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Optimized protocol for translatome analysis of mouse brain endothelial cells

Title
Optimized protocol for translatome analysis of mouse brain endothelial cells
Author(s)
Kim, NamsukJun, Mi-HeeJeong, Jin-YoungOh, Won-Jong
Issued Date
2022-09
Citation
PLoS ONE, v.17, no.9
Type
Article
Keywords
MESSENGER-RNAATLASAGE
ISSN
1932-6203
Abstract
Brain endothelial cells (BECs) are important conduits that deliver oxygen and nutrients, protect parenchyma cells from toxins, and drain wastes to maintain brain homeostasis. Impairment of BECs has been implicated in diverse neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease. Therefore, molecular analysis of BECs is important for understanding the molecular pathogenesis of these neurological diseases. Even though many transcriptome analyses for BECs have been developed, mRNA levels do not necessarily correlate with the levels of actively translated proteins. Translatome analysis using RiboTag mice, in which Rpl22, a ribosomal component, is tagged by the hemagglutinin epitope under Cre recombinase activation, could serve as an excellent tool that overcomes these caveats. However, implementation of this technique is limited by high noise-to-signal ratios as well as the low yield of mRNAs from BECs, which limits bulk gene expression analysis. In this study, we established a protocol to isolate highly pure mRNAs from BECs in the cortex of eight- to twelve-week-old male Tie2-Cre; Rpl22HA/HA mice by using a cell strainer to trap blood vessels prior to immunoprecipitation. According to the results of RT–PCR, the specificity of the mRNA pools isolated by our protocol was much higher than that of the pools isolated by the standard protocol. We were also able to generate a high-quality cDNA library for RNA-seq with the small amount of mRNA isolated with our protocol. Thus, this optimized method will be useful for future studies of BECs at the molecular level. © 2022 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI
http://hdl.handle.net/20.500.11750/17394
DOI
10.1371/journal.pone.0275036
Publisher
Public Library of Science
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