In recent years, the rapid advancement of high-throughput omics technologies—including genomics, transcriptomics, epigenomics, proteomics, and metabolomics—has demonstrated tremendous potential for multi-omics research in neurological diseases. By integrating data across multiple molecular layers, this approach provides a comprehensive understanding of the complex pathogenesis of neurological disorders and offers new avenues for biomarker discovery, prevention, diagnosis, treatment, prognosis assessment, and drug development[
1,
2].
Neurological diseases are characterized by multifactorial etiologies, strong gene–environment interactions, multistage pathological progression, and high clinical heterogeneity. Traditional single-omics approaches are insufficient to capture the full biological complexity of these disorders. Multi-omics research, by jointly analyzing genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, enables a systematic dissection of disease mechanisms[
1]. It delineates the molecular continuum linking genetic variants to cellular and tissue-level dysfunction[
2]. For example, in Alzheimer’s disease (AD), multi-omics studies have revealed concurrent alterations in DNA methylation, RNA expression, and protein levels of specific genes, reflecting intricate molecular interactions[
3]. Moreover, the integration of metabolomic and proteomic data allows for the identification of distinct AD molecular subtypes that correlate closely with cognitive decline and disease progression[
4]. Multi-omics analysis also uncovers modifiable metabolic and epigenetic pathways, offering promising targets for prevention and personalized intervention[
5]. Clinically, it enables molecular stratification of patients to improve diagnostic accuracy, predict therapeutic response, and refine prognostic evaluation, thereby enhancing treatment specificity[
6]. Furthermore, multi-omics accelerates drug discovery by identifying key regulatory networks and druggable nodes, facilitating target validation and drug repurposing in neurodegenerative disease models[
7]. Collectively, this integrative strategy provides a framework that links “macroscopic symptoms” to “molecular mechanisms”, bridging the continuum from risk prediction and early diagnosis to precise subtyping, effective treatment, and prognosis monitoring.
Recent years have witnessed substantial progress in multi-omics applications to neurological diseases. Mechanistically, integrative analyses combining DNA methylation, RNA sequencing, and proteomic data have identified
PBXIP1 as a gene significantly associated with AD neuropathology, potentially acting via the mechanistic Target of Rapamycin (mTOR) pathway in astrocytes and hippocampal neurons[
3]. Systems genetics coupled with multi-omics data has elucidated the intricate genetic architecture of neurodegenerative diseases—ranging from monogenic to polygenic and network-based models—thus deepening mechanistic insights[
8]. Spatial omics has further delineated the spatial distribution of pathological proteins within brain tissue and their microenvironmental contexts, providing novel perspectives on spatial pathophysiology[
9]. In terms of biomarkers and subtyping, large-scale integrative analyses have constructed high-dimensional brain multi-omics datasets, identifying four distinct molecular profiles, one of which is strongly associated with cognitive decline, accelerated disease progression, and shortened survival[
4]. The emerging “brain multi-molecular atlas”, integrating multi-omics with magnetic resonance imaging (MET) and positron emission tomography (PET) imaging phenotypes, is advancing precision diagnosis and molecular subtyping[
10]. Clinically, multi-omics approaches facilitate biological stratification of heterogeneous patient populations, enabling personalized management strategies[
6]. The integration of multi-omics with induced pluripotent stem cell (iPSC) models, cell lines, and
in vitro screening systems has further expanded opportunities for drug target discovery, mechanism validation, and drug repurposing[
7]. Methodologically, multi-omics data integration has evolved from traditional statistical and co-expression approaches to sophisticated frameworks incorporating graph networks, machine learning, and deep learning[
11]. Single-cell multi-omics, in particular, has seen exponential growth, especially in neurodegenerative research, demonstrating high potential for precise molecular subtyping and mechanistic exploration[
12]. Collectively, the field is transitioning from mechanistic exploration to translational application, achieving early breakthroughs in biomarkers, subtyping, and target identification.
Despite its promise, multi-omics research in neurological diseases still faces substantial challenges. Sample size and heterogeneity remain major constraints, as brain tissues are difficult to obtain, and small sample sizes often limit statistical power. Variability among patients, disease stages, and brain regions further complicates data interpretation. Data integration poses another critical challenge due to technical discrepancies among omics platforms, heterogeneous data formats, and batch effects, which are yet to be fully resolved. Although multimodal integration algorithms have advanced, further methodological optimization is needed[
13]. Moreover, most studies remain cross-sectional, lacking longitudinal data necessary to capture disease dynamics, and spatial resolution is often insufficient, particularly for region- and cell type–specific analyses. Spatial omics, while promising, is still in its infancy[
9]. Importantly, many candidate genes, pathways, and biomarkers identified thus far have not been effectively translated into clinical practice, underscoring the need for biological validation through experiments, animal models, and clinical trials. For instance, while
PBXIP1 is strongly implicated in AD, its mechanistic role remains to be fully elucidated[
3]. Multi-omics studies are also costly, computationally intensive, and highly dependent on interdisciplinary collaboration and high-performance computing, which constrain large-scale and clinical applications. Furthermore, the sensitivity of neurological data raises significant ethical, privacy, and data-sharing concerns, and the absence of standardized frameworks for data harmonization and secure sharing represents a major bottleneck for progress.
Looking ahead, future multi-omics research in neurological diseases should be deepened and expanded on several fronts. Longitudinal, large-scale multi-omics cohorts integrating genomic, epigenomic, transcriptomic, proteomic, metabolomic, imaging, clinical, lifestyle, and environmental data are essential for elucidating the temporal dynamics of disease progression. Studies with spatial and cell-type resolution should be prioritized, leveraging spatial omics and single-cell/single-nucleus multi-omics in brain, cerebrospinal fluid, and peripheral tissues to map molecular networks across specific cell types and regions. Integration with neuroimaging modalities will enable precise spatial mapping of pathological changes[
10]. Algorithmic innovation will be equally crucial. Developing interpretable and clinically applicable multimodal integration models—such as deep learning, graph neural networks, and explainable artificial intelligence—will help address the challenges of high-dimensional, heterogeneous data and batch effects[
13]. Future research should also emphasize translational pathways, advancing candidate biomarkers and targets from discovery to validation in biological systems and clinical trials. Moreover, integrative systems modeling that combines molecular, imaging, cognitive, lifestyle, immune, and environmental dimensions could provide a holistic, hierarchical understanding of neurological disease pathogenesis. Finally, technological innovations should aim to reduce costs, shorten analysis cycles, and expand sample accessibility (e.g., using blood instead of brain tissue), enhancing the feasibility and scalability of multi-omics in clinical settings.
In conclusion, multi-omics research is transforming the understanding and management of neurological diseases. Despite progress, challenges such as limited samples, data integration, and clinical translation remain. Future efforts should focus on higher-resolution and more integrative approaches to enable earlier and more precise interventions.
PDF
(224KB)
