Optimized RNA-seq workflow for routine diagnosis of mitochondrial diseases using aberrant expression analysis and phenotype-driven prioritization

Abstract Mitochondrial diseases (MD) are the most common inherited metabolic disorders and show marked clinical and genetic heterogeneity. Despite widespread use of whole-exome (WES) and whole-genome sequencing (WGS), nearly half of patients remain without molecular diagnosis, partly due to difficulties in interpreting variants affecting RNA splicing or gene expression. RNA sequencing (RNA-seq) has shown promise in research settings, few studies have evaluated its feasibility in routine clinical diagnostics under technical and financial constraints. We evaluated the implementation of RNA-seq in a routine diagnostic workflow for MD using fibroblasts from 38 patients, including 8 analyzed with a candidate-gene approach and 30 with an untargeted strategy. To optimize the workflow and improve sensitivity in untargeted analyses, we focused on aberrant expression detection using OUTRIDER with complementary analytical strategies, including multiple testing correction restricted to disease-relevant gene sets and Z-score–based outlier detection combined with phenotype-driven prioritization using Human Phenotype Ontology terms. This combined strategy enabled diagnostic performance comparable to previously published studies while optimizing resources. Pathogenicity was confirmed in 75% of targeted cases (6/8). In the untargeted cohort, new molecular diagnoses were established in 13% of patients (4/30), despite fewer samples than typically recommended and minimizing costs. Our study demonstrates that RNA-seq is feasible and clinically informative in a routine diagnostic setting despite analytical and financial constraints. RNA-seq represents a valuable complement to WES/WGS for improving variant interpretation and diagnostic yield in MD. We propose a practical framework for its implementation alongside genome sequencing in routine diagnostics.