Quantitative Analysis of Neurog2 Heterozygote, Mutant and and Mash1KI Retinal Transcriptomes by RNA Sequencing
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ABSTRACT:
PROVIDER: PRJNA437827 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6824970.fastq.gz | Fastqsanger.gz | |||
| SRR6824971.fastq.gz | Fastqsanger.gz | |||
| SRR6824972.fastq.gz | Fastqsanger.gz | |||
| SRR6824973.fastq.gz | Fastqsanger.gz | |||
| SRR6824974.fastq.gz | Fastqsanger.gz |
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Project description:To generate an unbiased view of changes to the retinal gene network in Neurog2 retinal mutants as well as understanding the effects of Mash1 ectopic expression in a Neurog2 retinal mutant, we generated and compared the E12.5 transcriptomes from Neurog2 heterozygote, Neurog2 mutant and Neurog2 mutant;Mash1 knock-in mice. A pair of E12.5 retinas from each biologic replicate underwent FACS sorting to isolate GFP+ cells. GFP+ cells were used to produce libraries for high throughput sequencing (n = 3 biologic replicates/genotype). Reads were aligned with BWA and Bowtie programs to the mm10 genome. Aligned reads were then analyzed for differentially expressed transcripts using the CuffDiff program in the Galaxy online bioinformatics package (www.usegalaxy.org).
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Project description:To generate an unbiased view of changes to the retinal gene network in Neurog2 retinal mutants, we generated and compared the P2 transcriptomes from control, heterozygote and mutant mice. A pair of P2 retinas from each biologic replicate were used to produce libraries for high throughput sequencing (n = 5 biologic replicates/genotype). Reads were aligned with BWA and Bowtie programs to the mm10 genome. Aligned reads were then analyzed for differentially expressed transcripts using the CuffDiff program in the Galaxy online bioinformatics package (www.usegalaxy.org).
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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 retinal transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis. Methods: Retinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl-/-) mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. 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 Nrl−/− retina, 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. Retinal mRNA profiles of 21-day old wild type (WT) and Nrl-/- mice were generated by deep sequencing, in triplicate, using Illumina GAIIx.
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Project description:The proneural NEUROG2 is essential for neuronal commitment, cell cycle exit and neuronal differentiation. Characterizing genes networks regulated downstream of NEUROG2 is therefore of prime importance. To identify NEUROG2 early response genes, we combined gain of function in the neural tube with a global detection of modified transcripts using microarrays. We included in our study a mutant form of NEUROG2 (NEUROG2AQ) that cannot bind DNA and cannot trigger neurogenesis. Using this approach, we identified 942 genes modified at the onset of NEUROG2 activation. The global analysis of functions regulated by NEUROG2 allowed unmasking its rapid impact on cell cycle control. We found that NEUROG2 specifically represses a subset of cyclins acting at the G1 and S phases of the cell cycle, thereby impeding S phase re-entry. This repression occurs before modification of p27kip1, indicating that the decision to leave the cell cycle precedes the activation of this Cyclin-dependant Kinase Inhibitor. Moreover, NEUROG2 down-regulates only one of the D-type cyclins, cyclinD1, and maintaining cyclinD1 blocks the ability of the proneural to trigger cell cycle exit, without altering its capacity to trigger neuronal differentiation. The fact that NEUROG2 represses a subset but not all cell cycle regulators indicates that cell cycle exit is not an indirect consequence of neuronal differentiation but is precisely controlled by NEUROG2. Altogether our findings indicate that NEUROG2, by specifically repressing G1 and S cyclins, allows committed neuronal precursors to perform their last mitosis but blocks their re-entry in the cell cycle, thus favouring cell cycle exit. Stage HH10-11 embryos (11 to 15 somites) were electroporated with a control vector (pGIG-GFP), a NEUROG2-expressing vector (pCIGNEUROG2-GFP), or a NEUROG2AQ-expressing vector (pCIGNEUROG2AQ-GFP). For each biological replicate, neural tubes from 20 embryos were pooled for GFP+ cells collection. GFP+ cells were collected 6h later using FACS sorting (Epics Altra HSS cell sorter, Toulouse Rio platform) and processed for RNA probe preparation and hybridization on Affymetrix microarrays. For each experimental condition, four biological replicates were processed.
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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 retinal transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis. Methods: Retinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl−/−) mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. 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 Nrl−/− retina, 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.
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