Comprehensive evaluation of blood-brain barrier-forming micro-vasculatures: Reference and marker genes with cellular composition.
Mei DaiYi LinSalim S El-AmouriMara KohlsDao PanPublished in: PloS one (2018)
Primary brain microvessels (BrMV) maintain the cellular characters and molecular signatures as displayed in vivo, and serve as a vital tool for biomedical research of the blood-brain barrier (BBB) and the development/optimization of brain drug delivery. The variations of relative purities or cellular composition among different BrMV samples may have significant consequences in data interpretation and research outcome, especially for experiments with high-throughput genomics and proteomics technologies. In this study, we aimed to identify suitable reference gene (RG) for accurate normalization of real-time RT-qPCR analysis, and determine the proper marker genes (MG) for relative purity assessment in BrMV samples. Out of five housekeeping genes, β-actin was selected as the most suitable RG that was validated by quantifying mRNA levels of alpha-L-iduronidase in BrMV isolated from mice with one or two expressing alleles. Four marker genes highly/selectively expressed in BBB-forming capillary endothelial cells were evaluated by RT-qPCR for purity assessment, resulting in Cldn5 and Pecam1 as most suitable MGs that were further confirmed by immunofluorescent analysis of cellular components. Plvap proved to be an indicator gene for the presence of fenestrated vessels in BrMV samples. This study may contribute to the building blocks toward overarching research needs on the blood-brain barrier.
Keyphrases
- blood brain barrier
- genome wide
- genome wide identification
- drug delivery
- high throughput
- cerebral ischemia
- endothelial cells
- genome wide analysis
- dna methylation
- copy number
- mass spectrometry
- transcription factor
- single cell
- gene expression
- resting state
- high resolution
- skeletal muscle
- electronic health record
- single molecule
- adipose tissue
- binding protein
- brain injury
- machine learning