Fig. 1

Data-driven discovery workflow. Using anterior horn tissue, RNA transcript expression was measured from isolated motor neurons, and counts of p62-positive cytoplasmic inclusions within motor neurons were conducted. RNA expression and pathology counts from the same patients were correlated by Spearman’s rank correlation to identify 83 transcripts (a). Pathology correlated transcripts seeded co-expressed networks. The resulting combined network was developed into tightly co-expressing modules using weighted gene co-expression analysis (WGCNA) (b). Modules were prioritised using enrichment with independently curated gene lists related to ALS rate of progression and ALS genetic susceptibility. The two top scoring modules were enriched for neuronal and immune function respectively. MN = motor neuron, LB = lymphoblastoid (c). The immune module was selected for use as a biomarker in peripheral tissue and additional non-tissue specific genes were added. Components of the immune module were assessed by mRNA and protein quantification for predictive value in blood and cerebrospinal fluid (CSF) (d)