A multiresolution framework to characterize single-cell state landscapes enhances our understanding of the regulatory genomic circuitry. Single-cell technologies have revolutionized cellular heterogeneity studies, allowing for the dissection of complex tissue compositions and identification of distinct cell states and types at unprecedented resolution. This framework integrates single-cell RNA sequencing (scRNA-seq) with other assays such as single-cell ATAC-seq (scATAC-seq), single-cell DNA methylation sequencing, single-nuclei sequencing, single-cell proteomics, and spatial analysis. By combining these datasets, researchers construct comprehensive maps of cellular states and transitions, revealing how individual cells contribute to disease progression and treatment response. Advanced computational tools and machine learning algorithms analyze the high-dimensional data generated by single-cell assays. Techniques like dimensionality reduction, clustering, and trajectory inference identify distinct cell populations and their dynamic changes over time. This approach highlights tissue heterogeneity and uncovers the regulatory mechanisms driving cell state transitions and their impact on disease. Integrating epigenomic, single cell, and spatial data decodes the regulatory circuitry underlying diseases. Mapping interactions between genomic loci, epigenomic marks, and protein expression at single-cell resolution pinpoints critical regulatory elements and pathways involved in disease onset and progression. This chapter unravels various integrative strategies of omics technologies in general while emphasizing its significance in precision medicine, enabling the development of targeted therapies pertaining to neurosciences. Moreover, incorporation of case studies, pipelines, and codes for bioinformatic analysis with relevant illustrations emphasize recent advances of multi-omics technologies presenting the state of art.