Molecular & Cellular Proteomics

Cancer cell lines are widely used in preclinical research, yet the clinical translation of findings from cell lines remains limited. Identifying cell lines that best resemble patient tumors requires integration of molecular profiles across biologically distinct sample types. Recent advances in transcriptomic integration have demonstrated the potential of deep learning for aligning data across different sample types. However, comparable approaches for proteomic data integration remain lacking, potentially because of the prevalence of missing values in proteomic datasets. Here, we introduce ProtInt, a deep learning-based framework that integrates proteomic data from cell lines and patient tumors by combining principles from proteomic imputation and transcriptomic integration methods. We applied ProtInt to integrate label-free proteomic profiles from 771 cancer cell lines and 550 treatment-naive tumors. ProtInt outperformed batch correction and transcriptomic integration methods in aligning cell line and tumor proteomes. Comparison of the cell line proteomes before and after integration revealed recurrent increase of proteins associated with immune reaction, cell-cell communication, and interaction with the extracellular matrix, and reduction of proteins involved in transcription, post-transcriptional processing, and mitochondrial gene expression as proteomes of cell lines were adapted to resemble tumors. These results establish ProtInt as a framework for joint analysis of proteomic datasets across distinct sample types and may facilitate the identification of cell lines best suited for clinically relevant studies.

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.

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.

Conventional univariate reference intervals (UniRIs) are widely used to identify abnormal biomarker values, but they evaluate each biomarker independently and do not account for coordinated deviations between biomarkers. We developed and evaluated a joint reference region (JRR) framework for plasma proteomics data using the Olink proteomics dataset generated by the UK Biobank Pharma Proteomics Project, covering approximately 3,000 plasma proteins. JRRs were estimated for selected protein pairs in a healthy reference subset, while UniRIs were estimated separately for individual proteins using the nonparametric method. Both approaches were then evaluated in ICD-defined disease subsets. Biomarker discovery revealed sparse and heterogeneous disease--protein associations, with some proteins recurring across multiple phenotypes and others showing more disease-specific patterns. The added value of JRRs varied across diseases and protein pairs. Across evaluated protein pairs, 56.5\% showed higher sensitivity under the JRR framework than the UniRI of the first protein, and 47.3\% showed higher sensitivity than the UniRI of the second protein. At the disease level, the median proportion of protein pairs with improved JRR sensitivity was 0.57. JRRs were most informative when univariate detection was limited but a subset of diseased observations was flagged only by the joint region. These findings suggest that JRRs provide a complementary approach to UniRIs by capturing abnormal joint biomarker configurations in high-dimensional proteomics data.

The processes such as DNA replication, transcription, and repair are often modulated by specific nuclear proteins, protein-protein interactions (PPIs), and post-translational modifications (PTMs). In Trypanosoma cruzi, however, the nuclear proteome and interactome have not been systematically mapped, limiting the interpretation of nuclear regulatory processes. Here, we report a nuclear proteome resource generated from intact nuclei isolated from T. cruzi and analyzed by high-resolution Orbitrap LC-MS/MS, integrating proteome profiling, computational interaction network inference, and exploratory crosslinking mass spectrometry (XL-MS). Proteome profiling identified 1,734 proteins in the nuclear fraction, including 316 proteins identified with PTM-containing peptides. Subcellular localization prediction and Gene Ontology analysis support nuclear enrichment and highlight functions related to transcription, RNA metabolism, and genome maintenance. The in silico interaction network derived from STRINGDB organizes the proteins into functional clusters, including a histone-associated interaction neighborhood. In parallel, XL-MS identified 26 residue-resolved interprotein crosslinks involving 36 proteins and detected PTMs at or near linked residues. Together, these data support reuse for comparative nuclear proteomics, multi-omics integration, and prioritization of candidates for future functional studies.

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.

Molecular imaging by imaging mass spectrometry (IMS) has become a key modality for spatial proteomics, lipidomics, glycomics, and metabolomics. It maps hundreds to thousands of molecular species concurrently throughout tissue without prior labeling. However, reporting thousands of ion images makes IMS measurements very high-dimensional, complicating interpretation. Furthermore, IMS data contain implicit chemical relationships. For example, the same molecular species can be reported by several separately-measured ion species, each an isotopic variant or isotopologue of that molecule. While conventional dimensionality reduction methods such as principal component analysis can address the dimensionality challenge, they typically do not preserve chemical relationships (e.g., isotopologue grouping), making biological interpretation harder. As advanced, higher-dimensional measurement types such as ion mobility IMS (IM-IMS) expand into spatial omics, addressing interpretability in a chemically informed way becomes pressing. Therefore, we present IsoMobil, a dimensionality-reduction framework for IM-IMS data that empirically detects potential isotopologues. Besides reducing dataset complexity, it facilitates interpretation at the (biologically relevant) molecular-species level rather than ion-species level. The algorithm finds spatially coherent ion species, filters them based on isotope-induced mass-to-charge (m/z) distances and mobility-bin consistency (isotopologues have near-identical collisional cross-sections). This yields a compact representation where isotopologue-candidate families, rather than individual ion-species, form latent dimensions. In a synthetic benchmark, IsoMobil outperformed (F1=1.0) spatial-only and m/z-based methods (F1{approx}0.67). In a human colon case study, IsoMobil found 77 isotopologue-candidate groups (COSH-P-quality[≥]0.85) among 6344 lipid ion species. By automating isotopologue discovery, IsoMobil lifts biological interpretation of exploratory, untargeted spatial omics by IM-IMS to the molecular-species level.

Glycan heterogeneity is a fundamental property of glycoproteins. A holistic understanding of glycan modification states is critical to translating glycoproteome regulation to biological function, but the high degree of glycosite-level heterogeneity leads to technical challenges in measuring glycoproteoforms. Common bottom-up glycoproteomics provide some insights but cannot recapitulate the full ensemble of glycoproteoforms from glycopeptide measurements alone. Promising efforts to profile masses of intact glycoproteins have recently explored data-independent acquisition (DIA) coupled with proton-transfer charge reduction (PTCR) or electron-capture-induced charge reduction mass spectrometry (MS). While valuable for generating broad glycoproteoform mass distributions, these approaches have remained limited in their ability to generate discrete glycoproteoform mass measurements, largely because they rely on low-resolving power measurements and deconvolution that does not account for isotopic information. Here, we develop a DIA-PTCR workflow that couples high-resolving power (Rp ~240,000 at m/z 200) tandem mass spectra with an open-source processing suite to define glycoproteoform populations within 20 ppm mass accuracy thresholds. We demonstrate the glycoproteoform characterization capabilities of this platform using a collection of glycoproteins with well-described translational interests (EpCAM, TIGIT, CD40, PDL1, and CD24). With a focus on EpCAM, we showcase how intact glycoproteoform masses acquired using our high-resolving power DIA-PTCR (hRp-DIA-PTCR) approach can be integrated with bottom-up intact glycoproteomics and Direct-Mass Technology (i.e., Orbitrap-based charge-detection MS) acquisitions to inform structural and biological insights. Altogether, our hRp-DIA-PTCR method extends the current capabilities of intact glycoprotein analyses by enabling robust characterization of isotopically resolved proteoforms and facilitating deep biological interpretation of glycosylation heterogeneity. Our open-source informatics platform includes a GUI-based tool called PTsliCR to clean PTCR spectra directly from DIA-PTCR raw files and a deconvolution R package called IsoTrac, both of which are freely available on GitHub at https://github.com/riley-research.

Spatial proteomics aims to resolve protein composition within intact tissues, yet extraction-based liquid chromatography-mass spectrometry (LC-MS) workflows face an inherent trade-off: smaller sampling units increase spatial specificity, whereas larger sampling units provide greater proteome depth and robustness. As analytical sensitivity improves, sampling-unit size therefore becomes a key experimental design parameter. Current extraction-based LC-MS workflows typically rely on laser capture microdissection (LCM), where sample recovery and scalability can become limiting at low input. Spatially resolved laser-activated cell sorting (SLACS) offers an alternative tissue-isolation strategy based on single-pulse near-infrared laser activation. Here, we use SLACS to systematically examine the resolution-sensitivity trade-off across sampling units ranging from single-cell-equivalent to larger low-input tissue regions. Few-cell sampling retained substantial proteomic information relative to larger regions while increasing spatial specificity. Applied to the mouse somatosensory cortex, SLACS generated deep, layer-resolved proteomic profiles from regions corresponding to approximately 60 cells and preserved major layer-specific molecular patterns at inputs as low as approximately 6 cells. These results highlight sampling-unit size as an important experimental design parameter in extraction-based spatial proteomics and support few-cell sampling as a practical compromise between spatial specificity, proteome depth and robustness.

Metaproteomics aims to capture a taxonomically comprehensive snapshot of proteins in a sample. Design of reference databases is a key aspect of metaproteomic workflows, as these define what is ultimately seen. Databases tailored to focal biomes offer optimal performance, yet their construction often requires drawing on heterogeneous data sources, posing a challenge to reproducibility and documentation. Here we present maniFasta, a tool enabling users to generate standardized, reproducible, and robustly documented protein reference sets from diverse input sources and datatypes. Users provide information on their desired input types and sources, and the output is an integrated database comprising a protein sequence file (FASTA), with harmonized identifiers and standardized headers, and an associated provenance metadata table (manifest). We highlight the value of maniFasta in the context of salivary metaproteomics, addressing the need for a taxonomically comprehensive reference database. The AllOralsDB resource includes human proteins, as well as proteins from bacteria and archaea, fungi and other microeukaryotes, viruses and viroid-like elements, dietary sources, and common contaminants. Together, this work provides a community resource for oral and salivary metaproteomics (https://www.homd.org/ftp/AllOralsDB/), and a versatile and accessible tool for constructing protein databases for metaproteomics generally (https://github.com/KauffmanLab/maniFasta).

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.

Post-translational modifications (PTMs) and genetic variants regulate protein function, signalling, and disease, but their interpretation requires integration of sequence annotations with structural, interaction, and biophysical context. Although resources such as Scop3P, UniProt, the Protein Data Bank, and AlphaFold provide extensive annotations and structural information, integrating these data into reproducible structure-aware analyses still requires custom scripting and manual coordination between multiple independent tools. To address this challenge, we developed Scop3P-Toolkit, an open-source executable analytical environment for interactive analysis of PTMs, mutations, and proteomics-derived peptides in their structural context. The toolkit integrates protein annotation retrieval with structural mapping, residue interaction network analysis, comparative structural analysis, and residue-level biophysical profiling within a unified framework. Experimentally supported phosphosites, phosphopeptides, and phosphoproteomics evidence are provided for human proteins through Scop3P, with optional integration of curated UniProt PTM annotations. UniProt-derived PTMs, sequence features, and genetic variants are available for proteins from any species, extending the framework beyond the human phosphoproteome. Scop3P-Toolkit supports structure-centric analyses including interpretation of PTMs and disease-associated variants, analysis of residue interaction networks and their rewiring across alternative conformations, structural localisation of peptides, and exploration of protein-protein, protein-ligand, and host-pathogen interfaces. Interactive visualisation links sequence annotations, three-dimensional structures, residue interaction networks, and biophysical profiles, enabling coordinated exploration across multiple molecular representations. The toolkit is distributed as Jupyter notebooks, browser-based Voila applications, and a Galaxy interactive tool, providing transparent, accessible, and reproducible workflows for both computational and experimental researchers. By integrating biological annotation resources into executable, structure-aware workflows, Scop3P-Toolkit enables reproducible interpretation of PTMs, mutations, and proteomics data.

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.

Parallelization of data acquisition substantially increases the throughput of mass spectrometry-based proteomics. However, parallelization also increases the density of mass spectra and consequently the overlap between ions, frustrating their analysis. To improve sequence identification and quantification from such spectra, we developed an open-source software for Joint Modeling of mass spectra (JMod). JMod models overlapping peaks as linear superpositions of their components in both MS1 and MS2 space, which permits multiplexed DIA with smaller mass offsets to increase the multiplexing capacity and thus proteomics throughput for a given plexDIA tag. This enables 9-plexDIA using 2 Da offset PSMtags, increasing throughput 9-fold while preserving quantitative accuracy and coverage depth. Furthermore, we use JMod to deconvolve simultaneous labeling by mass tags and heavy amino acids, thus increasing the throughput of metabolic pulse experiments measuring protein synthesis and degradation rates in single cells from mouse liver. By supporting enhanced decoding of highly multiplexed DIA spectra, JMod provides an open and flexible software that increases the throughput of sensitive proteomics.

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.

Engineered heart tissues (EHTs) represent an innovative platform enabling physiologically relevant in vitro evaluation of drug-induced cardiac responses. While functional characterization remains central to EHTs, molecular profiling is increasingly used to elucidate mechanisms underlying drug-induced phenotypes. Proteomics provides broad molecular characterization of drug responses at the protein level, yet the complexity, heterogeneity, and high dimensionality of proteomics datasets challenge conventional statistical approaches, which are not designed for cross-modal integration and streamlined multi-omics analysis. In this study, we developed an innovative framework based on topological data analysis (TDA) for the integration of large proteomics profiles and functional readouts to investigate system-level responses to drugs with opposing inotropic effects, epinephrine and doxorubicin. Samples were organized into a topological connectivity network according to multimodal similarity enabling simultaneous exploration of treatments, cardiac function and proteome alterations. Highly correlated features were then used for pathway enrichment analysis, which revealed strong similarities between the enrichment profiles associated with contractile force and epinephrine. These findings are consistent with the positive inotropic effect of epinephrine, whereas doxorubicin exhibited an opposing enrichment profile. Energy homeostasis, mitochondrial translation and proteostasis emerged as the major cellular processes displaying opposite associations with the two inotropic drugs, highlighting a link between cardiac contractility and perturbations in these processes. In conclusion, our TDA-based framework successfully integrated functional and proteomic data to uncover treatment-specific remodeling in EHTs, offering a modular and scalable approach that could be adapted to other in vitro organ models for systems-level mechanistic studies and next-generation drug development.

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.

Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for spatially Resolved Multi-omics, for rigorously analyzing spatial patterns in spatial multi-omics research. STORM incorporates a robust and efficient nonparametric test that quantifies local deviations in molecular feature measurements to detect spatial dependence across transcriptomic and proteomic data. It further estimates an interpretable spatial effect size, supports power calculations for both spatial locations and biological replicates, and enables formal group-level comparisons. In several simulated and experimental spatial multi-omics case studies, STORM demonstrates reliable performance in detecting spatial structures while offering quantitative support for study design decisions. Overall, STORM provides a principled statistical framework that unifies spatial hypothesis testing, effect size estimation, power analysis, and experimental design for spatial multi-omics data.

Quantitative membrane proteomics remains fundamentally limited by sample preparation because detergent extraction can perturb membrane protein interactions, ligand-responsive conformations, and higher-order assemblies before mass spectrometric analysis. Here, we demonstrate that peptide-based surfactants (Peptergents) enable a complete detergent-free workflow for native membrane proteomics. Membrane proteins are extracted directly from biological membranes while preserving their structural and functional integrity and remaining fully compatible with downstream LC-MS/MS workflows. Functional preservation is evidenced by maintenance of ligand-responsive conformations in the ABC transporter MsbA and the endogenous GPCR P2RY12, together with stabilization of the detergent-sensitive nine-subunit holo-translocon HTL, indicating that fragile membrane protein assemblies remain intact. At the proteome level, despite recovering fewer membrane proteins than conventional detergent extraction, Peptergent consistently generates higher peptide signal intensities, retains tissue-specific membrane proteome signatures, and preferentially enriches endoplasmic reticulum-associated metabolic networks, including cytochrome P450 enzymes and their interaction network. Together, these findings establish Peptergents as a broadly applicable membrane extraction technology for LC-MS/MS-based membrane proteomics, preserving native membrane organization and expanding the proteomics toolbox for biochemical, structural, and systems-level analyses of membrane proteins. In Brief StatementThis study establishes Peptergents as a detergent-free membrane extraction technology for LC-MS/MS-based membrane proteomics. Peptergent extraction preserves ligand-responsive membrane proteins, fragile membrane protein assemblies, and tissue-specific membrane proteome signatures while remaining fully compatible with quantitative proteomic workflows. These findings provide a broadly applicable strategy for preserving native membrane organization for biochemical, structural, and systems-level analyses of membrane proteins. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/744532v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@1fe34b0org.highwire.dtl.DTLVardef@35400corg.highwire.dtl.DTLVardef@1ffe97aorg.highwire.dtl.DTLVardef@394fc4_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPeptergents preserve ligand-responsive membrane proteins. C_LIO_LISupport chemoproteomics in thermal proteome profiling assays. C_LIO_LISimplify membrane proteomics workflow. C_LIO_LIMaintain native tissue-specific membrane biology. C_LIO_LIPreserve fragile membrane protein assemblies. C_LI

Comprehensive characterization of protein abundance and multiple post-translational modifications (PTMs) from the same biological samples is essential for understanding cellular regulation and PTM crosstalk but remains analytically challenging. Here, we present STEP-PTM (Sequential Tag-based Enrichment of Post-Translational Modifications), a modular TMT-multiplexed workflow that enables integrated quantitative analysis of the proteome, metabolome and multiple PTM classes from a single peptide preparation. Proteins are digested, isobarically labeled using tandem mass tags (TMT), and combined into a single multiplexed peptide pool prior to sequential PTM enrichment, thereby minimizing technical variability, reducing sample requirements and facilitating direct quantitative integration across datasets. STEP-PTM supports flexible sequential enrichment of phosphopeptides, peptides containing free and reversibly modified cysteines, sialylated N-linked glycopeptides, lysine-acetylated peptides and S-palmitoylated peptides, while preserving non-modified peptides for global proteome analysis. PTM-specific database searches further improve identification confidence and quantitative accuracy, and the modular workflow can readily be adapted by incorporating or omitting enrichment modules according to the biological question. Application of STEP-PTM to TMT16-plex cerebral brain organoids enabled the quantification of 10,413 proteins, 2,969 metabolites, 19,655 phosphopeptides, 28,876 peptides containing reversibly modified cysteines, 9,723 peptides containing free cysteines, 1,716 intact sialylated N-linked glycopeptides and 771 lysine-acetylated peptides from the same biological samples. We further demonstrate the applicability of the workflow to multiple mouse tissues, highlighting its broad utility for integrated systems-level characterization of protein expression and PTM regulation across diverse biological models.