Computational protocol: Concerted regulation of retinal pigment epithelium basement membrane and barrier function by angiocrine factors

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Protocol publication

[…] For RNAseq of native choroid ECs from P5 and P30 mice, RNA was extracted immediately after cell sorting by lysing cells directly in 750 μl TRI Reagent (Molecular Research Center, Inc.) and following the manufacturer's instructions. After phase separation, the aqueous phase was diluted 1:1 with 70% ethanol and loaded into a column of the RNeasy Mini Kit (Qiagen). RNA was purified following the manufacturer's instructions, including an in-column DNAse digestion step. RNAseq was carried out from three independent isolations, with 14 eyes per isolation. cDNA libraries were prepared with the TruSeq RNA Sample Preparation Kit (Illumina). Four samples were run per lane and sequenced on an Illumina HiSeq2000 platform. On quality control using FastQC, raw reads were aligned to the mouse genome (mm9) using TopHat with default parameters. CuffLinks with GC and upper quartile normalization was then used to calculate normalized expression levels (FPKM; Fragments Per Kilobase of transcripts per Million reads). Log transformed FPKM profiles were clustered using hierarchical clustering (hclust function in the R language) with average linkage and one minus Pearson correlation as distance. To determine the genes differentially expressed between P5 and P30 choroid ECs, statistical significance was calculated using DEseq2 bioconductor package in R (ref. ), which uses read counts (htseq-count). Only genes with Benjamini–Hochberg corrected P<0.01 and with a minimum relative difference of 2-fold were considered. All FPKM values were added 1 before filtering to avoid losing genes whose expression was undetectable in P5 or P30 samples. Next, the lists of upregulated genes in either P5 or P30 were re-filtered to exclude genes with FPKM<5 (close to the detection limit). These lists of differentially expressed genes were subsequently used to carry out gene ontology analyses with the DAVID software, using the biological process (GOTERM_BP_FAT), cellular component (GOTERM_CC_FAT) and molecular function (GOTERM_MF_FAT) categories. GSEA using the term EXTRACELLULAR_MATRIX (GO term GO:0031012) were performed to compare the transcriptomes of P5 and P30 choroid ECs. […]

Pipeline specifications

Software tools FastQC, TopHat, Cufflinks, Hclust, DESeq2, HTSeq, DAVID, GSEA
Application RNA-seq analysis