Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “tissue heterogeneity”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources
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↗
PubMed2026

Placenta-derived Exosomes Mitigate Hypoxia-Induced Trophoblast Apoptosis and Inflammatory Progression via SASH1.

SASH1 is a signal adaptor protein involved in cell growth, apoptosis, and immune regulation, and has been increasingly studied in tumor and immune cells. Emerging evidence suggests that SASH1 plays an important role in inflammatory responses and cellular homeostasis, processes that are closely associated with the development of PE. This study aimed to determine whether SASH1 contributes to trophoblast apoptosis and inflammatory responses in PE and whether P-EXOS exerts protective effects through SASH1 regulation. In this study, three PE-related transcriptomic datasets (GSE75010, GSE10588, and GSE60438) were analyzed to identify shared differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning algorithms were further applied to screen key candidate genes, and single-cell RNA sequencing data were used to characterize cellular heterogeneity in placental tissue and to determine cell type-specific expression patterns. SASH1 was identified as a consensus candidate gene and was significantly upregulated in trophoblast cells from PE samples. In vitro, a hypoxia-treated HTR-8/SVneo trophoblast cell model was established, combined with SASH1 knockdown, SASH1 overexpression, and co-culture with P-EXOS. Functional experiments showed that knockdown of SASH1 significantly suppressed hypoxia-induced trophoblast apoptosis and reduced the secretion of pro-inflammatory cytokines, including IL-6, IL-1β, and TNF-α, whereas SASH1 overexpression promoted apoptosis and inflammatory responses. In addition, P-EXOS treatment markedly reduced SASH1 expression at both mRNA and protein levels and attenuated hypoxia-induced trophoblast injury, while SASH1 overexpression largely abolished these protective effects. Taken together, these findings indicate that SASH1 plays a critical role in trophoblast apoptosis and inflammatory responses in PE. P-EXOS may alleviate hypoxia-induced trophoblastic injury by suppressing SASH1 expression, providing new insights into the molecular mechanisms and potential therapeutic targets for PE.

Trophoblasts↗
PubMed2026

Complex structural variation, phylogeny, and disease associations of the mucin pangenome.

Mucins are large glycoproteins that provide hydration and barrier function to epithelial tissues. Although genetically heterogeneous, all mucins harbor a large exon composed of variable number tandem repeats (VNTRs). Short-read sequencing has limited our understanding of mucin VNTR diversity and makes disease association studies challenging. We leverage 296 long-read phased genome assemblies to characterize 14 mucin family members, achieving &#x2265;97% accuracy across 572 haplotypes. Phylogenetic haplogroup analysis reveals extraordinary structural heterozygosity, with MUC4 harboring the greatest allelic diversity (n=240 distinct lengths) and MUC12 the greatest size range (&#x394; = 55,233 bp; 23,080 amino acids). Ten mucins show significant population stratification (pFDR < 0.05). At the MUC4/MUC20 locus, we characterize higher-order structural variation, including a recurrent inversion, copy number variation, and interlocus gene conversion. Optimized genotyping achieves &#x2265;95% haplogroup concordance across 10 loci. We apply this to 4,637 deeply phenotyped cystic fibrosis patients and identify a significant association between short MUC1 VNTRs and severe disease (p=0.0056), demonstrating the pangenome's utility for complex locus genotyping and disease discovery.

Journal Article↗
PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗
PubMed2026

Developmental patterning of adipose tissue by abd-A and Abd-B homeotic genes in Drosophila melanogaster.

The Bithorax Complex (BX-C) homeobox proteins specify segmental identities along the anterior-posterior axis during Drosophila embryogenesis. Differential expression of the BX-C genes abd-A and Abd-B distinguishes abdominal from thoracic adipocytes, yet the mechanism regulating this heterogeneity remains poorly understood. Here, we identify cis-regulatory elements (CREs) and transcription factors that direct abdominal-specific expression of abd-A and Abd-B in the larval fat body. Fine-mapping analyses identified a 627-bp CRE within the Abd-B locus and a ~6-kb CRE within the abd-A locus sufficient to drive heterogeneous expression. Yeast one-hybrid screening combined with functional analyses identified Lola, Lolal, and Combgap as key repressors of Abd-B, whereas Piragua (Prg) and Seven up (Svp) function as transcriptional activators, indicating that adipocyte heterogeneity in postembryonic adipose tissue is actively regulated. In turn, lola and prg are repressed by Abd-B, whereas lolal and svp are activated, forming a feedback circuit further modulated by Wnt signaling, which promotes lola and lolal expression while repressing svp. CUT&RUN analyses suggest that these interactions are direct, with dTCF/Pan and Abd-B occupancy detected at target loci. Together, our findings define a transcriptional circuit that regulates Abd-B gene transcription to pattern adipose tissue and may establish the developmental basis of fat depot specialization.

Abd-B↗
PubMed2026

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Benchmarking↗
PubMed2026

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

Proteomics↗
PubMed2026

Inhibition of EED enhances osteogenic differentiation and bone formation: a potential therapeutic strategy for osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is a heterogeneous group of inherited connective tissue disorders primarily caused by dominant mutations in COL1A1 or COL1A2 that impair type I procollagen folding and secretion. Misfolded collagen accumulates in the endoplasmic reticulum (ER), triggering ER stress and osteoblast dysfunction, and bone fragility. Current pharmacologic therapy focuses on inhibiting bone resorption but has limited efficacy and does not address the underlying biology of the disease. The epigenetic regulator polycomb-repressive complex 2 (PRC2) has emerged as an important regulator of bone formation. Genetic and pharmacologic disruption of PRC2 enhanced osteogenic differentiation in WT cells. Here, we demonstrate that inhibition of the PRC2 through targeting its essential component embryonic ectoderm development (EED) enhances osteogenic differentiation, improves bone architecture in male Col1a2 +/G610C OI mouse models, modulates the integrated stress response (ISR), and improves ER morphology in OI cells. These findings identify EED inhibition as a novel epigenetic strategy to restore collagen homeostasis and improve skeletal integrity in OI.

ER stress↗
PubMed2026

Diagnostic performance of panfungal PCR on tissue specimens for the diagnosis of invasive fungal diseases: a systematic review and meta-analysis of the Fungal PCR Initiative (FPCRI).

UNLABELLED: Invasive fungal diseases are difficult to diagnose because of the limited sensitivity of culture. Panfungal PCR amplicon sequencing assays (targeting ribosomal RNA, such as 18S, 28S, ITS) are recommended for fungal identification in histopathology samples showing fungal elements. However, data describing its overall performance and consistency are lacking. This systematic literature review and meta-analysis assessed the performance of panfungal PCR on formalin-fixed paraffin-embedded (FFPE) and non-fixed (fresh or frozen) tissue samples. A systematic literature search was performed to include studies reporting the use of panfungal PCR for fungal identification in FFPE or non-fixed tissue samples. PCR sensitivity and specificity were assessed using the reference standard of histopathology showing fungal elements. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates were obtained using random-effects meta-analysis. Twenty-eight studies were included. In FFPE samples (18 studies, 852 samples), sensitivity and specificity were 75.4% (95% confidence interval [CI], 59.2-86.6) and 93.5% (70.2-98.9), respectively. Sensitivity in non-fixed samples (13 studies, 207 samples) was 86.5% (74.7-93.3), while specificity could not be assessed (insufficient data). Comparative analyses showed a significantly higher sensitivity of panfungal PCR over culture (88.2%; 76-94.7 vs 52.2%; 39-65, P = 0.001). Sub-analyses could not demonstrate the superiority of one PCR target over another due to limited data. Panfungal PCR exhibited adequate sensitivity and good specificity in FFPE samples. Sensitivity was even higher in non-fixed samples and largely superior to culture. Nevertheless, large interstudy variability was observed, warranting interlaboratory studies to define the optimal PCR target and standardized protocols. IMPORTANCE: Invasive fungal diseases are difficult to diagnose because of the low sensitivity of culture. Panfungal PCRs are widely used for fungal identification in tissue specimens but suffer from heterogeneous procedures and performance. This meta-analysis shows an acceptable sensitivity (75.4% and 86.5% in fixed and non-fixed samples, respectively) and good specificity (93.5%) of panfungal PCR, supporting its use, not only on histopathology-positive fixed samples but also in non-fixed samples concomitantly with other diagnostic tools (cultures and fungal-specific PCRs if available). These results provide a strong basis for further standardization of panfungal PCR techniques via interlaboratory assays to assess reproducibility and optimize analytical protocols. CLINICAL TRIALS: This study is registered with PROSPERO as CRD42023461148.

Humans↗
PubMed2026

Personalized medicine strategy for MPNSTs: using precision oncology on PDOX models to inform tumor boards.

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNSTs) are a heterogeneous group of aggressive soft tissue sarcomas with poor prognosis. Currently there is a lack of effective treatments for MPNSTs. Here, we propose a personalized medicine approach that integrates a precision oncology strategy guided by MPNST genomic analysis, with a functional validation of treatment response in an orthotopic xenograft model (PDOX) derived from the same MPNST. METHODS: Comprehensive whole genome sequencing analysis was performed in primary MPNSTs, relapses and (in one case) metastases, following disease progression in two independent individuals. Matched MPNST PDOX models were generated by orthotopically implanting tumor fragments near the sciatic nerve of immunodeficient mice. Candidate targeted combination therapies were prioritized based on genomic alterations and tested in vivo in the PDOX models. RESULTS: The feasibility of the developed strategy is illustrated for two MPNST patients, one Neurofibromatosis type 1 (NF1) individual that developed two independent MPNSTs and another sporadic MPNST case with multiple metastatic relapses. Genomic analysis revealed a remarkable degree of genomic stability across primary MPNSTs and their successive relapses in each patient, and even metastases in one individual. While based on a small number of cases requiring additional analyses, this finding aligns with previous evidence suggesting a fair genomic conservation throughout tumor evolution. This stability supports the identification of consistent therapeutic vulnerabilities throughout disease progression. Among the therapies tested, co-treatment of MEK inhibitor (MEKi) plus bromodomain inhibitor (BETi) elicited the highest antitumor activity, resulting in approximately 60% tumor volume reduction in the sporadic MPNST PDX model, whose patient has been receiving this therapy for eight months with sustained remission. CONCLUSIONS: This study demonstrates the feasibility and clinical utility of integrating genomic-driven precision oncology with PDOX-based functional testing for MPNSTs. This strategy may support molecular tumor boards (MTBs) in their treatment decisions. The observed genomic stability supports the use of longitudinal tumor profiling to guide treatment, and the success of MEKi+BETi highlights its potential as a combination therapy for MPNSTs.

Precision Medicine↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2026

Single-Cell Triomics Analysis of Tumor Cells Infiltrating Patient-Derived Breast Cancer Scaffolds.

Cellular heterogeneity plays a critical role in tissues and diseases, including cancer. Single-cell technologies are required to provide detailed information about the phenotype and genotype of individual cells. Despite several approaches to analyzing different analytes at the single-cell level, it is challenging to assess DNA, RNA, and protein simultaneously. Here, a single-cell triomics method to assess DNA, RNA, and proteins from the same cell using a targeted sequencing approach is shown. Breast cancer cells cultured in monolayers and in patient-derived scaffolds that mimic in vivo-like growth conditions, both with and without chemotherapy treatment, were analyzed. Data showed that DNA, RNA, and protein biomarkers could be reliably analyzed, providing biological insights into breast cancer cell heterogeneity. In addition, chemotherapy treatment caused changes in subpopulations and expressions of biomarkers. Furthermore, cells growing in patient-derived scaffolds generated from various breast cancers affected cell heterogeneity and drug resistance differently as a result of the unique tumor-specific microenvironments. The data show that single-cell triomics provides new means to assess cancer cell heterogeneity at DNA, RNA, and protein levels.

Humans↗
PubMed2024

Proteomic Heterogeneity of the Extracellular Matrix Identifies Histologic Subtype-Specific Fibroblast in Gastric Cancer.

Gastric cancer (GC) is a highly heterogeneous disease regarding histologic features, genotypes, and molecular phenotypes. Here, we investigate extracellular matrix (ECM)-centric analysis, examining its association with histologic subtypes and patient prognosis in human GC. We performed quantitative proteomic analysis of decellularized GC tissues that characterizes tumorous ECM, highlighting proteomic heterogeneity in ECM components. We identified 20 tumor-enriched proteins including four glycoproteins, serpin family H member 1 (SERPINH1), annexin family (ANXA3/4/5/13), S100A family (S100A6/8/9), MMP14, and other matrisome-associated proteins. In addition, histopathological characteristics of GC reveals differential expression in ECM composition, with the poorly cohesive carcinoma-not otherwise specified (PCC-NOS) subtype being distinctly demarcated from other histologic subtypes. Integrating ECM proteomics with single-cell RNA sequencing, we identified crucial molecular markers in the PCC-NOS-specific stroma. PCC-NOS-enriched matrisome proteins and gene expression signatures of adipogenic cancer-associated fibroblasts (CAFadi) are closely linked, both associated with adverse outcomes in GC. Using tumor microarray analysis, we confirmed the CAFadi surface marker, ATP binding cassette subfamily A member 8 (ABCA8), predominantly present in PCC-NOS tumors. Our ECM-focused analysis paves the way for studies to determine their utility as biomarkers for patient stratification, offering valuable insights for linking molecular and histologic features in GC.

Humans↗
PubMed2026

Intraskeletal Variation in Cortical Bone Quantity in a Medieval Italian Sample: A Multivariate Exploratory Approach.

Bioarcheologists interpret skeletal health by examining variability within and between individuals. Studies of bone loss have generated contradictory and conflicting results regarding the onset and severity of age-related bone loss on a global and temporal scale, perhaps due to mismatched methodologies. Intraskeletal comparisons of bone tissue prove challenging precisely because of heterogeneous baselines in quantity and remodeling of cortical bone throughout the skeleton, as well as evolutionary histories and environmental impacts on growth and development. Here we analyze cortical bone indicators from the rib, metacarpal, and femoral cortical bone in a subset of individuals (n&#x2009;=&#x2009;72) regions from the medieval Italian archaeological site of Pieve di Pava. To facilitate intraskeletal comparisons across elements with different biological baselines, we standardize cortical bone parameters using z-scores. Variation in relative intraskeletal cortical bone was assessed using accessible multivariate methods (principal component analysis and hierarchical cluster analysis). Results suggest an association between femoral and metacarpal cortical bone values, with stochastic trends in metacarpal and femoral relative bone quantity in relation to the rib bone quantity at the sample level. Our study demonstrates that while intraskeletal analyses are challenging, they are made more robust by synthesizing multivariate methods alongside exploratory data analysis (EDA) methods to tack between sample-level and individual-level scales and variability. Ultimately, we advocate for leveraging multivariate techniques not as a final step, but rather as a means of generating new hypotheses and challenging tendencies to a priori establish typological groups in the research process.

Skeleton↗
PubMed2026

Profiling Dectin-2-Positive Tumor-Associated Macrophages Across Human Cancers by Immunohistochemistry.

PURPOSE: To characterize the prevalence and distribution of Dectin-2-positive macrophages across human tumors and develop a research immunohistochemistry (IHC) assay to assess Dectin-2 in cancer tissues. MATERIALS AND METHODS: C-type lectin domain family 6 member A (CLEC6A), the gene encoding Dectin-2, was evaluated across 38 tumor types using The Cancer Genome Atlas. A fit-for-purpose Dectin-2 IHC assay was developed using a monoclonal antibody selected from screening 11 anti-Dectin-2 antibodies. Assay performance was supported by Dectin-2-expressing and parental cell line controls, macrophage-associated staining patterns, and comparison with an orthogonal CLEC6A in situ hybridization method using RNAscope. Dectin-2 expression was assessed in tissue microarrays (n = 553 samples) across 6 cancer types and whole tissue sections (n = 137) across 7 cancer types. RESULTS: The Cancer Genome Atlas analysis identified enriched CLEC6A expression in several tumor types, including non-small cell lung cancer (NSCLC), triple-negative breast cancer (TNBC), and subsets of head and neck cancer (HNC) and colorectal cancer (CRC). By IHC, Dectin-2-positive macrophages were detected across tumor types, with notable heterogeneity within and across cancer types. In tissue microarrays, NSCLC showed the highest frequency of Dectin-2-positive macrophage infiltration, with 38% of cases with staining &#x2265;1% of tumor area. Whole tissue section analysis confirmed and expanded these findings, with &#x2265;50% of NSCLC, melanoma, HNC, TNBC, and CRC samples showing Dectin-2-positive macrophages in &#x2265;1% tumor area. CONCLUSIONS: Dectin-2 expression was observed in subsets of tumor-associated macrophages across multiple human cancers, with relatively enriched expression in NSCLC, melanoma, HNC, TNBC, and CRC. To our knowledge, this study represents the first broad protein-level characterization of Dectin-2 across multiple human tumor types, identifies cancers with relatively enriched Dectin-2-positive macrophage infiltration, and provides a foundation for future translational studies of Dectin-2-targeted therapies.

Humans↗
PubMed2025

Bayesian reconstruction and differential testing of excised introns.

MOTIVATION: Characterizing the differential excision of introns is critical for understanding the functional complexity of a cell or tissue, from normal developmental processes to disease pathogenesis. Most transcript reconstruction methods infer full-length transcripts from high-throughput sequencing data. However, this is a challenging task due to incomplete annotations and the heterogeneous expression of transcripts across cell-types, tissues, and experimental conditions. Several recent methods circumvent these difficulties by considering local splicing events, but these methods lose transcript-level splicing information and may conflate similar, but distinct transcripts. RESULTS: In this work, we formalize a new transcript reconstruction problem that interpolates between the full-length and local splicing perspectives by considering sequences of exon-exon junctions (SEEJs) that co-occur in transcripts. We then present a hierarchical Bayesian admixture model and posterior inference algorithms for computing SEEJs (BSEEJ), and a generalized linear model for characterizing differential SEEJ usage based on model parameter estimates. We show that BSEEJ achieves high F1 score for reconstruction tasks and improved accuracy and sensitivity in differential splicing when compared with six transcript and local splicing methods on simulated data. Lastly, we evaluate BSEEJ on experimental data based on transcript reconstruction, novelty of transcripts produced, model sensitivity to hyperparameters, and a functional analysis of differentially expressed SEEJs. AVAILABILITY AND IMPLEMENTATION: BSEEJ is freely available at https://github.com/bayesomicslab/BSEEJ.

Bayes Theorem↗
PubMed2026

Management of Soft Tissue and Visceral Leiomyosarcomas.

IMPORTANCE: Leiomyosarcoma is a rare and heterogeneous malignant mesenchymal neoplasm associated with substantial morbidity and mortality. Given recent advances in biologic understanding and the complexity of leiomyosarcoma, a consensus-driven approach is needed to harmonize management and address remaining clinical and research gaps. OBJECTIVE: To provide an evidence-based synthesis of current diagnostic and therapeutic approaches for leiomyosarcoma by an international panel of physicians, researchers, and patient advocates, focusing on site-specific management, systemic therapy strategies, and key areas of clinical uncertainty, while identifying unmet needs and research priorities. EVIDENCE REVIEW: This review is based on a comprehensive evaluation of the literature, including clinical trials, observational studies, and international consensus guidelines. Sources were identified through MEDLINE (via PubMed) and Embase database searches and reference screening, then supplemented by multidisciplinary expert consensus. Emphasis was placed on studies informing diagnosis, surgical management, radiotherapy, and systemic therapy in leiomyosarcoma. FINDINGS: The rarity and heterogeneity of leiomyosarcoma poses substantial challenges in its management. In localized disease, complete surgical resection remains the cornerstone of treatment, with evidence supporting the use of site-specific perioperative treatment strategies. Prospective data supporting neoadjuvant or adjuvant chemotherapy are lacking, and the role of radiotherapy differs across anatomic disease sites and institutions. In advanced disease, multiple systemic therapies demonstrate activity, including anthracycline-based and gemcitabine-based combinations, trabectedin, and tyrosine kinase inhibitors, although optimal sequencing after first-line therapy remains undefined. Emerging data suggest potential benefit from treatment continuation strategies and selected use of local therapies in oligometastatic settings. Molecular heterogeneity is increasingly recognized but has not yet translated into routine clinical implementation, and integration of molecular profiling into diagnostic pathways for predictive and therapeutic insights remains an unmet need. CONCLUSIONS AND RELEVANCE: This international consensus addresses the diagnosis and management of leiomyosarcoma. Management requires a multidisciplinary, site-specific approach informed by limited but evolving evidence. Key uncertainties persist, particularly regarding perioperative therapy, optimal sequencing and combination of systemic treatments, and integration of molecular data. Continued international collaboration and leiomyosarcoma-specific clinical trials are needed to refine treatment strategies and improve patient outcomes.

Journal Article↗