Multiple Sclerosis (MS) is a severe chronic inflammatory disease of a yet unknown cause characterized by autoimmune destruction of myelin and neurons in the central nervous system. The exact contribution of different immune cells in MS pathogenesis is still unclear. DNA methylation is an epigenetic modification that without altering the genetic code can stably change the expression of genes and thus may play an important role in MS development. Moreover, due to stability, high specificity and the ability to be measured in limited amounts of material genome-wide, methylation changes might become novel robust biomarkers of MS activity and treatment response.
We aimed to establish and optimize a genome-wide methylation approach to investigate epigenetic changes that render immune cells pathogenic in MS with the prospect of better understanding disease pathogenesis. Cells from the cerebrospinal fluid (CSF), and other cells and biofluids, of relapsing-remitting MS and secondary progressive MS patients, as well as age- and sex-matched other neurological disease controls were included in the analysis. The libraries for genome-wide methylation analysis were prepared as was previously described (Smallwood et al. Nat Methods 2014). We have optimized the library preparation procedure for the whole-genome methylation analysis on a low number of CSF cells. We are still optimizing libraries for genome-wide small non-coding RNA analysis in low number of cells and bodifluids based on previously described method for single-cells (Faridani et al. Nat Biotechnol. 2016).
We have prepared libraries from MS cases and controls (n=48) and assessed their quality and quantity. We will use a deep sequencing approach to determine genome-wide methylation and sncRNA changes that associate with disease status and stage. Such analysis will enable us to study MS heterogeneity and progression using samples that are inaccessible to many other methods.
The work involves sensitive personal data.
OBS! I want to move data from the following Milou projects: