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PubMed2025

Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.

MOTIVATION: Pinpointing the subcellular location of proteins is essential for studying protein function and related diseases. Advances in spatial proteomics have shown that automatic recognition of protein subcellular localization from images could highly facilitate protein translocation analysis and biomarker discovery, but existing machine-learning works have been mostly limited to processing 2D images. By contrast, 3D images have higher spatial resolution and allow researchers to observe cellular structures in their natural context, but currently, there are only a few studies of 3D image processing for protein distribution analysis due to the lack of data and complexity of modeling. RESULTS: We developed a knowledge-enhanced protein subcellular localization model, KE3DLoc, which could recognize distribution patterns in 3D fluorescence microscope images using deep learning methods. The model designs an image feature extraction module that incorporates information from 3D and 2D projected cells and implements asymmetric loss and confidence weights to address data imbalance and weak cell annotation issues. Besides, considering that the biological knowledge in the Gene Ontology (GO) database can provide valuable support for protein location understanding, the KE3DLoc model incorporates a novel knowledge enhancement module that optimizes the protein representation by related knowledge graphs derived from the GO. Since the image module and the knowledge module calculate features from different levels, KE3DLoc designs protein ID aggregation to enhance the consistency of protein features across different cells. Experimental results on three public datasets have demonstrated that the KE3DLoc significantly outperforms existing methods and provides valuable insights for spatial proteomics research. AVAILABILITY AND IMPLEMENTATION: All datasets and codes used in this study are available at GitHub: https://github.com/PRBioimages/KE3DLoc.

Microscopy, Fluorescence↗

PubMed2026

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities with adult glioma, it comprises distinct disease entities. In this study, we longitudinally profile a molecularly diverse cohort of 16 pHGG patients through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of neoplastic and microenvironmental features during disease progression and treatment. We define a set of core pHGG neoplastic cell states and observe differential tumor-myeloid interactions between malignant cell phenotypes. We find that essential neuromodulators and the interferon response are upregulated post-therapy, implicating them as malignant cell-intrinsic targets. We observe an increase in oligodendrocytes upon progression and that they coordinate spatial motifs with proneural tumor cells. This multiomic atlas of longitudinal pHGG captures features of therapy response and provides a scalable reference for the study of pediatric brain tumors.

Humans↗

PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗

PubMed2026

Spatial analysis reveals the evolving organization of IDH-mutant glioma.

Adult diffuse gliomas are composed of malignant cell states interwoven with the non-malignant brain microenvironment. Here, we combine spatial transcriptomics and spatial proteomics of isocitrate dehydrogenase (IDH)-mutant gliomas to define organizational principles across histological grades. In low-grade tumors, spatial organization is shaped by underlying brain anatomy. We identify a functional white-gray matter junction that restricts cortical invasion and is associated with marked changes in tumor composition and cellular phenotypes. This junction is preferentially traversed by oligodendrocyte progenitor (OPC)-like malignant cells, suggesting a role in tumor expansion. In contrast, tumors with intermediate histological features are largely disorganized, with few recurring interactions between cancer cell states and microenvironmental cell types. In high-grade tumors, hypoxia-associated structure emerges, resembling IDH-wild-type glioblastoma. Together, these findings reveal two independent axes of spatial organization-from anatomy-driven structure in low-grade tumors to hypoxia-driven organization in high-grade tumors-and establish a framework linking tumor grade to recurrent spatial interactions.

Isocitrate Dehydrogenase↗

PubMed2026

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.

MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.

Proteomics↗

PubMed2026

PASTA: versatile tyramide-oligonucleotide amplification for multimodal spatial biology.

Spatial proteomics is limited by detection sensitivity, multiplexing and multimodal integration, leaving a gap between discovery and clinical assays. Here we present protein and nucleic acid serial tyramide amplification (PASTA), using horseradish peroxidase-mediated oligonucleotide deposition and cyclical imaging for high-plex, multimodal spatial profiling. Compatible with conjugated antibodies and in situ hybridization, PASTA enables simultaneous protein and RNA codetection from formalin-fixed, paraffin-embedded samples, providing a cost-effective bridge from discovery to clinical validation.

Tyramine↗

PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗

PubMed2026

Proteomics-based approaches to neutrophil biology.

INTRODUCTION: Neutrophils are central effectors of innate immunity and key contributors to inflammation, host defense, and tissue injury across a wide range of physiological and pathological contexts. Due to their short lifespan, rapid activation, and extensive post-translational regulation, comprehensive molecular characterization of neutrophil function requires approaches that go beyond transcriptomics or marker-based analyses. AREAS COVERED: This review summarizes how proteomic technologies have advanced the understanding of neutrophil biology by enabling unbiased, system-wide profiling of protein abundance, subcellular organization, post-translational modifications, and functional heterogeneity. We discuss global and subcellular proteomics, PTM-centric analyses, and emerging low-input and single-cell proteomic strategies, highlighting recent studies of infection, cancer, metabolic disorders, aging, autoimmune disease, and inflammation. The literature covered includes current large-scale quantitative proteomics, targeted PTMs, and integrative multi-omics studies in both human samples and relevant experimental models. EXPERT OPINION: Proteomics has established neutrophils as highly plastic and context-dependent cells whose functions are governed by coordinated remodeling of signaling, metabolism, and effector pathways. Future progress will depend on expanding neutrophil-specific PTM maps, improving low-input workflows, and integrating single-cell and spatial proteomics. Together, these advances are expected to redefine neutrophil functional states and accelerate translation toward clinically meaningful biomarkers and therapeutic strategies.

Humans↗

PubMed2026

Spatial proteogenomic profiling uncovers sensitization strategies for antibody-drug conjugate in HER2-positive breast cancer.

Antibody-drug conjugates (ADCs) have transformed the treatment of HER2-positive breast cancer, yet resistance remains poorly understood. Using imaging mass cytometry, we profiled 157 regions of interest comprising 912,360 single cells from 47 HER2-positive/hormone receptor-negative breast cancers treated with SHR-A1811 in the FASCINATE-N trial. Spatial proteomic analyses identified two determinants of ADC response: elevated tumor-cell H3K27ac expression was associated with improved ADC efficacy, whereas collagen-positive fibroblasts mediated resistance. Combining ADC with the histone deacetylase inhibitor chidamide or the collagen-modulating agent losartan produced synergistic antitumor effects in preclinical models. These biomarkers and therapeutic vulnerabilities were independently validated in patients with advanced HER2-positive disease receiving trastuzumab deruxtecan. Moreover, based on these spatial features, we developed a clinically applicable ADC barrier prediction model that can be implemented using multiplex immunofluorescence. Taken together, our findings reveal actionable spatial determinants of ADC efficacy and suggest potential combination therapeutic strategies.

Humans↗

PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗

PubMed2026

Single-nucleus profiling reveals hepatocyte identity and immune features associated with corticosteroid response in severe alcohol-related hepatitis.

BACKGROUND & AIMS: Severe alcohol-related hepatitis (sAH) is associated with high short-term mortality. However, 30-40% of patients fail to respond to corticosteroids, the only proven pharmacological treatment. The pathophysiological mechanisms underlying sAH and the marked heterogeneity in treatment response remain incompletely understood. We aimed to define cellular changes associated with corticosteroid response in sAH and to identify baseline markers predictive of treatment outcome. METHODS: Single-nucleus RNA sequencing was performed on liver biopsies from patients with biopsy-proven sAH (n = 17), including paired baseline and day 8 biopsies in a subset of patients (n = 8). Patients were classified as corticosteroid responders (sAH-R; Lille score <0.45) or non-responders (sAH-NR; Lille score ≥0.45). Liver biopsies from patients with acute decompensation of alcohol-related cirrhosis (AD; n = 5) and healthy controls (n = 4) were included for comparison. Findings were validated using immunohistochemistry and spatial proteomics in a large multicenter validation cohort (n = 172). RESULTS: At baseline, sAH-R exhibited a significantly higher proportion of liver-infiltrating S100A8+ monocytes compared to sAH-NR, a difference that persisted at day 8. sAH livers showed a marked reduction in mature hepatocytes and an expansion of stressed and intermediate hepatocyte populations, indicating progressive loss of mature hepatocyte identity. This loss was more pronounced in sAH-NR, who also exhibited fewer cycling hepatocytes at day 8, consistent with impaired regenerative capacity. Expression of SULT2A1, a marker of mature hepatocyte identity, was significantly reduced in sAH-NR at baseline. Finally, liver biopsies with ≥50% SULT2A1-positive hepatocytes at baseline were strongly predictive of corticosteroid response. CONCLUSIONS: This study delineates distinct immune and hepatocyte changes associated with corticosteroid response in sAH. Baseline SULT2A1 expression may facilitate stratified treatment approaches in sAH. IMPACT AND IMPLICATIONS: Severe alcohol-related hepatitis (sAH) represents one of the most devastating manifestations of alcohol-related disorders. sAH is characterized by acute hepatic inflammation and high short-term mortality. Clinical management remains challenging, as corticosteroids - the only pharmacological therapy currently applied - are ineffective in a substantial proportion of patients and are associated with significant adverse effects. These limitations highlight the need for a more detailed understanding of sAH pathophysiology and for approaches that enable improved patient stratification and therapeutic decision-making. In this study, we identify distinct pre-treatment differences between corticosteroid responders and non-responders at the level of both hepatocytes and myeloid cells, providing new insight into the cellular mechanisms underlying treatment heterogeneity in sAH. Furthermore, leveraging these findings, we developed a histology-based SULT2A1 scoring system using a commercially available antibody, which predicts corticosteroid response at baseline and may support stratified treatment approaches in clinical practice.

Humans↗

PubMed2022

Signatures for viral infection and inflammation in the proximal olfactory system in familial Alzheimer's disease.

Alzheimer's disease (AD) is characterized by deficits in olfaction and olfactory pathology preceding diagnosis of dementia. Here we analyzed differential gene and protein expression in the olfactory bulb (OB) and tract (OT) of familial AD (FAD) individuals carrying the autosomal dominant presenilin 1 E280A mutation. Compared to control, FAD OT had increased immunostaining for β-amyloid (Aβ) and CD68 in high and low myelinated regions, as well as increased immunostaining for Iba1 in the high myelinated region. In FAD samples, RNA sequencing showed: (1) viral infection in the OB; (2) inflammation in the OT that carries information via entorhinal cortex from the OB to hippocampus, a brain region essential for learning and memory; and (3) decreased oligodendrocyte deconvolved transcripts. Interestingly, spatial proteomic analysis confirmed altered myelination in the OT of FAD individuals, implying dysfunction of communication between the OB and hippocampus. These findings raise the possibility that viral infection and associated inflammation and dysregulation of myelination of the olfactory system may disrupt hippocampal function, contributing to acceleration of FAD progression.

Humans↗

PubMed2026

Senotypes define the diverse landscape of senescent cells.

Cellular senescence was initially defined in vitro as a stable cell-cycle arrest that occurs after repeated replication, but it is now recognized as a heterogeneous state shaped by cell type, species, senescence-inducing stress, tissue microenvironment and time. To organize this complexity, we propose the term 'senotype' to classify senescent cells by their inputs, molecular features and functional effects. We outline a practical framework incorporating: (1) cell identity and context; (2) inducing mechanism; (3) temporal stage; (4) multimodal molecular and structural features; and (5) physiological or pathological functions. Experimentally defined senotypes can serve as references for interpreting tissue-derived senotypes, where parameters may be incomplete. Senotypes should be anchored in combinations of core hallmarks (that is, durable cell-cycle arrest, altered secretory profiles, macromolecular or organelle damage, disrupted homeostasis) rather than single markers. Advances in single-cell, spatial, proteomic and computational methods enable rigorous senotype characterization, improving consistency and accelerating development of targeted senotherapeutics.

Cellular Senescence↗

PubMed2025

SpatioMark: quantifying the impact of spatial proximity on cell phenotype.

MOTIVATION: As research advances in spatially resolving the biological archetype of various diseases, technologies that capture the spatial relationships between cells are demonstrating increasing value. Whilst there are an increasing number of analytical methods being developed to identify the complex web of interactions between cells, the downstream impacts of these cell-cell relationships are under explored. RESULTS: We present SpatioMark, a statistical framework that simplifies the assessment of gene or protein expression changes within a cell type that are associated with the spatial proximity to other cell types. We demonstrate its performance across spatial proteomics and transcriptomics datasets. We link identified relationships with differences in patient survival. We highlight key challenges in identifying changes in molecular markers associated with the localization of cells. We propose correction strategies that reduce artefact-induced relationships. AVAILABILITY AND IMPLEMENTATION: SpatioMark is implemented in the Statial R package on Bioconductor: https://bioconductor.org/packages/release/bioc/html/Statial.html.

Humans↗

PubMed2026

Distinct immune landscapes characterize highly versus minimally invasive brain metastases.

Brain metastases (BrMs) occur in approximately 30% of cancer patients, causing nearly one-fifth of cancer deaths. While immune checkpoint inhibitors (ICIs) benefit some BrM patients, responses remain highly variable. This variability partly reflects distinct histopathological growth patterns that include minimally invasive (MI) and highly invasive (HI) brain BrMs. Here we show that MI BrMs exhibit robust immune infiltration, whereas HI lesions are immunosuppressed. However, histological differentiation between MI and HI can be challenging because of subjective margin assessment. Here, using highly multiplexed spatial proteomics on 119 tumor sections from 46 patients with BrMs, we identify CHI3L1 as a key mediator of the immunosuppressive microenvironment in HI BrMs. In preclinical models, genetic deletion of CHI3L1 converts immune-cold metastases into lymphocyte-rich, ICI-responsive lesions infiltrated by granzyme B+ CD8+ T cells. In BrM patients treated with ICI, immunohistochemical quantification of CHI3L1 expression was a stronger predictor of ICI response than traditional MI/HI classification. Thus, CHI3L1 represents a promising biomarker and therapeutic target for BrMs.

Humans↗

PubMed2026

A conserved grain-associated immunosuppressive niche in Sudanese patients with mycetoma.

Mycetoma is a neglected tropical disease caused by various bacterial and fungal pathogens that has a significant health impact across a broad geographically defined "mycetoma belt" spanning South America, Africa and Asia. Histologically, mycetoma is characterised by invasive and destructive granuloma development in the skin, deep tissues and bone, leading to tissue destruction, deformities and high morbidity. The presence of macroscopic, highly compacted pathogen microcolonies, or "grains," is a key diagnostic feature, and the formation of grains supports pathogen persistence and disease chronicity. However, there is a paucity of information on immune responses in mycetoma patients and on the relative importance of phylogeny and/or grains in establishing the local immune landscape. Here, we used spatial proteomics to examine the distribution of 43 immune-related proteins in surgical biopsies from 11 patients with mycetoma of bacterial (actinomycetoma; Actinomadura pelletierii and Streptomyces somaliensis; n = 6) and fungal (eumycetoma; Madurella mycetomatis; n = 5) origin. Using mixed-effects modelling, an exploratory analysis across species and pathogen classes revealed few significant differences in immune marker expression. In contrast, and independently of pathogen class, the cellular infiltrate closest to grain boundaries had higher per-cell expression of CD66b+ neutrophils, ARG1, and VISTA. The preferential accumulation of CD66b+ARG1+VISTA+ cells at grain boundaries was confirmed by quantitative immunofluorescence analysis. Hence, the local tissue microenvironment surrounding the mycetoma grain represents a specialised immunosuppressive niche, with parallels to the tumour microenvironment. Trial Registration The study was prospectively registered on ClinicalTrials.gov NCT04401969.

Adult↗

PubMed2026

Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans↗

PubMed2026

Next-generation brain proteomics: Integrating single-cell, spatial, and multi-omics for clinical biomarker discovery.

The mammalian brain's functional complexity arises from the sophisticated architecture of neurons and glia. This network is essentially defined by its dynamic proteome, which reveals the functional execution underlying neural computation and disease. This review integrates the technological leap in neuroproteomics. It has moved beyond bulk tissue proteome cataloguing to high-sensitivity single-cell and spatial resolution. We detail how next-generation platforms, such as TIMS-PASEF and Orbitrap-Astral, have enabled deeper and faster phenotypic profiling of limited brain samples. However, the proteome coverage remains constrained by dynamic range, sample loss, ionisation bias and incomplete detection of low-abundance regulatory proteins. We further examine how such studies have revealed the proteomic remodelling that drives lineage specification and synaptic plasticity by linking temporal protein expression waves to biological function. Crucially, we delineate the clinical translational trajectory, illustrating how aberrant signatures are verified in cerebrospinal fluid (CSF) and validated in plasma to support precision medicine. Finally, we argue for the necessity of "fused" multi-omics integration and Artificial Intelligence (AI) to decode the non-linear molecular logic of brain pathology.

Humans↗