Multi-omics and high-spatial-resolution omics: deciphering complexity in neurological disorders

Abstract

Background: The world has witnessed a steady rise in neurological diseases, which represent a heterogeneous group of disorders characterized by complex pathogenesis involving disruptions at multiple molecular levels, including genomic, transcriptomic, proteomic, and metabolomic levels. These disorders, often caused by genetic mutations, metabolic imbalances, immune dysregulation, and environmental factors, pose significant challenges to global public health due to their high prevalence, mortality, and disability burden.

Results: The advent of high-throughput technologies, such as next-generation sequencing and mass spectrometry, has provided valuable insights into the underlying mechanisms of disease, especially the development of multi- and high-spatial-resolution omics technologies, enabling the interaction of multiple levels of biology and analysis of the complex molecular networks and pathophysiological processes.

Conclusions: This review provides a comprehensive analysis of the latest advancements in multi- and high-spatial-resolution omics, with a focus on their applications in precision diagnostics, biomarker discovery, and therapeutic target identification in brain diseases. The study also highlights the current challenges in the clinical implementation and discusses the future directions, with artificial intelligence being anticipated to enhance clinical translation and diagnostic accuracy significantly.

Keywords: multi-omics; neurological diseases; single-cell omics; spatial transcriptomics.

Graphical Abstract

Applications of multi-omics and high-spatial-resolution omics technologies in the diagnosis and treatment of brain diseases in the field of neurology. By integrating high-throughput omics technologies, including 4 basic omics, single-cell omics, and spatial omics technologies, it is possible to dissect the complex pathogenic mechanisms of neurological disorders comprehensively. This multi-omics approach spans multiple levels, from genetic variations to metabolic changes, and reveals the interactions between these levels, providing an unprecedented perspective for in-depth disease understanding. In the study of diseases such as Alzheimer’s disease, Parkinson’s disease, stroke, epilepsy, multiple sclerosis, and hydrocephalus, the application of these cutting-edge technologies has greatly facilitated the discovery of key biomarkers and significantly deepened our understanding of the molecular mechanisms of disease pathogenesis.

Figure 1:

Three main types of metabolomics, including targeted metabolomics, untargeted metabolomics, and widely targeted metabolomics. This figure outlines these 3 metabolomics techniques, each with unique strengths and limitations. Careful selection among them can lead to the optimal choice to meet the specific requirements of your experimental objectives.

Figure 2:

Research workflow and applications of high-spatial-resolution omics technologies: integrated development of single-cell and spatial omics. This figure illustrates the integrated research workflow of high-spatial-resolution omics technologies, combining single-cell omics technology with spatial omics approaches. Single-cell omics involves (i) sample preparation, (ii) single-cell isolation and labeling (utilizing representative technologies such as Drop-seq and 10x Genomics Chromium), (iii) nucleic acid extraction and library construction, and (4) high-throughput sequencing to resolve cellular gene expression profiles and uncover cellular heterogeneity. In parallel, spatial omics employs (i) tissue sample processing, (ii) spatial labeling/capture (via techniques like 10x Visium, Slide-seq, and MERFISH), (iii) sequencing/detection, and (iv) computational data analysis to map gene expression with spatial coordinates, thereby elucidating tissue microenvironment interactions. The synergy between these approaches addresses complementary questions—who is expressing a gene versus where expression occurs spatially—enabling breakthroughs in tumor biomarker identification, cellular subpopulation localization, and tissue developmental mechanisms, among others.

Figure 3:

Applications of multi-omics and high-spatial-resolution omics technologies in the diagnosis and treatment of brain diseases in the field of neurology. By integrating the high-throughput omics technologies, including 4 basic omics, single-cell omics, and spatial omics technologies, it is possible to comprehensively dissect the complex pathogenic mechanisms of neurological disorders. This multi-omics approach spans multiple levels, from genetic variations to metabolic changes, and reveals the interactions between these levels, providing an unprecedented perspective for in-depth disease understanding. In the study of diseases such as AD, PD, stroke, epilepsy, MS, and hydrocephalus, the application of these cutting-edge technologies has greatly facilitated the discovery of key biomarkers and significantly deepened our understanding of the molecular mechanisms of disease pathogenesis.

Figure 4:

Pathogenesis of AD revealed by multi-omics and high-spatial-resolution omics technologies. This figure provides an overview of the pathogenesis of AD revealed by omics technologies, with a particular focus on several key aspects, including abnormal tau protein, deposition of Aβ protein, formation of neurofibrillary tangles, neuronal loss and degeneration, disorders of lysosomal-related metabolic pathways, and neuroinflammation. In the context of tau protein, Aβ protein, and plaque accumulation, the M7 MAPK module and STAT3 gene are involved. Moreover, LRP1, FKBP1B, PSEN1, APP, PS1, and tau proteins constitute the main components of neurofibrillary tangles, and their abnormal alterations represent crucial pathological features of AD. The process of neuronal loss and degeneration involves changes in various key elements, including genes (APOE4, ATP6V1A, LINGO1, OLIG), histone modifications (H3K9ac, H4K16ac), metabolite regulation (PKM2), and myelination-related alterations. These factors act in concert, leading to the impairment of neuronal functions and the decline of cognitive abilities. In the lysosome- and glycolysis-related metabolic pathways, the abnormalities of FBP1, FBP2, RHOH, lipid, SPI1/PU.1SPI1, ELF2, CSTD, SPARC, CALB2, and CTSB, RUNX1, as well as the metabolic pathways of sphingolipids and aromatic amino acids, reflect cellular metabolic impairments observed in AD. In terms of neuroinflammation, PBXIP1, along with activated microglia and astrocytes, shows abnormal hyperactivity. PTPRG/VIRMA inhibitors show their impacts on mitochondrial function and neuronal survival, offering potential therapies for AD. Alterations in SST⁺ and Pvalb⁺/Vip⁺ levels also show potential for the diagnosis of AD. By integrating multilevel data across the genome, transcriptome, proteome, and metabolome, multi-omics and high-spatial-resolution omics technologies provide a comprehensive view of AD pathogenesis, offering crucial insights for early diagnosis and precision therapy.

Figure 5:

Multi-omics and high-spatial-resolution omics reveal key mechanisms, biomarkers, and risk factors in PD. PD is a complex neurodegenerative disorder influenced by genetic, environmental, and neurobiological factors. Mutations in genes such as ZNF184, IL1R2, IL1B, GPNMB, and LRRK2 are central to PD risk prediction and genetic susceptibility. Aberrant expression of proteins, like GPNMB, CD38, SYN2, and DGKQ, and biomarkers, such as MAPT, SSR1, TP53, and NR2F2, is linked to PD progression and diagnostic value. These changes drive neuroinflammation and immune activation. BBB disruption facilitates the infiltration of leukocytes and neutrophils, initiating neuroinflammation. Activated microglia and astrocytes release inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6), which exacerbate the inflammatory environment and damage neurons. Lysosomal dysfunction, involving OMD, CD44, VGF, PRL, and MAN2B1, and ceRNA–Akt1 axis disruption contribute to α-synuclein aggregation. Inflammatory biomarkers, including CircSV2b, DDC, Proline, BCAAs, and molecules in steroidogenesis and fatty acid catabolism, are also identified. Dysregulated short-chain fatty acid metabolism is associated with cognitive decline in PD. HSP90 inhibitors show therapeutic potential, as suggested by scRNA-seq of neuronal heterogeneity and molecular pathways. This figure integrates multilevel omics data to illuminate PD pathogenesis, supporting early diagnosis, monitoring, and targeted treatment.