Reliable reference genes for normalization of gene expression data in tea plants (Camellia sinensis) exposed to metal stresses

Reference genes, often referred to as housekeeping genes (HKGs), are frequently used to normalize gene expression data based on the assumption that they are expressed at a constant level in the cells. However, several studies have shown that there may be a large variability in the gene expression levels of HKGs in various cell types. In a previous study, employing human embryonic stem cells (hESCs) subjected to spontaneous differentiation, we observed that the expression of commonly used HKG varied to a degree that rendered them inappropriate to use as reference genes under those experimental settings. Here we present a substantially extended study of the HKG signature in human pluripotent stem cells (hPSC), including nine global gene expression datasets from both hESC and human induced pluripotent stem cells, obtained during directed differentiation toward endoderm-, mesoderm-, and ectoderm derivatives. Sets of stably expressed genes were compiled, and a handful of genes (e.g., EID2, ZNF324B, CAPN10, and RABEP2) were identified as generally applicable reference genes in hPSCs across all cell lines and experimental conditions. The stability in gene expression profiles was confirmed by reverse transcription quantitative PCR analysis. Taken together, the current results suggest that differentiating hPSCs have a distinct HKG signature, which in some aspects is different from somatic cell types, and underscore the necessity to validate the stability of reference genes under the actual experimental setup used. In addition, the novel putative HKGs identified in this study can preferentially be used for normalization of gene expression data obtained from differentiating hPSCs.

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The normalization of gene expression data in melanoma: Investigating the use of glyceraldehyde 3-phosphate dehydrogenase and 18S ribosomal RNA as internal reference genes for quantitative real-time PCR

Analytical Biochemistry ◽

10.1016/j.ab.2008.05.024 ◽

2008 ◽

Vol 380 (1) ◽

pp. 137-139 ◽

Cited By ~ 26

Author(s):

Orsolya Giricz ◽

Janelle L. Lauer-Fields ◽

Gregg B. Fields

Keyword(s):

Gene Expression ◽

Real Time ◽

Gene Expression Data ◽

Ribosomal Rna ◽

Real Time Pcr ◽

Reference Genes ◽

Expression Data ◽

Quantitative Real Time Pcr ◽

Internal Reference ◽

18S Ribosomal Rna

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Selection of Reference Genes for Normalization of Gene Expression Data in Blood of Machado-Joseph Disease/Spinocerebellar Ataxia Type 3 (MJD/SCA3) Subjects

Journal of Molecular Neuroscience ◽

10.1007/s12031-019-01374-0 ◽

2019 ◽

Vol 69 (3) ◽

pp. 450-455 ◽

Cited By ~ 1

Author(s):

Ana F. Ferreira ◽

Mafalda Raposo ◽

João Vasconcelos ◽

Maria do Carmo Costa ◽

Manuela Lima

Keyword(s):

Gene Expression ◽

Spinocerebellar Ataxia ◽

Gene Expression Data ◽

Reference Genes ◽

Spinocerebellar Ataxia Type ◽

Expression Data ◽

Spinocerebellar Ataxia Type 3 ◽

Machado Joseph Disease ◽

Type 3 ◽

Selection Of

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Validation of endogenous reference genes in Buglossoides arvensis for normalizing RT-qPCR-based gene expression data

SpringerPlus ◽

10.1186/s40064-015-0952-4 ◽

2015 ◽

Vol 4 (1) ◽

Cited By ~ 12

Author(s):

Vijay J Gadkar ◽

Martin Filion

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Reference Genes ◽

Expression Data ◽

Endogenous Reference ◽

Endogenous Reference Genes

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Selection of Reference Genes for Normalizing Quantitative Real-Time PCR Gene Expression Data with Multiple Variables in Coffea spp.

Plant Molecular Biology Reporter ◽

10.1007/s11105-011-0382-6 ◽

2011 ◽

Vol 30 (3) ◽

pp. 741-759 ◽

Cited By ~ 37

Author(s):

Luis F. Goulao ◽

Ana S. Fortunato ◽

José C. Ramalho

Keyword(s):

Gene Expression ◽

Real Time ◽

Gene Expression Data ◽

Real Time Pcr ◽

Reference Genes ◽

Expression Data ◽

Quantitative Real Time Pcr ◽

Multiple Variables ◽

Selection Of

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Selection of Reference Genes for Transcription Studies on Adventitious Root Induction in Olive (Olea Europaea L.) Microshoots Considering Co-expression and Average Transcriptional Stability

10.21203/rs.3.rs-60001/v1 ◽

2020 ◽

Author(s):

Carlos Noceda ◽

Augusto Peixe ◽

Birgit Arnholdt-Schmitt

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Reference Genes ◽

Ad Hoc ◽

Root Induction ◽

Expression Data ◽

Internal Reference ◽

Experimental Conditions ◽

Selection Of

Abstract BackgroungSelection of reference genes (RGs) for normalization of PCR-gene expression data includes two crucial steps: determination of the among-sample transcriptionally more stable genes and subsequent choosing of the most suitable genes as internal controls. Both steps can be carried-out through generally accepted strategies each having different strengths and weaknesses. The present study proposes to reinforce normalization of gene expression data by integrating and adding analytical revision at critical steps of those accepted procedures. Especially crucial is to counterbalance a higher representative number of RGs with a correspondent increase in their average transcriptional instability or a generalised co-expression trend among the samples. This methodological study used in vitro olive adventitious rooting as an experimental system, since the underlying morphogenetic process -wich is common to diverse species- is still not completely understood.ResultsFirstly, RG candidates were ranked according to transcriptional stability following a simple statistical method that reduces biasing effects of concomitant, systematic biological variations associated to experimental conditions, such as the variations caused by gene co-regulation. Those types of systematic co-variation are unconsidered by several popular ad hoc informatics programmes. To select the adequate genes among those already ranked, an algorithm of one of the ad hoc informatics programmes (GeNorm) was adapted to allow partial automatization of RG selection for any strategy of transcriptional-gene stability ordering. In order to delve into the resulting possible RG sets suitability for inter-assay comparisons and technical-error compensation, separate statistics were formulated. The achieved results were compared with those obtained by standard stability ranking methods. Finally, a double evaluation was performed to accurately contrast two choice RG sets. The whole strategy was applied to a panel considering several independent factors, but the suitability of the obtained putative RG sets was tested for cases restricted to fewer variables. H2B, OUB and ACT are valid for normalization in transcriptional studies on olive microshoot rooting when comparing treatments, time points and assays.ConclusionsThe set of genes identified as internal reference is now available for wider expression studies on any target gene in similar biological systems. The overall methodology aims to constitute a guide for general application.

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Standardization of Gene Expression Quantification by Absolute Real-Time qRT-PCR System Using a Single Standard for Marker and Reference Genes

Biomarker Insights ◽

10.4137/bmi.s5596 ◽

2010 ◽

Vol 5 ◽

pp. BMI.S5596 ◽

Cited By ~ 7

Author(s):

Yi-Hong Zhou ◽

Vinay R. Raj ◽

Eric Siegel ◽

Liping Yu

Keyword(s):

Gene Expression ◽

Real Time ◽

Gene Expression Data ◽

Reference Genes ◽

Pcr Analysis ◽

Expression Data ◽

Internal Reference ◽

Expression Ratio ◽

Qrt Pcr ◽

Single Standard

In the last decade, genome-wide gene expression data has been collected from a large number of cancer specimens. In many studies utilizing either microarray-based or knowledge-based gene expression profiling, both the validation of candidate genes and the identification and inclusion of biomarkers in prognosis-modeling has employed real-time quantitative PCR on reverse transcribed mRNA (qRT-PCR) because of its inherent sensitivity and quantitative nature. In qRT-PCR data analysis, an internal reference gene is used to normalize the variation in input sample quantity. The relative quantification method used in current real-time qRT-PCR analysis fails to ensure data comparability pivotal in identification of prognostic biomarkers. By employing an absolute qRT-PCR system that uses a single standard for marker and reference genes (SSMR) to achieve absolute quantification, we showed that the normalized gene expression data is comparable and independent of variations in the quantities of sample as well as the standard used for generating standard curves. We compared two sets of normalized gene expression data with same histological diagnosis of brain tumor from two labs using relative and absolute real-time qRT-PCR. Base-10 logarithms of the gene expression ratio relative to ACTB were evaluated for statistical equivalence between tumors processed by two different labs. The results showed an approximate comparability for normalized gene expression quantified using a SSMR-based qRT-PCR. Incomparable results were seen for the gene expression data using relative real-time qRT-PCR, due to inequality in molar concentration of two standards for marker and reference genes. Overall results show that SSMR-based real-time qRT-PCR ensures comparability of gene expression data much needed in establishment of prognostic/predictive models for cancer patients–-a process that requires large sample sizes by combining independent sets of data.

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