Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of mouse preimplantation development: unfertilized eggs, fertilized egg, 2-cell embryos, 4-cell embryos, 8-cell embryos, morulae, blastocysts

ABSTRACT: Feature Extraction 5.1.1 software (Agilent Technologies) performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). Details are described in the publication (Hamatani T. et al., Developmental Cell. Published online Dec. 18, 2003).

ORGANISM(S): Mus musculus

SUBMITTER: Mark Carter

PROVIDER: E-MEXP-74 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Dynamics of global gene expression changes during mouse preimplantation development.

Hamatani Toshio T Carter Mark G MG Sharov Alexei A AA Ko Minoru S H MS

Developmental cell 20040101 1

Understanding preimplantation development is important both for basic reproductive biology and for practical applications including regenerative medicine and livestock breeding. Global expression profiles revealed and characterized the distinctive patterns of maternal RNA degradation and zygotic gene activation, including two major transient waves of de novo transcription. The first wave corresponds to zygotic genome activation (ZGA); the second wave, named mid-preimplantation gene activation (M ...[more]

PMID: 14723852

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Project description:Microarray data analysis Intensity of 21,939 gene features per array was extracted from scanned microarray images using Feature Extraction 5.1.1 software (Agilent Technologies), which performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). The input file for NIA microarray analysis tool is available at http://lgsun.grc.nia.nih.gov/microarray/data.html

Project description:In mammals, totipotent pre-implantation embryos are formed by fusion of highly differentiated oocytes and spermatozoa. Acquisition of totipotency concurs with remodeling of chromatin states of parental genomes (âepigenetic reprogrammingâ), changes in maternally contributed transcriptome and proteome, and zygotic genome activation. Genomes of mature germ cells are more proficient in supporting embryonic development than those of somatic cells. It is currently unknown whether transgenerational inheritance of chromatin states present in mature gametes underlies the efficacy of early embryonic development after natural conception. Here, we show that Ring1 and Rnf2, two core components of the Polycomb Repressive Complex 1 (PRC1), serve redundant gene regulatory functions during oogenesis that are required to support embryonic development beyond the two-cell stage. Numerous developmental regulatory genes that are established Polycomb targets in various somatic cell types are de-repressed in Ring1/Rnf2 double mutant (dm) fully grown germinal vesicle (GV) oocytes. Translation of tested aberrant maternal transcripts is, however, delayed until after fertilization. Exchange of maternal pro-nuclei between control and Ring1/Rnf2 maternally dm early zygotes demonstrates an essential role for Ring1 and Rnf2 during oogenesis in defining cytoplasmic and nuclear maternal contributions that are both essential for proper initiation of embryonic development. A large number of genes up-regulated in Ring1/Rnf2 dm GV oocytes harbor PRC2-mediated histone H3 lysine 27 trimethylation (H3K27me3) in spermatozoa and in embryonic stem cells (ESCs), and are repressed during normal oogenesis and early embryogenesis. These data strongly support the model that Polycomb acts in the female and male germline to silence differentiation inducing genes and to program chromatin states, thereby sustaining developmental potential across generations. Expression profiling of late 2-cell embryos was performed with the following genotypes: maternal Ring1-Rnf+ (control), maternal Ring1-Rnf2- (maternal Ring1/Rnf2 double mutant). These embryos were obtained by crossing Ring1-/-Rnf2F/F control females or Ring1-/-Rnf2F/F Zp3-cre expreimental females, respectively, to Ring1+/+Rnf2F/F control males. To distinguish between maternal transcripts present in the 2-cell embryo and newly (embryonicaly) transcribed transcripts, embryos from both genotypes were either not treated (expression profiling therefore shows all maternal and embryonic transcripts) or alpha-amanitin treated (alpha-amanitin inhibits de novo transcription, therefore expression profiling of treated embryos will only show maternal transcripts). 11 samples were analyzed: 3 biological replicates (except alpha-amanitin treated maternal Ring1/Rnf2 double mutant, where only 2 replicates were analyzed) of each genotype and treatment group were analyzed. Each sample contains 40 late 2-cell embryos.

Dataset Information

Transcription profiling of mouse preimplantation development: unfertilized eggs, fertilized egg, 2-cell embryos, 4-cell embryos, 8-cell embryos, morulae, blastocysts

Publications

Dynamics of global gene expression changes during mouse preimplantation development.

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