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Boddeke, E. W. G. M.

Publications and source records attributed to Boddeke, E. W. G. M..

2 recordsLinked to original sources

Distinct gene expression profiles in leukocortical demyelinated white and grey matter areas of Multiple Sclerosis patients

Demyelination of the CNS is a prominent pathological hallmark of Multiple Sclerosis (MS) and affects both white (WM) and grey matter (GM). However, demyelinated WM and GM areas exhibit clear pathological differences, most notably the presence or absence of inflammation and activated glial cells in WM and GM, respectively. In order to gain more insight into the differential pathology of demyelinated WM and GM areas, we micro-dissected neighbouring WM and GM demyelinated areas as well as normal appearing matter from leukocortical lesions of human post-mortem material and used these samples for RNA-sequencing. Our data show that even neighbouring WM and GM demyelinated areas share only 10% overlap in gene expression, implying a distinct gene expression profile, which is extending to a specific glial cell related signature. We propose that, based on their distinct expression profile, pathological processes in neighbouring WM and GM are likely different which could have implications for the efficacy of current MS treatments.

neuroscience

BRAin INteractive Sequencing Analysis Tool (BRAIN-SAT); facilitating interactive transcriptome analyses (http://brainsat.eu/)

Over the last decade, a large number of glia transcriptome studies has been published. New technologies and platforms have been developed to allow access and interrogation of the published data. The increase in large transcriptomic data sets allows for innovative in silico analyses to address biological questions. Here we present BRAIN-SAT, the follow-up of our previous database GOAD, with several new features available on an interactive platform that enables access to recent, high quality bulk and single cell RNA-Seq data. The combination of several functions including gene searches, differential and quantitative expression analysis and a single cell expression analysis feature enables the exploration of published data sets at different levels. These different functionalities can be used for researchers and research companies in the neuroscience field to evaluate and visualize gene expression levels in a set of relevant publications. Here, we present a new platform with easy access to published gene expression studies for data exploration and gene of interest searches.

neuroscience