Journal of Proteome Research最新文献

Lung adenocarcinoma (LUAD) is the most common histological subtype of nonsmall-cell lung cancer. Herein, a multiomics method, which combined proteomic and N-glycoproteomic analyses, was developed to analyze the normal and cancerous bronchoalveolar lavage fluids (BALFs) from six LUAD patients to identify potential biomarkers of LUAD. The data-independent acquisition proteomic analysis was first used to analyze BALFs, which identified 59 differentially expressed proteins (DEPs). The bioinformatic analyses of 59 DEPs have shown that a potential marker protein, beta-1,4-galactosyltransferase 1 (B4GALT1), was consistently downregulated in all cancerous lung lobes (CLLs). As the downregulation of B4GALT1 may indicate changes in protein N-glycosylation, site-specific N-glycoproteome analysis of BALFs from the normal lung lobes (NLLs) and CLLs was further performed by using a fully automated glycopeptide enrichment and separation system. Comparing the glycan structures containing free GlcNAc in BALFs between NLLs and CLLs qualitatively, the percentage of unique glycan structure for free GlcNAc existing only in NLLs was 52.8%, which was significantly higher than the 46.3% existing only in CLLs. Furthermore, the sequential proteomic and N-glycoproteomic analyses allowed us to identify a panel of functionally related potential biomarkers consisting of one protein (B4GALT1) and four glycoproteins (NFKB1, F2, LTF, and DLD).

Single cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all laboratories have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown effect on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single cell level, utilizing Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37 °C (control), (2) 4 °C overnight, and (3) -196 °C storage followed by liquid nitrogen preservation. Both cold and frozen storage induced significant alterations in the cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

Data-independent acquisition (DIA) on ion mobility mass spectrometers enables deep proteome coverage and high data completeness in large-scale proteomics studies. For advanced acquisition schemes such as parallel accumulation serial fragmentation-based DIA (diaPASEF) stability of ion mobility (1/K₀) over time is crucial for consistent data quality. We found that minor changes in environmental air pressure systematically affect the vacuum pressure in the TIMS analyzer, causing ion mobility shifts. By comparing experimental ion mobilities with historical weather data, we attributed observed drifts to fluctuations in the ground air pressure. Moderate air pressure changes of e.g. fifteen mbar induce ion mobility shifts of 0.025 Vs/cm². These drifts negatively impact peptide quantification across consecutively acquired samples due to drift-dependent abundance changes and increased missing values for ions located at the boundaries of diaPASEF isolation windows, which cannot be corrected by postprocessing. To address this, we applied an in-batch mobility autocalibration feature on a run-wise basis, leading to full elimination of ion mobility drifts.

Mass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, interactive differential expression analysis tools become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to nonprogrammers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements or multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software, respectively. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs.

Identifying sex from an unknown dried blood spot (DBS), especially when the corpse remains undiscovered, often provides valuable evidence in forensic casework. While DNA-based sex determination is a reliable method in forensic settings, it requires expensive reagents and is time-consuming. To develop a rapid reagent-free blood test for sex, we employed paper spray ionization mass spectrometry (PSI-MS) to capture sex-discriminatory lipid profiles from 200 DBS samples comprising 100 males and 100 females. We conducted a supervised machine learning (ML) analysis on all detected lipid signals to hunt biomarkers of sex within the data set. This analysis unveiled significant differences in specific sphingomyelin and phospholipid species levels between male and female DBS samples. Using the parsimonious set of 60 lipid signals, we constructed a classifier that achieved 100% overall accuracy in predicting sex from DBS on paper. Additionally, we assessed three-day-old air-exposed DBS on glass and granite surfaces, simulating the typical blood samples available for forensic investigations. Consequently, we achieved ∼92% overall sex prediction accuracy from the holdout test data set of DBS on glass and granite surfaces. As a highly sensitive detection tool, PSI-MS combined with ML has the potential to revolutionize forensic methods by rapidly analyzing blood molecules encoding personal information.

We introduce here a novel approach, termed time-segmented acquisition (Seg), to enhance the identification of peptides and proteins in trapped ion mobility spectrometry (TIMS)-time-of-flight (TOF) mass spectrometry. Our method exploits the positive correlation between ion mobility values and reversed-phase liquid chromatography (LC) retention time to improve ion separation and resolution. By dividing the LC retention time into multiple segments and applying a segment-specific narrower ion mobility range within the TIMS tunnel, we achieved better separation and higher resolution of ion mobility. In comparison to conventional TIMS methods, which typically scan a static ion mobility range (either from 0.6 to 1.6 [Wide] or from 0.85 to 1.3 [Narrow], V × s/cm²), the Seg method demonstrates marked improvements in identification rates. Compared to Wide scanning, the Seg method increases peptide identifications by 17–27% and protein identifications by 6–16% depending on the gradient length and the sample load. The enhancement in peptide identification is even more pronounced when compared to Narrow scanning, with an increase of 34–86%. These findings highlight the potential of the Seg dda-PASEF method in expanding the capabilities of TIMS-TOF mass spectrometry, especially for peptide-focused analyses, such as post-translational modifications and peptidomics.

Precise prefractionation of proteome samples is a potent method for realizing in-depth analysis in top-down proteomics. PEPPI-MS (Passively Eluting Proteins from Polyacrylamide gels as Intact species for MS), a gel-based sample fractionation method, enables high-resolution proteome fractionation based on molecular weight by highly efficient extraction of proteins from polyacrylamide gels after SDS-PAGE separation. Thereafter it is essential to effectively remove contaminants such as CBB and SDS from the PEPPI fraction prior to mass spectrometry. In this study, we developed a complete, robust, and simple sample preparation workflow named PEPPI-SP3 for top-down proteomics by combining PEPPI-MS with the magnetic bead-based protein purification approach used in SP3 (single-pot, solid-phase-enhanced sample preparation), now one of the standard sample preparation methods in bottom-up proteomics. In PEPPI-SP3, proteins extracted from the gel are collected on the surface of SP3 beads, washed with organic solvents, and recovered intact with 100 mM ammonium bicarbonate containing 0.05% (w/v) SDS. The recovered proteins are subjected to mass spectrometry after additional purification using an anion-exchange StageTip. Performance validation using human cell lysates showed a significant improvement in low-molecular-weight protein recovery with a lower coefficient of variation compared to conventional PEPPI workflows using organic solvent precipitation or ultrafiltration.

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

Antivenoms are the only effective treatment for snakebite envenomation and have saved countless lives over more than a century. Despite their value, antivenoms present risks of adverse reactions. Current formulations contain a fraction of nonspecific antibodies and serum proteins. While new promising candidates emerge as the next generation of antivenoms, it remains clear that animal-derived antivenoms will still play a critical role for years to come. In this study, we improved the bothropic antivenom (BAv), by capturing toxin-specific antibodies through affinity chromatography using immobilized Bothrops jararaca venom toxins. This process produced an improved antivenom (iBAv) enriched in neutralizing antibodies and depleted of serum proteins. Proteomic analysis showed that iBAv was 87% depleted in albumin and 37–83% lower in other serum proteins compared to BAv. Functional evaluation demonstrated that iBAv had a 2.9-fold higher affinity for venom toxins by surface plasmon resonance and a 2.8-fold lower ED50 in vivo, indicating enhanced potency. Our findings indicate that enriching specific antibodies while depleting serum proteins reduces the total protein dose required and increases the potency of antivenom. Although technical and economic considerations remain for large-scale implementation, this affinity-enriched antivenom represents a significant advancement in improving antivenom efficacy against B. jararaca envenomations.

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data are multidimensional, and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries do not allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to sort and filter plots by categorical or numerical variables called metas. MODE allows users to build and share these displays with several options for plot design and meta selection. The MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.