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PubMed2026

Odon: an ultra-fast viewer for spatial proteomics.

MOTIVATION: Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. RESULTS: Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine designed for viewport-driven tile loading and GPU-based compositing. In scripted benchmarks using synthetic multiplex OME-Zarr datasets, Odon showed lower peak memory use, lower affine-derived zoom-step error, and faster warm-start image loading than napari and QuPath under the tested conditions. Its GPU-based compositing pipeline also enables smooth rendering and interaction with >1 000 000 segmented cells. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data. AVAILABILITY AND IMPLEMENTATION: Source code and compiled installers are available at https://github.com/alexcoulton/odon.

Proteomics↗

PubMed2026

Hex-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies.

Whole-tissue level spatial proteomics provides critical insights into region-specific biological regulations but remains challenging. Previously, we introduced the micro-scaffold assisted spatial proteomics (MASP) concept for whole-tissue mapping. However, this prototype required substantial development in spatial resolution, practicality, and throughput for practical application. Here we present a next-generation MASP technique (hex-MASP) featuring i) a new design of hexagonal-micro-wells fabricated with optimized projection micro-stereolithography 3D-printing, achieving high spatial resolution, sampling robustness, and mechanical strength for reproducibly compartmentalizing even tough tissues; ii) enhanced throughput/effectiveness in sample preparation and LC-MS analysis with high quantitative quality. Applied to mouse brain, hex-MASP achieved in-depth, whole-tissue mapping for >6,000 proteins in mouse brains, with high spatial accuracy and excellent data quality. The substantially improved resolution revealed critical regional details across the entire brain, that were not previously captured, enabling precise depiction of protein distribution heterogeneity. This technique enabled the identification of many unreported regionally enriched proteins across brain structures. We further applied hex-MASP to investigate the intrabrain distribution of intracerebroventricularly dosed antibody therapeutics and related proteins, which enabled whole-tissue mapping of protein drugs revealed insights into antibody brain penetration and distribution. Hex-MASP represents a robust, scalable platform for whole-tissue spatial proteomics.

Animals↗

PubMed2026

Spatial Proteomics of the Human Atherosclerotic Microenvironment Reveals Heterogeneity in Intraplaque Proteomes and Extracellular Matrix Remodeling.

Plaque heterogeneity underlies the propensity of atherosclerotic lesions to rupture and trigger cardiovascular events. Most proteomic studies examine bulk changes, obscuring key spatial differences in protein abundance. We report a high-resolution spatial proteomics workflow exploring the molecular landscape of human plaques and a murine myocardium. By combining laser capture microdissection with high-sensitivity ion-mobility mass spectrometry, spatial profiling of cellular and extracellular matrix (ECM) proteomes was achieved. Over 2700 proteins were detected from 50,000 μm2 areas, revealing substantial intraplaque heterogeneity across distinct regions (lipid-rich, media, shoulder, necrotic core, intima) and distance from the artery lumen. Inverse correlations between proteases (cathepsin B) and core structural ECM proteins (perlecan, HSPG2) indicated active ECM remodeling. Analysis of media layers indicated distinct protein signatures associated with smooth muscle contraction and cell-cell communication. Blood coagulation signatures, including platelet degranulation and fibrin formation, were enriched at the intima. Inflammatory (clusters of differentiation 4/68, CD4/CD68; vascular cell adhesion molecule 1, VCAM1) and vascular damage markers (tenascin-C, TNC) were enriched in shoulder regions. The necrotic core was dominated by blood proteins, consistent with intraplaque hemorrhage. This workflow resolves proteomic changes over ∼200 μm distances, providing unprecedented insights into plaque morphology and offers a powerful tool for elucidating plaque biology.

Humans↗

PubMed2026

Spatial proteomic mapping of the human and mouse retina using IBEX.

We generated a comparative spatial proteomic atlas of the human and mouse retina using a highly multiplexed immunohistochemistry technique called iterative bleaching extends multiplexity (IBEX). We refined the IBEX workflow by integrating an antibody dissociation option alongside chemical bleaching. This dual strategy enabled removal of the entire antibody complex, permitting the flexible use of antibodies from the same host species across iterative cycles. We coupled this workflow with super-resolution imaging via deconvolution and applied it to the retina of healthy humans and WT mice and the Crb1rd8 mouse model. We successfully imaged over 25 protein markers on human and mouse tissue sections, generating spatial atlases of the major retinal cell populations. Cross-species protein expression was compared to scRNA-seq datasets to identify protein and transcript disparities. Super-resolution IBEX delineated the ultrastructural features of the outer limiting membrane (OLM), identifying CD44 as a core structural component tightly colocalized with a highly organized F-actin belt within Müller glial endfeet. Using the Crb1rd8 mouse model, disruption of this complex was spatially associated with rosette formation and OLM structural failure. In summary, spatial proteomic atlases of the human and mouse retina were used to reveal insights into the arrangement of major retinal cell populations and OLM structure.

Animals↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed2026

Unbiased Spatial Proteomics Uncovers Hepatic in Situ Regulation in Alcohol-Associated Hepatitis.

Alcohol-associated hepatitis (AH) is an acute inflammatory form of alcohol-associated liver disease. Previous studies have explored molecular mechanisms associated with AH pathogenesis through bulk liver tissue analysis; however, the heterogeneity of liver tissue and hence the spatial regulation within the AH liver microenvironment remained unaddressed. Here, an unbiased spatial proteomics analysis on the pathologic regions (PRs) of AH liver tissue is presented, including immune cell infiltration foci, lipid droplets, chicken-wire fibrosis, and fibrotic bands. Through combining a highly efficient nanodroplet processing in one pot for trace samples platform with ultrasensitive liquid chromatography-mass spectrometry, this study identified and quantified a total of 5186 unique proteins from PRs isolated in 200-μm-long × 200-μm-wide × 10-μm-thick areas. This in-depth spatial proteome coverage allowed us to discover mechanistic regulations within individual PRs, including compromised resolution of inflammation with infiltrated neutrophils at infiltration foci, increase of mitochondrial and peroxisomal fatty acid β-oxidation at lipid droplets, and differential cellular and extracellular regulations between chicken-wire fibrosis and fibrotic bands. Overall, this study demonstrated a new capability for AH research, revealed the significance of understanding spatial regulation within AH liver tissue, and further facilitated the development of therapeutic strategies at high resolution.

Proteomics↗

PubMed2026

Single-section multiplex spatial proteomics of immune microenvironments in kidney transplantation.

Characterizing kidney disease is challenged by marked cellular heterogeneity and limited tissue availability from renal biopsies. Conventional diagnostic workflows rely on multiple serial sections for parallel staining, increasing tissue consumption, sampling bias, and loss of spatial information, thereby constraining molecular characterization within intact tissue architecture. High-plex spatial proteomics may overcome these limitations by enabling comprehensive molecular profiling on a single section. Here, we present and evaluate a high-plex cyclic immunofluorescence imaging workflow (MACSima™, Miltenyi Biotec) applied to kidney transplant biopsies, including BK virus nephropathy (BKVN) and focal segmental glomerulosclerosis (FSGS), to characterize spatial immune organization with a focus on complement system components. Feasibility and subcellular resolution were first assessed in a lupus nephritis section, demonstrating compatibility with diagnostic immune panels and preservation of tissue morphology. A 48-marker multiplex panel interrogating immunity, oxidative stress, senescence, and fibrosis was then applied to BKVN samples, including paired pre- and post-treatment biopsies, revealing distinct proteomic patterns and dynamic changes following therapy. In FSGS, a glomerulus-focused panel identified spatially resolved innate and adaptive immune signatures, including complement-related patterns supporting exploratory analysis of glomerular immune architecture. Structural, nuclear, membrane, and phosphorylated signaling markers enabled precise delineation of renal compartments and assessment of cellular states such as proliferation, DNA damage, and pathway activation. The workflow also supported detection of extracellular vesicles in cultured renal cells, highlighting its versatility. Overall, this approach provides a robust, tissue-sparing platform for integrated spatial and molecular profiling of renal biopsies, reducing sampling bias while enabling discovery-level phenotyping from a single section. This unified strategy is particularly suited to kidney transplantation, where diagnosis, therapeutic decision-making, and longitudinal monitoring are closely interconnected.

Kidney Transplantation↗

PubMed2025

Altered immune signatures in breast cancer lymph nodes with metastases revealed by spatial proteome analyses.

BACKGROUND: Metastasis to lymph nodes is strongly associated with reduced survival in breast cancer patients. To increase the understanding on how lymph node metastasis impairs the local immune response in affected lymph nodes, we here studied spatial proteomic changes of critical lymph node immune populations in uninvolved lymph nodes (UnLN) and paired lymph nodes with metastases (LNM) from five breast cancer patients. METHODS: The proteome was analyzed for cortical lymphocyte compartments, subcapsular sinus (SCS) and medullary sinus (MS) CD169+ macrophages, using the Digital Spatial Profiling (DSP) platform from NanoString. RESULTS: Our results identified a stable proteome of SCS CD169+ macrophages in LNM, with the exception for downregulation of the anti-apoptotic protein Bcl-xL and FAPα, but a clear reduction in numbers of SCS CD169+ macrophages in LNM. In contrast, the proteome of MS CD169+ macrophages, B-cell compartments and interfollicular T-cells showed altered immune signatures in LNM, indicating that the decline in SCS CD169+ macrophages coincide with a malfunction in the local, anti-tumor immune responses. CONCLUSIONS: The findings from our study support the notion that metastasis to lymph nodes in breast cancer patients modifies local immune responses. These changes may contribute to explain unsuccessful therapeutic responses, and thereby worsened prognosis, for breast cancer patients with LNM.

Breast Neoplasms↗

PubMed2026

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index.

Collagen breast stroma can become a breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study evaluates the spatial regulation of the collagen proteome from normal breast tissue. Normal breast tissue sections from the Susan G. Komen tissue bank were used (n = 40), with data including genetic ancestry (n = 20 African ancestry; n = 20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). 10-plex cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation microscopy contrasted in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed the collagen alpha-1(I) chain proteome was spatially heterogeneous across the normal breast microenvironment with site-specific post-translational modification of proline hydroxylation. Signatures computationally extracted from stroma-rich regions reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve >0.7; p-value >0.05). Multivariate modeling of collagen peptides, fiber metrics, and clinical features supported a strong positive association with BMI as a determinant of collagen alterations in the normal breast. This study provides a foundation for larger studies investigating the clinical value of spatial collagen proteome alterations in human breast.

Humans↗

PubMed2026

Spatial proteomics reveals four-stage molecular evolution in cancer immunotherapy-related gastritis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet immune-related adverse events (irAEs) including immunotherapy-related gastritis (IRAEG) pose significant clinical challenges-often necessitating treatment interruption that may compromise antitumor efficacy. IRAEG presents with atypical symptoms, lacks specific biomarkers, and shows histopathological overlap with other forms of gastritis, complicating diagnosis and management. Despite increasing clinical recognition, a systematic understanding of spatial molecular alterations across the full disease course remains limited. Here, we used spatial proteomics to map the molecular landscape of IRAEG during disease progression and to define stage-specific patterns of molecular evolution relevant to cancer immunotherapy management. METHODS: We analyzed tissue samples from seven patients, including four non-immunotherapy-related gastritis controls and three cancer patients who developed IRAEG following ICI therapy for solid tumors, sampled longitudinally across four disease stages: baseline (G1), acute severe inflammation (G2), early recovery (G3), and complete recovery (G4). Using laser capture microdissection coupled with data-independent acquisition mass spectrometry, we profiled 177 spatially resolved gastric tissue regions. Multiplex immunohistochemistry and immunofluorescence characterized features of the immune microenvironment, while Gene Ontology, KEGG pathway analysis, Gene Set Variation Analysis, and xCell inference enabled functional, metabolic, and immune profiling. Key immune and NET-related findings were further validated by multiplex immunofluorescence in an independent, expanded cohort of IRAEG and non-immunotherapy-related gastritis samples. RESULTS: IRAEG was characterized by widespread HLA molecule activation and enhanced antigen processing, resembling the immune phenotype observed in organ transplant rejection. The acute G2 stage exhibited excessive neutrophil extracellular trap formation, profound metabolic suppression, and collapse of immune homeostasis-features that may inform early intervention strategies to preserve ICI treatment continuity. During early recovery (G3), inflammatory injury transitioned toward repair, marked by activation of fatty acid metabolism and PPAR signaling. Notably, even at complete clinical recovery (G4), more than 1,000 proteins remained differentially expressed, reflecting sustained enhancement of metabolic and immune functions and establishing a distinct molecular "memory" state with implications for ICI rechallenge decisions. CONCLUSIONS: These findings define four molecularly distinct stages of IRAEG progression and recovery. The stage-specific signatures identified here serve as candidate biomarkers for diagnosis, disease staging, and therapeutic response assessment, and may guide clinical decisions regarding irAE management, treatment modification, and safe ICI rechallenge to support continued antitumor therapy.

Humans↗

PubMed2026

Spatial Proteomic Profiling, a Novel Method for Detecting Diagnostic and Prognostic Proteins in Pediatric Sarcoma.

Pediatric sarcomas comprise approximately 10% of all childhood solid malignancies and are characterized by distinct genetic and proteomic alterations that have potential diagnostic, prognostic, and therapeutic significance. We have utilized digital spatial profiling (DSP) to identify protein expression in pediatric Ewing sarcoma (ES), Osteosarcoma (OS), Alveolar rhabdomyosarcoma (ARMS), and Embryonal rhabdomyosarcoma (ERMS), in association with clinical outcomes. Formalin-fixed, paraffin-embedded sections from a tissue microarray block containing eight ES, eight OS, five ARMS, and three ERMS cases were subjected to proteomic DSP on a GeoMx NanoString platform yielding information on expression of 580 proteins. Proteins related to epigenetic regulation, signaling pathways, and mesenchymal differentiation were broadly expressed across all tumor types. Tumor-specific protein profiles were defined based on highly expressed proteins. Differentially expressed proteins include Cyclin D1 in ES, S100A4 in OS and IKKi/IKKe in ARMS and ERMS. Immunohistochemical validation confirmed variable expression of H3K27me3 across the tumors, and elevated expression of Cyclin D1 in ES and S100 in OS. These findings underscore the utility of DSP as a high-resolution proteomic tool for the identification of clinically relevant biomarkers in pediatric sarcomas. The results provide a foundation for further investigation of candidate proteins with potential diagnostic, prognostic, and therapeutic applications.

Humans↗

PubMed2025

Spatial Proteomics Using BiFCPL Identifies Regulators of DMV Formation Involved in Coronavirus Replication.

β-Coronaviruses hijack host factors to remodel host endo-membranes to form double membrane vesicles (DMVs), which act as central hubs for the replication of viral genomes. Understanding the molecular mechanism underlying DMV formation is critical for developing effective antiviral strategies and has garnered significant attention. However, the host factors involved in DMV formation remain scanty. Here, we employed a bimolecular fluorescence complementation-based proximity labeling (BiFCPL) strategy to investigate the proteome of DMVs generated by co-expression of SARS-CoV-2 NSP3 and NSP4. Our analysis identified 62 proteins with high confidence, among which five proteins were localized in the endoplasmic reticulum (ER), and were further confirmed to be recruited to DMVs through interactions with NSP3/NSP4. Moreover, we demonstrated that the absence of GRAMD1B or TEX2 resulted in the formation of enlarged DMVs induced by either NSP3/NSP4 or coronaviruses infection, and impaired coronaviruses replication as well. Collectively, our study concerning host-virus interactions sheds light on novel host factors involved in DMV formation.

Virus Replication↗

PubMed2025

Biomarker identification through spatial proteomics for the characterization of indeterminate thyroid nodules.

PURPOSE: The identification of novel molecular biomarkers may assist in the characterization of indeterminate thyroid nodules, which pose significant diagnostic challenges. Here, we aimed to explore the potential of proteomic analyses to support biomarker discovery in challenging thyroid lesions. METHODS: Linear Discriminant Analysis (LDA) was applied to Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) data from 44 thyroid neoplasms to select the most impactful molecular features for the classification of different tumor histologies, as well as for the distinction between NRAS-mutant (mNRAS) and NRAS-wild-type (wtNRAS) tumors. Relevant peaks were subsequently identified through nanoscale liquid chromatography electrospray ionization tandem mass spectrometry (nLC-ESI-MS/MS). RESULTS: The LDA selected nine relevant molecular markers distinguishing noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTPs) from other tumor histologies (balanced accuracy = 73%), as well as 19 relevant markers able to identify mNRAS cases (balanced accuracy = 84%). Nine differentially expressed proteins were putatively identified: among them, ATP-dependent RNA helicase DDX42 showed a similar distribution between NIFTPs and papillary thyroid carcinomas (PTCs) / follicular variant PTCs (FVPTCs), while the distribution of the Histone H4 signal was similar between NIFTPs and follicular adenomas (FAs). In addition, Protein disulfide-isomerase A1 and Complement C4-B were overexpressed in wtNRAS compared to mNRAS cases, regardless of histology. CONCLUSION: The LDA-selected features enable to distinguish NIFTPs from morphologically similar lesions and to discriminate between mNRAS and wtNRAS cases. The identified markers might complement genetic analyses and provide insights into the distinct pathogenic drivers behind the development of mNRAS compared to wtNRAS lesions.

Humans↗

PubMed2026

Loss of the Mechanistic Target of Rapamycin Complex 1 Causes a Lethal Alpha-1 Antitrypsin Deficiency-Associated Liver Disease.

BACKGROUND & AIMS: SERPINA1 mutations cause retention of the otherwise secreted alpha-1 antitrypsin and lead to the proteotoxic alpha-1 antitrypsin deficiency-related liver disease. As mechanistic target of rapamycin is a key coordinator of proteostasis, we studied its role in alpha-1 antitrypsin deficiency-related liver disease. METHODS: PiZ mice overexpressing the characteristic SERPINA1 mutation were mated with rodents harboring a hepatocyte specific-ablation of the interaction partners regulatory-associated protein of mechanistic target of rapamycin or rapamycin-insensitive companion of mammalian target of rapamycin, corresponding to mechanistic target of rapamycin complexes 1 or 2, or with mice lacking mechanistic target of rapamycin. Serum proteomics, liver bulk proteomics, spatial proteomics, and metabolomics were applied to characterize molecular and metabolic alterations. RESULTS: At 2 months of age, PiZ-mTORΔhep and PiZ-RaptorΔhep but not PiZ-RictorΔhep mice showed signs of increased liver injury and mortality despite diminished hepatic alpha-1 antitrypsin accumulation. PiZ-RaptorΔhep animals displayed increased levels of the proapoptotic protein C/EBP homologous protein, but C/EBP homologous protein ablation did not rescue the phenotype. Serum proteomics revealed no signs of advanced synthetic liver failure but immature hepatocellular products. Liver bulk proteomics and small metabolite measurement demonstrated a metabolic reprogramming of PiZ-RaptorΔhep mice. Spatial proteomics revealed alterations in liver zonation with increased ammonia levels as the likely cause of death in PiZ-RaptorΔhep animals. CONCLUSIONS: In summary, in alpha-1 antitrypsin deficiency-related proteotoxic liver injury, regulatory-associated protein of mechanistic target of rapamycin preserves a liver zonation, thereby protecting from lethal metabolic dysregulation.

Animals↗

PubMed2026

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p < 0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8 + T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗

PubMed2025

PEELing: an integrated and user-centric platform for spatially resolved proteomics data analysis.

SUMMARY: Molecular compartmentalization is vital for cellular physiology. Spatially resolved proteomics allows biologists to survey protein composition and dynamics with subcellular resolution. Here, we present PEELing, an integrated package and user-friendly web service for analyzing spatially resolved proteomics data. PEELing assesses data quality using curated or user-defined references, performs cutoff analysis to remove contaminants, connects to databases for functional annotation, and generates data visualizations-providing a streamlined and reproducible workflow to explore spatially resolved proteomics data. AVAILABILITY AND IMPLEMENTATION: PEELing and its tutorial are publicly available at https://peeling.janelia.org/ (Zenodo DOI: 10.5281/zenodo.15692517). A Python package of PEELing is available at https://github.com/JaneliaSciComp/peeling/ (Zenodo DOI: 10.5281/zenodo.15692434).

Proteomics↗

PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗

PubMed2026

Resolving cellular signaling in space and time: From organelle proteomics to spatial phosphoproteomics.

Cellular signaling is inherently organized in space and time, requiring coordinated control of protein localization, molecular interactions, and enzymatic activity across subcellular compartments. Recent advances in chemical biology, protein engineering, and quantitative proteomics have made it possible to interrogate these dimensions in an integrated manner. Here, we highlight emerging strategies to resolve signaling organization across three interconnected dimensions: organelle-resolved proteome mapping to define spatial context, proximity labeling to capture local protein interaction networks, and spatially resolved phosphoproteomics to quantify signaling outputs. Developments in proximity labeling, including split, conditionally activated and light-gated enzymes, enable temporally controlled, context-dependent profiling of transient protein assemblies in living cells. Advances in high-throughput and low-input phosphoproteomics, together with improved computational frameworks for kinase activity inference and subcellular enrichment strategies, are enabling spatially resolved measurement of signaling activity. Together, these approaches are shifting the field from static localization maps toward dynamic models of signaling networks.

Proteomics↗