Gene expression analysis and DNA methylation profiling in WT-AG-haESCs, DKO-AG-haESCs and round spermatids
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE60076 | GEO | 2015/06/29
SECONDARY ACCESSION(S): PRJNA257418
REPOSITORIES: GEO
Similar Datasets
Project description:We generated RRBS data to analyse the DNA methylation profiling among WT-AG-haESCs, DKO-AG-haESCs and round spermatids, we found deletion of H19 and Gtl2 DMRs do not change the methylation patterns in AG-haESCs base on all detected CpG sites.
2015-06-29 | GSE60075 | GEO
Project description:We generated RRBS data to analyse the DNA methylation profiling among WT-AG-haESCs, DKO-AG-haESCs and round spermatids, we found deletion of H19 and Gtl2 DMRs do not change the methylation patterns in AG-haESCs base on all detected CpG sites. We used round spermatids as control and analysed the DNA methylation profiles of all the cell lines were by RRBS.
2015-06-29 | E-GEOD-60075 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-06-29 | E-GEOD-60076 | biostudies-arrayexpress
Project description:RNA seq result shows that WT-AG-haESCs and DKO-AG-haESCs samples are clustered together using hierarchical cluster both in the all expression genes and imprinting genes. This suggests that DKO of DMRs of H19 and Gtl2 do not change the overall gene expression patterns in AG-haESCs.
2015-06-29 | GSE60072 | GEO
Project description:RNA seq result shows that WT-AG-haESCs and DKO-AG-haESCs samples are clustered together using hierarchical cluster both in the all expression genes and imprinting genes. This suggests that DKO of DMRs of H19 and Gtl2 do not change the overall gene expression patterns in AG-haESCs. We used round spermatids as control. Using RNA-seq, profile of all the expression genes and imprinting genes beteween different samples were analysed.
2015-06-29 | E-GEOD-60072 | biostudies-arrayexpress
Project description:Spermatogenesis, an efficient and complex system in male germline development, requires a series of elaborately regulated genetic events in which diploid spermatogonia differentiate into haploid spermatozoa. N6-methyladenosine (m6A) is an important epigenetic RNA modification that occurs during spermatogenesis. ALKBH5 is an m6A eraser and knocking out Alkbh5 increases the level of total m6A methylation and causes male infertility. In this study, comprehensive analyses of MeRIP-seq and RNA-seq data revealed differences between wild-type (WT) and Alkbh5 knockout (KO) mice. In pachytene spermatocytes (PA), 8,151 m6A peaks associated with 9,959 genes were tested from WT and 10,856 m6A peaks associated with 10,016 genes were tested from KO mice. In the round spermatids (RO), 10,271 m6A peaks associated with 10,109 genes were tested from WT mice and 9,559 m6A peaks associated with 10,138 genes were tested from KO mice. The peaks were mainly concentrated in the coding region and the stop codon of the GGAC motif. In addition, enrichment analysis showed significant m6A methylation genes in related pathways in spermatogenesis. Furthermore, we conducted joint analyses of the m6A methylome and RNA transcription, suggesting an m6A regulatory mechanism of gene expression. Finally, seven differentially expressed mRNAs from RNA-seq data in both PA and RO were verified using qPCR. Overall, our study provides new information on m6A modification changes between WT and KO in PA and RO, and may provide new insights into the molecular mechanisms of m6A modification in germ cell development and spermatogenesis.
| S-EPMC8968445 | biostudies-literature
Project description:[This corrects the article DOI: 10.3389/fgene.2022.832677.].
| S-EPMC9421247 | biostudies-literature
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived WT and dKO round spermatids transcriptome profiling (RNA-seq) Methods: Adult WT and dKO round spermatids mRNA profiles mice were generated by deep sequencing, in dulplicate. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and dKO round spermatids, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions. Adult wild type (WT) and dKO mouse round spermatids were generated by deep sequencing, in dulplicate, using Illumina GAIIx.
2014-07-03 | E-GEOD-59017 | biostudies-arrayexpress
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived WT and dKO round spermatids transcriptome profiling (RNA-seq) Methods: Adult WT and dKO round spermatids mRNA profiles mice were generated by deep sequencing, in dulplicate. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and dKO round spermatids, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2014-07-03 | GSE59017 | GEO
Project description:Spermiogenesis is the final phase of spermatogenesis during which post-meiotic haploid round spermatids (rSpt) differentiate into elongated spermatozoa and includes several critical cell-specific processes like DNA condensation, formation of the acrosome, and production of the flagellum. Disturbances in this process will lead to complications in sperm development and subsequently cause infertility. As such, studying spermiogenesis has clinical relevance in investigating the etiology of male infertility and will improve our scientific understanding of male germ cell formation. Here, we were able to purify round spermatid and elongated spermatid fractions from a single cryopreserved human testicular tissues sample with an efficiency of 85.4% ± 4.9% and 97.6% ± 0.6%, respectively. We confirmed the cell types by morphology and immunohistochemistry for histone H4 and PNA protein expression. The purity was measured by manual counting of histone H4 positive (round) and negative (elongated) spermatids in both sorted 1 N cell fractions. This method can be applied to both human and rodent studies. Especially in studies with limited access to testicular tissue, this method provides a reliable means to simultaneously isolate these cell types with high purity. Our method allows for further investigation of germ cell development and the process of spermiogenesis in particular, as well as provides a tool to study the etiology of male infertility, including morphological and biochemical assessment of round and elongating spermatids from subfertile men. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
| S-EPMC6590320 | biostudies-literature