabolism with extracellular matrix remodeling in IVDD and OA and regulating neuroimmune responses during SCI recovery. Notably, lactylation’s regulatory patterns differ from classical PTMs by serving as a molecular bridge linking metabolic reprogramming to pathological tissue remodeling. This contrasts with phosphorylation, which primarily dominates signal transduction pathways. These insights reposition lactate from a metabolic byproduct to a disease-modifying signaling molecule, suggesting lactylation could inform therapeutic strategies for inflammatory, degenerative, and regenerative musculoskeletal disorders.

The cellular surfaceome is crucial for cellular homeostasis and communication, with surface proteins acting as receptors, transporters, and enzymes. However, comprehensive analysis of the surfaceome is challenging due to the hydrophobic nature and low abundance of membrane proteins, leading to their underrepresentation in mass spectrometry-based proteomics data. This study exploits a novel method for profiling the cellular surfaceome using furan chemistry. We synthesized six furan-biotin compounds (FB1-FB6) and showed that FB1 reacts with lysine and cysteine. Upon furan activation by oxidation, FB1 exhibited the highest staining intensity and specificity for cell surface proteins. Pull-downs of biotinylated proteins further confirmed the efficiency of FB1 in enriching cell surface proteins, and FB1 was also found to reduce nonspecific labeling of intracellular proteins compared to biotinylation methods using N-hydroxysuccinimide esters. Our method thus provides a robust tool for unbiased, high-specificity proteomic studies of cell surfaceomes.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by immune dysregulation and the production of autoantibodies targeting nuclear components; yet, the molecular mechanisms linking nuclear protein alterations to disease activity remain poorly defined. Here, we performed integrative proteomic and phosphoproteomic analyses of peripheral blood mononuclear cells from 90 healthy individuals and 130 SLE patients, complemented by transcriptomic profiling of 1,461 SLE patients. Our multiomics analysis revealed progressive dysregulation of nuclear proteins, particularly those involved in RNA processing and immune regulation, with distinct subsets associated with remission and active disease states. Phosphoproteomic profiling uncovered dynamic phosphorylation changes, including reduced multisite phosphorylation of FKBP15 and aberrant activation of kinases such as CDK and CK2. Transcriptome–proteome integration highlighted persistent interferon signaling and inflammatory gene expression, while transcription factor (TF) analysis indicated dysregulation of STAT1 and IRF family members. Network analysis identified central hub proteins, such as NPM1 and PARP1, that bridge post-translational modifications with global transcriptional alterations. Collectively, these findings delineate a multilayered regulatory network connecting protein abundance, phosphorylation dynamics, and TF activity, thereby providing mechanistic insights into SLE pathogenesis and suggesting potential biomarkers and therapeutic targets for disease modulation.

XGBoost, a gradient boosting algorithm, is widely recognized for its efficiency and robustness in multiclass classification tasks. Metabolomics serves as a powerful tool for biomarker discovery; however, metabolic biomarkers associated with the progression from chronic hepatitis B (CHB) to liver cirrhosis (LC) to hepatocellular carcinoma (HCC), as well as those related to treatment effects in HCC (HCCAT), remain unclear. In this study, an XGBoost-based machine learning approach combined with mass spectrometry was used to analyze the metabolic profiles of 30 healthy controls (HC), 29 CHB patients, 30 LC patients, 30 HCC patients, and 30 HCCAT patients. Biomarker screening was conducted through three comparative analyses: (1) HC, CHB, LC, HCC, and HCCAT; (2) HC, CHB, LC, and HCC; and (3) HC, HCC, and HCCAT. A total of 17 metabolic biomarkers were identified, among which nine had not been previously associated with HBV-related liver diseases. Notably, a potential biomarker panel composed of eicosenoic acid, dihydromorphine, cysteine, acetic acid, sitosterol, and hypoxanthine showed promise for disease prognosis and therapeutic evaluation. These findings highlight the great potential of integrating metabolomics with machine learning to identify novel metabolic biomarkers related to HBV-associated liver disease progression and treatment response.

Breast cancer is the second most common cause of brain metastasis, often in advanced-stage disease. The mechanisms underlying breast cancer brain metastasis (BCBM), particularly how tumor cells cross the blood–brain barrier and adapt to the brain environment, remain unclear. Cell surface glycosylation plays diverse roles, and its dysregulation in cancer can disrupt signaling and promote metastasis. We investigated how changes in cell surface N-glycans contribute to BCBM by analyzing N-glycans released from human breast cancer cell lines (MDA-MB-231, MDA-MB-361, HTB-131, HTB-22), a brain-seeking variant (MDA-MB-231BR), and glioblastoma cells (CRL-1620) using nano liquid chromatography–tandem mass spectrometry (LC–MS/MS). Surface N-glycans were enzymatically released from live cells with PNGase F without compromising the membrane integrity. Results showed the 231BR cell line expressed higher levels of sialylated N-glycans than other cells, with N-glycan 4502 being the most abundant. Four sialylated structures (4501, 4502, 3501, and 5602) were significantly elevated in 231BR, suggesting a role in brain metastasis. This study expands our understanding of altered N-glycan profiles in BCBM and highlights potential molecular features linked to brain colonization. Further research on these N-glycans could clarify their function in mediating metastasis and identifying therapeutic targets.

The estimation of the post-mortem interval (PMI) in forensic skeletal remains is extremely challenging, as traditional morphological methods lose their effectiveness and accuracy as decomposition progresses. To address this issue, this study utilizes metabolomics to investigate the biochemical changes affecting bone tissue during the decomposition process. Fragments of pig mandibles were buried in an open grassland field at varying depths (0, 10, 30, and 50 cm) and collected every month up to 6 months. Bone metabolites were extracted using a single-phase methanol–water protocol, and both gas chromatography–mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied for their analysis. The primary goal of this study is to identify specific metabolic shifts associated with increasing post-mortem intervals to identify potential bone metabolomic biomarkers for PMI and to assess the impact of burial depth on these changes. The generated regression models using LC-MS/MS data were able to estimate the PMI of the skeletal fragments with an accuracy of 14 days over 6 months, an outstanding result, particularly considering the current lack of methodologies to estimate PMI from bones. Burial depth, instead, did not play a significant role on the metabolomic bone signature. This research deepens our understanding of post-mortem biochemical processes in bones, making a significant contribution to the advancing field of forensic metabolomics, and highlights the potential of metabolomics for investigating buried skeletal remains and enhancing post-mortem interval assessments.

Disulfide bonds are essential for the structural and functional integrity of therapeutic peptides and proteins (TPPs). However, accurately mapping disulfide bonds in TPPs remains challenging due to the scrambling of the disulfide bond during sample preparation. In addition, identifying TPP metabolites with disulfide bonds is crucial for preclinical and clinical development. However, existing software for metabolite identification can only identify metabolites with native disulfide bonds, despite the evidence of nonnative disulfide bonds in metabolites. This highlights the necessity for developing a strategy for comprehensively profiling the metabolites of TPPs. In this study, a proteomic strategy based on pLink software was developed for the characterization of disulfide bonds in insulin and its metabolites from rat liver S9. Through peptic digestion at 0 °C and subsequent liquid chromatography–high-resolution mass spectrometry (LC–HRMS) analysis, all the disulfide bonds of insulin were identified without disulfide bond reshuffling. As for its metabolites, 118 were identified from rat liver S9 after incubation with insulin, including 41 with disulfide bond reshuffling. Metabolized insulin peptides with disulfide bond reshuffling were successfully verified by reduction and alkylation of the peptides along with oxidation of the disulfide bonds using meta-chloroperoxybenzoic acid (mCPBA), facilitating their fragmentation. This study provides a new strategy for the reliable and comprehensive characterization of TPPs and their metabolites with disulfide bonds.