Anne-Marie Koop

7 257 with hematoxylin and eosin (H&E) for routine histological analysis, Sirius Red for detection of fibrillar collagen and FITC-labelled wheat-germ-agglutinin (WGA, Sigma) to visualize and quantify the cell cross-sectional area (CSA). Modification of Isolectine B4 staining with additional fluorescence labeled-streptavidin (Dylight 595-conjugated streptavidin, Jackson Thermo, 1:100) and counterstaining with FITC- labeled WGA was performed to assess capillary to cardiomyocyte ratios. Collagen deposition, cell CSA and capillary density were determined using ImageJ software. Slides were visualized using a Leica DM2000 and a Leica DM3000 microscope for bright field and fluorescence imaging, respectively Library construction and sequencing Total RNA was extracted using Direct-zol™ reagent (ZYMO), following the manufacturer’s procedure. The total RNA quality and quantity were analysed by a Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, CA, USA) with RIN number >7.0. Approximately 10 mg of total RNA was used to remove ribosomal RNA according to the manuscript of the Epicentre Ribo-Zero Gold Kit (Illumina, San Diego, USA). Following purification, the ribo-minus RNA fractions is fragmented into small pieces using divalent cations under elevated temperature. Then the cleaved RNA fragments were reverse-transcribed to create the final cDNA library in accordance with a strand-specific library preparation by dUTP method. The average insert size for the paired-end libraries was 300±50 bp. And then we performed the pair-end 2×150bp sequencing on an illumina Hiseq 4000 platform housed in the LC Sciences (Hangzhou, China) following the vendor’s recommended protocol. Bioinformatics analysis For transcripts assembly, firstly, Cutadapt 22 and perl scripts in house were used to remove the reads that contained adaptor contamination, low quality bases and undetermined bases. Then sequence quality was verified using FastQC (http://www. bioinformatics. babraham.ac.uk/projects/fastqc/ ). We used Bowtie2 23 and Tophat2 24 to map reads to the genome of Mus musculus (Version: v88). The mapped reads of each sample were assembled using StringTie. 25 Then, all transcriptomes from 6 samples were merged to reconstruct a comprehensive transcriptome using perl scripts and gffcompare (https://github.com/ gpertea/gffcompare/). After the nal transcriptome was generated, StringTie 25 and Ballgown 26 was used to estimate the expression levels of all transcripts. To analyze the differential expression StringTie 25 was used to perform expression level for mRNAs by calculating FPKM (FPKM=[total_ exon_fragments/mapped_reads(millions) *exon_length(kB)]). The differentially expressed mRNAs were selected with log2 (fold change) >1 or log2 (fold change)