Dataset Information

Zebrafish embryo gene and protein expression comparison

ABSTRACT: Sensitivity and throughput of transcriptomics and proteomomics technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNAseq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and on 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 zebrafish embryos, which is an upcoming model in disease studies. 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 15,927 genes. Gene expression was also found to correlate with the abundance of 1,017 quantified proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed. 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. 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. The transcriptomics data is deposited in NCBI GEO. Proteomics informatics: Spectra were identified by Mascot and validated by PeptideProphet and ProteinProphet in the Trans-Proteomic Pipeline (TPP). The main search parameters are: full tryptic specificity, precursor mass searched within -0.5 to +2.5 Da, product ions within [-0.5,0.5] Da and allowing for one missed cleavage. Carbamidomethylation was assumed as the only and fixed PTM.

OTHER RELATED OMICS DATASETS IN: PRJNA189535PRJNA189536PRJNA189203

INSTRUMENT(S): Bruker Daltonics HCT Series, instrument model

ORGANISM(S): Danio Rerio (zebrafish) (brachydanio Rerio)

TISSUE(S): Embryo, Early Embryonic Cell

SUBMITTER: Magnus Palmblad

LAB HEAD: Magnus Palmblad

PROVIDER: PXD000145 | Pride | 2016-06-09

REPOSITORIES: Pride

ACCESS DATA

Publications

Parallel deep transcriptome and proteome analysis of zebrafish larvae.

Palmblad Magnus M Henkel Christiaan V CV Dirks Ron P RP Meijer Annemarie H AH Deelder André M AM Spaink Herman P HP

BMC research notes 20131024

<h4>Background</h4>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 s ...[more]

PMID: 24156766

Similar Datasets

Project description:Background: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer (IBC) pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. Results: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive re-wiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, ER-positive IBC, and ER-negative IBC, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in IBC mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. Conclusions: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially-localized transcriptomic data. The analysis provides new biological insights into the re-wiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer. 36 flash-frozen human primary breast cancer samples were subjected to laser capture microdissection to separately isolate matched tumor epithelial and tumor-associated stromal components. RNA was isolated, subjected to 2 rounds of amplification, and hybridized on Agilent 4x44K microarrays along with a common reference (single round-amplified commercially obtained Universal Human Reference RNA) in a dyeswap design. For two samples of tumor-associated stroma, a second technical replicate was performed. Samples were labelled as ER-positive based on ESR1 gene expression levels in the tumor epithelium, using univariate Gaussian mixture model-based clustering via the mclust package in R.

Dataset Information

Zebrafish embryo gene and protein expression comparison

Publications

Parallel deep transcriptome and proteome analysis of zebrafish larvae.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure