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PubMed2026

Integrative Transcriptomic and Proteomic Profiling Identifies S100P as a Potential Functional Biomarker for Sessile Serrated Lesions.

BACKGROUND: Sessile serrated lesions (SSLs) account for 15% of colorectal cancers (CRCs) but detection remains difficult due to flat morphology, mucinous features, and subtle histology. AIMS: This study aimed to identify novel and functionally relevant biomarkers of SSLs using transcriptomic screening and multi-omics validation. METHODS: Paired SSL and normal mucosa specimens (n = 6) underwent RNA sequencing. Differentially expressed genes (DEGs) were filtered for membrane or secretory proteins and validated across TCGA and adenoma transcriptomes. Functional significance was assessed using CRISPR dependency profiling, proteotranscriptomic concordance, pharmacogenomic sensitivity, and connectivity map analysis. RESULTS: We identified 216 upregulated genes in SSLs, including 68 encoding secretory/membrane proteins that better discriminated SSLs from controls and were enriched for adhesion and neuronal signaling while suppressing TNFα-NFκB inflammatory pathways. Cross-cohort comparison revealed five overlapping candidates between SSLs and TCGA CMS1 tumors. Among them, S100P emerged as the primary biomarker candidate, showing consistent upregulation in SSLs and CMS1 tumors while remaining low in normal mucosa and conventional adenomas. TFF1 also showed RNA-level upregulation but appeared more context-dependent. S100P demonstrated strong RNA-protein concordance in CRC cell-line profiling, supporting its detectability as a biomarker candidate. Pharmacogenomic profiling of LS411N cells revealed marked sensitivity to SN-38 and fluoropyrimidines, consistent with serrated CRC vulnerabilities. Connectivity map analysis identified perturbations, including MAPK1 and histone acetyltransferase suppression, that may reverse parts of the SSL transcriptional program. CONCLUSION: These findings prioritize S100P as a promising biomarker candidate for SSLs that warrants further validation in larger cohorts and clinically applicable platforms.

Humans↗
PubMed2026

From molecular responses to environmental monitoring: advances and translational gaps in omics approaches in fish environmental toxicology.

Fish occupy a central position in aquatic ecosystems and serve as important bioindicators for environmental monitoring, as well as powerful translational models for understanding toxic mechanisms conserved across higher vertebrates. In recent years, omics techniques have proven to be powerful tools to address complex environmental questions that conventional toxicology methods cannot answer. Despite this potential, a critical translational gap remains between molecular findings and their use in ecological risk assessment frameworks. This review critically synthesizes advances across omics techniques including epigenomics, transcriptomics, metabolomics and proteomics and their integration. Special emphasis is placed on methodological considerations and practical aspects of these techniques in fish environmental toxicology and environmental monitoring. Evidence from single-omics studies suggests conserved biomarker signatures across species while characterizing complex phenomena like non-monotonic dose-response relationships, mixture toxicity and transgenerational and stereoselective effects with implications for population level monitoring. Multi-omics studies, especially those involving triple omics, further enhance mechanistic resolution by reconstructing adverse outcome pathways. We further evaluate using case studies when additional molecular layers provide critical insight and when they offer limited advantage, a strategic distinction with direct implications in environmental monitoring programmes. Finally, current limitations and future directions that will ultimately bridge the translational gap and hold promise for advancing mechanistic ecotoxicology and predictive environmental monitoring are discussed.

Animals↗
PubMed2026

Integrated Multi-omics Profiling of 2,4-dinitrochlorobenzene (DNCB)-induced Atopic Dermatitis in Mice Reveals a Coordinated Network of Barrier Dysfunction, Immune Activation, and Metabolic Reprogramming.

Atopic dermatitis (AD) is caused by a combination of epidermal barrier defect and immune imbalance. However, the molecular networks between these structural abnormalities and metabolic variations are unclear. This study aim of this research was to examine the concurrent molecular alterations in skin barrier damage and metabolic disorders in an AD-like mouse model by a multi-omics strategy. A 2,4-dinitrochlorobenzene (DNCB)-induced AD-like mouse model was established and the skin tissues were examined through the combination of transcriptomic, quantitative proteomic, and metabolomic analyses. Cross-omics correlation and network analyses were performed to identify consistently abnormal molecular pathways and crucial regulatory molecules. DNCB treatment caused severe epidermal hyperplasia, and prominent infiltration of CD3⁺ T cells, F4/80⁺ macrophages, and mast cells. Transcriptomic and proteomic analysis indicated significant disruption in keratinocyte differentiation, extracellular matrix organization, and cornified envelope formation pathways. Combined analysis detected 171 molecules which were simultaneously altered at both mRNA and protein levels, and network analysis identified FLG2 and KRT6B as central barrier-related molecules. Pathway enrichment analysis consistently showed the participation of AMPK and PPAR signaling pathways. Metabolomic analysis also revealed coordinated changes in lipid and amino acid metabolism which were closely associated with cornified envelope-associated genes and collagen-modifying enzymes. These findings indicate a close relationship between barrier, immune and metabolic regulation in DNCB-induced dermatitis and provide a multi-omics resource for future mechanistic studies of atopic skin inflammation.

Animals↗
PubMed2026

CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a genetically heterogeneous neurodevelopmental condition in which hundreds of individually rare risk variants converge on a small number of shared biological pathways, including synaptic scaffolding, chromatin remodeling, excitation-inhibition balance, and cellular energy metabolism. Translating this genetic heterogeneity into mechanistic insight requires experimental systems capable of interrogating individual gene functions in human-relevant neural contexts at scale. CRISPR-enabled functional genomics in human pluripotent stem cell (hPSC)-derived neural models, spanning neural progenitors, cortical and inhibitory neurons, astrocytes, microglia, and brain organoids, provides precisely this capability. By integrating pooled perturbation screens with multimodal readouts including single-cell and spatial transcriptomics, chromatin accessibility profiling, proximity labeling proteomics, multi-electrode array electrophysiology, and metabolic flux analysis, these platforms enable systematic, causal mapping of ASD gene function at system resolution. Early applications have already revealed convergent mechanisms: BAF complex disruption expands the ventral progenitor pool and biases its fate toward oligodendrocyte and interneuron lineages; ADNP loss impairs microglial synaptic pruning through altered endocytic trafficking; and mTOR pathway dysregulation in PTEN- and TSC2-perturbed models links genetic risk directly to metabolic and mitochondrial dysfunction. Computational frameworks including MIMOSCA and SCEPTRE enable causal network reconstruction and pseudotime inference from these datasets, moving the field from gene lists toward pathway-level models of ASD pathobiology. Translational applications leverage isogenic iPSC panels and variant-level base and prime editing to stratify ASD variants by functional impact, informing gene therapy design for haploinsufficient targets such as CHD8 and SCN2A via AAV or antisense oligonucleotide delivery. Remaining challenges, including model developmental immaturity, batch variability, and the difficulty of modeling polygenic risk, are addressed by a roadmap integrating spatial perturbomics, AI-driven causal inference, and population-scale standardized biobanks. This review synthesizes the current state of CRISPR-based functional genomics in human stem cell neural models as a coherent experimental framework for converting ASD genetic associations into mechanistic understanding and therapeutic opportunity.

Humans↗
PubMed2026

Recent advancements in exosomal content analysis: the future of liquid biopsy.

Exosomes are widely acknowledged as an essential agent that carries biomarkers for specific diseases, representing the molecular status of their parent cells and providing extremely useful diagnostic insights. They can be isolated from different body fluids and contain a range of cargo molecules, including proteins, lipids, metabolites, and nucleic acids. Recent advancements in technology have greatly accelerated exosome research. Proteomics provides protein signatures linked to many pathological conditions, enabling quick and clinically scalable diagnostic tools, whereas high-throughput RNA-sequencing can be used to perform detailed transcriptome profiling. Exosomal biomarkers are showing promising clinical results in early detection of neurological diseases, infectious and cardiovascular disorders, oncology, and other medical conditions, hence accelerating therapeutic monitoring. Despite these advances, several challenges continue to hinder clinical translation including the lack of standardized isolation protocol, variability in exosome yield and purity, biological heterogeneity, and limited large-scale clinical validation. Addressing these limitations will be critical for the successful integration of exosome-based liquid biopsy into routine clinical practice. Overall, exosomes having significant potential as diagnostic tool, represent a transformative horizon in biomedical liquid biopsy research to redefine the landscape of less-invasive diagnostics and tailored clinical applications.

Humans↗
PubMed2025

Unlocking microbial potential: advances in omics and bioinformatics for aromatic hydrocarbon degradation.

Aromatic hydrocarbons (AHs) are persistent environmental pollutants with high toxicity. Bacterial degradation of AHs provides a sustainable and cost-effective approach for the remediation of sites contaminated with both mono- and polycyclic aromatic hydrocarbons. Aerobic degradation of AHs typically involves oxygenases-mediated hydroxylation followed by aromatic ring cleavage. In contrast, anaerobic degradation relies on diverse activation mechanisms that ultimately converge on the central intermediate benzoyl-CoA. Over the past decades, research on bacterial degradation of AHs has grown steadily, supported by advances in omics and bioinformatics. In this review, we summarize the current knowledge on the pathways, enzymes, and microbial diversity involved in AH degradation, highlighting how omics and bioinformatic approaches are advancing our understanding of this process. However, to improve our knowledge of microbial AHs catabolism, it is crucial to prioritize the characterization of novel enzymes and pathways, especially those mediating anaerobic and hybrid degradation strategies. Addressing this gap requires the development of specialized resources that incorporate a broader taxonomic diversity and an expanded inventory of anaerobic genes and enzymes supported by experimental evidence. Equally important is the integration of multi-omics technologies, artificial intelligence, and ecological modeling into unified analytical pipelines. These efforts will be key to fully unlocking microbial metabolic potential and guiding more effective bioremediation and monitoring strategies for AHs.

Biodegradation, Environmental↗
PubMed2026

Multi-omics analysis reveals Protein Kinase A-associated regulatory remodeling during adaptation of Trichoderma reesei to lignocellulosic substrate.

The filamentous fungus Trichoderma reesei is a major industrial source of holocellulolytic enzymes, and its response to complex carbon sources is regulated by nutrient-sensing mechanisms, including the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) signaling pathway. Here, we integrated transcriptomics, quantitative proteomics, and phosphoproteomics to analyze PKAc1-associated responses in the parental strain QM9414 and a Δpkac1 strain cultivated under glucose or sugarcane bagasse conditions. Deletion of pkac1 was associated with altered growth-related phenotypes and reduced extracellular activities of selected biomass-depolymerizing enzymes. Multi-omics profiling revealed condition-dependent changes affecting subsets of carbohydrate-active enzymes (CAZymes) genes and proteins, nutrient transporters, stress-associated proteins, and regulatory factors. Phosphoproteomics identified phosphorylation-state changes associated with pkac1 deletion, including reduced phosphorylation at sites enriched for the PKA consensus motif. In silico peptide docking was used to prioritize candidate PKAc1-associated substrates for future validation, including a Sec 7-derived peptide with favorable docking behavior relative to the control peptide. Together, these data support a working model in which PKAc1 contributes to regulatory and phosphorylation-state remodeling during adaptation to sugarcane bagasse, with effects on the magnitude and/or timing of selected CAZyme-related outputs in T. reesei.

Trichoderma↗
PubMed2026

Secondary metabolite profiling of rare Micromonospora spp. from cold desert of NW Himalayas via multi-omics analysis.

INTRODUCTION: The genus Micromonospora is a prolific producer of specialized metabolites with pharmacological and agronomic relevance. Natural products derived from the genus Micromonospora have a distinctive chemical diversity and enormous therapeutic potential, thus represent a potential source for drugs and drug leads. OBJECTIVE: To explore the biosynthetic potential of four Micromonospora strains isolated from cold desert of NW Himalayas through genome mining and to correlate predicted biosynthetic gene clusters with chemical features detected by untargeted LC-HRMS metabolomics. METHOD: High-quality genomes were annotated for BGCs and matched against untargeted LC-HRMS features (peak picking, alignment, and annotation to chemical classes). Each isolate was grown in triplicate, and fermented broth was pooled for further metabolomic studies. RESULTS: By integrating genomic and metabolomic approaches, specialized biosynthetic gene clusters and strain-based putative metabolite classes were identified. LRS1 showed elevated xanthines (RiPP/siderophore), LRS3 had phenolic glycosides (hybrid PKS/NRPS), LRS4 showed 70-fold hydroxycinnamate enrichment (Type II PKS), and LRS5 displayed p-benzoquinone enrichment (Type III PKS). The metabolite profile of each strain aligned with its predicted biosynthetic gene cluster composition. CONCLUSION: Under a single growth regime, each Micromonospora strain exhibits a distinct metabolomic profile. This metabologenomics workflow can be further explored to isolate specialized metabolites with potential therapeutic and agricultural value.

Micromonospora↗
PubMed2026

Integrative multi-omics analysis unravels the metabolic landscape and reveals serum biomarkers for early diagnosis of hyperuricemia.

BACKGROUND: Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. METHODS: This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. RESULTS: HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. CONCLUSIONS: This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.

Humans↗
PubMed2026

Do Multi-Omics Approaches Improve the Diagnosis of Microbial Overgrowth Syndromes?

PURPOSE OF REVIEW: This review investigates how advances in breath testing (BT), small bowel (SB) culture, metagenomics, metatranscriptomics, transcriptomics and proteomics are reshaping the definition and diagnosis of small intestinal bacterial overgrowth (SIBO). It also discusses whether SIBO should be redefined as part of a larger group of microbial overgrowth syndromes. RECENT FINDINGS: Recent studies identify distinct hydrogen-, methane-, and hydrogen sulfide-associated overgrowth phenotypes, termed SIBO, intestinal methanogen overgrowth (IMO), and intestinal sulfide overproduction (ISO). SB sampling shows that these conditions involve different microbial patterns and functional activity, symptoms, and host responses. Quantitative shotgun metagenomics provides greater taxonomic and functional resolution than culture, while metatranscriptomics reveals active microbial pathways. On top of that, host transcriptomics and proteomics contribute to the better understanding of the predominant microbial effects in host cellular mechanisms in each of the distinct small bowel overgrowth types. SIBO has been increasingly identified as a disorder of microbial ecology and function rather than bacterial quantity alone. Integrating BT with SB sampling and multi-omics approaches may improve classification, clarify symptom mechanisms, and support a more individualized treatment, although standardized methods and further clinical validation remain necessary.

Humans↗
PubMed2024

Cohort Studies and Multi-omics Approaches to Low-Dose Ionizing Radiation-Induced Cardiovascular Disease: A Comprehensive Review.

The effect of low-dose ionizing radiation exposure on the risk of cardiovascular disease (CVD) represents a significant concern in the field of radiation protection. The prevailing approach to mitigating the adverse effects of low-dose or low-dose-rate radiation does not currently incorporate the potential risk of CVD, despite the possibility that such risk may be a substantial contributor to overall health hazards. Current evidence suggests a potential association between radiation exposure and CVD; however, the overall findings remain inconclusive. This is particularly due to the uncertainty surrounding the influence of significant non-radiation risk factors on the associations reported in epidemiological studies. It is difficult to discern the underlying connection in observational epidemiology when there is substantial variation in baseline risk factors. The paucity of epidemiological research in this domain is being partially offset by the advancement of multi-omics approaches. These methods assist in identifying radiosensitive targets, comprehending underlying biological processes, and pinpointing biomarkers. This, in turn, fortifies the evidence gleaned from epidemiological studies. In this review, we delve into the body of epidemiological research pertaining to CVD induced by low-dose ionizing radiation and the application of multi-omics techniques. The integration of these two methodologies holds the promise of identifying specific molecules or biological pathways that can be employed to validate endpoints related to radiation risk assessment.

Humans↗
PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗
PubMed2026

Mitochondrial Function-Related Genes in Sleep Disorders: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is linked to sleep disorders in previous report, but the potential roles of specific genes remain unclear. This study aimed to dissect different subtype-specific genetic associations and their underlying mechanisms. A multi-omics Summary-data-based Mendelian Randomization (SMR) approach was performed to identify potential causal links between mitochondrial function-related genes and sleep disorders. We integrated GWAS data from FinnGen database (the discovery set), independent GWAS datasets (covering different sleep-disorder subtypes and used for validation), and cis-QTLs (including mQTLs, eQTLs, and pQTLs) to perform systematic exploration. Specially, we performed targeted validation of tissue-specific effects, leveraging gene expression data from disease-relevant brain regions within the GTEx database. Our SMR analysis identified mitochondrial function-related genes potentially modulating sleep disorders across biological layers, initially identifying 102 genes at the methylation level, 48 at the gene expression level, and 6 at the protein abundance level. Integrative analysis subsequently prioritized DCXR and ACADVL and revealed their distinct, subtype-specific associations. DCXR exhibited a protective role in sleep apnea while ACADVL showed a paradoxical risk conferring role in daytime sleepiness. In addition, the analysis identified an epigenetic regulatory mechanism for DCXR in which its expression and protein levels are modulated by DNA methylation. Finally, validation in brain-hypothalamus tissue confirmed DCXR as a significant potential protective factor (OR = 0.929, 95% CI: 0.887-0.973, P_HEIDI = 0.999, FDR = 0.2449). Our findings implicate key mitochondrial genes, particularly DCXR and ACADVL, in the pathophysiology of specific sleep disorder subtypes, highlighting potential avenues for precision medicine. Clinical trial number: Not applicable.

Humans↗
PubMed2026

Modeling meningioma in vitro in the omics era.

Meningioma biology has been substantially clarified by recent omics-based studies, which have identified recurrent mutations, copy-number alterations, and distinct molecular subgroups. However, although these approaches have provided a valuable framework, they are inherently limited in their ability to establish direct causal relationships. The mechanistic studies are therefore indispensable for translating these molecular observations into biological understanding. Nevertheless, the mechanistic literature has often evolved in a fragmented manner, with individual pathways and model systems studied in relative isolation from the broader multi-omic landscape. In this review, we synthesize these complementary bodies of work into an integrated framework and outline a clear roadmap for future studies. We first review the historical development of established meningioma cell lines, their current molecular characterization, and the recent emergence of 3D models and organoids. Intrinsic challenges in modeling meningioma in vitro are discussed, including the difficulty of establishing immortalized cell lines from predominantly benign tumors, genetic alterations introduced during immortalization, and drift under culture conditions that differ substantially from those of the parental tumors. Next, insights from functional studies centered on these models are integrated within the molecular framework established by large-scale omics analyses. To avoid fragmentation and overemphasis on isolated findings, prior studies are organized into six categories based on major signaling pathways: Hippo, PI3K/Akt/mTOR, MAPK, Wnt/β-catenin, FOXM1, and Notch. Finally, lessons from other cancer models, including experimental approaches to chromosome-scale genomic disturbances, are considered to provide a more integrated view of meningioma biology and to highlight directions for future research.

Meningioma↗
PubMed2026

A One Health approach to Antimicrobial Resistance: Concepts, challenges, and advances in omics.

Antimicrobial resistance (AMR) is a global threat driven by the interplay between microbial evolution and human activity. Antimicrobial use in human and veterinary medicine, as well as in agriculture, accelerates the selection and dissemination of resistant bacteria and genes across interconnected human, animal, and environmental reservoirs. These dynamic exchanges render single-sector interventions ineffective. A One Health approach integrating human, animal, and environmental health is therefore essential to understand and mitigate the emergence and spread of AMR. This chapter focuses on bacterial antimicrobial resistance, addressing key concepts, major challenges, and emerging technologies within a One Health framework. Advances in next-generation sequencing and omics technologies have transformed our capacity to resolve AMR at unprecedented scale and resolution. These tools enable the tracking of resistance genes and high-risk clones across ecosystems, uncover transmission pathways, and identify key drivers of dissemination. Such insights support real-time epidemiological surveillance, outbreak detection, and targeted interventions. However, translating these advances into routine practice remains a major challenge, requiring harmonized methodologies, data integration, and cross-sector coordination. Addressing AMR demands sustained collaboration across disciplines and stakeholders, including clinicians, veterinarians, farmers, researchers, policymakers, industry, and the public. And framing AMR as a shared ecological and societal responsibility underscores the urgency of coordinated global action. We call for the urgent integration of One Health principles into surveillance, policy, and innovation to preserve antimicrobial effectiveness and safeguard future health.

Humans↗
PubMed2026

Next-Generation Disease Profiling by Integrating Histopathology with Spatial Multi-Omics Data.

The field of pathology has experienced several transformative changes in recent years with the advent of digital pathology and spatial multi-omics. These technologies have enhanced every aspect of pathology practice, from streamlining daily workflows to generating high-fidelity multi-omics data that provide pathologists with novel tools to refine disease profiling and clinical diagnosis. Each layer of multimodal data (genomic, metabolomic, proteomic, or transcriptomic) has uncovered a distinct facet of disease pathologies, and combined with machine learning/artificial intelligence-based data analysis and pattern recognition models, has provided holistic understanding of regulatory mechanisms underpinning them. However, high-dimensional data have far exceeded the volume, scale, and complexity of immunostaining methods implemented by pathologists and, thus, have generated significant challenges related to deconvolution, interpretation, and clinical translation. Furthermore, these multimodal studies have predominantly relied on computational methods to process data and extract disease-relevant insights, thus raising questions around relevance or role of a pathologist in this new era of multi-omics. This review will provide a perspective on the evolving fields of molecular histopathology and spatial -omics, leveraging them to approach disease profiling, and redefining the role of a pathologist during this process.

Humans↗
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↗
PubMed2026

Multi-omics profiling of cerebrospinal fluid in autoimmune encephalitis: insights into pathogenesis and therapeutic targets.

BACKGROUND: Autoimmune encephalitis (AIE) is a rare, severe inflammatory brain disease, with its pathogenesis not yet fully elucidated. This study aimed to characterize proteomic and metabolomic alterations in the cerebrospinal fluid (CSF) of AIE patients and identify potential therapeutic targets. METHODS: 65 consecutive AIE patients and age-matched concurrent controls were enrolled, respectively. Clinical characteristics, including blood and CSF laboratory findings, were compared between the two groups, and CSF samples were collected for multi-omics analysis. Differentially expressed proteins (DEPs) and metabolites (DEMs) between AIE patients and controls were identified using data-independent acquisition-based proteomics and targeted liquid chromatography-mass spectrometry-based metabolomics, followed by integrated multi-omics analysis. RESULTS: Compared with controls, AIE patients had lower levels of triglyceride and C1q, but higher HDL-CH levels, neutrophil counts, and eosinophil counts in blood. CSF leukocyte, erythrocyte, lymphocyte, and mononuclear cell counts were also elevated in AIE patients. Proteomic analysis identified 163 DEPs, with enrichment of 87 canonical pathways primarily associated with immune-inflammatory responses, neuronal-synaptic dysfunction, and cell signaling and metabolic pathways. Metabolomic analysis recognized 21 DEMs, predominantly amino acids, lipids, and carbohydrates, which were involved in lipid-carbohydrate metabolism and immune regulation. Integrated multi-omics analysis validated these findings and identified several potential therapeutic targets for AIE, including the IL6-STAT3 axis. CONCLUSIONS: Integrated multi-omics analysis systematically delineates cellular and molecular alterations underlying AIE. Immune-inflammatory response and lipid metabolism are pivotal in AIE progression and the IL6-STAT3 axis holds promise as a potential therapeutic target.

Humans↗