Evaluation of reference genes for gene expression studies in mouse and N2a cell ischemic stroke models using quantitative real-time PCR

Kang, Yingbo; Wu, Zhuomin; D, Cai; Lu, Binger

doi:10.1186/s12868-018-0403-6

Cited by 39 publications

(23 citation statements)

References 25 publications

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“…Therefore, analogue to the bone/callus, we suggest an individual evaluation of the time point analysed in primarily traumatised organs to identify the most suitable housekeeping. At both time points Hprt, which has previously been recommended for isolated TBI and ischemic 22,41 , showed a high expression stability in the hypothalamus. Hprt, a hypoxanthine-phosphoribosyltransferase (Table 1), is essential for the synthesis of purine nucleotides 42 .…”

Section: Discussionmentioning

confidence: 82%

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Validation of reference genes for expression analysis in a murine trauma model combining traumatic brain injury and femoral fracture

Otto

Köhli

Appelt

et al. 2020

Sci Rep

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Systemic and local posttraumatic responses are often monitored on mRNA expression level using quantitative real-time PCR (qRT-PCR), which requires normalisation to adjust for confounding sources of variability. Normalisation requests reference (housekeeping) genes stable throughout time and divergent experimental conditions in the tissue of interest, which are crucial for a reliable and reproducible gene expression analysis. Although previous animal studies analysed reference genes following isolated trauma, this multiple-trauma gene expression analysis provides a notable study analysing reference genes in primarily affected (i.e. bone/fracture callus and hypothalamus) and secondarily affected organs (i.e. white adipose tissue, liver, muscle and spleen), following experimental long bone fracture and traumatic brain injury. We considered tissue-specific and commonly used top-ranked reference candidates from different functional groups that were evaluated applying the established expression stability analysis tools NormFinder, GeNorm, BestKeeper and RefFinder. In conclusion, reference gene expression in primary organs is highly time point as well as tissue-specific, and therefore requires careful evaluation for qRT-PCR analysis. Furthermore, the general application of Ppia, particularly in combination with a second reference gene, is strongly recommended for the analysis of systemic effects in the case of indirect trauma affecting secondary organs through local and systemic pathophysiological responses.

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Section: Discussionmentioning

confidence: 82%

“…in order to allow an accurate analysis. Although first attempts have been made to recommend reference genes for qRT-PCR analysis in isolated trauma models [20][21][22] , there are currently no multiple-injury studies determining suitable reference genes.…”

mentioning

confidence: 99%

Validation of reference genes for expression analysis in a murine trauma model combining traumatic brain injury and femoral fracture

Otto

Köhli

Appelt

et al. 2020

Sci Rep

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“…Our collection of samples is substantial (N=3-5 per time point) and we have used a broad panel of 15 candidate reference genes ( ACTB, 18S, SDHA, GAPDH, HTATSF1, CDC40, RPL13A, CSNK2A2, AP3D1, HPRT1, CYC1, EIF4A, UBC, B2M and PAK1IP1 - Table 1). This panel includes candidates widely used historically ( 18S , GAPDH , ACTB ), those that we have shown be strong references in mouse skeletal muscle ( CSNK2A2 , AP3D1 , RPL13A ) [25, 37], and also shown by others to be viable references in rodent brains ( UBC , HPRT1 , RPL13A, 18S ) [46, 47]. To maximise the power of our study, we have assessed reference gene suitability using all four algorithms described above (geNorm, deltaCt, BestKeeper and Normfinder).…”

Section: Introductionmentioning

confidence: 99%

Identification of qPCR reference genes suitable for normalising gene expression in the developing mouse embryo

Hildyard

Wells

Piercy

2020

Preprint

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Mammalian embryogenesis is an intricate, tightly orchestrated process. Progression from zygote through somitogenesis and on to organogenesis and maturity involves many interacting cell types and multiple differentiating cell lineages. Quantitative PCR analysis of gene expression in the developing embryo is a valuable tool for deciphering these interactions and tracing lineages, but normalisation of qPCR data to stably expressed reference genes is essential. Patterns of gene expression change globally and dramatically as embryonic development proceeds, rendering identification of appropriate reference genes challenging at both the whole embryo- and individual tissue-level. We have investigated expression stability in mouse embryos from mid to late gestation (E11.5-E18.5), both at the whole-embryo level, and within more restricted tissue domains (head, developing forelimb), using 15 candidate reference genes (ACTB, 18S, SDHA, GAPDH, HTATSF1, CDC40, RPL13A, CSNK2A2, AP3D1, HPRT1, CYC1, EIF4A, UBC, B2M and PAK1IP1), and four complementary algorithms (geNorm, Normfinder, Bestkeeper and deltaCt). Unexpectedly, all methods suggest that many genes within our candidate panel are acceptable references, and despite disagreement over highest-scoring candidates, AP3D1, RPL14A and PAK1IP1 are the strongest performing genes overall. Conversely, HPRT1 and B2M are consistently poor choices: these genes show strong developmental regulation. We further show that use of AP3D1, RPL13A and PAK1IP1 can reveal subtle patterns of developmental expression even in genes ostensibly ranked as acceptable (CDC40, HTATSF1), and thus these three represent universally suitable reference genes for the mouse embryo.

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“…However, to address this issue, researchers average the stability ranks across different methods and calculate an overall "geometric mean rank" (8,9). Some studies also attribute a weighted rank (10)(11)(12)(13)(14). This approach is rather questionable, as it does not consider the strengths and weaknesses of each method for a given experimental setting.…”

Section: Introductionmentioning

confidence: 99%

Optimal use of statistical methods to validate reference gene stability in longitudinal studies

Sundaram

Sampathkumar

Massaad

et al. 2019

Preprint

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Multiple statistical approaches have been proposed to validate reference genes in qPCR assays. However, conflicting results from these statistical methods pose a major hurdle in the choice of the best reference genes. Recent studies have proposed the use of a minimum of three different methods but there is no consensus on how to interpret conflicting results. Researchers resort to averaging the ranks or attributing a weighted rank to candidate genes. However, we report here that the suitability of these validation methods can be influenced by the experimental setting. Therefore, averaging the ranks can lead to suboptimal assessment of stable reference genes if the method used is not suitable for analysis. As the respective approaches of these statistical methods are different, a clear understanding of the fundamental assumptions and the parameters that influence the reference gene stability calculation is necessary. In this study, the stability of 10 candidate reference genes (Actb, Gapdh, Tbp, Sdha, Pgk1, Ppia, Rpl13a, Hsp60, Mrpl10, Rps26) was assessed using four common statistical approaches (GeNorm, NormFinder, Coefficient of Variation or CV analysis and Pairwise ΔCt method) in a longitudinal experimental setting. We used the development of the cerebellum and the spinal cord of mice as a model to assess the suitability of these statistical methods for reference gene validation. GeNorm and the Pairwise ΔCt were found to be ill suited due to a fundamental assumption in their stability calculations. Highly correlated genes were given better stability ranks despite significant overall variation. NormFinder fares better but the presence of highly variable genes influences the ranking of all genes because of the algorithm's construct. CV analysis estimates overall variation, but it fails to consider variation across groups. We thus highlight the assumptions and potential pit-falls of each method using our longitudinal data. Based on our results, we have devised a workflow combining NormFinder, CV analysis along with visual representation of mRNA fold changes and one-way ANOVA for validating reference genes in longitudinal studies. This workflow proves to be more robust than any of these methods used individually.

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Evaluation of reference genes for gene expression studies in mouse and N2a cell ischemic stroke models using quantitative real-time PCR

Cited by 39 publications

References 25 publications

Validation of reference genes for expression analysis in a murine trauma model combining traumatic brain injury and femoral fracture

Validation of reference genes for expression analysis in a murine trauma model combining traumatic brain injury and femoral fracture

Identification of qPCR reference genes suitable for normalising gene expression in the developing mouse embryo

Optimal use of statistical methods to validate reference gene stability in longitudinal studies

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