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Parallel deep transcriptome and proteome analysis of zebrafish larvae.

Palmblad M, Henkel CV, Dirks RP, Meijer AH, Deelder AM, Spaink HP - BMC Res Notes (2013)

Bottom Line: We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes.Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions.By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Proteomics and Metabolomics, Leiden University Medical Center, Zone L04-Q, P,O, Box 9600, 2300 RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl.

ABSTRACT

Background: Sensitivity and throughput of transcriptomic and proteomic technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNA-seq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and for any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in embryos of zebrafish, which is an upcoming model in disease studies.

Results: We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes. Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed.

Conclusions: By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies.

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Related in: MedlinePlus

Analysis flowchart. Schematic overview of the analysis pipeline, and numbers of genes/proteins detected by each technology (top). With each technology, raw data are mapped to a suitable reference. In case of LC-MS/MS, the raw data is compared to known Uniprot/TrEMBL proteins. With RNA-seq, the reads are aligned to the Zv9 genome. Microarray probes were annotated with a known Ensembl gene or transcript. To make comparisons across technologies possible, the protein and array annotations were translated to Ensembl genes. The Venn diagram (bottom) shows the overlaps amongst the genes detected by the different technologies. Green: Illumina RNA-seq, Blue, Agilent microarray and Red, proteomic data from LC-MS/MS.
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Figure 1: Analysis flowchart. Schematic overview of the analysis pipeline, and numbers of genes/proteins detected by each technology (top). With each technology, raw data are mapped to a suitable reference. In case of LC-MS/MS, the raw data is compared to known Uniprot/TrEMBL proteins. With RNA-seq, the reads are aligned to the Zv9 genome. Microarray probes were annotated with a known Ensembl gene or transcript. To make comparisons across technologies possible, the protein and array annotations were translated to Ensembl genes. The Venn diagram (bottom) shows the overlaps amongst the genes detected by the different technologies. Green: Illumina RNA-seq, Blue, Agilent microarray and Red, proteomic data from LC-MS/MS.

Mentions: In Figure 1 we show a schematic overview of the analysis pipeline, and numbers of genes/proteins detected by each technology. With each technology, raw data are mapped to a suitable reference. In case of LC-MS/MS, the raw data correspond to 20,796 confidently identified spectra (FDR = 1%) mapped to 1,694 zebrafish proteins. With RNA-seq, 11.3 million out of 15.9 million reads aligned to 25,255 distinct loci on the Zv9 genome. However, for 1,647 genes, the alignments did not overlap with annotated exons (many of these are non-protein coding genes), leaving 23,608 genes with quantified expression. Finally, using a microarray, 69,061 probes of 175,974 were annotated with a known Ensembl gene or transcript. As multiple probes can assay expression for the same gene locus, the number of annotated genes for which an expression measure was obtained is reduced to 19,212. 119 of these are non-protein coding, leaving 19,093 genes for comparison with other technology.


Parallel deep transcriptome and proteome analysis of zebrafish larvae.

Palmblad M, Henkel CV, Dirks RP, Meijer AH, Deelder AM, Spaink HP - BMC Res Notes (2013)

Analysis flowchart. Schematic overview of the analysis pipeline, and numbers of genes/proteins detected by each technology (top). With each technology, raw data are mapped to a suitable reference. In case of LC-MS/MS, the raw data is compared to known Uniprot/TrEMBL proteins. With RNA-seq, the reads are aligned to the Zv9 genome. Microarray probes were annotated with a known Ensembl gene or transcript. To make comparisons across technologies possible, the protein and array annotations were translated to Ensembl genes. The Venn diagram (bottom) shows the overlaps amongst the genes detected by the different technologies. Green: Illumina RNA-seq, Blue, Agilent microarray and Red, proteomic data from LC-MS/MS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016144&req=5

Figure 1: Analysis flowchart. Schematic overview of the analysis pipeline, and numbers of genes/proteins detected by each technology (top). With each technology, raw data are mapped to a suitable reference. In case of LC-MS/MS, the raw data is compared to known Uniprot/TrEMBL proteins. With RNA-seq, the reads are aligned to the Zv9 genome. Microarray probes were annotated with a known Ensembl gene or transcript. To make comparisons across technologies possible, the protein and array annotations were translated to Ensembl genes. The Venn diagram (bottom) shows the overlaps amongst the genes detected by the different technologies. Green: Illumina RNA-seq, Blue, Agilent microarray and Red, proteomic data from LC-MS/MS.
Mentions: In Figure 1 we show a schematic overview of the analysis pipeline, and numbers of genes/proteins detected by each technology. With each technology, raw data are mapped to a suitable reference. In case of LC-MS/MS, the raw data correspond to 20,796 confidently identified spectra (FDR = 1%) mapped to 1,694 zebrafish proteins. With RNA-seq, 11.3 million out of 15.9 million reads aligned to 25,255 distinct loci on the Zv9 genome. However, for 1,647 genes, the alignments did not overlap with annotated exons (many of these are non-protein coding genes), leaving 23,608 genes with quantified expression. Finally, using a microarray, 69,061 probes of 175,974 were annotated with a known Ensembl gene or transcript. As multiple probes can assay expression for the same gene locus, the number of annotated genes for which an expression measure was obtained is reduced to 19,212. 119 of these are non-protein coding, leaving 19,093 genes for comparison with other technology.

Bottom Line: We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes.Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions.By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Proteomics and Metabolomics, Leiden University Medical Center, Zone L04-Q, P,O, Box 9600, 2300 RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl.

ABSTRACT

Background: Sensitivity and throughput of transcriptomic and proteomic technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNA-seq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and for any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in embryos of zebrafish, which is an upcoming model in disease studies.

Results: We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes. Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed.

Conclusions: By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies.

Show MeSH
Related in: MedlinePlus