PROTEOMICS

BackgroundSepsis-associated acute kidney injury (S-AKI) is a major contributor to morbidity and mortality in critically ill patients. However, the molecular mechanisms underlying its temporal progression remain poorly understood because conventional biomarkers primarily reflect renal dysfunction rather than disease pathogenesis. Urinary extracellular vesicles (uEVs), which carry kidney-derived molecular cargo, provide a promising platform for monitoring renal-specific biological alterations during disease progression. MethodsWe conducted a longitudinal multi-omics study of uEVs collected from 81 patients with sepsis, including 48 patients with S-AKI and 33 sepsis-only controls. Patients were randomly assigned to a discovery cohort (n = 52) and an independent validation cohort (n = 29). Urine samples were collected at Day 1, Day 4, and Day 8 after AKI diagnosis. High-resolution proteomic and metabolomic profiling was performed to characterize temporal molecular alterations. Enriched pathways identified in the discovery cohort were evaluated in the validation cohort using pathway-level concordance analysis. ResultsComparative analysis between S-AKI and sepsis-only patients identified distinct stage-specific molecular alterations throughout disease progression. At the early stage (Day 1), validated pathways included complement and coagulation cascades, ferroptosis, HIF-1 signaling, sphingolipid metabolism, and arachidonic acid metabolism, highlighting coordinated inflammatory, hypoxic, and lipid metabolic responses. During the mid-stage (Day 4), persistent activation of complement and coagulation cascades, ferroptosis, and HIF-1 signaling was accompanied by metabolic reprogramming involving alanine, aspartate and glutamate metabolism and tyrosine metabolism. Although limited sample availability reduced statistical power at Day 8, phenylalanine metabolism remained validated in the metabolomic analysis, suggesting persistent metabolic dysregulation during late-stage disease progression. ConclusionsThis study provides the first longitudinal, independently validated multi-omics characterization of human uEVs in S-AKI. By integrating proteomic and metabolomic profiling, we reveal the temporal evolution of renal-specific molecular pathways from early inflammatory and hypoxic responses to subsequent metabolic reprogramming. These findings establish uEV-based multi-omics as a promising strategy for molecular phenotyping of S-AKI beyond conventional clinical biomarkers and provide a valuable resource for future biomarker discovery and therapeutic target identification.

Top-down proteomics (TDP) advances biomedical research by providing a birds-eye view of proteoforms in cells, tissues, and biofluids. Thousands of proteoforms can be characterized using well-established TDP technologies, and potential proteoform biomarkers of diseases have been discovered. However, there is a lack of an easy and biologically informative approach to present the quantitative global TDP data. Here, we present proteoform barcode as a straightforward visualization approach that simultaneously displays proteoform abundance and their associated Gene Ontology (GO) biological processes, converting a list of proteoforms to a biologically informative image. The proteoform barcode allows 1) a global view of proteoforms (i.e., relative abundance and functional information) in complex biological systems (i.e., bacteria, yeast, human cells, and human plasma) and 2) the accurate distinction of samples in diverse biological conditions (i.e., control and disease) assisted by machine learning approaches. The proteoform barcode, assisted by the random forest model, accurately separated the human plasma samples of healthy controls and early-stage breast cancer. The data demonstrates the high potential of the proteoform barcode-based approach for early diagnosis of diseases in an easy and biologically informative manner.

The discovery of alternative proteins (AltProts), translated from non-canonical ORFs, has expanded the human proteome and revealed a hidden layer known as the "ghost proteome". Despite increasing evidence, AltProts detection remains challenging due to their small size, physicochemical heterogeneity, and lack of annotation. Here, we developed an integrated bioinformatic and proteomic workflow to benchmark the detection of reference proteins (RefProts), isoforms, and alternative microproteins (MicroAltProts) in colorectal cancer cells using four extraction protocols--HCl, RIPA buffer, RIPA with chloroform, and RIPA followed by 30 kDa filtration--combined with high-resolution data-independent acquisition mass spectrometry. We identified and quantified using the Orbitrap Astral mass spectrometer a total of 66,438 peptides corresponding to 12,584 different protein groups across methods, with RIPA-based extraction approaches providing the most comprehensive coverage. To reduce redundancy in the OpenProt database and focus on MicroAltProts, we curated the dataset by removing known isoforms and long proteins, yielding a non-redundant set of 183,937 MicroAltProts. K-means clustering based on eight ProtParam-derived features grouped MicroAltProts into four physicochemical clusters. Among them, 43 MicroAltProts (<200 amino acids) were experimentally validated by mass spectrometry and classified into tiers following recent recommended international guidelines. Cluster assignment of detected MicroAltProts revealed that HCl extraction favored disordered, alkaline proteins, while RIPA-based protocols enabled the identification of membrane-associated and amphipathic -helical MicroAltProts. Structural prediction indicated the presence of diverse folding determinants, including transmembrane helices, disordered regions, and nucleic acid-binding-like motifs. Altogether, this study provides a roadmap framework for the unbiased simultaneous detection of RefProts, isoforms, and AltProts, and supports a broader functional role for MicroAltProts.

There are many databases of post-translational modifications, but each has different protein and PTM coverage, making the current state of post-translational modifications difficult to obtain, assess, or harmonize. ProteomeScout has been a relational database that harmonized a myriad of resources to address this challenge. In this update, we wished to create a lightweight, sustainable open source resource for the community that retained the power of harmonization, protein visualization, and Python tools for interacting with the database. ProteomeScout-v4 presents an ecosystem of open-source tools for aggregating and updating proteins and post-translational modifications and presenting the web-based and code-based access tools for interacting with the data. ProteomeScout has moved from using a complex relational database to a flat-text file format. The database file update is version controlled on FigShare and all features have processes for continuous updating, including migrating PTMs to updated protein records, when external databases are updated. The open-source nature and flexibility allow for user-based control and community-based feature integration. Fast and easy analysis is possible with the API, with example analyses provided here.

Novel serum peptidomics offers a direct insight into proteolytic activity, tissue injury, and systemic signaling. Nevertheless, existing workflows suffer from low peptide yields, low throughput, and limited recovery of low-abundance species. Here we present a native serum peptidomics protocol that integrates mild acid treatment, solid-phase extraction with molecular weight cutoff filtration and data-independent acquisition mass spectrometry (DIA-MS). The protocol requires less than 100 {micro}l of serum or plasma, is completed within hours, time-cost-effective and compatible with 96-well formats without specialized equipment. Applied to a proof-of-concept cohort of patients with acute ischemic stroke (AIS), intracranial haemorrhage (ICH), and healthy controls, the workflow identified over 12,000 peptides, exceeding the three-fold threshold of existing peptidomics approaches. DIA-MS analysis across independent batches demonstrated 78-83% peptide overlap and consistent fold-change directionality. We further introduce peptide locus analysis, which aggregates overlapping peptides within defined protein regions. This approach revealed bidirectional regulation within individual precursor proteins such as the fibrinogen alpha chain (FIBA), resolving intraprotein proteolytic dynamics. Three candidate peptides from TYB4, CO4B, and ITIH4 proteins accurately distinguished stroke subtypes and controls, while characteristic shifts in peptide physicochemical properties were observed across strokes. This workflow substantially advances the sensitivity, throughput, and biological resolution of serum peptidomics for quantitative multi-biomarker discovery, validation and its output promises effective implementation of AI/ML models aiming for new dimensions in diagnostics, prognostics, prediction and monitoring.

Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

Serum proteomics is increasingly used for minimally invasive biomarker discovery and disease phenotyping, and the choice of serum preprocessing workflow can shape proteome depth, quantitative characteristics, and downstream biological readouts. However, disease-oriented comparisons within a single cohort remain limited. Here, we compared four serum preprocessing workflows--Top14 depletion (TOP14D), tomato lectin affinity purification (TomAP), and two nanoparticle-based enrichment workflows (NPA and NPB)--using serum from six patients with systemic juvenile idiopathic arthritis (sJIA) and six age- and sex-matched healthy controls, and analyzed them using unified data-independent acquisition mass spectrometry (DIA-MS) and a statistical pipeline. We evaluated proteome depth, missingness, quantitative characteristics, group separation, differential abundance signatures, pathway enrichment, curated sJIA-related gene set coverage, pre-ranked gene set enrichment analysis (GSEA) results, and detection of inflammasome/interferon-related proteins. TomAP yielded the greatest proteome depth (7612 proteins), followed by NPB (6735 proteins) and NPA (6602 proteins), whereas TOP14D yielded the smallest protein set (3303 proteins). Principal component analysis (PCA) showed a separation between the sJIA and control groups for all workflows. Differentially expressed proteins (DEPs) showed limited overlap, with only 75 DEPs common to all four workflows. Functional enrichment patterns were workflow-dependent; TOP14D and TomAP mainly captured neutrophil/myeloid and inflammatory processes, whereas NPA and NPB captured RNA processing- and translation-related signals. TomAP showed relatively broad coverage and positive enrichment of curated sJIA-related gene sets associated with inflammation, innate immunity, and macrophage activation syndrome (MAS). Inflammasome/interferon-related proteins, including NLRC4, PYCARD, GSDMD, MEFV, IL-18, OAS3, and MYD88, showed workflow-dependent detectability and differential abundance. These findings support a disease-oriented benchmark for fit-for-purpose workflow selection according to the disease axis and analytical objective rather than proteome depth alone.

Spatially resolved single-cell proteomic imaging technologies, including cyclic immunofluorescence (CycIF), generate high-dimensional data, critical for tissue-scale biological analysis. However, single-cell analysis remains computationally demanding, lacks standardization across platforms and is often inaccessible to experimental biologists without programming expertise. Here we present SCORPy (Single-Cell proteOmics Research Platform), a standalone, cross-platform desktop application that provides an end-to-end, code-free workflow for the analysis of single-cell proteomic data extracted from imaging experiments. SCORPy introduces methodological advances for preprocessing multiplexed imaging data: an exposure-aware, cycle-matched background correction strategy, and a normalization framework that harmonizes signal distributions across markers while enabling batch correction across experiments. These approaches are integrated with quality control, interactive thresholding and cell phenotyping using a hierarchical cell reference library, and downstream compositional and spatial analyses within a unified interface. Sample-level metadata can be incorporated throughout the workflow to support integrative analyses and facilitate generation of publication-ready visualizations. By combining robust preprocessing methods with an accessible implementation, SCORPy reduces computational barriers and promotes broader adoption of spatial single-cell proteomics analysis.

ShortThe SwissProt database contains a stable 20,418 human protein-coding genes and 42,541 human protein sequences. Ribo-Seq suggests about 7,000 additional, non-canonical Open Reading Frames (ORFs) are present in humans, though only a few of them are confirmed by Mass Spectrometry (MS). Detecting these proteins requires extensive database searches, increasing computational load and inflating False Discovery Rates (FDR). Using the ionbot search engine with the OpenProt database allows for reliable detection of non-canonical proteins while controlling FDR. Ionbot surpasses the Trans-Proteomics Pipeline (TPP) in reproducibility, identifying more peptides and proteins supported by multiple spectra. In addition, open modification searches yield better PSMs compared to closed searches. This work highlights the importance of employing cutting-edge search engines in non-canonical protein research, as well as the value of open modification search in correcting errors in non-canonical protein detection. LongO_ST_ABSBackgroundC_ST_ABSThe SwissProt database reports a quite stable 20,418 human protein-coding genes and 42,541 human protein sequences, figures that have remained stable. New techniques like Ribo-Seq indicate that approximately 7,000 additional, non-canonical Open Reading Frames (ORFs) are translated in humans, few of which have been confirmed by Mass Spectrometry (MS). Detecting these non-canonical proteins requires comprehensive database searches, which increase computational load and False Discovery Rate (FDR). Here, we use the open search engine ionbot in combination with the OpenProt proteogenomics database to reproducibly detect non-canonical proteins while maintaining a well-controlled FDR. ResultsCompared to the current gold standard, the Trans-Proteomics Pipeline (TPP), ionbot shows higher reproducibility, with a higher number of peptides and proteins supported by multiple spectra, and across multiple samples. We observe that PSMs from the open modification search against OpenProt have higher fragment ion intensity correlation compared to PSMs obtained from the closed search, or by only searching canonical proteins. ConclusionsIn this work, we show the potential for open modification searching to correct potential mistakes in non-canonical proteins detection by preventing modified canonical peptides or variants from being incorrectly identified as non-canonical peptides. We also highlight the importance of assessing the FDR of non-canonical identifications separately from canonical ones, as global FDR calculations are biased by the scarcity of non-canonical identifications in each dataset.

BackgroundNanopore-based protein reading holds promise for single-molecule proteomics, but analyzing the resulting ionic current signals presents substantial computational challenges. Unlike nucleic acids, peptide-associated ionic signals exhibit complex patterns with subtle transitions between amino acids that can be obscured by noise. We developed an integrated computational strategy for processing nanopore protein ionic current signals from synthetic protein constructs containing all 20 amino acids in controlled sequence contexts. We started with denoising to remove artifacts, followed by change point detection using variable-number and fixed-number segmentation methods. ResultsWe employed dynamic time warping barycenter averaging to generate amino acid-specific reference templates. In parallel, we performed classification of amino acid regions using bidirectional LSTM networks trained on segment-level statistical features. Accuracy for 20-way amino acid classification remained limited, while charge-based binary classification reached 93.3% accuracy. Pairwise classification revealed that certain amino acids, particularly negatively charged residues, generate distinctive signatures. We used a profile hidden Markov model to capture sequential structure of protein translocation. ConclusionThis approach provides computational strategies for automated segmentation, classification, and modeling of nanopore peptide translocation ionic current data.

High-throughput proteomics now spans several technologies that differ in biological breadth, precision, specificity, sensitivity, cost, and method(s) of quantification; however, investigators currently lack an evidence-based framework to select or combine platforms. Here, we present the Atlas of Proteomic Technologies (APT) - a curated resource that evaluates six commercial platforms across ten analytical dimensions using published head-to-head evidence, expert evaluation, and iterative peer review. APT hosts two decision tools: the Help Me Choose tool maps a study's primary aim(s), scale, sample matrix, and technical constraints to a calibrated, weight-adjustable platform ranking, and the Help Me Combine tool ranks complementary platform pairs by their net-new protein coverage and user-specified technical priorities. Both tools were tested exhaustively across all possible combinations and behave in a balanced, merit-based manner. APT is openly accessible at https://aptatlas.org, providing downloadable tool specifications, seeded analysis scripts, and complete protein coverage lists, ensuring every score and recommendation is inspectable and reproducible.

Mass spectrometry (MS) is the leading technology for identifying proteins in complex biological samples. It relies on the use of tandem MS alongside a reference database of canonical protein sequences to computationally identify peptides and their parent proteins. The canonical sequences represent the most widely expressed and functionally validated forms of proteins. Consequently, disease-induced or disease-supportive variants, such as those associated with cancer, will evade detection if they are absent from the database. To address this challenge, this study introduces a revised release of the Unkown Mutation Analysis (XMAn) database by incorporating coding missense and nonsense mutations from the latest versions (v103) of the COSMIC Genome Screen Mutants (GSM) and Cancer Gene Census (CGC) datasets in two distinct FASTA-formatted peptide databases comprising 3,848,499 and 312,658 variants, respectively. The mutated peptides were matched to reviewed, non-redundant UniProt Homo sapiens protein entries (18,362 and 746), and characterized in terms of nucleotide- and amino acid mutation frequencies, peptide length distributions, and associations between specific single-nucleotide (SNV) and single amino acid (SAAVs) variants. Applied to the analysis of MDA-MB-231 breast cancer cell-membrane protein fractions, the database enabled the identification of 300+ high-quality variant peptides - several localized to functional protein-binding and catalytic domains - and 23 aberrant protein products mapped to the CGC dataset. The database is hosted and available for download on Zenodo (XMAn/gsm doi: 10.5281/zenodo.21781023; XMAn/cgc doi: 10.5281/zenodo.21781514) or can be accessed through https://sites.google.com/vt.edu/xman-db/home.

Deciphering the functions of post-translational modifications (PTMs) is a critical bridge connecting large-scale modification proteomics data to mechanistic studies. However, most existing tools for visualizing PTM omics data are limited to site catalogs or single-dimensional feature displays. They lack the capability to simultaneously map user-derived differential modification sites onto multi-dimensional contexts, including protein three-dimensional (3D) structure, evolutionary conservation, functional sites, and disease associations. This limitation makes it difficult for researchers to rapidly assess the biological importance of candidate sites from among a vast number of differentially modified sites. Here, we present PTMExplorer, an interactive platform for the multi-dimensional visualization of protein PTMs. PTMExplorer comprises three core modules: PTM Inspector, built upon ProtVista, provides a multi-track, sequence-feature integrated view incorporating intrinsically disordered region (IDR) prediction (via flDPnn), surface accessibility calculation (via FreeSASA), and UniProt functional annotations; PTM 3D Locator, leveraging the Nightingale/Mol* engine, anchors modification sites onto AlphaFold/Protein Data Bank (PDB) 3D structures through residue mapping via PDBe-SIFTS; and PTM Overview, utilizing the R circlize package, presents a panoramic polar circos plot illustrating modification distribution and inter-group differential regulation. Additionally, three major disease-associated modification databases (PTMD, qPTM, and PhosCancer) are integrated as PTM-Disease Nexus, enabling co-localization comparison between user-defined differential sites and reported disease-related sites. PTMExplorer currently supports eight model organisms, accepts user-uploaded differential analysis results, and provides multi-dimensional annotations and various visualization options (https://www.bioladder.cn/PTMExplorer/). Using a multi-omics dataset from hepatocellular carcinoma (18 patients, 9 modification types) as a case study, we demonstrate the practical utility of PTMExplorer in screening potential biomarkers, revealing multi-modification coordination mechanisms, and distinguishing between absolute and relative quantification patterns.

Proteomics studies increasingly use different technologies, including mass spectrometry, the antibody-based Olink platform, and the aptamer-based SomaScan platform. However, downstream analysis often depends on platform-specific scripts or point-and-click tools, limits flexibility and makes analyses difficult to reuse across platforms. We developed ProtPipe2, a downstream proteomics analysis framework for mass spectrometry, Olink, and SomaScan data that is available as both an R package and an interactive web application. ProtPipe2 supports quality control, preprocessing, imputation, batch correction, statistical analysis, visualization, pathway analysis, and result export. ProtPipe2 expands the original Protpipe workflow to additional proteomics platforms and provides reusable R functions for imputation, batch correction, and other downstream analyses. Its redesigned web application provides user-controlled methods and parameters through the same R-based workflow. Case studies across mass spectrometry, Olink, and SomaScan datasets demonstrated a consistent downstream analysis workflow across platforms. By supporting three proteomics platforms through both graphical and scripted workflows, ProtPipe2 makes downstream analysis easier to perform, repeat, and review. ProtPipe2 is freely available at https://nih-card.github.io/ProtPipe2/.

Stem cells secrete diverse extracellular proteins that regulate pluripotency, differentiation, and cell-cell communication, making them powerful model systems for studying development, disease mechanisms, and regenerative medicine. However, robust stem cell secretome analysis remains technically challenging. Unlike many other cell types, stem cells cannot tolerate serum starvation or growth factor deprivation, while low-abundance secreted proteins are often masked by media-derived proteins and intracellular contamination. Here, we systematically optimized the secretome proteomics workflow in iPSCs, by evaluating culture medium composition, conditioned-media collection time, cell plating density, media harvest and preparation methods, LC-MS acquisition methods, and data analysis strategies. Full-strength Essential 8 medium, 48 h media collection, 80% cell confluency, two-step centrifugation, and data-independent acquisition (DIA)-LC-MS/MS provided the optimal secretome proteomics data quality. We then applied the optimized platform to an isogenic iPSC disease model to investigate how progranulin deficiency reshapes the extracellular and intracellular proteomes. Progranulin-deficient iPSCs showed a coordinated reduction of extracellular lysosomal hydrolases despite relatively modest intracellular proteome changes, suggesting altered lysosome trafficking and possible impairment of lysosomal exocytosis. Together, this work establishes a robust and standardized workflow for stem cell secretome proteomics and demonstrates its utility for investigating extracellular proteome remodeling in human disease models.

Plasma proteomics is increasingly used for biomarker discovery and predictive modeling, yet diurnal protein trajectories remain insufficiently characterized. In our review of recent proteomic biomarker studies, 43% of the identified biomarkers had previously been reported to display 24-h rhythmicity. We demonstrate that ignoring these short-term dynamic effects compromises the robustness of reported models predicting health outcomes. We integrated a population-scale multi-ethnic longitudinal cohort with repeated measures over 10 years, with two cohorts of healthy adults undergoing frequent plasma sampling across days under controlled circadian, sleep and food-intake conditions. This design enabled estimation of short-term intraindividual variability (ST), long-term intraindividual variability (LT), population-level variability (POP) and genetic effects (GEN) across 7,289 protein targets. ST, LT, POP, and GEN define diverse protein trajectories, including rapid dynamics, long-term change, and individual-specific signatures. Using and generalizing this framework will facilitate covariate selection, study design, biomarker prioritization, and variability-aware modeling by users of proteomic data. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/741635v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@55520eorg.highwire.dtl.DTLVardef@17e4bacorg.highwire.dtl.DTLVardef@9a0d0borg.highwire.dtl.DTLVardef@1ce88fb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Narrow-window data-independent acquisition (nDIA) is emerging as a powerful technique for bottom-up proteomics. Here, we systematically benchmarked nDIA, wide-window DIA (wDIA), narrow-window data-dependent acquisition (nDDA), and wide-window DDA (wDDA) for rapid, single-shot proteomic analysis. For data processing, we introduced Tesorai Search, a new search engine leveraging a large pre-trained model and compared it with DIA-NN and FragPipe across both DIA and DDA datasets. Among 12 acquisition-analysis pipelines evaluated, nDIA combined with DIA-NN and Tesorai Search delivered the highest proteome coverage, identifying 10,255 and 10,766 protein groups from benchmark samples, respectively. Both search engines maintained rigorous false-discovery rate (FDR) control. While nDIA generally outperformed nDDA in sensitivity, FragPipe-DDA+ approach proved to be the most sensitive within the nDDA pipelines. However, entrapment analyses indicate that this sensitivity comes at the cost of less robust FDR control compared to Tesorai Search. As a proof of concept, we applied nDIA-MS to 17 cancer cell lines harboring DNA damage response (DDR) gene knockouts, successfully detecting significant downregulation of all targeted proteins and uncovering 81 DDR-related proteins modulated in at least one cell line. These results underscore nDIA-MS, together with DIA-NN and Tesorai Search, as a robust and scalable platform for high-throughput functional proteomic screening.

Purpose: Many spinal cord-injured (SCI) patients are recurrently catheterized to manage urinary retention and incontinence. Microbial pathogens colonize urethral catheter surfaces, persist in form of biofilms and trigger inflammatory responses in the bladder. Our objective was to determine bladder tissue changes due to long-term inflammation using LC-MS proteomics. Experimental Design: We examined longitudinal proteomic profiles from eight SCI patients, sampled over 2 to 6 months, in a patient-specific manner as well as compared with trauma patients diagnosed with acute urinary tract infections (UTI). The sample sources were urinary pellets (UP) from all subjects, and catheter biofilm extracts (CB) from SCI patients. Bioinformatic protein network and western blot analyses served to corroborate evidence of protein and cell differentiation changes in the urinary tract of SCI patients. Results: More than 50 proteins with functional or structural roles in the epidermal cornified envelope were quantitatively increased in proteomic profiles from three SCI patients. This included stress response keratins and proteins involved in keratin crosslinking, e.g. transglutaminases and small proline-rich proteins, suggesting occurrence of keratinizing squamous epithelial metaplasia. Protein co-expression network analyses supported epithelial trans-differentiation changes. Nearly thirty proteins with gastro-intestinal epithelial cell expression specificity were enriched in proteomic profiles from a single SCI patient. Among those were mucin-2 and sucrase-isomaltase, secreted proteins detected in samples for only that patient via western blots. This finding was consistent with intestinal epithelial metaplasia. Conclusions and clinical relevance: Without using invasive cystoscopy and histopathology, proteomic analyses from easily available UP and CB samples allowed us to infer cell differentiation changes in urothelial tissues of SCI patients with chronic infections, supporting a concept of clinical relevance for urothelial metaplasia screening, a precursor stage of urothelial neoplasms.

O-glycosylation is among the most abundant and structurally diverse post-translational modifications in eukaryotes, yet its heterogeneity renders O-glycoproteins exceptionally difficult to characterize. To overcome these challenges, we have developed an integrated mass spectrometry (MS) strategy to define the proteoform landscape of O-glycoproteins and applied it to human osteopontin (OPN). OPN is a disease-associated extracellular matrix protein subject to extensive modification. By combining native MS with serial exoglycosidase digestions, we directly resolved truncation, phosphorylation, sulfation, and O-glycosylation of OPN. Matched glycoproteomic analyses, using tailored (glyco)protease combinations, allowed us to quantify glycan heterogeneity inaccessible to conventional trypsin-based approaches or protein-centric methods. We integrated these datasets using forward compositional simulations to infer the intact OPN proteoform distribution and benchmarked the resulting models against an experimental intact-mass distribution obtained by proton-transfer charge-reduction MS. This comparison revealed that bottom-up O-glycoproteomics systematically underestimates the true extent of glycan sialylation, whereas assuming (near-)complete sialylation accurately reproduced the experimental intact OPN mass distribution. Together, these results provide a comprehensive, quantitative view of OPN compositional diversity and demonstrate how intact-protein and peptide-level measurements can be reconciled to resolve highly heterogeneous glycoform populations. The workflow establishes a broadly applicable framework for characterizing extensively O-glycosylated and multiply modified proteins.