PROTEOMICS

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.

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.

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.

Proteolysis can be completed in a single-step, rapid (5-30 minutes) reaction using hyperthermoacidic archaeal (HTA) proteases. Protocol simplicity and stability of HTA proteases in ambient storage conditions removes many logistical barriers to widespread deployment of proteomic analysis. HTA protease cleavage selectivity is complicated but quantitatively reproducible and generates peptides that negligibly overlap with trypsin. Tryptic and HTA protease peptides emphasize different proteins and provide complementary coverage of amino acid sequence and post-translational modification (PTM) sites. Digestion of a sample's separate aliquot with HTA protease is a minor addition to trypsin-based workflows, but LC-MS analysis of both sets of peptides is not practical for large cohorts. This manuscript evaluates the identifications and reproducibility of LC-MS analysis of tryptic and HTA protease peptide mixtures. Adding HTA protease peptides to 20% increased total protein identifications by 24% on average in neat plasma and identified 216 additional non-artifact modifications compared to conventional trypsin-only bottom-up proteomic analysis. This approach is reproducible and does not increase sample preparation or analysis time. Peptides from each protease present independent opportunities to quantify overlapping proteins and a quantification model was developed in which the protease with the most complete data is selected for each protein while the second protease is used to infer missing values. This cross-protease inference is more accurate than the alternative minimum, mean, or nearest neighbor imputations. Finally, biomarkers discovered through HTA protease peptides are amenable to rapid HTA protease only proteomic workflows thus streamlining translation from discovery to fieldable applications.

Ensuring the safety and suitability of proteins considered for food and biotechnology applications is an important challenge. Computational approaches can facilitate the prioritisation of candidate proteins for further investigation, particularly given the rapid growth of genomic and metagenomic sequence datasets. In this study, we developed computational models to distinguish edible plant tissue-associated (EPTA) proteins from non-EPTA proteins. The positive class comprised 939 proteins with experimentally validated evidence of expression in plant tissues commonly consumed by humans, hereafter referred to as EPTA proteins. The negative class comprised 939 proteins annotated in UniProt as toxic, allergenic, or antinutritional, hereafter referred to as non-EPTA proteins. Models were evaluated using five-fold cross-validation with a cluster-based partitioning strategy, ensuring that no protein in the validation set shared more than 40% sequence identity with proteins in the corresponding training set. First, composition-based sequence features were evaluated to establish a baseline for EPTA or non-EPTA classification, with the Extra Trees (ET) classifier achieving a validation AUROC of 0.848. To incorporate evolutionary information beyond amino acid composition, PSSM profiles were subsequently evaluated, yielding an AUROC of 0.842 with an SVM classifier. Finally, to capture richer sequence patterns and contextual information using protein language modelling, we evaluated ESM2-based models. The fine-tuned ESM2-T33 model achieved the highest validation AUROC of 0.925 and was selected as the final predictive model. The resulting approach was implemented as the EdiProPred web server (https://webs.iiitd.edu.in/raghava/edipropred/) and standalone software for computational classification of plant proteins. The EdiProPred framework provides a useful computational tool for prioritising candidate plant proteins and facilitating the exploration of novel protein resources for food and biotechnology applications.

Phenylketonuria (PKU) is an autosomal recessive disorder caused by PAH gene defects and can cause irreversible neurological damage as early as the fetal period; however, non-invasive prenatal monitoring methods are lacking. In this study, Pah+/- female mice were mated with Pah+/-; male mice and wild-type male mice, respectively. Urine samples were collected longitudinally from pregnant mice at 10 time points during gestation from Day 1 to Day 19. Data-independent acquisition (DIA) quantitative proteomics combined with GO/KEGG analysis, WGCNA, and a dual-strategy LASSO-random forest approach was used to screen candidate biomarkers. The results showed that stable differentially expressed proteins between groups were detectable as early as Day 1, and the differences persisted throughout gestation and exhibited temporal dynamics. Permutation tests indicated that the probability of random generation of the differential protein combination was low (0.04-0.06), suggesting that it was not randomly generated. The intersection of the two strategies yielded five proteins, GINM1, PRG4, LY6A, NTM, and YIPF3, and after including GLO1, a total of six candidate biomarkers were obtained. The candidate proteins had interaction or co-expression associations with 14 proteins involved in PKU mechanisms, with GLO1 serving as a network hub. This study found that the maternal urine proteome can non-invasively reflect the systemic response induced by fetal-origin PKU, providing proof of concept and candidate biomarker resources for non-invasive early-pregnancy screening of PKU.

Key plasma protein, including albumin, complement system proteins, and coagulation factors circulate in biochemically and functionally distinct isoforms, or proteoforms. Despite the importance of these proteoforms being widely accepted, they are challenging to resolve systematically, and as a consequence, we still lack a more complete picture of the total proteoform diversity of the human plasma proteome. Here, we generated 3,114 intact protein fractions by extensively separating plasma according to isoelectric point and molecular weight. We then analysed these fractions by liquid chromatography data-independent acquisition mass spectrometry (DIA-MS) upon tryptic digestions, and combined the resultant data into a multidimensional, proteoform resolved atlas of the human plasma proteome. We report that 5,077 unequivocally detected plasma proteins circulate at least 179,049 distinct proteoforms, of which we detect 44,083, with a median of 11 proteoforms per protein, at high confidence. Substantially increasing the number of experimentally confirmed proteoforms, our atlas implies that the canonical protein forms account for less than half of the total protein mass in plasma. We detect the greatest proteoform diversity among the most abundant plasma proteins and those functioning in nutrient transport, complement and coagulation. Finally, our data clarifies discrepancies between the Olink and SomaScan proteomic platforms, as according to our atlas, these techniques provide correlated values for relatively abundant proteins with low to medium proteoform diversity, but struggle in the quantification of low-abundant proteins and on those which exist in many proteoforms. In sum, our data reveals extensive proteoform diversity of the human plasma proteome, and implies that proteoform diversity represents an enormous untapped reservoir for biomedical research and biomarker discovery.

Mass spectrometry-based proteomics increasingly supports study designs with repeated measurements, such as longitudinal sampling and spatial tissue profiling. These designs offer rich biological insights but introduce statistical challenges due to within-subject correlations and variance heterogeneity across time points or anatomical locations. Despite the growing use, best practices for analyzing repeated-measures proteomics data remain limited. We systematically compare commonly applied methods, including paired t-tests and linear mixed-effects models with correlation induced by random effects, to more flexible covariance-pattern models with unstructured covariance. Through simulation studies, we evaluate false-positive rates and statistical power under varying conditions of covariance heterogeneity. Our results demonstrate substantial limitations of conventional approaches and highlight the robustness and superior performance of unstructured covariance pattern models for repeated measures proteomics. This approach accommodates diverse study designs supporting accurate inference and reproducibility. We demonstrate that conventional F-tests can be severely biased, not just for small sample sizes but also moderately large ones, while using pairwise t-tests or applying parametric bootstrapping to evaluate the p-value of the F-tests mitigates the problem. We provide real world data examples showing how to perform valid and informative statistical analyses for temporal and spatial proteomics studies with repeated measurements.

Single-cell proteomics requires computational choices to remain aligned with experimental design as research questions evolve. Here we present ScProteoAgent, which translates natural-language requests into domain-specific calculations and preserves research intent, analysis design, matrix-processing history and statistical outputs as reusable analytical state. Follow-up requests reuse applicable inputs and initiate new calculations when contrasts or experimental units change. We assembled a benchmark comprising 55 tasks and 33 reference conclusions from 11 published studies, combining progressive questions within studies with complementary biological and analytical settings. In a retrospective comparison of 88 archived outputs from eight systems, ScProteoAgent achieved the highest mean score of 93.67 under a common six-dimensional rule-based evaluation. A HeLa migration case connected phenotype composition with proteomic clusters, candidate differences and original-observation support. In liver zonation, a researcher-guided follow-up reused a saved matrix to test mouse-level spatial contrasts and compare full-matrix and observed-only results. Applications in brain development, sample preservation and hematopoiesis illustrated how interpretation depends on processing choices and experimental units. ScProteoAgent connects research questions with statistical results and their analytical context, enabling existing analyses to support more specific biological investigations.

Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.

Reliable quality control (QC) is essential for reproducible biological mass spectrometry, yet QC information is still typically stored in tool-specific, poorly interoperable formats that hinder comparison, reuse, and transparent reporting. We present mzQC, a community-developed standard from the HUPO Proteomics Standards Initiative for the compact, machine-readable representation of QC metrics and their associated metadata across mass spectrometry workflows. mzQC is implemented in JSON, organized to support both individual mass spectrometry acquisitions and collections thereof, with formal QC metrics definitions supported by controlled vocabularies to ensure unambiguous metric interpretation and semantic consistency. The format is supported by validation tools and reference libraries in Python, R, and Java, facilitating integration into existing software ecosystems. We demonstrate the utility of mzQC across diverse analytical modalities and QC settings, spanning longitudinal instrument monitoring, acquisition-method evaluation, study-level quality assessment, repository-scale data characterization, and spatially resolved mass spectrometry imaging. Together, these use cases show that mzQC provides not merely a file format, but a shared foundation for interoperable QC reporting, large-scale data assessment, and more rigorous, reproducible, and reusable mass spectrometry-based research.

Cerebrospinal fluid (CSF) contains brain derived proteins that can be leveraged as biomarkers for Alzheimer's disease (AD) and other neurodegenerative diseases. However, converting protein intensities to a disease signal remains a challenge because physiology driven changes to the localization of proteins across CSF subcompartments are obscured when CSF is analyzed alone. Where subcompartment data, such as that from extracellular vesicles (EVs), are also measured, separate but parallel analyses of EVs and CSF can obscure their shared disease signal. Here, we present an integrated matrix factorization approach that combines proteomic signals from matched CSF and phosphatidylserine exposed EVs and nanoparticles (EVps). In a ROS/MAP cohort of 48 participants, integrating CSF and EVps signals improved biological concordance across compartments and increased the number of candidate proteins distinguishing participants with AD from controls. Notably, the joint signal identified as many brain related biomarker candidates as studies using substantially larger cohorts. By demonstrating that integration of CSF and EVps proteomes maximizes information from limited samples, this framework offers a path toward more efficient biomarker discovery across two compartments increasingly leveraged in neurodegenerative disease research.

Euglena longa is a heterotrophic model euglenid, of significant research interest as a unicellular eukaryote. To provide broad insight into the localisation of its proteome we used subcellular spatial proteomics (SSP), based on a label-free version of LOPIT-DC (Localisation of Organelle Proteins by Isotope Tagging after Differential Centrifugation), in which organelles are separated by differential centrifugation and each protein is assigned to a compartment from its abundance profile across the fractions. We detected 5,909 proteins, using protein abundance profiles across fractions to resolve 16 different subcellular compartments, such as the plastid, pellicle, and peroxisomes, and assigned high-confidence localisations to 2,955 of them. This study serves as a proteomic dataset to resolve protein compartmentalisation for this euglenid, enabling evolutionary, biological and functional insight and analysis. We are drafting manuscripts concerning the metabolic nature of the plastid, the evolutionary origins of the pellicle, and the functions of the peroxisome. Investigators interested in using this data prior to publication are encouraged to contact us to avoid overlap and to facilitate coordinated and collaborative use of this resource.

Background: Pre-operative triage of adnexal masses with serum CA-125, HE4, and ultrasound (O-RADS) has limited accuracy, contributing to unnecessary surgery. We develop and validate a first of its kind, biomarker panel-free plasma proteomic classifier that distinguishes malignant from benign adnexal masses from a single blood draw, operating directly on discovery-mode mass spectrometry. Methods: In this multicenter prospective observational study, plasma from women with adnexal masses and from healthy controls at a large urban academic center (discovery and four validation cohorts) and an external academic center from another Urban-suburban area (external validation) was analyzed by data-independent-acquisition mass spectrometry (Orbitrap Astral), quantifying >1,000 proteins per sample. Cancerous (malignant, borderline adnexal mass, or metastasis to ovary) versus benign adnexal masses were classified with ADAPT-MS (Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry), which retrains an ElasticNet model for each sample based on the proteins measured. The primary outcome was discrimination (area under the receiver operating characteristic curve [AUC]). Pre-operative O-RADS scores and serum CA-125 were compared and combined with proteomics on matched subsets. Findings: Using a discovery cohort (n=1062), an ADAPT-MS classifier, validated across five independent cohorts (n=623), achieved a summary AUC of 0.835 (cohort range 0.779-0.904). Among patients with pre-operative O-RADS (n=231), proteomics (AUC 0.852) was comparable to O-RADS alone (0.863) and combining them raised discrimination to 0.922. Proteomics outperformed serum CA-125 (0.915 versus 0.777; n=125) and proteome-derived ROMA-like and two-marker surrogates. Biologically, the classification-relevant proteins are enriched for host response proteins rather than tumor cell associated protein changes, comprising ECM remodeling, complement and innate immune response pathways. This explains good detection of tumor with heterogenous pathology and also metastasis to ovary. Interpretation: A single plasma proteomic measurement interpreted using adaptive machine learning discriminates malignant from benign adnexal masses with good internal and external validation, and is complementary to ultrasound, warranting prospective evaluation as a panel-free diagnostic adjunct to imaging.

Ubiquitination is a post-translational modification with well described roles in the regulation of immune cell function, such as dendritic cells (DCs). DCs are crucial initiators and regulators of T cell-mediated immune responses. In this study, we use activity-based probes and proteomics to systematically profile DUBs in a DC line. Using the lysate-based biotin(bio)-Ubiquitin(Ub)-propargylamide(Prg) probe and proteomics, the activity and abundance of deubiquitinases was measured in unstimulated and CpG-stimulated MutuDCs to simulate responses to bacteria. This analysis was complemented with profiling using the cell-permeable DUB activity-based probe IMP-2373. The combined analysis with bio-Ub-Prg and IMP-2373 provided insights into the dynamics of DUB activity and abundance in DCs following stimulation and identified several DUBs for further study.

In bottom-up proteomics, protease specificity determines peptide length and LC/MS/MS detectability; in N-terminomics, this is critical because each protein N-terminal peptide is unique and cannot be substituted by other peptides. CHromatographic AMplification of Protein N-terminal peptides (CHAMP-N) combines LysargiNase digestion with StageTip-based strong cation exchange chromatography (SCX), removing internal peptides by exploiting their newly generated N-terminal Lys/Arg residues and thereby selectively isolating protein N-terminal peptides. However, LysargiNase cleavage near the protein N-terminus can generate excessively short peptides. Here, we developed two complementary CHAMP-N approaches that modurate N-terminal peptide length while retaining the charge-based SCX separation principle. LysN digestion avoided cleavage at N-terminal-proximal Arg residues, whereas D3-acetylation blocked Lys cleavage by LysargiNase to produce ArgN-like specificity. Both approaches extended otherwise short N-terminal peptides while preserving N-terminal basic residues on internal peptides. Conventional LysargiNase, LysN, and ArgN-like CHAMP-N generated complementary sets of N-terminal peptides. Integration of the three workflows identified 2,552 protein N-termini, approximately twice the number identified by conventional CHAMP-N alone. The median relative standard deviations of N-terminal peptide intensities were approximately 15% for all three workflows, indicating comparable technical reproducibility. These results demonstrate that coordinating protease cleavage specificity with SCX selectivity reduces sequence-dependent losses and expands N-terminome coverage while preserving the simplicity and reproducibility of CHAMP-N.

Quantitative proteomics requires decisions about normalization, missing-data handling, and statistical modeling that can change the reported results. ProteoScopeR is an R package and Shiny application that connects these decisions in a traceable workflow and complements downstream exploration in xOmicsShiny. Side-by-side comparisons expose changes in distributions, feature retention, effect estimates, and selected protein sets. An optional external artificial-intelligence assistant reviews exported evidence and proposes settings for researcher approval; statistical calculations remain in R. A group-only case study used DIA-NN-derived aqueous humor data from 69 samples and 3,667 imported protein groups. Six normalization methods and seven missing-data strategies were screened, and the approved cyclic-loess analysis compared five differential workflows. For nAMD versus pmCNV, limma and proDA selected 54 and 37 protein groups, respectively, with 31 shared selections at adjusted P < 0.05 and absolute log2 fold change [&ge;] 0.5. Input routing, scientist decisions, locked settings, and an execution receipt preserve the analysis record. These comparisons describe sensitivity to analytical choices rather than identify a universally superior method.

Tangential flow filtration (TFF) is an effective method for scalable EV isolation. Here, we describe and validate a reference protocol for TriPort-TFF, a laboratory-assembled TFF system featuring a three- port reservoir design enabling sample circulation, retentate return, and diafiltration. Constructed from individually sourced, commercially available components with detailed assembly and operating parameters, TriPort-TFF can be built at a lower cost than commercially integrated TFF platforms. We evaluated the system using conditioned media (CM) from human MDM cultured with EV-depleted human serum, human cerebral organoids in serum-free medium, and mouse BMDM cultured with EV-depleted foetal bovine serum (FBS) unstimulated or LPS-stimulated. The system reproducibly isolated EVs from all three sources, and biological replicates of BMDM showed distinct proteomic clusters between LPS-stimulated and unstimulated conditions. The requirement for subsequent size-exclusion chromatography (SEC) after TFF depended on the sample serum-presence. In serum-containing CM, TFF alone co-concentrated serum proteins alongside EVs, subsequent SEC improved the particle-to- protein ratio, reduced inter-replicate variability and improved proteomic depth. TFF alone in serum- free organoid CM already yielded higher particle-to-protein ratios than TFF-only preparations from serum-containing CM, with SEC further improving this ratio. TriPort-TFF offers an accessible system for EV-isolation that can be combined with SEC for diverse sample types.

Kidney disease affects over 850 million people and leads to degradation of the kidney cortical compartments and kidney failure. Interstitial fibrosis and tubular atrophy (IFTA) is a universal feature of chronic kidney disease, yet the sequence of tubulointerstitial matrix protein (matrisome) changes that underlie the development and progression of IFTA are not fully understood. In particular, post-translational modifications which may stabilize key matrisome components and result in irreversible and functionally detrimental interstitial fibrosis or tubular basement membrane thickening, have yet to be characterized. Post-translational modifications (e.g., acetylation) have been shown to contribute to kidney diseases, yet they have not been investigated in chronic kidney disease progression. Herein, we established feasibility of matrisome protein quantification in the tubulointerstitium, with concurrent matrisome protein post-translational modification quantification, in archived diagnostic human kidney biopsies, using laser capture microdissection, liquid chromatography tandem mass spectrometry (LC-MS/MS), and bioinformatics analysis. In this pilot study, we focused on two conditions associated with IFTA progression: diabetic nephropathy and kidney transplantation. We examined both healthy and chronically damaged tubulointerstitium in two human kidney biopsies, one representing each condition. We identified signature protein markers of tubulointerstitium (e.g., UMOD, SLC12A1) in abundance, confirming sample composition, and observed uniformity in protein abundance across replicates from the same biopsies. We found increases in certain collagen isoforms (COL1A1, COL6A3) and other ancillary proteins in areas of IFTA. We also successfully quantified post-translational modifications, confirming that hydroxylproline was the most abundant modification in healthy tubulointerstitium from the kidney transplant biopsy, and that hydroxyllysine was also highly abundant and could be confidently quantitated on two collagen isoforms (COL1A1, COL4A2). Lastly, we performed power analysis to determine the required sample size to achieve significant power and false discovery rate. Several proteins exhibited large Hedge effect sizes in the tubulointerstitium (ANPEP, PRODH2), indicating their potential usefulness as fibrosis signatures. Our study is a first step toward comprehensive characterization of tubulointerstitial matrisome changes, including post-translational modifications seen with IFTA progression. Subsequent studies will potentially reveal new molecular markers of key early events in chronic kidney disease progression, and new therapeutic targets for mitigating this process. Data are available via ProteomeXchange identifier PXD079025.

Proteomics is a powerful tool for profiling the protein landscape underlying cellular and physiological processes. Advances in mass spectrometry-based methods, such as tandem-mass-tags (TMT) isobaric labeling-based proteomics at both MS2 and MS3, label-free quantification (LFQ) with data-dependent-acquisition (DDA) or data-independent acquisition (DIA)-based proteomics, and stable isotope labeling with amino acid (SILAC) spike-in-assisted proteomics, enable quantitative proteome comparisons across biological conditions. These five quantitative proteomic methods offer distinct advantages in coverage, throughput, and quantitative reproducibility. Here, we treated them as orthogonal approaches, retaining protein candidates with significant, consistent-direction changes supported across multiple independently instrumented methods to identify high-confidence protein-level changes associated with longevity. Insulin/insulin-like growth factor signaling (IIS) is a highly conserved longevity regulatory pathway from Caenorhabditis elegans to humans. By quantitatively profiling IIS mutants in C. elegans and performing genetic screening, we obtained a systems-view of proteomic changes linked to longevity and identified new longevity regulators. This systematic cross-method comparison also serves as a valuable resource for guiding the selection of quantitative proteomics approaches.