Expression of long non-coding RNAs during spermatogenesis
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE100964 | GEO | 2017/09/04
SECONDARY ACCESSION(S): PRJNA393538
REPOSITORIES: GEO
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2018-05-17 | GSE101342 | GEO
Project description: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.
2020-11-16 | GSE145130 | GEO
Project description:Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and diseasee. LncRNAs exhibit cell type- and tissue-specific expression, but little is known about the expression and function of lncRNAs in the developing human brain. Here, we deeply profiled lncRNAs from polyadenylated and total RNA obtained from human neocortex at different stages of development and integrated this resource to analyze the transcriptomes of single cells. While lncRNAs were generally detected at low levels in whole tissues, single cell transcriptomics revealed that many lncRNAs are abundantly expressed in individual cells and are cell type-specific. Furthermore, we used CRISRPi to show that LOC646329, a lncRNA enriched in radial glia but detected at low abundance in tissues, regulates cell proliferation. The discrete and abundant expression of lncRNAs among individual cells has important implications for both their biological function and utility for distinguishing neural cell types. 16 Bulk Tissue Samples from GW13-23; 226 Single Cells from GW19.5-23.5 ------------------ bulk_tpm.polya.txt: bulk RNA-seq expression; using polyA full reference scell_ncounts.genes.thresh.txt: single cell RNA-seq expression; using polyA stringent reference; includes 50 GW16, GW21, GW21p3 cells previously analyzed (Pollen et. al. 2014) polya_RNA_stringent_ref.gtf: bulk RNA-seq polyA stringent transcriptome reference polya_RNA_full_ref.gtf: bulk RNA-seq polyA full transcriptome reference total_RNA_stringent_ref.gtf: bulk RNA-seq total stringent transcriptome reference total_RNA_full_ref.gtf: bulk RNA-seq total full transcriptome reference GW13_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal
2016-03-28 | E-GEOD-71315 | biostudies-arrayexpress
Project description:Increasingly, studies have shown that mature spermatozoa contain many transcripts including mRNAs and miRNAs. However, the expression profile of long noncoding RNAs (lncRNAs) in mammalian sperm has not been systematically investigated. Here, we used highly purified RNA to investigate lncRNA expression profiles in mouse mature sperm by stranded-specific RNA-seq. We identified 20907 known and 4088 novel lncRNAs transcripts, and the existence of intact lncRNAs was confirmed by RT-PCR and fluorescence in situ hybridization on two representative lncRNAs. Compared with testes, 2873 upregulated and 6629 downregulated lncRNAs and 1237 upregulated and 2513 downregulated mRNAs were identified in sperm. Based on the “Cis and Trans” RNA-RNA interaction principle, we found 1793 targeted coding genes of differently expressed lncRNAs. In terms of GO analysis, differentially expressed lncRNAs targeted genes mainly related to nucleic acid metabolic, protein modification, chromatin and histone modification, sperm function, and spermatogenesis. In contrast, differentially expressed transcripts of mRNAs were highly enriched for nucleic acid metabolic, spermatogenesis, sperm function, and chromatin organization. Furthermore, KEGG pathway analysis showed that the differentially expressed lncRNAs were involved in mRNA surveillance and AMPK signaling pathways. Besides these two pathways, RNA transport splicesome was another pathway incorporated with the differentially expressed mRNA. In summary, this study provides a preliminary database valuable for identifying lncRNAs critical in the late stage of spermatogenesis or important for sperm function regulation.
2017-02-04 | GSE88732 | GEO
Project description:Mammalian spermatogenesis is a complex and highly orchestrated combination of processes in which male germline proliferation and differentiation result in the production of mature spermatozoa. If recent genome-wide studies have contributed to the in-depth analysis of the male germline protein-coding transcriptome, little effort has yet been devoted to the systematic identification of novel unannotated transcribed regions expressed during mammalian spermatogenesis. We report high-resolution expression profiling of male germ cells in the rat using Illumina next-generation sequencing technology and highly enriched testicular cell populations. Among 20,424 high-confidence transcripts reconstructed, we defined a stringent set of 1,419 long multi-exonic unannotated transcripts expressed in the testis, named TUTs. These were classified into seven groups with different expression patterns. The vast majority of TUTs share many of the characteristics of vertebrate long non-coding RNAs (lncRNAs). We also markedly reinforced the finding that TUTs and known lncRNAs accumulate during the meiotic and postmeiotic stages of spermatogenesis in mammals, and that X-linked meiotic TUTs do not escape the silencing effects of meiotic sex chromosome inactivation. Importantly, we discovered that TUTs and known lncRNAs with a peak expression during meiosis define a distinct class of non-coding transcripts that exhibit exons twice as long as those of other transcripts. Our study provides new insights in transcriptional profiling of the male germline and represents a high-quality resource of novel loci expressed during spermatogenesis that significantly contributes to the rat genome annotation. A graphical display of the data is conveniently accessible through the ReproGenomics Viewer (RGV) at http://rgv.genouest.org. Genome-wide expression profiling analysis using Illumina next-generation sequencing technology
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Project description:The dual functional lncRNAs have been intensively studied and identified to be involved in various fundamental cellular processes recently. It is essential to understand in which context when a dual functional lncRNA serves as a non-coding RNA or a template for coding peptide, particularly in some pathological conditions. However, apart from time consuming and cell type specific experiments, there is virtually no in-silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs based on their sequence, physicochemical and secondary structural features. Our data demonstrated that LncReader showed multiple advantage compared to various classical machine learning methods. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq were applied in four leukemia cell lines. Remarkably, LncReader achieved the best performance among all these datasets. Therefore, LncReader provides a sophisticated and practical tool that enables fast dual functional lncRNAs identification.
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