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At least 19 recordsLinked to original sources
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

Spatial biology reveals altered macrophage states in immunosuppressed non-melanoma skin cancer.

Immunosuppressed patients with non-melanoma skin cancer experience worse clinical outcomes, yet the tumor immune microenvironment associated with systemic immunosuppression remains incompletely defined. Using integrated single-cell, spatial transcriptomic, multiplex immunofluorescence, and spatial epigenomic profiling across immunocompetent and immunosuppressed tumors, we found that overall immune-cell composition was largely preserved despite differences in immune-cell distribution, spatial organization, and T cell clonality. Immunosuppressed tumors demonstrated reduced intratumoral macrophage densities, decreased T cell clonal diversity, altered antigen-presenting cell and T cell spatial interactions, and distinct fibroblast- and macrophage-associated spatial niches. Multi-cohort validation across complementary spatial and single-cell platforms identified consistent alterations in innate-adaptive immune organization in immunosuppressed tumors. Together, these findings define spatial and functional remodeling of the tumor immune microenvironment under systemic immunosuppression and provide a framework for future therapeutic investigation in high-risk patients.

Humans↗
PubMed2024

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Humans↗
PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗
PubMed2026

Extracellular vesicle miR-93-5p cargo regulates glomerular endothelial cell damage in Alport syndrome.

Modulation of miRNA expression in glomerular cells is associated with renal disease. Here, we investigated the role of miR-93-5p in mitigating glomerular damage in Alport syndrome and whether the disease-modifying activity of extracellular vesicles from human amniotic fluid stem cells (hAFSC-EVs) is mediated by their miR-93-5p cargo. We identified downregulation of miR-93-5p specifically in glomerular endothelial cells in Alport syndrome along disease progression. Silencing of miR-93-5p in hAFSC-EVs changed the transcriptomic and proteomic profile, regulating EV disease-modifying activity. Compared with naive hAFSC-EVs, silenced hAFSC-EVs did not rescue glomerular endothelial function in vitro and did not restore kidney function in vivo. We established that hAFSC-EVs regulate VEGFR1 and VEGFR2 signaling by miR-93-5p cargo transfer, highlighting that miR-93-5p can restore glomerular endothelial cell biology. Spatial transcriptomics analysis of hAFSC-EV-injected kidneys showed that these EVs can reverse pathways altered during disease progression by stimulating proregenerative processes, specifically in the glomerulus, by regulating miR-93-5p targets. Alteration of glomerular endothelial cell transcriptomics and miR-93-5p targets was also confirmed in biopsies of patients with Alport syndrome using spatial molecular imaging. We demonstrated the critical role of miR-93-5p in glomerular endothelial cells and the capability of hAFSC-EVs to regulate miR-93-5p and its targets in Alport syndrome.

Humans↗
PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗
PubMed2026

Spatial Omics in High-Grade Gliomas: Mapping Immune-Tumor Niches for Precision Therapy.

High-grade gliomas (HGGs), particularly glioblastoma (GBM), remain among the most lethal human cancers despite decades of molecular profiling and therapeutic innovation. A primary reason for treatment failure is that HGG biology is spatial: malignant cell states, immune suppression, metabolic stress, and therapeutic resistance are organized into distinct anatomical and functional niches. Spatial omics technologies now enable high-dimensional mapping of gene expression, protein signaling, immune architecture, and metabolic activity within intact tumor tissue. These approaches reveal how proneural and mesenchymal transcriptional states coexist yet localize to distinct regions, alongside hypoxic, invasive, and stem-enriched niches. Spatial analyses show that key clinical determinants, including O6-methylguanine-DNA methyltransferase (MGMT)-associated temozolomide resistance, radiotherapy tolerance in hypoxic regions, and immunotherapy failure driven by myeloid-dominated immune exclusion, are influenced not only by molecular programs but also by cellular location. Beyond biological insight, spatial omics is reshaping clinical paradigms by enabling region-specific patient stratification, early assessment of treatment response, and identification of therapy-resistant reservoirs that seed recurrence. Prior bulk and single-cell studies defined HGG cell states and pathways but often treated resistance as tumor-wide. This review presents a spatially explicit framework that synthesizes spatial transcriptomic and immune-profiling studies to identify tumor-immune niches and spatial bottlenecks that drive therapeutic failure and recurrence.

Humans↗
PubMed2026

CAGNet: a structure-aware clustering-alternated graph network for cell-cell interaction inference in spatial transcriptomics.

MOTIVATION: Understanding cell-cell interactions (CCIs) in spatial transcriptomics is crucial for uncovering the spatial organization and functional heterogeneity of tissues. However, existing graph-based models typically rely on static clustering or fixed adjacency structures, which limits their ability to capture dynamic cellular relationships. RESULTS: We propose CAGNet, a two-stage framework for CCI inference from spatial transcriptomics data. In Stage 1, a Graph Attention Network encoder with joint feature and graph reconstruction learns structure-aware node embeddings from spatial gene expression profiles. In Stage 2, an alternating optimization mechanism iteratively updates cluster centers via KL-guided soft assignment and refines node embeddings through spatial graph reconstruction, establishing a closed-loop between representation learning and clustering. Experiments on three 10x Genomics Visium datasets demonstrate that CAGNet consistently outperforms six CCI inference baselines across ACC, AUC, AP, Precision, Recall, and F1. CAGNet also achieves the highest Adjusted Rand Index on all three datasets against six spatial domain identification methods, confirming that the learned embeddings capture biologically relevant spatial organization. Information-theoretic analysis further shows that CAGNet retains the highest mutual information between input features and learned embeddings among all compared methods. Ablation studies and 5-fold cross-validation confirm the contribution of each component and the reproducibility of the results. AVAILABILITY: The proposed method is implemented in the CAGNet package available at http://github.com/mahan1233333-maker/CAGNet .

Spatial Transcriptomics↗
PubMed2025

Sparse deconvolution of cell type medleys in spatial transcriptomics.

Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets. We introduce Weight-Induced Sparse Regression (WISpR), a machine learning algorithm that integrates spot-specific hyperparameters and sparsity-driven modeling. Unlike conventional approaches that neglect biology-grounded constraints, WISpR accurately predicts cell-type distributions while preserving biological coherence, i.e., spatially and functionally consistent cell-type localization, even in unmatched datasets. Benchmarking against five alternative methods across ten datasets, WISpR consistently outperformed competitors and predicted cellular landscapes in both normal and cancerous tissues. By leveraging sparse cell-type arrangements, WISpR provides biologically informed, high-resolution cellular maps. Its ability to decode tissue organization in both healthy and diseased states highlights WISpR's practical utility for spatial transcriptomics, particularly in challenging settings involving noise, sparsity, or reference mismatches.

Humans↗
PubMed2026

ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.

Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding-self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10 × Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.

Spatial Transcriptomics↗
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&#x2009;<&#x2009;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&#x2009;+&#x2009;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↗
PubMed2026

Pancreatic ductal adenocarcinoma: The Vision of Heracles.

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest malignancies, as tumors evolve faster than therapies. Resistance is ecological, not merely KRAS driven, involving overlooked players like high-grade pancreatic intraepithelial neoplasias (PanINs), peripancreatic fat, stromal mechanics, myeloid-neural circuits, metabolic rewiring, and systemic host responses. We propose precision interception targeting PanIN/intraductal papillary mucinous neoplasm (IPMN) biology, spatial-functional-proteogenomic classification beyond transcriptomics, the Heracles Protocol (measure, prime, strike, and adapt), and integrated technologies from AI pathology to exosomal delivery and CRISPR-based synergy mapping, together making PDAC more tractable.

Humans↗
PubMed2026

Distinct cellular phenotypes of language and executive decline in amyotrophic lateral sclerosis.

Cognitive manifestations, including impairments in language and executive functions, are seen in amyotrophic lateral sclerosis (ALS), but the underlying mechanisms remain unclear. We mapped prefrontal cortex regions from ALS patients by integrating spatial and single-nucleus transcriptomics in a cognitively stratified patient cohort. We uncover that cognitive impairment in ALS is associated with distinct patterns of neuronal dysfunction and glial-vascular dysregulation that vary by region and cognitive subtype. Executive dysfunction is linked to reduced mitochondrial and synaptic activity in deep-layer dorsolateral prefrontal cortex neurons, whereas language-related deficits track with a diffuse pan-regional response involving glial and vascular abnormalities. Our analyses, validated by multiplexed imaging, further identify signatures in the prefrontal cortex that span both motor and cognitive phenotypes, including a multicellular gliosis response. The findings reveal that clinical heterogeneity in ALS is driven by phenotype-specific cellular interactions in motor and non-motor regions of the brain.

Amyotrophic Lateral Sclerosis↗
PubMed2025

Spatially organized lymphocytic microenvironments in high grade primary prostate tumors.

The spatial organization and composition of the tumor-immune microenvironment (TME) play a critical role in shaping the progression of many solid cancers, but the organization of the TME in primary prostate cancer (PCa) remains poorly characterized. We therefore profiled the abundance and spatial distributions of major cell types involved in adaptive immunity in 29 radical prostatectomy specimens stratified into high (HGG; n=14) and low Gleason-grade (LGG; n=15). Compared to LGG, HGG PCa exhibited significantly greater B and T cell infiltration with many immune cells organized into clusters, some of which resembled tertiary lymphoid structures (TLSs). In HGG tumors, these clusters were dense, symmetric, rich in PD-1+ T cells, and frequently proximate to the tumor compartment. LGG clusters were less well organized, and T cell depleted. Thus, a subset of high-grade PCa harbor organized immune clusters that may play a role in tumor control and contain therapeutically targetable T and B cells.

Prostate cancer↗
PubMed1975

Physical and biological aspects of repair in dog cortical-bone transplants.

The amount of repair and the time required to accomplish repair of four-centimeter segmental fibular transplants in twenty-one male adult dogs were determined at from two to forty-eight weeks after transplantation by torsional stress testing, microradiography, and tetracycline labeling. The transplanted cortical bone was greatly weakened at from six weeks to six months but was nearly normal at one year. The strength of the transplant appeared to be related to the amount of porosity of the matrix rather than to the quality or completeness of biological repair. Spatially, the repair was ordered rather than random. The initial resorption caused increased porosity which was slowly offset by apposition of new bone, a process which was dependent on general skeletal metabolic activity. Although physical strength was near normal at forty-eight weeks, only 60 per cent of the transplants had been remodeled.

Animals↗
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↗
PubMed1976

Biostereometrics and the communication of biological form.

Biostereometrics-the spatial and spatio-temporal analysis of biological form and function based on principles geometry-is a modern approach to the measurement and analysis of biological form which recognizes that organic structures are irregular and three-dimensional (or four-dimensional, as in movement or growth). Stereometric sensors of various types are used to determine the coordinates of points distributed over the surface (internal or external). The resulting data provide a more comprehensive, parsimonious and unambiguous spatial quantification than heretofore possible using traditional lengths, breadths, and circumferences. Recent and prospective biomedical applications are described.

Anthropometry↗