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The validation of Short Interspersed Nuclear Elements (SINEs) as a RT-qPCR normalization strategy in a rodent model for temporal lobe epilepsy.

René A J Crans | Jana Janssens | Sofie Daelemans | Elise Wouters | Robrecht Raedt | Debby Van Dam | Peter P De Deyn | Kathleen Van Craenenbroeck | Christophe P Stove
PloS one | 2019

In gene expression studies via RT-qPCR many conclusions are inferred by using reference genes. However, it is generally known that also reference genes could be differentially expressed between various tissue types, experimental conditions and animal models. An increasing amount of studies have been performed to validate the stability of reference genes. In this study, two rodent-specific Short Interspersed Nuclear Elements (SINEs), which are located throughout the transcriptome, were validated and assessed against nine reference genes in a model of Temporal Lobe Epilepsy (TLE). Two different brain regions (i.e. hippocampus and cortex) and two different disease stages (i.e. acute phase and chronic phase) of the systemic kainic acid rat model for TLE were analyzed by performing expression analyses with the geNorm and NormFinder algorithms. Finally, we performed a rank aggregation analysis and validated the reference genes and the rodent-specific SINEs (i.e. B elements) individually via Gfap gene expression.

Pubmed ID: 30629669

Research resources used in this publication

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Associated grants

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This is a list of tools and resources that we have found mentioned in this publication.


GraphPad Prism (tool)

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Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

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RRID:SCR_003387

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geNORM (tool)

RRID:SCR_006763

Software to determine most stable reference (housekeeping) genes from set of tested candidate reference genes in given sample panel. From this, gene expression normalization factor can be calculated for each sample based geometric mean of user-defined number of reference genes.

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RRID:SCR_008539

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At Biogazelle, we are dedicated to accelerate the understanding of the transcriptome through excellence in science and technology. We are convinced that unravelling the coding and non-coding regions of the genome will help researchers, clinicians, plant breeders, and other actors of the scientific community to get better answers to their questions. It is our mission to develop data-analysis software tools, and to carefully select the best analytical platforms to offer customized RNA gene expression services. Our service lab activities support the life science market at every level of the entire process: from discovery to validation of biomarkers, from optimal experiment design to extensive analysis and interpretation of results, from PCR assay design to research-use-only kits. We are a young and dynamic company, eager to learn and to teach. Our continuous research, solid track record, and worldwide network keep Biogazelle at the forefront of new developments, making new instruments and cutting edge technologies accessible to all our customers. Each project is handled by experts and is fully customized to suit the objectives of the project and to accommodate specific requirements. The service portfolio ranges from sample preparation to data analysis using rigorous MIQE compliant procedures, high-throughput and validated laboratory methods, qbase+ data analysis software, and state-of-the-art analytical tools.

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