Parallel deep transcriptome and proteome analysis of zebrafish larvae
- Authors
- Palmblad, M., Henkel, C.V., Dirks, R.P., Meijer, A.H., Deelder, A.M., and Spaink, H.P.
- ID
- ZDB-PUB-131121-16
- Date
- 2013
- Source
- BMC research notes 6(1): 428 (Journal)
- Registered Authors
- Meijer, Annemarie H., Spaink, Herman P.
- Keywords
- none
- Datasets
- GEO:GSE44226, GEO:GSE44351, GEO:GSE44352
- MeSH Terms
-
- Animals
- Benchmarking
- Embryo, Nonmammalian
- Female
- Gene Expression Profiling
- Gene Expression Regulation, Developmental
- High-Throughput Nucleotide Sequencing/standards*
- Histones/genetics
- Oligonucleotide Array Sequence Analysis/standards*
- Proteome*
- Proteomics/instrumentation
- Proteomics/methods
- Ribosomal Proteins/genetics
- Transcriptome*
- Vitellogenins/genetics
- Zebrafish/embryology
- Zebrafish/genetics*
- Zebrafish Proteins/genetics*
- PubMed
- 24156766 Full text @ BMC Res. Notes
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.