Ecotoxicogenomics: interlaboratory comparability of microarray data
Ontology highlight
ABSTRACT:
ORGANISM(S): Eohaustorius estuarius
SUBMITTER: Alvine Mehinto
PROVIDER: E-GEOD-64213 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise. First dataset: RNA extracts (Ex) derived from whole bodies of 8 controls and 8 cyfluthrin-exposed amphipods were prepared by one single lab and analyzed (i.e., labeling, microarray hybridization and feature extraction) by all 6 laboratories. Second dataset: Each of the 6 labs received 8 control and 8 cyfluthrin-exposed whole body samples (WB) for RNA extraction and microarrays. Lab C was unable to analyze the WB samples and only submitted data for the Ex samples.
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