Multiomics Approaches in the Central Nervous System and Neuropathies

With the latest developments in methods for deep analysis of data at the genome, transcriptome and proteome levels, today we are able to understand in detail the molecular principles that determine the mechanisms of development both in normal and pathological conditions. The use of bioinformatics methods provides ample opportunities for high-throughput target search, drug screening, analysis of signaling pathways and the microenvironment cell-cell interactions. Multiomic approach allows a holistic view of the developmental trajectory and disease progression of an organ, tissue, or disease within a complex system as well as an insight of the genomic landscape at cellular resolution. For example, the cellular transcriptome is deciphered by single-cell RNA sequencing, the epigenome by ATAC-sequencing, and the totality of the T-cell Receptor profile by TCR-seq. The capabilities of such methods today continue to develop at an incredible pace - both in status quo computational biology and in the “ ‘dry’ biology –‘ wet’ biology ” funnel.

In this Research Topic, we would like to highlight the niche of bioinformatics as a pioneer scientific field at the frontier of neuroscience. We are interested in seeing data on the development, ontogeny of organs and tissues, diseases, and their manifestations using multiomic methods. This can be either a comprehensive analysis of extrinsic and intrinsic factors using systems between healthy and pathological conditions, or the dynamics of the development of a specific organ in the CNS at the level of the proteome or cascade of intercellular interactions.

This Research Topic welcomes original research and reviews. Specific themes to be addressed may include, but are not limited to:

• New methods, algorithms, packages in multiomics data analysis
• CNS development and ontogeny
• CNS-related diseases

Additionally, we encourage the use of a combination of multiomics data analysis algorithms published to date (Seurat, CellChat, RNA Velocity, cellrank, scFates, Scriabin, BPCells). By mixing these algorithms, for example, using the output of the CellChat package in the form of a matrix of ligand-receptor interactions instead of the standard input matrix of single-cell RNA sequencing data in the form of cells/genes, we could build a new data space showcasing cellular changes at the level of intercellular interactions after their migration, even before final differentiation and specialization.

With the fast-developing sequencing methods and the multiplexity in algorithm combination, we would be glad to see new trends in analyzing the CNS in healthy and disease conditions using multiomics and molecular approaches to dissect the mechanism and frequency of diseases associated with the CNS.