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PubMed2026

Revealing Hidden Variables in DESI-Based Spatial Metabolomics: Solvent Composition and Tissue Type as Critical Drivers.

In the development of a desorption electrospray ionization (DESI) workflow for spatial metabolomics, we investigated the impact of two commonly used solvent systems, 90% acetonitrile (ACN) and 90% methanol (MeOH), on the spatial metabolomic profiling of various murine tissues. The performance of both solvents was evaluated across several metabolite classes (central carbon metabolites, amino acids, and fatty acids). Although the ACN-based solvent system led to higher signal intensities for small polar metabolites involved in glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism, the MeOH-based solvent system provided superior signal intensities for fatty acids. These findings demonstrate that the solvent composition differentially influences metabolite extraction and ionization processes in DESI and should be carefully matched to the biological questions and metabolite classes of interest. As a proof-of-principle, the ACN solvent system was applied to a pilot study based on a rat model of renal ischemic injury, revealing region-specific metabolic changes between normoxic and ischemic conditions. Together, these results demonstrate the importance of solvent selection in DESI-based spatial metabolomics and showcase the ability of this approach to uncover spatially resolved metabolic adaptations associated with tissue injury.

Animals↗

PubMed2025

Spatial Metabolomics Reveals the Role of Penicillic Acid in Cheese Rind Microbiome Disruption by a Spoilage Fungus.

Microbial interactions in cheese rinds influence community structure, food safety, and product quality. But the chemical mechanisms that mediate microbial interactions in cheeses and other fermented foods are generally not known. Here, we investigate how the spoilage mold Aspergillus westerdijkiae chemically inhibits beneficial cheese-rind bacteria using a combination of omics technologies. In cheese rind community and co-culture experiments, A. westerdijkiae strongly inhibited most cheese rind community members. In co-culture with Staphylococcus equorum, A. westerdijkiae strongly affected bacterial gene expression, including upregulation of a putative bceAB gene cluster that is associated with resistance to antimicrobial compounds in other bacteria. Mass spectrometry imaging (MSI) revealed spatially localized production of secondary metabolites, including penicillic acid and ochratoxin B at the fungal-bacterial interface. Integration of LC-MS/MS and genome annotations confirmed the presence of additional bioactive metabolites, such as notoamides and circumdatins. Fungal metabolic responses varied by bacterial partner, suggesting species-specific chemical strategies. Notably, penicillic acid levels increased 2.5-fold during interaction with Brachybacterium, and experiments with purified penicillic acid showed inhibition of a range of cheese rind bacteria. These findings show that A. westerdijkiae deploys a context-dependent arsenal of mycotoxins and other metabolites, disrupting microbial community assembly in cheese rinds.

Aspergillus westerdijkiae↗

PubMed2026

A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.

Metabolic bioengineering has emerged as a transformative approach for reshaping plant defense by targeting intrinsic biosynthetic pathways to enhance immunity in modern agriculture. Moving beyond proof-of-concept metabolomics to broad-spectrum programmable pathway engineering addresses gaps in plant rational design and optimizes resilience in response to diverse environmental cues. This review aims to comprehensively highlight the transition of innovative approaches to phenolics, alkaloids, flavonoids, terpenoids, and benzoxazinoids, inferring adaptive reprogramming that mediates the growth-defense balance and functions as molecular sentinels in plants. Furthermore, decoding the volatile metabolome reveals a dynamic signaling interface that influences defense responses and stress-induced plant-microbe interactions, with the shikimate, jasmonate, and salicylate pathways functioning as central hubs for microbial deterrence and priming immune memory. Recent developments in multi-scalar genome-editing strategies, including CRISPR-driven combinatorial edits, enzyme orthogonalization, fluxomics, and spatially resolved multi-omics, reconfigure central and specialized metabolic fluxes toward improved defense function and regulation. Additionally, emerging tools, such as WUSCHEL2 and BABY BOOM transcriptional modules, and artificial engineering strategies integrating deep learning model-driven predictions facilitate rapid development of synthetic genetic circuits and support a predictive engineering of plants. Moreover, Mass spectrometry imaging (MSI) in spatial metabolomics enables to obtain structures and locations of unidentified endogenous metabolites within cells and tissues. Overall, this review emphasizes a diverse array of primary and secondary metabolites, spanning molecular concepts to recent advances in plant immune mechanisms. It also illustrates new frontiers in programmable metabolic engineering that accelerate the understanding of plant-microbe-metabolite cross-talks, offering strategies to improve plant resistance and advance sustainable agricultural solutions.

metabolic bioengineering↗

PubMed2026

The chemical landscape of plant surface metabolites: Acylsugars as models of ecological function and structural diversity.

Plants produce a multifunctional assortment of specialized metabolites that play important roles in defense, environmental adaptation, and ecological interactions. Among these compounds, acylsugars, nonvolatile metabolites produced primarily in glandular trichomes of Solanaceae species, have emerged as informative model systems for understanding plant surface chemistry. Differences in acyl chain length, branching pattern, saturation, and attachment position generate extensive chemical diversity that influences herbivore deterrence, pathogen resistance, and the physicochemical properties of leaf surfaces. Recent advances in analytical chemistry, particularly liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), have greatly improved the ability to separate structurally related acylsugar isomers and characterize metabolite complexity at high resolution. When integrated with genomics, transcriptomics, and emerging spatial metabolomics approaches, these analytical tools provide new insights into acylsugar biosynthesis, pathway regulation, evolutionary diversification, and ecological function across plant species. This review positions acylsugars, particularly those of Solanum species, as model systems for understanding how structural diversity, spatial localization, and specialized metabolism shape ecological and physiological function at plant surfaces. We examine acylsugar structural diversity, biosynthetic pathways, ecological and physiological functions, and interactions with environmental and atmospheric processes. Major challenges, including extensive isomeric complexity, incomplete pathway characterization, and difficulties linking chemical structure to biological function, are discussed alongside emerging opportunities in integrative omics, crop improvement, sustainable pest management, and environmental monitoring. Overall, acylsugars provide a powerful model for linking molecular structure, spatial localization, and ecological function, offering broader insight into how specialized metabolism shapes plant adaptation, defense, and environmental interactions.

Acylsugars↗

PubMed2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2025

Mechanistic roles of GmSWEET10a/b and GmSUT1 in the oil-protein balance in soybean mature seeds at transcriptional and metabolic levels.

Previous investigations indicated that the soybean (Glycine max) SUGARS WILL EVENTUALLY BE EXPORTED TRANSPORTER10a/b (GmSWEET10a/b) genes promote oil accumulation, while inhibiting protein accumulation in seeds. To clarify the mechanisms modulated by GmSWEET10a/b in mediating the oil and protein accumulations in soybean seeds, an integrated comparative multiomics was conducted using the double gmsweet10a,b mutant and wild-type (WT) embryos. Spatial metabolomic analysis revealed that gmsweet10a,b embryos were surrounded by a sugar-reduced seed coat and experienced a sugar-starvation state in embryonic tissues in vivo. The decreased sugar content in the gmsweet10a,b embryos reduced the availability of carbon skeletons required for oil synthesis and was associated with decreased expression levels of genes involved in sucrose metabolism, fatty acid biosynthesis, and triacylglycerol assembly. Meanwhile, the expression of genes encoding storage protein was induced in gmsweet10a,b embryos, when compared with WT. These changes resulted in decreased oil content and increased protein content in gmsweet10a,b embryos versus WT. In vitro sugar-starvation assay also supported the suppression of fatty acid biosynthesis and the enhanced storage protein accumulation in developmental embryo under sugar-starved conditions. Furthermore, the knockout of SUCROSE TRANSPORTER 1 (GmSUT1), which was upregulated in gmsweet10a,b embryos, significantly decreased the sugar level, resulting in lower oil content but higher protein content in gmsut1 embryos than WT ones. Our findings provided a mechanistic understanding of the modulation of sugar transport between seed coat to embryo by both GmSWEET10a/b and GmSUT1, which plays a pivotal role in balancing oil and protein accumulations in soybean mature seeds.

Seeds↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

Loss of the Mechanistic Target of Rapamycin Complex 1 Causes a Lethal Alpha-1 Antitrypsin Deficiency-Associated Liver Disease.

BACKGROUND & AIMS: SERPINA1 mutations cause retention of the otherwise secreted alpha-1 antitrypsin and lead to the proteotoxic alpha-1 antitrypsin deficiency-related liver disease. As mechanistic target of rapamycin is a key coordinator of proteostasis, we studied its role in alpha-1 antitrypsin deficiency-related liver disease. METHODS: PiZ mice overexpressing the characteristic SERPINA1 mutation were mated with rodents harboring a hepatocyte specific-ablation of the interaction partners regulatory-associated protein of mechanistic target of rapamycin or rapamycin-insensitive companion of mammalian target of rapamycin, corresponding to mechanistic target of rapamycin complexes 1 or 2, or with mice lacking mechanistic target of rapamycin. Serum proteomics, liver bulk proteomics, spatial proteomics, and metabolomics were applied to characterize molecular and metabolic alterations. RESULTS: At 2 months of age, PiZ-mTORΔhep and PiZ-RaptorΔhep but not PiZ-RictorΔhep mice showed signs of increased liver injury and mortality despite diminished hepatic alpha-1 antitrypsin accumulation. PiZ-RaptorΔhep animals displayed increased levels of the proapoptotic protein C/EBP homologous protein, but C/EBP homologous protein ablation did not rescue the phenotype. Serum proteomics revealed no signs of advanced synthetic liver failure but immature hepatocellular products. Liver bulk proteomics and small metabolite measurement demonstrated a metabolic reprogramming of PiZ-RaptorΔhep mice. Spatial proteomics revealed alterations in liver zonation with increased ammonia levels as the likely cause of death in PiZ-RaptorΔhep animals. CONCLUSIONS: In summary, in alpha-1 antitrypsin deficiency-related proteotoxic liver injury, regulatory-associated protein of mechanistic target of rapamycin preserves a liver zonation, thereby protecting from lethal metabolic dysregulation.

Animals↗

PubMed2026

The domestication-associated WHP10 tandem cluster of amino acid transporter genes enhances whole-plant protein accumulation in maize.

Improving protein accumulation in maize is essential for sustainable agriculture, yet the regulatory mechanisms governing the intermediate "flow" of organic nitrogen remain elusive. Here, we show that the maize stem acts as a regulatory node for nitrogen allocation. By integrating spatial transcriptomics and metabolomics with quantitative genetics, we demonstrate that a transport-oriented stem program orchestrates the high-protein phenotype of the wild maize accession Ames21814. We identified a major locus, Whole-plant High Protein 10 (WHP10), that encodes a tandemly duplicated cluster of amino acid transporter genes. WHP10 exhibits strong vascular-biased expression, driven by promoter divergence that enhances the wild allele's activity. Functional assays and genetic validation support a model in which the WHP10 cluster facilitates the transport of multiple nitrogen-rich amino acids, thereby contributing to vascular-associated amino acid transport and post-uptake organic-nitrogen partitioning. Our findings establish stem flow as a regulatory layer for protein accumulation and identify WHP10 as a high-value target for precision breeding to enhance whole-plant protein accumulation without compromising grain yield.

Zea mays↗

PubMed2026

Multi‑omics approaches to decipher the molecular mechanisms of exercise‑mediated bone protection: From mechanistic insights to personalized exercise prescription (Review).

The global burden of bone metabolic disorders necessitates a shift from generic exercise recommendations toward personalized prescription strategies. Exercise confers skeletal protection through mechanotransduction, yet the underlying molecular networks remain incompletely understood. Multi‑omics technologies, including transcriptomics, proteomics, metabolomics and single‑cell spatial approaches, have revolutionized the capacity to decode exercise‑mediated bone adaptation at the systems level. The present review synthesizes current single‑omics landscapes and integrative multi‑omics analyses that elucidate the core regulatory networks, mechanobiological coupling mechanisms and multiorgan crosstalk that are implicated in the bone response to mechanical loading. Translational applications across clinical scenarios such as osteoporosis, osteoarthritis and disuse bone loss are evaluated, and the technical, analytical and translational challenges limiting clinical implementation are addressed. Finally, the present review provides a framework for translating multi‑omics molecular signatures into personalized exercise prescriptions for optimized skeletal health.

Humans↗

PubMed2026

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article↗

PubMed2025

Integrated multi-omics analyses provide new insights into genomic variation landscape and regulatory network candidate genes associated with walnut endocarp.

Persian walnut (Juglans regia) is an economically important nut oil tree; the fruit has a hard endocarp/shell to protect seeds, thus playing a key role in its evolution, and the shell thickness is an important trait for walnut breeding. However, the genomic landscape and the gene regulatory networks associated with walnut shell development remain to be systematically elucidated. Here, we report a high-quality genome assembly of the walnut cultivar 'Xiangling' and construct a graphic structure pan-genome of eight Juglans species to reveal the genetic variations at the genome level. We re-sequence 285 accessions to characterize the genomic variation landscape. Through genome-wide association studies (GWAS), we identified 19 loci associated with more than 268 loci that underwent selection during walnut domestication and improvement. Multi-omics analyses, including transcriptomics, metabolomics, DNA methylation, and spatial transcriptomics across eleven developmental stages, revealed several candidate genes related to secondary cell biosynthesis and lignin accumulation. This integrated multi-omics approach revealed several candidate genes associated with secondary cell biosynthesis and lignin accumulation, such as UGP, MYB308, MYB83, NAC043, NAC073, CCoAOMT1, CCoAOMT7, CHS2, CESA7, LAC7, COBL4, and IRX12. Overexpression of JrUGP and JrMYB308 in Arabidopsis thaliana confirmed their roles in lignin biosynthesis and cell wall thickening. Consequently, our comprehensive multi-omics findings offer novel insights into walnut genetic variation and network regulation of endocarp development and shell thickness, which enable further genome-informed breeding strategies for walnut cultivar improvement.

Juglans↗

PubMed2024

The burgeoning spatial multi-omics in human gastrointestinal cancers.

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Humans↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Spatial multiomics in biomedical research: advances beyond transcriptomics.

Coordinated changes in gene expression, epigenetic regulation, protein and metabolic activities together drive disease progression and determine clinical outcomes. While spatially resolved transcriptomics has been widely adopted across biomedical fields, it offers an incomplete picture limited to transcriptomic levels. Here, we survey the latest developments in spatial multiomics technologies, with particular emphasis on platforms that extend beyond conventional transcriptomics and profile genomics, epigenomics, proteomics, or metabolomics within intact tissues. These approaches are rapidly becoming commercialized, and here we highlight major technical breakthroughs, enhanced sample compatibility, emerging applications, and computational tools for data analysis. This Review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of spatial multiomics methods in biomedical research.

Humans↗

PubMed2026

Unraveling lung cancer complexity: Spatial omics in tumor microenvironment characterization and precision medicine.

Heterogeneous tumor microenvironment (TME) in lung cancer plays a crucial role in disease progression and resistance to therapy. Despite advances in single-cell and bulk omics profiling, these methods often overlook spatial context, which is vital for understanding cell-cell interactions and regional heterogeneity. In recent years, spatial omics technologies-including spatial genomics, transcriptomics, proteomics, and metabolomics-have revolutionized the ability to map molecular landscapes while maintaining tissue architecture. These advancements have become essential components of next-generation lung cancer management. By providing unprecedented resolution in characterizing the lung cancer TME, spatial omics could reveal prognostic and predictive biomarkers and identify new therapeutic vulnerabilities. This review will provide the first critical evaluation of spatial multi-omics approaches for lung cancer prognosis. It will also assess various integration strategies for multi-omics data to explore the clinical translational potential of these tools for therapy selection and patient stratification. Therefore, a deeper understanding of spatial omics technologies and their application in lung cancer can significantly improve precision diagnostics and therapeutic decision-making.

Lung cancer↗

PubMed2026

Omics in optic neuropathies: From molecular landscapes to personalized therapeutics.

Optic neuropathies comprise a heterogeneous group of disorders involving transient or permanent injury to retinal ganglion cells (RGCs) and their axons. Clinically, these neurodegenerative conditions manifest as dyschromatopsia, decreased visual acuity, and visual field defects, and in severe cases may ultimately lead to blindness and disability. The marked heterogeneity across disease subtypes, incompletely understood etiologies, and complex pathogenic mechanisms pose substantial challenges to precise diagnosis and effective treatment. Recent advances in omics technologies - including genomics, transcriptomics, proteomics, metabolomics, lipidomics, single-cell and spatial sequencing, and integrative multi-omics approaches - have ushered optic nerve degenerative disease research into an era of high-resolution comprehensive investigation. In this review, we summarize representative applications of omics approaches to elucidate genetic alterations, signaling dysregulation, metabolic reprogramming, and immune responses in optic neuropathies. We further discuss the emerging potential of multi-omics in identifying early diagnostic biomarkers and informing individualized therapeutic strategies. Finally, we provide a forward-looking perspective on the future trajectory of omics technologies and their prospects in both fundamental research and clinical translation, with the overarching aim of accelerating the bench-to-bedside transition in this critical eye disease field.

biomarkers↗