Next Generation Sequencing Facilitates Quantitative Analysis of the lncRNA Transcriptome in 6 Distinct Germ Cell Types During Mouse Spermatogenesis

Purpose: The goals of this study are to obtain the lncRNA landscape in 6 distinct germ cell types during mouse spermatogenesis. Methods: RNA-seq data of lncRNAs from 6 distinct cell types were generated by deep sequencing using Illumina HiSeq 2500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks. qRT-PCR validation was performed using SYBR Green assays. Results: Using the rRNA minus strategy, we generated a total of 1100.65 Gb paired and sequence reads with a length of 150 bp. Next, we trimmed the adaptor sequence and filtered the sequence data for low-quality reads at a high stringency with an average Phred quality score less than 20, which resulted in a total of 745.67 Gb high-quality sequence reads. After filtering the reads, almost all sequence reads had a Phred quality score higher than 20, and the percentage of Ns in the reads was nearly zero. By using unique whole-genome alignment (mouse genome (mm10)), a total of 14587 mRNA genes and 6921 lncRNA genes were expressed as fragments per kilobase of exon per million reads mapped (FPKM)>0.1 in at least one sample. In addition, to obtain fingerprint lncRNAs during spermatogenesis, we first identified 2327 differentially expressed lncRNAs between every two adjacent stages. Furthermore, we identified 437 fingerprint lncRNAs that were expressed at a FPKM > 10 of its highly expressed stage. Conclusions: Our RNA-seq dataset comprehensively dissects the dynamic expression mode of lncRNAs during mouse spermatogenesis and generated 28 relatively robust potential lncRNA candidates in spermatogenesis. RNA-seq data of lncRNAs from 6 distinct cell types were generated by deep sequencing, in duplicate, using Illumina HiSeq 2500.