Multiple sclerosis (MS) is a multifactorial autoimmune disease that affects the central nervous system (CNS), resulting from the complex interplay between genetic susceptibility and environmental factors. While our understanding of the disease processes involved in MS has greatly advanced, there is still an urgent need to deepen our knowledge to be able to effectively halt MS progression and control relapses. For decades, exploring compartmentalized processes within the CNS has been technically challenging. Key issues include the low cell counts and fragility of cerebrospinal fluid (CSF) cells and a dependence on post-mortem CNS brain specimens, constraining research to investigating disease mechanisms at their later stages. As a result, most human-based studies have focused on characterizing blood of patients with MS, which has provided valuable insights into disease processes. However, it’s important to note that some underlying disease activity may not be accurately reflected in peripheral blood samples, posing a challenge in using such blood samples. Nevertheless, with the recent advancement of computational data analysis tools, we are better equipped to investigate cellular and molecular mechanisms involved in disease progression. As a result, we now have a better understanding of dynamic changes within the landscape of CSF cells and across different types of MS lesions leveraging multiomics approaches.

The emergence of sensitive multiomic technologies, such as single-cell RNA-sequencing, CITE-seq, single-nuc, and MERFISH special transcriptomics, has significantly advanced our understanding of MS by providing insights into the involved cell subsets at a higher resolution level. Multiomic approaches shed light on distinct cell populations and deepened our understanding of their roles within the micro-environment. However, despite the advancements, challenges persist, including the interpretation of large datasets, the relevance of gene-related observations, and the translation of these findings to functional validation at the benchside. Furthermore, as the computational and multiomic fields exponentially expand, establishing bridges between experimental and computational scientists is crucial. This is critical to characterize cell subsets at a high-resolution level, assess their implications with the brain or the periphery, and validate such observations at the functional level.

This Research Topic aims to gather studies that offer fresh perspectives on optimizing computational strategies to more effectively characterize the mechanisms involved in MS. Manuscripts can discuss one or several multiomic approaches. We are interested in Original Research and Review articles, focusing on, but not limited to, the following areas:
• Original studies that utilize computational approaches to understand MS disease progression, relapses, and triggering events of MS
• Studies on animal models that closely mimic the relapsing nature and disease progression of MS
• Reviews that summarize new advances in the field of MS with a translational and computational analysis approach
• Transcriptomic characterization of CSF cells derived from MS patients
• Mapping MS lesion across the brain using multiomic approach
• Radiologically isolated syndrome (RIS) and evolution towards MS
• High-resolution characterization of peripheral and CNS immune cell signatures in MS
• Leveraging computational approaches to better understand mechanisms involved in relapsing and progressive MS
• Transcriptomic and epigenomic signatures involved in animal models of CNS autoimmunity
• Leveraging multiomics approaches to understand the role of viral infections in MS
• Understanding the interplay between host-microbiome and MS using systembioinformatics

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Frontiers in Immunology

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