Summary Cerebral cavernous malformations (CCMs) are vascular disorders that affect up to 0.5% of the total population. About 20% of CCMs are inherited because of familial mutations in CCM genes, including CCM1 / KRIT1 , CCM2 / MGC4607 , and CCM3 / PDCD10 , whereas the etiology of a majority of simplex CCM-affected individuals remains unclear. Here, we report somatic mutations of MAP3K3 , PIK3CA , MAP2K7 , and CCM genes in CCM lesions. In particular, somatic hotspot mutations of PIK3CA are found in 11 of 38 individuals with CCMs, and a MAP3K3 somatic mutation (c.1323C>G [p.Ile441Met]) is detected in 37.0% (34 of 92) of the simplex CCM-affected individuals. Strikingly, the MAP3K3 c.1323C>G mutation presents in 95.7% (22 of 23) of the popcorn-like lesions but only 2.5% (1 of 40) of the subacute-bleeding or multifocal lesions that are predominantly attributed to mutations in the CCM1/2/3 signaling complex. Leveraging mini-bulk sequencing, we demonstrate the enrichment of MAP3K3 c.1323C>G mutation in CCM endothelium. Mechanistically, beyond the activation of CCM1/2/3-inhibited ERK5 signaling, MEKK3 p.Ile441Met ( MAP3K3 encodes MEKK3) also activates ERK1/2, JNK, and p38 pathways because of mutation-induced MEKK3 kinase activity enhancement. Collectively, we identified several somatic activating mutations in CCM endothelium, and the MAP3K3 c.1323C>G mutation defines a primary CCM subtype with distinct characteristics in signaling activation and magnetic resonance imaging appearance.

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The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases.