Molecular & Cellular Proteomics

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.

The sea urchin is a long-studied model used to study fertilization and various developmental processes. While its genome and transcriptomics data are well documented, the global quantitative proteomic dynamics that complement mRNA expression during development are not available. Here we utilize an "in-cell proteomics" strategy to profile the proteomic changes in 5 developmental time points (egg, 32-cell, blastula, gastrula and larva) of the purple sea urchin, Strongylocentrotus purpuratus. This strategy bypasses cell lysis needed in conventional proteomic experiments and detects over 6,000 protein groups using only 50 eggs/embryos. Over 3,000 proteins have significant changes throughout development that cluster into 6 unique expression patterns. Analysis of signaling proteins and some transcription factors involved in cell specification and differentiation align well with published literature, supporting that proteomes obtained reflect biology of the organism. Furthermore, a majority of mRNA and their corresponding protein levels are discordantly correlated across development. Overall, this study provides a comprehensive analysis of proteomic changes occurring throughout sea urchin development and establishes a powerful and sensitive high-throughput approach suitable for detecting proteomic changes with a small number of embryos.

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.

Single-cell patch-proteomics is a novel and powerful technique combining electrophysiological and quantitative proteomic characterization from individually sampled neurons. Following whole-cell patch clamp characterization of neuronal excitability, we collect intracellular contents and, using the latest advancements in Liquid Chromatography and Mass Spectrometry, identify and quantify peptides and their corresponding proteins. By cell-matched one-to-one associations between electrophysiological properties and protein abundance, this approach provides a means to elucidate molecular mechanisms of neuronal function on individual neuron basis.

Endoplasmic reticulum (ER) stress is associated with many human diseases, but current understanding of how ER stress responses reshape the stressed proteome over time is still emerging. Specifically, how organellar protein quality control and clearance pathways coordinate to maintain proteostasis in early and prolonged ER stress is unclear. Here we describe a spatiotemporal proteomic strategy termed simultaneous proteome localization and turnover analysis with time resolution (SPLAT-TR) to interrogate the synthesis, degradation, and localization changes of over 4,000 proteins in early (1-4 hours) and prolonged (16-24 hours) ER stress. As ER stress progresses, each time point is distinguished by distinct protein translocation and clearance regulations, coupled to differential organellar proteostasis and usage of protein degradation pathways. Mitochondria show a bimodal response, with a shift from early activation of respiratory protein towards protein quality control pathways in prolonged stress, coinciding with energetics decline. In the Golgi, the increased clearance of collagen proteins is paradoxically coupled to an increased synthesis of secretory pathway components, suggesting removal by secretion. In the ER, the proteome remodels under prolonged stress via increased synthesis of UFMylation and ER-phagy-related proteins coupled to the selective degradation of ER membrane and microdomain proteins. Inhibition of UFMylation alters ER-phagy receptor usage and synergistically induces stress-induced cell death. These results present a systematic delineation of cell compartment proteostasis under unfolded protein response and highlight the utility of SPLAT-TR to investigate time-dependent cellular events.

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.

Understanding the pathological significance of protein glycosylation and translating disease-associated glycan alterations into diagnostic and therapeutic opportunities require integrated analysis of glycans, their carrier glycoproteins, and spatial context. Here, we present an expanded multimodal glycoprotein analysis framework incorporating lectin-assisted imaging mass cytometry (Lectin-IMC) for stepwise discovery and spatial validation of disease-associated glyco-niches. Disease-associated glycans identified by laser microdissection-assisted lectin microarray (LMD-LMA) tissue glycome mapping are spatially evaluated by Lectin-IMC in relation to cell types and tissue microenvironments, and candidate carrier glycoproteins identified by MS-based glycoproteomics are subsequently incorporated for higher-order evaluation of glycan-protein-cell type relationships. Multiplex panel design is guided by LMA-based assessment of lectin-lectin interactions and biologically informed probe selection. Using a dilated cardiomyopathy model, we constructed a five-lectin panel centered on Wisteria floribunda agglutinin (WFA), which recognizes fibrosis-associated asialo N-glycans identified previously. Glycan-dependent WFA detection was validated by competitive inhibition and PNGase F treatment, and WFA-reactive glycans were spatially associated with fibrotic regions containing ACTA2VIM myofibroblast-like cells. Among six extracellular matrix glycoprotein candidates identified in WFA-binding fractions, periostin showed the strongest spatial correspondence with WFA-positive regions by pixel-based quantitative analysis and was further associated with ACTA2VIMWFA fibrotic microenvironments. The optimized Lectin-IMC panel was also transferable to a human FFPE cardiomyopathy specimen. Collectively, Lectin-IMC provides intuitive spatial visualization and interpretability of glycan-protein-cell type relationships and serves as an on-tissue spatial validation and prioritization layer within an iterative multimodal framework in which spatially defined glyco-niches can guide subsequent proteomic or glycoproteomic discovery.

Mass spectrometry based immunopeptidomics is limited by peptide identification. HLA class I ligands are non-tryptic, frequently carry low charge states and often fragment incompletely, yet nearly all workflows rely on a single activation method, higher-energy collisional dissociation (HCD). Unlike proteomics, where a protein is inferred from several peptides, in immunopeptidomics the peptide is itself the analyte and the candidate therapeutic target, so a ligand that is not identified is a target that is not found. Here we evaluate whether two alternative activation chemistries, ion trap collision-induced dissociation (CID) and infrared multiphoton dissociation (IRMPD), recover ligands that HCD does not. To our knowledge this is the first application of either method to HLA class I immunopeptidomics, made practical by the integrated infrared laser of the Orbitrap Tribrid Apex MultiOmics. Four HLA-typed cell lines were analysed in technical triplicate against the canonical human proteome and a Ribo-seq-derived cryptic ORF database. Because fragment-intensity predictors are trained almost exclusively on HCD spectra, AlphaPeptDeep was fine-tuned separately for each method; fine-tuning and Percolator rescoring increased unique peptide identifications in every cell line and method combination (median +17.1% canonical, +54.5% cryptic), and fine-tuned models transferred across cell lines with a median loss of 0.4%, placing the three methods on a common analytical footing. On that footing the three methods yielded immunopeptidomes of similar size and composition, but each recovered peptides the others missed. HCD produced the most complete fragment-ion ladders and recovered more peptides from the basic-anchor alleles A*11:01 and A*33:03, both members of the A3 supertype. CID and IRMPD recovered a larger share of alleles without a basic C-terminal anchor and, for peptides containing no lysine, arginine or histidine, produced higher search scores and more matched b/y ion current in three of four cell lines. Each method therefore contributed exclusive peptides, and at a fixed budget of three LC-MS/MS runs, one run per method yielded on average more unique peptides than triplicate runs of a single method in all four cell lines (+0.4% to +10.9%). Of 239 novel canonical predicted HLA binders in the two cancer cell lines (HCT116 and MDA-MB-231) absent from the Immune Epitope Database, 55% were detected by only one method; nine derive from cancer-associated antigens. CID and IRMPD can therefore provide sequences that may not be identified by HCD alone, or with better spectral quality for a chemically defined subset of ligands. Their value lies alongside HCD rather than in its place, and realising it will require fragment-intensity models and acquisition schemes built for these chemistries rather than adapted from HCD.

During pregnancy, mother and fetus co-exist albeit as two physiologically distinct individuals, connected through the placenta and the umbilical cord. This connection allows for the exchange of vital substances, while individual blood homeostasis remains intact. We hypothesized that comparing maternal and cord plasma (N-glyco)proteomes would provide insights into the biological communication and unique physiological state of each individual. As comparing only matched maternal and umbilical cord blood plasma misses a crucial identifier in determining whether observed changes are related to infancy, pregnancy or both, we designed a triad study by adding a non-pregnant women control group. This allowed us to distinguish pregnancy-specific proteins and their potentially altered N-glycosylation from those in infancy. We found that the fetus and mother have distinct proteomes, N-glycoproteoforms and alternative complement regulation. Moreover, we observed that pregnancy in women led to specific N-glycosylation changes on a select set of primarily HDL-associated and acute-phase proteins. Lastly, our data provide evidence for an early fetal development of the humoral immune system as especially fetal IgM displayed distinctive N-glycosylation signatures.

Protein-protein interactions (PPIs) influence critical biological processes in pathogenic microorganisms, such as the human fungal pathogen, Cryptococcus neoformans. Fungal thermotolerance and stress response pathways are key virulence determinants that directly impact pathogen adaptation and survival and the infection process. To establish a comprehensive baseline of PPIs in C. neoformans and explore these interactions to infer functional roles for uncharacterized proteins, we applied size exclusion chromatography coupled with mass spectrometry to the secreted and cellular proteomes of the fungi. As a result, 216 and 1699 unique proteins were identified across 24 secretome and proteome fractions, respectively. The predicted secretome networks included expected proteins associated with vesicles and virulence, indicating a role in extracellular defense. Whereas the cryptococcal proteome highlighted interactions among proteins with defined roles in fungal virulence for protein stability and thermotolerance, including two previously uncharacterized proteins, CNAG_00287 and CNAG_05199, putatively involved in complex formation with heat-shock proteins (HSP). Based on sequence and structure homology, we propose that CNAG_00287 is a tetratricopeptide repeat-containing co-chaperone that modulates Hsp 70 activity and CNAG_05199 functions as a Hsp70. We validated the thermotolerance role of CNAG_00287 in heat-related stress, as its absence significantly impaired fungal growth in nutrient-limited media at 37 {degrees}C. Together, this work resolves virulence-associated PPIs within C. neoformans and reveals new molecular regulators of thermotolerance that underpin fungal pathogenicity.

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.

Membrane trafficking determines the subcellular localization and compartment-residence time of receptors, channels, transporters and other transmembrane proteins and thereby influences where in the cell and how long these proteins function. Functional-enrichment computational approaches may predict the membrane-trafficking regulations but do not resolve the directionality of the transport between cellular compartments. We present Traffickome, a deterministic method organizing 754 trafficking proteins into 11 directed transitions across seven compartments and 1,837 signaling proteins across 22 pathways. Implemented as an interactive browser and open-source Python package, Traffickome infers compartment-to-compartment transport, cargo fate and signaling-pathway activity and enables in silico perturbation from a single proteomic comparison. Across 29 proteomic comparisons spanning 14 studies encompassing over 66,000 gene-level observations across over 10,000 genes, Traffickome ranked the expected trafficking transition first in 23 cases (79%), outperforming the best conventional enrichment methods (17 of 29, 59%). Mean accuracy fell to 18% after randomizing the protein-to-transition assignments. To illustrate the power of Traffickome, we performed time-resolved analysis of the phosphoproteome in EGF-stimulated cells with the goal to distinguish signaling by internalized versus plasma-membrane EGFR. In the resulting datasets, comprising over 18,000 phosphosite-level measurements, Traffickome revealed localization-dependent differences in EGF-induced phosphorylation and prioritized trafficking regulators for testing their effects on signaling processes.

Pathological protein conformational remodeling is a key molecular feature of brain disorders. However, conventional approaches to understand the changes in potein conformation are targeted and difficult to scale proteome-wide analysis. Emerging mass spectrometry-based structural proteomic methods broaden coverage but often involve complex workflows and trade-offs between labeling efficiency and preservation of the in vivo molecular state. Thus, quantitative analysis of proteome-wide structural remodeling remains limited. Here we present Brain Landscape In Situ Crosslinking Mass Spectrometry (BLIS-XL-MS), an experimental and computational framework for analyzing protein and protein-complex remodeling in brain tissue. BLIS-XL-MS stabilizes the molecular state before secondary crosslinking, improves reagent accessibility through sectioning and permeabilization, and integrates quantitative crosslink analysis with structural and protein-interaction-network interpretation. Applied to brains from GluA1A636T knock-in mice, modeling a neurodevelopmental disorder, BLIS-XL-MS revealed coordinated remodeling of protein systems involved in AMPA receptor-trafficking, mitochondrial function and cell-death pathways. We demonstrate that BLIS-XL-MS provides a scalable framework for mapping disease-associated structural remodeling across the brain proteome.

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.

Uropathogenic Escherichia coli (UPEC) is the predominant causative agent of urinary tract infections. The UPEC strain, CFT073, has a mosaic genome containing 60 genomic islands enriched in virulence genes. In this study, we explore the small proteome of CFT073 using ribosome profiling (Ribo-seq) coupled with RNA sequencing (RNA-seq), and reveal 338 translated, previously unannotated, putative small open reading frames (sORFs) with less than 100 codons. Among them, 78 sORFs are located in intergenic regions. Fifteen intergenic sORFs encode a predicted transmembrane helix (smemORFs), of which seven are hypothetical or have not been characterized in other E. coli strains. The majority of the remaining putative sORFs encodes uncharacterized or novel soluble proteins (ssolORFs). Considering the evolution of many sORFs is likely to be recent, functional sORFs are more likely to have conserved gene neighborhood in other genomes. Searching into the genomes of 10 other E. coli strains and four enterobacteria species, synteny analysis reveals that 10 of 15 putative smemORFs, and 27 of 63 putative ssolORFs, have a homologous sequence in a similar genomic context in at least one other genome, supporting their likelihood of encoding functional proteins. Further validation of putative sORFs using proteomics, cell-free protein synthesis, and mNeonGreen tagging provide strong evidence for the translation of nine predicted intergenic sORFs. Notably, four previously uncharacterized small membrane proteins have increased abundance under oxidative stress, suggesting a potential role in the stress response. Together, our findings reveal the small proteome of an extraintestinal pathogenic E. coli and provide a catalog of putative sORFs for further investigation.

Coordination of biological function requires the partition of cellular components including into biomolecular condensates, but an overall landscape of how protein compartmentalize into higher-order assemblies under stress is still emerging. We apply proteome-wide solubility profiling to compare the compositions of NP-40-insoluble proteins and their phosphorylation status, using a new mass spectrometry-based hybrid bottom-up and chemical middle-down proteomics approach to analyze the solubility behavior of 8,740 proteins and 31,647 phosphopeptides under normal and ER stress conditions. Cell stress induces a pervasive differential partition of proteins in and out of detergent-insoluble cellular compartments. This differential partition is partially orthogonal to stress-induced abundance changes and comprises both phosphorylation-dependent and phosphorylation-independent mechanisms. Whereas phosphorylation-independent partition changes involve largely secretory pathway proteins and implicate higher-order assemblies of chaperones and clients, phosphorylation-based partitions suggest a dynamic rearrangement of biomolecular condensate compositions across cytoplasmic and nuclear ribonucleoprotein assemblies. The accumulation of serine/arginine rich (SR) proteins and other annotated nuclear speckle members in the condensate-rich proteome fractions emerges as a central feature of stress-induced remodeling. Our results establish global solubility dynamics as an integral component of proteome stress response and implicates broad involvements of splice factor spatial reorganization as a prominent facet of ER stress response.

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.

Combinatorial therapies are widely used in the treatment of acute myeloid leukemia (AML) to address disease heterogeneity, adaptive resistance, and rewired signaling and metabolic states. Yet drug prioritization remains largely guided by clinical or phenotypic evidence, while the molecular mechanisms underlying effective drug combinations remain incompletely defined. To narrow this gap, we developed Combinatorial high-ratio Partial proteolysis with reference PRoteome Analysis (CoPPRA), a structural proteomics workflow based on limited proteolysis of cell lysates that profiles drug-associated changes in regional protein accessibility at peptide-level resolution. Here, we applied CoPPRA to ruxolitinib and ulixertinib, individually and in combination, in AML-related cell lysates. Our findings extend conjunctive targeting (CT), a recently proposed mechanism of combinatorial drug action in which combined exposure produces protein targeting patterns not observed with either drug alone. Previously identified through combination-associated changes in protein solubility/stability, CT is examined here at peptide-level resolution through regional differences in proteolytic accessibility. The ruxolitinib-ulixertinib combination produced broad peptide-level accessibility changes, including a subset meeting the predefined criteria for CT. CT candidates predominantly exhibited regional accessibility changes, with altered peptide regions occurring against comparatively small changes across the remaining quantified peptides from the same proteins. MAP2K1 and ATP6V1G1 showed pronounced differences between overlapping peptide sequences, highlighting localized variation in combination-associated accessibility, including an ATP6V1G1 peptide mapping to an annotated helical region. Combination-associated increases in peptide signals were also observed in PIK3R1, BRD4, and PTPN11, linking regional accessibility changes to signaling and transcriptional regulators relevant to AML. Functional enrichment and network analyses further implicated nucleotide and glucose metabolism, ficolin-1-rich granules, ribosome-associated processes, and phagocytic vesicles. These results extend conjunctive targeting from protein-level solubility/stability changes to regional differences in proteolytic accessibility, showing that combination-associated effects can be concentrated within specific peptide regions rather than distributed uniformly across proteins. More broadly, CoPPRA provides a peptide-resolved approach for investigating the molecular features of combinatorial drug action and prioritizing protein regions for subsequent mechanistic validation.

Venetoclax combined with azacitidine has improved treatment outcomes in acute myeloid leukemia (AML), yet relapse and treatment persistence remain major clinical challenges. To investigate proteomic mechanisms associated with venetoclax-azacitidine response and adaptation, we applied a multi-layer combinatorial proteome integral solubility/stability alteration analysis (CoPISA) strategy in SKM-1 AML cells. Cells were treated with venetoclax, azacitidine, their combination, or vehicle control and profiled across four orthogonal layers: short-term lysate CoPISA, short-term intact-cell CoPISA, long-term intact-cell CoPISA after 5 days of treatment, and long-term expression proteomics of surviving cells. The venetoclax-azacitidine combination induced treatment-specific protein solubility and abundance changes that were not fully reproduced by either single agent. Long-term surviving cells displayed extensive proteomic remodeling, consistent with the emergence of an adaptive drug-tolerant state, although contributions from pre-existing resilient cell populations cannot be excluded. Integration of short- and long-term solubility changes with abundance remodeling revealed distinct adaptive regimes, including retained biochemical targets, dosage-compensated targets, sensitive-state-specific targets, and remodeled adaptive targets. This framework prioritized candidate resistance-associated proteins, including NRP2, RPL18, PLP2, RPS28, and NOTCH1. NRP2 emerged as a top candidate across all proteomics layers, consistently showing increased abundance. Overall, this study shows that combining CoPISA with expression proteomics can resolve temporally distinct proteomic states of venetoclax-azacitidine response and identify candidate adaptive vulnerabilities in AML.

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.