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Introduction: Cerebral cavernous malformations (CCMs) are common lesions causing stroke and epilepsy. Mechanistic studies in cultured endothelial cells (ECs) and in murine models have correlated CCM gene loss with signaling aberrations related to MEKK3-KLF2/4, Rho/ROCK and angiogenesis activity, and disruption of junctional proteins and EC-mesenchymal transition. Other studies demonstrated a robust antigen driven clonally expanded B-cell immune response, and pathogenetic B-T cell interactions. We hypothesized that the transcriptome of endothelial ECs from human CCM lesions reflects these postulated mechanisms.Methods: Resected human CCMs were harvested at the time of surgical resection, snap frozen, embedded in OCT and stored at -80 °C. Normal brain tissue was acquired at autopsy from patients free of neurological disease. Five human CCMs and 3 normal brain tissues were included in the study. ECs from CCMs and normal capillaries were collected using laser-captured microdissection. RNA was extracted using the TRIzol protocol. RNA libraries, generated using low-input strand specific RNA-Seq kit (Clontech), were multiplexed and sequenced with 50 basepair (bp) single end reads (SR 50 bp), and mapped using STAR v2.4.0g1 alignment. Differentially expressed (DE) genes between CCM and normal ECs were identified with DESeq2. QIAGEN’s Ingenuity® was used to conduct functional enrichment analysis on the DE genes, defining canonical pathways associated with DE genes.Results: There were 774 DE genes (P<0.05; Fold-change>4). Most significant enriched canonical pathways were inflammation related (the highest enriched were signaling in T-helper cells, complement system and leukocyte extravasation signaling). Other pathways related to extracellular matrix and actin signaling (Rac, Paxillin, actin cytoskeleton and integrin signaling), angiogenesis, MMPs and the coagulation cascade.Conclusion: Differential transciptome analysis is feasible in CCM ECs in situ. It validates previously postulated disease mechanisms, and identifies other candidates for future study. This approach can categorize disease-related gene activity in lesions with different genotypes, clinical severity, and therapeutic interventions.