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2) How does this analysis work? It’s relatively simple: we use DNA results from genetic studies such as DecodeME and match them with RNA databases that give an indication of gene expression in different tissues and cell types.

3) The analysis tests if genes associated with ME/CFS are more commonly used in certain tissues or cell types. You can also use other categories of genes, for example grouped based on biological function.

4) The graph below shows how powerful this technique is. It picks out the relevant cell type in multiple diseases: immune cells in rheumatoid arthritis and asthma, neurons in schizophrenia and epilepsy and pancreatic cells for diabetes.

5) These results were from a landmark 2018 study by Finucane et al. Adding ME/CFS to this analysis pipeline gives results that strongly point to the central nervous system. Using an immunological dataset (ImmGenn) gave no significant hits.

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6) Multiple analyses and genetic data sources now point in the direction of the central nervous system. Either the genetic studies like DecodeME are way off (possible but unlikely), or this is a region (the brain) and cell type (neurons) that is key to ME/CFS.

7) Next, we tried to go one level deeper and see which type of neuron has the best fit with the ME/CFS genetic data using the Human Brain Atlas. The (eccentric) medium spiny neurons (eMSN) were one of the top hits.

8) Caveat: this doesn’t mean this cell type is involved in ME/CFS. At this finer level the results are more uncertain. It just means it matches ME/CFS genetic signals better than the other cell types.

9) Medium spiny neurons are cells in the striatum, a region deep inside the brain. It acts like a gate and determines which input signals are worth acting on. It’s for example important in initiating movement.