GSM4818684: C9_diff_day5_1_SPOND24; Homo sapiens; RNA-Seq
High quality RNA from H1 hESC and their differentiated derivatives was isolated with RNeasy Plus Mini kit (Qiagen, cat # 74134) using QIAshredder (Qiagen, cat # 79654) and on-column digestion of genomic DNA following manufacturer's recommendations. The kit captures RNA molecules higher than 200 nucleotide length effectively removing smaller RNAs such as 5.8S rRNA, 5S rRNA and tRNAs. For RNA-Seq, RNA was initially quantified by NanoDropND-1000 spectrophotometer (Thermo Scientific) and the concentration/quality of total RNA was further confirmed on Agilent Bioanalyzer2100. Only samples that had RNA integrity number (RIN) > 8 were used for RNA-Seq studies. RNA-seq library preparation and Illumina sequencing were performed by the Genome Technology Access Center (GTAC) at Washington University School of Medicine. Total RNA was enriched for polyA-tailed mRNA and mRNA fragmentation, first- and second-strand cDNA synthesis, and addition of indexed adapters was carried out as previously described [S. J. Matkovich, G. W. Dorn, 2nd, Deep sequencing of cardiac microRNA-mRNA interactomes in clinical and experimental cardiomyopathy. Methods Mol. Biol. 1299, 27-49 (2015)]. non-strand-specific, single-end RNA-SeqSingle-end, 50 nt reads were obtained on an Illumina HiSeq 3000 instrument and the sequencing reads were mapped to the human genome (hg19 UCSC) using HISAT2 [M. Pertea, D. Kim, G. M. Pertea, J. T. Leek, S. L. Salzberg, Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc 11, 1650-1667 (2016)]. The mean number of transcriptome-aligned reads per heart was 17.2 million.  Read quantitation on a per-gene basis was performed with HTSeq [S. Anders, P. T. Pyl, W. Huber, HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166-169 (2015)]. Detectable RNAs were assessed as those present at or above 1 read per million (18 reads) in at least 2 of 4 samples from any treatment group, resulting in 16,083 RNAs for downstream analyses.  Calculation of fold-change and FDR for differentially expressed RNAs used the R/Bioconductor packages limma and edgeR/voom [C. W. Law, M. Alhamdoosh, S. Su, G. K. Smyth, M. E. Ritchie, RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 5, 1408 (2016)]. Partek Genomics Suite v6.6 (Partek, St. Louis, MO) was used for inter-sample quality control by principal components analysis and for heat map construction. To visualize differentially expressed mRNAs, normalized read counts from edgeR were subjected to unsupervised hierarchical clustering with Euclidean distance and average linkage using Partek Genomics Suite v6.6. Unless otherwise specified, standardized heatmaps are shown in which map colors represent the extent of deviation from the row average. For clarity of presentation and to enable comparison with other studies, individual gene expression levels in column graphs are rendered as FPKM (Fragments Per Kilobase of exon per Million reads mapped to the transcriptome), calculated from Tophat-aligned read counts using Cufflinks 2.1.1 [C. Trapnell et al., Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7, 562-578 (2012)].