Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “omics analysis”

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 523 records · Page 29Linked to original sources
PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗
PubMed2025

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders.

Biomarker research in psychopathology increasingly employs high-dimensional Omics approaches. Yet, proteomics based on human hair remain largely unexplored, despite its potential to efficiently capture stable biological signals accumulated over weeks to months. This study leveraged machine learning to investigate the potential of the hair proteome-all detectable peptides and proteins-as a biomarker source for stress-associated psychopathology. We analyzed protein profiles from hair segments of women with non-suicidal self-injury disorder and healthy controls (N&#x202f;=&#x202f;68). Of 1114 identified proteins, 611 were sufficiently abundant for analyses. Partial Least Squares Discriminant Analysis achieved stable 84.4&#xa0;% cross-validated accuracy for classification of clinical groups (p&#x202f;<&#x202f;.001), outperforming models based on data-derived clusters (60&#xa0;%), stress-related proteins (73&#xa0;%), and simulated hair cortisol from meta-analytic effect sizes (53-59&#xa0;%). Predicted class probabilities strongly correlated with clinical symptoms and well-being (r&#x202f;>&#x202f;.60). Key predictive proteins were linked to pain perception, oxidative stress, and cholesterol homeostasis. Approximately 15&#xa0;% of proteins differed significantly between groups, with the strongest candidates related to ribosomal function-an emerging target in depression. These findings establish hair proteomics as a promising, non-invasive biomarker source for psychiatric research with potential clinical applications in risk assessment and personalized interventions.

Humans↗
PubMed2026

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

Animals↗
PubMed2003

Nutritional genomics: implications for companion animals.

This is an exciting time for biological scientists as the "omics" era continues to evolve and shape the way science is understood and conducted. As genome sequencing of the human comes to a close, other mammals are in line to be sequenced. Along with pigs and cows, dogs are now on the high priority list for sequencing, and cats may soon follow suit. Until sequence data are available, genetic maps may be used to reveal important physical characteristics of a genome. Genome mapping is important in identifying gene placement, but gives little information regarding function. Therefore, functional genomics, including the global analysis of RNA and protein expression, protein localization and protein-protein interactions will emerge as important areas of study. The major use of the dog and cat genome maps hitherto has been for the study of human and veterinary medicine. These powerful resources also can be applied to the field of nutritional genomics and proteomics, enhancing our understanding of metabolism and optimizing companion animal nutritional and health status. Genomics has begun to be applied to nutritional research, but issues specifically relevant to companion animals have not been elucidated thus far. The study of genomics and proteomics will be crucial in areas such as nutrient requirement determination, disease prevention and treatment, functional ingredient testing and others. Nutritional genomics and proteomics will definitely play a vital role in the future of pet foods.

Animal Nutritional Physiological Phenomena↗
PubMed2005

Towards discovery-driven translational research in breast cancer.

Discovery-driven translational research in breast cancer is moving steadily from the study of cell lines to the analysis of clinically relevant samples that, together with the ever increasing number of novel and powerful technologies available within genomics, proteomics and functional genomics, promise to have a major impact on the way breast cancer will be diagnosed, treated and monitored in the future. Here we present a brief report on long-term ongoing strategies at the Danish Centre for Translational Breast Cancer Research to search for markers for early detection and targets for therapeutic intervention, to identify signalling pathways affected in individual tumours, as well as to integrate multiplatform 'omic' data sets collected from tissue samples obtained from individual patients. The ultimate goal of this initiative is to coalesce knowledge-based complementary procedures into a systems biology approach to fight breast cancer.

Biomarkers, Tumor↗
PubMed2024

Sulfonamide-induced DNA hypomethylation disturbed sugar metabolism in rice (Oryza sativa L.).

DNA methylation is well-accepted as a bridge to unravel the complex interplay between genome and environmental exposures, and its alteration regulated the cellular metabolic responses towards pollutants. However, the mechanism underlying site-specific aberrant DNA methylation and metabolic disorders under pollutant stresses remained elusive. Herein, the multilevel omics interferences of sulfonamides (i.e., sulfadiazine and sulfamerazine), a group of antibiotics pervasive in farmland soils, towards rice in 14&#xa0;days of 1&#xa0;mg/L hydroponic exposure were systematically evaluated. Metabolome and transcriptome analyses showed that 57.1-71.4&#xa0;% of mono- and disaccharides were accumulated, and the differentially expressed genes were involved in the promotion of sugar hydrolysis, as well as the detoxification of sulfonamides. Most differentially methylated regions (DMRs) were hypomethylated ones (accounting for 87-95&#xa0;%), and 92&#xa0;% of which were located in the CHH context (H&#xa0;=&#xa0;A, C, or T base). KEGG enrichment analysis revealed that CHH-DMRs in the promoter regions were enriched in sugar metabolism. To reveal the significant hypomethylation of CHH, multi-spectroscopic and thermodynamic approaches, combined with molecular simulation were conducted to investigate the molecular interaction between sulfonamides and DNA in different sequence contexts, and the result demonstrated that sulfonamides would insert into the minor grooves of DNA, and exhibited a stronger affinity with the CHH contexts of DNA compared to CG or CHG contexts. Computational modeling of DNA 3D structures further confirmed that the binding led to a pitch increase of 0.1&#xa0;&#xc5; and a 3.8&#xb0; decrease in the twist angle of DNA in the CHH context. This specific interaction and the downregulation of methyltransferase CMT2 (log2FC&#xa0;=&#xa0;-4.04) inhibited the DNA methylation. These results indicated that DNA methylation-based assessment was useful for metabolic toxicity prediction and health risk assessment.

DNA Methylation↗
PubMed2026

Deciphering novel targets in salivary gland pleomorphic adenoma by integrating plasma proteomics and parotid transcriptomics analyses.

BACKGROUND/PURPOSE: Pleomorphic adenoma (PA) is the most common salivary gland benign tumor, with its molecular drivers elusive due to a lack of experimental models. This study aimed to decipher novel targets in PA by systematically integrating plasma protein quantitative trait loci (pQTL)-based Mendelian randomization (MR) with multi-omics profiling of parotid gland tissues. MATERIALS AND METHODS: We performed two-sample MR using 5450 plasma pQTLs and genome-wide association study summary for benign or broader salivary gland diseases from FinnGen consortium. Bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) comparing PA to normal tissue were used for transcriptomic validation. Immunohistochemistry (IHC) was applied for protein-level validation in human PA, adenoid cystic carcinoma (ACC), and murine inflammatory lesions. RESULTS: MR identified 12 plasma proteins associated with benign salivary gland tumor risk. Transmembrane serine protease 6 (TMPRSS6) was the only protein significantly risk-increasing for both benign and broader salivary gland diseases. Strikingly, mitogen-activated protein kinase kinase 4 (MAP2K4) showed opposite MR effects between benign and all-lesion outcomes. Bulk RNA-seq showed limited concordance with MR findings, while scRNA-seq revealed a unique plastic epithelium and partially validated candidates at cellular resolution. Critically, IHC confirmed MAP2K4 protein overexpression specifically in human PA, but not in ACC or inflammatory lesions, while TMPRSS6 was downregulated in established pathologies despite its genetic risk association. CONCLUSION: By integrating plasma proteome-based causal inference with parotid tissue multi-omics, this study unveils MAP2K4 as a potential PA-specific driver. This integrative framework provides novel, context-specific targets for further functional investigation in salivary gland tumorigenesis.

Gene expression profiling↗
PubMed2026

Dynamic transcriptomic landscape from bulk RNA-seq reveals critical mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 modules for deep second-degree burn wound healing.

Burn injuries constitute a significant global health challenge, with deep partial-thickness burns (deep second-degree) posing particular clinical concerns due to prolonged healing and high scarring risks stemming from reticular dermis damage. Current therapeutic strategies remain largely empirical, reflecting limited understanding of stage-specific regulatory mechanisms. This study systematically investigated the molecular basis of deep partial-thickness burn repair by establishing murine models and performing RNA-seq analysis across healing phases (0, 3, 7, 14&#xa0;days post-burn, dpb). Integrated bioinformatics revealed pivotal ceRNA and PPI networks, identifying hif1a (hypoxia-responsive immunomodulator) and col1a1 (ECM remodeling hub) as nodal regulators. Mechanistically, mmu-miR-101a-3p and mmu-miR-181a-5p were validated as post-transcriptional repressors of col1a1 and hif1a, respectively. Our work pioneers the discovery of the mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 axes as master regulators of burn repair, offering novel therapeutic targets. The multi-omics dataset and molecular networks established herein provide a foundational resource for wound healing research.

MicroRNAs↗
PubMed2026

Convergent mitochondrial impairment and apoptosis driven by simultaneous down-regulation of multiple genes at 11p11.2 in Alzheimer's disease.

Genome-wide association studies (GWAS) and multi-omics analyses have identified numerous risk loci and thousands of potential causal genes associated with Alzheimer's disease (AD). However, the synergistic pathogenic contributions of multiple low-risk causal genes within a single locus remain poorly understood. Polygenic synergism at the 11p11.2 locus was systematically examined in AD pathogenesis. Three causal genes (MTCH2, NDUFS3, and PSMC3) exhibited coordinated down-regulation in both AD patients and AD mouse models. Individual knockdown in cultured cells altered mitochondrial function and disrupted AD-associated pathways, as revealed by transcriptomic profiling. Integrated RNA-seq analysis and experimental validation demonstrated that the concurrent down-regulation of all three genes synergistically enhanced mitochondrial reactive oxygen species (ROS) generation and activated the caspase-7-mediated apoptotic pathway. Notably, pharmacological caspase inhibition with Q-VD-OPh attenuated neuronal apoptosis, ameliorated memory deficits, and reduced A&#x3b2; plaque deposition in APP/PS1 mice. Simultaneous down-regulation of multiple genes at the 11p11.2 locus contributed to mitochondrial dysfunction and apoptosis in AD, highlighting polygenic synergism as a key pathogenic mechanism.

Animals↗
PubMed2026

Integrated methylome and transcriptome analysis provides insight into DNA methylation-mediated networks in sexual dimorphism of Vernicia montana.

BACKGROUND: Sexual dimorphism is fundamental to reproduction in dioecious plants and is regulated by both genetic and epigenetic mechanisms. DNA methylation is a central epigenetic mark known to influence phenotypic variation in plants. However, its specific role in shaping sexual dimorphism in dioecious trees remains poorly understood. To address this question, we performed integrated genome-wide DNA methylome and transcriptome analyses of four tissue types in the dioecious tung tree (Vernicia montana), including male and female flower buds and their corresponding leaves. RESULTS: Our analysis revealed distinct DNA methylation patterns between male and female tissues. Notably, the coordination between DNA methylation reprogramming and transcriptional regulation appeared to be more strongly associated with reproductive development than with vegetative growth in V. montana. We identified a set of sex-biased genes that may reflect different reproductive strategies between the sexes. Further analysis identified several key transcription factors (TFs) potentially associated with promoter differentially methylated regions (DMRs), including flowering-time regulators (e.g., FRS5, REM16, and VRN1) and TFs involved in hormone signaling pathways such as jasmonic acid, auxin, and salicylic acid signaling. Cis-regulatory element analysis showed that some promoter DMRs overlapped with hormone response elements related to abscisic acid, auxin, and gibberellin. Co-expression network analysis further revealed potential regulatory correlations among promoter DMR-mediated TFs, hormone-responsive pathways, and key floral development regulators. CONCLUSIONS: Collectively, our results suggest that interactions among DNA methylation, transcriptional regulation, and hormone-responsive pathways may contribute to the establishment of sexual dimorphism in V. montana. This study provides the first integrated view of these regulatory layers in V. montana and supports a species-specific regulatory framework for understanding the epigenetic basis of sexual dimorphism in this economically important dioecious tree. The proposed framework is based on multi-omics analyses and warrants further validation through targeted functional studies.

DNA Methylation↗
PubMed2006

FuGE: Functional Genomics Experiment Object Model.

This is an interim report on the Functional Genomics Experiment (FuGE) Object Model. FuGE is a framework for creating data standards for high-throughput biological experiments, developed by a consortium of researchers from academia and industry. FuGE supports rich annotation of samples, protocols, instruments, and software, as well as providing extension points for technology specific details. It has been adopted by microarray and proteomics standards bodies as a basis for forthcoming standards. It is hoped that standards developers for other omics techniques will join this collaborative effort; widespread adoption will allow uniform annotation of common parts of functional genomics workflows, reduce standard development and learning times through the sharing of consistent practice, and ease the construction of software for accessing and integrating functional genomics data.

Computer Simulation↗
PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗
PubMed2026

Free polyphenols and multi-omics traits underlying antioxidant variation across Paeonia lactiflora leaf cultivars.

Leaves of Paeonia lactiflora are underutilized by-products with potential as natural antioxidant sources. In this study, 18 cultivars were evaluated for phytochemical composition and in vitro antioxidant capacity. Total phenolic content correlated strongly with DPPH and ABTS activities, and the comprehensive antioxidant index identified 'Coral Charm' and 'Hangshao' as representative high- and low-antioxidant cultivars, respectively. Untargeted metabolomics detected 2677 metabolites and identified 908 differential metabolites between the two cultivars. Targeted phenolic profiling quantified 27 compounds, among which 11 differed significantly between the two cultivars. Catechin and epicatechin were enriched in 'Coral Charm', with contents of 6.62 and 0.397&#xa0;ng/mg, respectively, compared with 0.012 and 0.002&#xa0;ng/mg in 'Hangshao'. (+)-Dihydroquercetin was also more abundant in 'Coral Charm', while caffeic acid showed an upward trend. Proteomic analysis identified 423 differentially expressed proteins, mainly associated with secondary metabolite biosynthesis, redox homeostasis, and central carbon metabolism. Integrated analysis identified pyruvate metabolism as the only pathway significantly enriched in both metabolomic and proteomic datasets. Molecular docking predicted favorable binding between representative phenolics and selected proteins. These findings link cultivar-dependent antioxidant variation in peony leaves with free-phenolic accumulation and pathway-level metabolic differences, supporting the selection and utilization of antioxidant-rich peony leaf resources.

Antioxidants↗
PubMed2004

Using networks to identify fine structural differences between functionally distinct protein states.

The vast increase in available data from the "-omics" revolution has enabled the fields of structural proteomics and structure prediction to make great progress in assigning realistic three-dimensional structures to each protein molecule. The challenge now lies in determining the fine structural details that endow unique functions to sequences that assume a common fold. Similar problems are encountered in understanding how distinct conformations contribute to different phases of a single protein's dynamic function. However, efforts are hampered by the complexity of these large, three-dimensional molecules. To overcome this limitation, structural data have been recast as two-dimensional networks. This analysis greatly reduces visual complexity but retains information about individual residues. Such diagrams are very useful for comparing multiple structures, including (1) homologous proteins, (2) time points throughout a dynamics simulation, and (3) functionally different conformations of a given protein. Enhanced structural examination results in new functional hypotheses to test experimentally. Here, network representations were key to discerning a difference between unliganded and inducer-bound lactose repressor protein (LacI), which were previously presumed to be identical structures. Further, the interface of unliganded LacI was surprisingly similar to that of the K84L variant and various structures generated by molecular dynamics simulations. Apo-LacI appears to be poised to adopt the conformation of either the DNA- or inducer-bound structures, and the K84L mutation appears to freeze the structure partway through the conformational transition. Additional examination of the effector binding pocket results in specific hypotheses about how inducer, anti-inducer, and neutral sugars exert their effects on repressor function.

Bacterial Proteins↗
PubMed2026

Multi-omics Approaches to CCAAT/Enhancer-Binding Protein Beta in Oral Squamous Cell Carcinoma: Crosstalk Between Tumor Cells and Tumor-Associated Macrophages Driving Disease Progression.

BACKGROUND: CCAAT/Enhancer-Binding Protein Beta (CEBPB) is an important transcription factor that regulates tumor progression. However, the mechanism by which CEBPB regulates the progression of Oral Squamous Cell Carcinoma (OSCC) remains incompletely understood. Tumor progression depends on complex intercellular interactions within the tumor microenvironment. The purpose of this study was to investigate the role and epigenetic regulatory mechanisms of CEBPB in interactions between OSCC cells and tumor-infiltrating immune cells. METHODS: Bulk RNA-seq, ChIP-seq, and scRNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The HOMER algorithm was employed to identify enhancers and predict the CEBPB-binding motif. Cell cluster analysis, functional enrichment, and intercellular interaction analysis were performed using the "Seurat" R package. H3K27ac enrichment at GAS6 enhancers was validated by ChIP-qPCR. Metastatic OSCC cells with CEBPB knockdown or GAS6 overexpression were established and co-cultured with THP-1 cells. IL-10 and IL-6 secretion from co-cultured THP-1 cells was detected via ELISA. Chemotaxis of OSCC cells toward THP-1 cells was assessed through a Transwell assay. RESULTS: CEBPB was upregulated in OSCC and correlated with poor prognosis. By integrating H3K27ac ChIP-seq and bulk RNA-seq data, 131 CEBPB-regulated enhancer-controlled genes were identified in lymph node metastatic OSCC cells. scRNA-seq analysis revealed eight major cell clusters in primary foci and lymph node metastases, including T/NK cells, malignant epithelial cells, B/plasma cells, macrophages, fibroblasts, dendritic cells, endothelial cells, and mast cells, with the malignant epithelial cells stratified into distinct sub-clusters. CEBPB expression was elevated in malignant epithelial cells of lymph node metastases compared to primary foci. Furthermore, 15 pairs of enhanced ligand-receptor interactions were identified in lymph node metastases relative to primary foci. GAS6 was a CEBPB-regulated enhancer-controlled gene, primarily mediating interactions between malignant cells and macrophages. CEBPB knockdown in metastatic OSCC cells significantly impaired their chemotaxis toward cocultured THP-1 cells, and downregulated IL-10/IL-6 secretion and CD206 expression in cocultured THP-1 cells. Conversely, GAS6 overexpression reversed these inhibitory effects. CONCLUSION: CEBPB activated GAS6 transcription in metastatic OSCC cells. The CEBPB/ GAS6 axis in metastatic OSCC cells enhanced their chemotaxis toward macrophages and promoted the M2 polarization of macrophages, thereby facilitating the establishment of an immunosuppressive microenvironment.

Humans↗
PubMed2026

A Multifaceted Interplay Among Hemophagocytosis, Interleukin-18, and Type I Interferon Distinguishes Still Disease From Other Autoinflammatory Diseases.

OBJECTIVE: The unknown pathophysiology and the lack of specific features for systemic juvenile idiopathic arthritis and adult-onset Still disease (collectively known as Still disease; SD) delay diagnosis and appropriate treatment. The goal of this study was to identify features and mechanisms that distinguish SD from other systemic autoinflammatory diseases (SAID). METHODS: Using the SomaScan assay and RNA sequencing (RNA-Seq), we determined the plasma proteomes and immune cell microRNA (miRNA) and RNA transcriptomes of 372 patients with SAID, respectively. Proteomic findings were validated by enzyme-linked immunosorbent assays. SD (n&#xa0;=&#xa0;72) and non-SD SAIDs (n&#xa0;=&#xa0;300) were compared to identify distinguishing features of SD. We performed integrated and unbiased analyses of all data sets using weighted gene correlation network analysis to identify feature modules that characterize SD and stratify patients. RESULTS: Elevated plasma heme oxygenase 1 (HO-1) and interleukin-18 (IL-18) strongly correlate and characterize SD but do not associate with general inflammation. SD was characterized by ferroptosis in plasma, type I interferon (IFN) signaling in monocyte transcriptomes, and elevated natural killer cell miRNA-146a-5p, which is an IL-18 induced miRNA. Finally, we identified feature modules that distinguish SD from other SAIDs and stratified patients with SD into two distinct subgroups not attributable to disease activity or inflammation but hemophagocytosis. CONCLUSION: This unprecedented large omics data set of SAIDs revealed that complex interactions among hemophagocytosis, IL-18, and type I IFN signaling characterize SD. Furthermore, two distinct subgroups in patients with SD were distinguished by the degree of hemophagocytic activity. Finally, the large proteomics and RNA-Seq data sets generated in this study can serve as an invaluable resource for the further investigation of SD and other SAIDs.

Humans↗
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↗