Integrated transcriptome analysis of mouse spermatogenesis

• Published in: BMC genomics Vol. 15; no. 1; p. 39
• Main Authors: Margolin, Gennady, Khil, Pavel P, Kim, Joongbaek, Bellani, Marina A, Camerini-Otero, R Daniel
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.01.2014; BioMed Central
• Subjects: Algorithms; Alternative Splicing; Analysis; Animals; Cluster Analysis; Gene expression; Gene Expression Profiling; Genetic aspects; Genetic diversity; Genomes; Genomics; Male; Meiosis; Messenger RNA; Mice; Open Reading Frames; RNA polymerase; RNA Polymerase II - metabolism; RNA sequencing; Rodents; Sequence Analysis, RNA; Sperm; Spermatogenesis; Spermatogenesis - genetics; Spermatozoa - metabolism; Studies; Transcriptional Elongation Factors - genetics; Transcriptional Elongation Factors - metabolism
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-15-39

Differentiation of primordial germ cells into mature spermatozoa proceeds through multiple stages, one of the most important of which is meiosis. Meiotic recombination is in turn a key part of meiosis. To achieve the highly specialized and diverse functions necessary for the successful completion of meiosis and the generation of spermatozoa thousands of genes are coordinately regulated through spermatogenesis. A complete and unbiased characterization of the transcriptome dynamics of spermatogenesis is, however, still lacking. In order to characterize gene expression during spermatogenesis we sequenced eight mRNA samples from testes of juvenile mice from 6 to 38 days post partum. Using gene expression clustering we defined over 1,000 novel meiotically-expressed genes. We also developed a computational de-convolution approach and used it to estimate cell type-specific gene expression in pre-meiotic, meiotic and post-meiotic cells. In addition, we detected 13,000 novel alternative splicing events around 40% of which preserve an open reading frame, and found experimental support for 159 computational gene predictions. A comparison of RNA polymerase II (Pol II) ChIP-Seq signals with RNA-Seq coverage shows that gene expression correlates well with Pol II signals, both at promoters and along the gene body. However, we observe numerous instances of non-canonical promoter usage, as well as intergenic Pol II peaks that potentially delineate unannotated promoters, enhancers or small RNA clusters. Here we provide a comprehensive analysis of gene expression throughout mouse meiosis and spermatogenesis. Importantly, we find over a thousand of novel meiotic genes and over 5,000 novel potentially coding isoforms. These data should be a valuable resource for future studies of meiosis and spermatogenesis in mammals.