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PubMed2026

Integrating genomics, multi-omics, CRISPR and speed breeding for stress-resilient vegetable legume improvement.

Vegetable legumes are nutritionally and ecologically important crops. However, their genetic improvement has not kept pace with the increasing challenges posed by climate change due to the polygenic nature of stress tolerance, narrow genetic diversity, and the persistent gap between molecular discoveries and field-level cultivar development. Although recent reviews have examined individual genomic tools or specific stress responses, a comprehensive synthesis integrating genomics-assisted breeding, multi-omics technologies, genome editing, and speed breeding within a unified crop improvement framework has been lacking. This review addresses that gap by critically evaluating how these complementary approaches can accelerate the development of stress-resilient vegetable legumes, including pea, common bean, cowpea, faba bean, cluster bean, yard-long bean, and hyacinth bean. This review synthesizes advances in QTL mapping, genome-wide association studies, transcriptomics, metabolomics, and CRISPR-based functional genomics that have identified key regulators and pathways underlying resistance to major biotic and abiotic stresses. Rather than considering these technologies independently, the review emphasizes their convergence into a systems-level breeding framework integrating genomic discovery, functional validation, predictive breeding, and accelerated generation advancement to improve breeding efficiency. Speed breeding, enabling up to seven to eight generations annually under optimized controlled-environment experimental conditions in cowpea, is discussed as a complementary strategy with genomic selection and genome editing. The review further identifies major translational bottlenecks, including transformation recalcitrance, limited genomic resources for underutilized vegetable legumes, inadequate multi-environment validation, and fragmented omics integration, and presents an integrated systems-breeding framework to bridge the gap between gene discovery and cultivar development.

Fabaceae↗
PubMed2025

scMGCL: accurate and efficient integration representation of single-cell multi-omics data.

MOTIVATION: Single-cell multi-omics data integration is essential for understanding cellular states and disease mechanisms, yet integrating heterogeneous data modalities remains a challenge. We present scMGCL, a graph contrastive learning framework for robust integration of single-cell ATAC-seq and RNA-seq data. Our approach leverages self-supervised learning on cell-cell similarity graphs, in which each modality's graph structure serves as an augmentation for the other. This cross-modality contrastive paradigm enables the learning of biologically meaningful, shared representations while preserving modality-specific features. RESULTS: Benchmarking against state-of-the-art methods demonstrates that scMGCL outperforms others in cell-type clustering, label transfer accuracy, and preservation of marker-gene correlations. Additionally, scMGCL significantly improves computational efficiency, reducing runtime and memory usage. The method's effectiveness is further validated through extensive analyses of cell-type similarity and functional consistency, providing a powerful tool for multi-omics data exploration. AVAILABILITY AND IMPLEMENTATION: Code and datasets are released at https://github.com/zlCreator/scMGCL.

Single-Cell Analysis↗
PubMed2025

Augmented kurtosis-based projection pursuit: a novel, advanced machine learning approach for multi-omics data analysis and integration.

Due to the heterogeneity of multi-omics data, exacting their maximum information potential remains a challenge. Whereas some solutions have been offered, most cannot overcome the large linear dynamic range associated with such data, while others require large biological effect sizes to produce meaningful models. Here, we (i) perform a comprehensive benchmarking of multi-omics data analysis tools, and (ii) introduce kurtosis-based projection pursuit analysis, augmented with classification and regression trees (kPPA-CART) as a robust, easy-to-implement alternative. Using ground truth data, we demonstrate that kPPA-CART exhibits superiority in inferring biological significance from low-intensity (low-count) features and studies with small biological effect sizes. Applying it to experimental breast cancer data from The Cancer Genome Atlas, we identify novel genes that cluster the samples into subtypes that mimic the canonical PAM50 classes with notable improvements. Validating with external metastatic breast cancer data from the AURORA US consortium, kPPA-CART identifies genes that are associated with poor event-free survival and additional clustering associated with increased tumor mutational burden. Finally, we provide an R package and an online implementation of kPPA-CART.

Humans↗
PubMed2025

Gencube: centralized retrieval and integration of multi-omics resources from leading databases.

MOTIVATION: The volume of multi-omics data for diverse species is growing at an unprecedented rate, with new genome assemblies, related annotations, and high-throughput sequencing resources being submitted daily to various genomic data repositories. In response to this data influx, both existing and new databases are establishing optimized hierarchical structures to manage the vast amount of information. However, the lack of accessible command-line tools, combined with the functional limitations and unintuitive design of existing options, presents significant challenges for researchers. This gap underscores a critical need for a tool that enables streamlined retrieval and integration of omics data across these diverse repositories. RESULTS: We have developed Gencube, a command-line tool that enables centralized retrieval and integration of a comprehensive set of six different data types-genome assemblies, gene sets, annotations, sequences, comparative genomic data, and NGS-based omics resources-from various leading databases. AVAILABILITY AND IMPLEMENTATION: Gencube is a free and open-source tool, with its code available on GitHub: https://github.com/snu-cdrc/gencube and also archived on Zenodo: https://doi.org/10.5281/zenodo.14607649.

Databases, Genetic↗
PubMed2004

Omic space: coordinate-based integration and analysis of genomic phenomic interactions.

MOTIVATION: With the recent progress in genomics, various data sets of omic interactions describing networks of omic elements have become available. In order to obtain reliable hypotheses from the data, it is effective to integrate interactions from different sorts of data sets. In order to facilitate a coordinate-based integration and analysis of omic interactions, we introduce the concept of an omic space comprising a comprehensive set of omic planes. Genomic, transcriptomic, proteomic, metabolomic, phenomic and other omic planes are defined by two orthogonal genomic-coordinate axes. RESULTS: We show that the omic space concept helps us to assimilate biological findings comprehensively into hypotheses or models combining higher-order phenomena and lower-order mechanisms by demonstrating that a comprehensive ranking of correspondences among interactions in the space can be used effectively for estimating candidates of responsible gene pairs for epistatic interacting loci of tumors in mice. We also show that the omic space offers a convenient framework for database integration, by presenting a system named the 'Genome <==> Phenome Superhighway' (GPS) that serves as a framework for integration and visualization of omic interactions based on omic spaces of some model species including Homo sapiens, Mus musculus, Caenorhabditis elegans and Arabidopsis thaliana. AVAILABILITY: For the GPS web site, see http://omicspace.riken.jp/gps/.

Algorithms↗
PubMed2026

Application of Omics Technologies for Cowpea Improvement.

Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.

cowpea↗
PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗
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↗
PubMed2024

Relationship between inflammation/immunity and epilepsy: A multi-omics mendelian randomization study integrating GWAS, eQTL, and mQTL data.

OBJECTIVES: Increasing evidence suggests that activated innate/adaptive immunity induces an inflammatory response, thereby participating in epileptogenesis. However, the biological explanation of inflammation/immunity as a potential cause for epilepsy remains largely unknown. This research aimed to determine the causal effects of inflammation/immune-related genes in epilepsy based on multi-omics mendelian randomization (MR). METHODS: We employed summary-data-based MR (SMR) approach to combine GWAS for epilepsy (12,891 cases and 312,803 control) with gene expression quantitative trait loci (cis-eQTL, 31,684 participants) and DNA methylation QTL (cis-mQTL, 1,980 participants) data. Five additional MR methods were then used for sensitivity analyses to confirm the reliability of causal associations. In addition, enrichment analysis of key genes was conducted to provide insight into the biological functions of epilepsy risk variants. RESULTS: A total of 386 inflammation/immune-related genes were selected for further analyses. Primary SMR analysis indicated that 37 DNA methylation sites and six genes regulated by them had potential causal relationship with epilepsy. MR analysis further refined the results, identifying three genes that had a causal effect on epilepsy. Notably, VEGFA (OR: 0.925; 95&#xa0;% CI: 0.862-0.994) expression was negatively correlated with epilepsy risk, whereas IL16 (OR: 1.076; 95&#xa0;% CI: 1.028-1.126) and HLA-DPA1 (OR: 1.041; 95&#xa0;% CI: 1.009-1.074) expressions were positively associated with epilepsy risk. Functional enrichment analysis revealed that the identified genes were involved in GO-BP terms related to VEGF activation signaling and chemotaxis regulation. CONCLUSION: This analysis confirms the causal role of inflammation/immunity in epilepsy, and the identified candidate genes provide clues for drug development in clinical practice.

Humans↗
PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗
PubMed2026

Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder associated with substantial disability. Although genome-wide association studies have identified multiple BD-associated loci, the underlying genes and mechanisms remain incompletely understood. METHODS: We integrated a European-ancestry BD genome-wide association dataset with cross-tissue and tissue-specific transcriptome-wide association studies (TWAS) and complementary gene-based analysis. Candidate genes were further evaluated using differential expression analysis, consensus clustering, immune infiltration analysis, machine learning, summary-data-based Mendelian randomization, Mendelian randomization using single-cell expression quantitative trait locus data, single-nucleus transcriptomics, phenome-wide association analysis, and virtual screening. RESULTS: The integrative analyses prioritized 37 candidate genes. Peripheral-blood differential-expression analysis identified 14 genes that remained significant after FDR correction, and their expression profiles separated BD samples into two expression-defined clusters. Machine-learning analysis selected UNC50, LMAN2L, LYG2, HSPE1, and KANSL3 for an exploratory classification nomogram. SMR associated genetically predicted higher HSPE1 expression with increased BD risk in two blood eQTL datasets. Cell-type-specific analyses indicated HSPE1-related associations in T-cell and natural killer cell subsets, while single-nucleus analysis descriptively showed higher HSPE1 expression in medial thalamic T cells from BD samples. PheWAS identified no genome-wide significant associations for HSPE1, whereas virtual screening identified candidate compounds with favorable predicted docking scores against the HSPE1 structure. CONCLUSION: This integrative multi-omics study identified HSPE1 as a candidate BD risk gene with immune-cell-related regulatory evidence, providing insight into BD pathogenesis and supporting functional validation.

Humans↗
PubMed2026

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans↗
PubMed2025

Advances in tumor subclone formation and mechanisms of growth and invasion.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Humans↗
PubMed2026

Norgestrel drives mitochondrial collapse and plasma membrane impairment in Pacific oyster (Crassostrea gigas) sperm by triggering premature acrosome reaction.

The toxic mechanisms of norgestrel (NGT), an emerging marine pollutant, on the sperm from externally fertilized invertebrates remain elusive. This study employed an integrated physiological and multi-omics framework to elucidate how NGT (10 and 1000&#xa0;ng/L) disrupts acrosome reaction (AR) signaling machinery, thereby impairing the functional integrity of Pacific oyster (Crassostrea gigas, also known as Magallana gigas) sperm. Exposure to NGT triggered a significant, dose-dependent premature AR, characterized by elevated acrosin activity and a loss of acrosomal integrity. Multi-omics integration supports a model in which this premature exocytosis is linked to signaling disturbances, including disruption of calcium signaling and reduced transcript abundance of calmodulin (CaM) and the primary recognition protein zonadhesin (Zan). This signaling interference induced an premature AR, subsequently driving a cascade of bioenergetic and structural failures. At the mitochondrial level, NGT induced abnormal mitochondrial permeability transition pore (mPTP) opening and elevated the transcript levels of antioxidant defense genes (e.g., peroxiredoxin-5, PRDX5). These alterations indicate the occurrence of mitochondrial collapse. Concurrently, scanning electron microscopy verified localized plasma membrane wrinkling and pore formation in sperm. In addition, NGT exposure decreased the transcript abundance of cytoskeleton-related genes, including solute carrier family 26 member 6 (SLC26A6), actin (ACT), and tubulin polymerization promoting protein family member 3 (TPPP3). These molecular changes further disrupted membrane phospholipid homeostasis, as represented by altered glycerophospholipid metabolism. At the same time, cumulative cellular stress was associated with decreased transcript abundance of cytoprotective factors (e.g., baculoviral IAP repeat-containing proteins, birc2) and changes in apoptosis-related genes consistent with activation of a caspase-8-mediated apoptotic programme. In conclusion, NGT, as a representative synthetic progestin, exerts reproductive toxicity by interfering with signaling mediators to induce premature AR, which subsequently exhausts metabolic energy and triggers plasma membrane impairment. These findings provide a critical mechanistic basis for the aquatic ecological risk assessment of synthetic progestins.

Animals↗
PubMed2024

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Crohn Disease↗
PubMed2026

Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.

BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecular layers, leading to substantial advances in our understanding of their complex dynamics. However, not all molecular aspects are studied equally, and studies examining the same modalities often show inconsistencies, underscoring the need for data standardization and highlighting the potential for using transformative artificial intelligence and machine-learning (AI/ML) methods for ovary studies. OBJECTIVE AND RATIONALE: This study aims to evaluate how multi-omic studies have advanced our understanding of the ovarian lifecycle from fetal development to postmenopause. We systematically reviewed published studies that have investigated molecular/omic layers, including the genome, methylome, transcriptome, and proteome throughout ovarian development and aging. Our analysis identified key molecular and cellular patterns, highlighted inconsistencies across studies and addressed gaps in data analysis, interpretation, and reproducibility to guide future research. SEARCH METHODS: We conducted a systematic literature search of Medline (PubMed), Embase (Ovid), and Web of Science Core Collection (Clarivate) using a combination of controlled and free text terms for human ovary, oogenesis, folliculogenesis, ovary development and (epi)genome, transcriptome, proteome, and multi-omic mechanisms to find relevant articles published before August 2025. To focus the scope of the current review, studies of domesticated and farm animals, rodents and other model organisms, non-human primates, as well as those examining various human ovarian pathologies were excluded. OUTCOMES: The search identified 23 546 studies for screening, of which 637 full-text studies were assessed for eligibility. Subsequently, we extracted data from 121 studies. Most studies analyzed the transcriptome of oocytes, granulosa cells, and ovarian tissue from reproductive-age individuals (n&#x2009;=&#x2009;91), with fewer studies examining samples from individuals of advanced reproductive age (n&#x2009;=&#x2009;45) and fetal (n&#x2009;=&#x2009;16) samples. Transcriptome analyses were most common (n&#x2009;=&#x2009;103, 85%), followed by proteome (n&#x2009;=&#x2009;19, 16%) and epigenome (n&#x2009;=&#x2009;14, 12%) studies. We found substantial variation in how studies defined and reported participants' groups as well as in their sequencing technologies and data analysis methods, with a lack of standardized reporting of background clinical information, data analysis methods, and pipeline details. The key findings underscore the prevailing consensus on genes defining major ovarian cell types and their roles throughout the ovarian lifespan, from prenatal development to postmenopausal transformation. This review highlighted the underrepresentation of certain patient groups, particularly prepubertal and peri-/postmenopausal individuals, among researched populations, due to obvious clinical and ethical reasons. WIDER IMPLICATIONS: This scoping review offers a comprehensive overview and benchmark of the current state of high-throughput omics-based research on ovarian cellular composition and molecular dynamics. To address these shortcomings, we propose general recommendations for multi-omics ovary studies and emphasize the necessity for more thorough multi-omic data integration by effectively applying novel AI/ML approaches. They can potentially improve the quality of multi-omics analyses at both single-cell and tissue levels despite limited sample sizes and enable integration of molecular profiling data with clinical and radiology datasets, enabling a more comprehensive understanding of ovarian biology. Such advancements can enhance reproducibility of research findings and guide future research to deepen our understanding of ovarian biology and ultimately support the development of medical technologies for better preserving fertility and alleviating infertility. REGISTRATION NUMBER: A protocol was published a priori on the Open Science Framework (https://osf.io/z38gb/).

Female↗
PubMed2026

Recent Advances in Multi-Omics of Systemic Lupus Erythematosus.

This comprehensive narrative review examines recent advances in multi-omics research for Systemic Lupus Erythematosus (SLE), emphasizing integrated approaches over single-omics studies. The review critically evaluates technological advancements, methodological innovations, and clinical applications while identifying current limitations and future research directions. We conducted a comprehensive narrative review following SANRA guidelines, searching PubMed, Web of Science, Scopus, and Embase, covering publications from January 2018 to June 2025. The review focuses on studies integrating two or more omics layers in SLE research, with emphasis on computational methods, biomarker validation, and clinical applications. Multi-omics integration has revealed critical insights into SLE pathogenesis, including immune cell heterogeneity, gene-environment interactions, and metabolic dysregulation. However, significant challenges remain in data integration methodologies, small sample sizes, and biomarker reproducibility. Current computational approaches include early integration (concatenation), intermediate integration (joint dimensionality reduction), and late integration (ensemble methods). While multi-omics approaches offer unprecedented insights into SLE complexity, standardized integration protocols and robust validation frameworks are urgently needed. Small sample sizes and heterogeneity issues limit reproducibility, particularly affecting biomarker discovery and clinical translation. Multi-omics integration represents a paradigm shift toward precision medicine in SLE, but realizing this potential requires addressing current methodological limitations, standardizing validation processes, and developing robust computational frameworks for reliable clinical applications.

Humans↗