Stability of reference genes for normalization of reverse transcription quantitative real-time PCR (RT-qPCR) data in bovine blastocysts produced by IVF, ICSI and SCNT

Zygote ◽  
2013 ◽  
Vol 22 (4) ◽  
pp. 505-512 ◽  
Author(s):  
Charlotte Luchsinger ◽  
María Elena Arias ◽  
Tamara Vargas ◽  
Marcos Paredes ◽  
Raúl Sánchez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Real Time ◽  
Gene Expression Data ◽  
Internal Control ◽  
Reverse Transcription ◽  
Reference Genes ◽  
Quantitative Estimation ◽  
Expression Patterns ◽  
Preimplantation Embryos ◽  
Expression Data

SummaryReverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a sensitive and accurate tool for quantitative estimation of gene transcription levels in preimplantation embryos. To control for possible experimental variations, gene expression data must be normalized using internal control genes commonly known as reference genes. However, the stability of reference genes can vary depending on the state of development and/or experimental conditions; hence the assessment of their stability is essential before initiating a gene expression analysis. In the present study, we used RT-qPCR to measure the transcript levels of 10 commonly used reference genes and analyzed their expression stability in bovine blastocysts produced by in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI) and somatic cell nuclear transfer (SCNT). Using the geNorm program, we found the best combination of genes to normalize gene expression data in bovine embryos at the blastocyst stage produced by IVF (HMBS, SF3A1, and HPRT1), ICSI (H2A, HMBS, and GAPDH), SCNT (ACTB, SF3A1, and SDHA) and/or between blastocysts produced by these methods (GAPDH, HMBS and EEF1A2). We also demonstrated that not only the culture conditions may affect the expression patterns in bovine blastocysts but also the choice of embryo production method may have an important effect.

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

2010 ◽  
Vol 5 ◽  
pp. BMI.S5596 ◽  
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.

Identification of stable reference genes in differentiating human pluripotent stem cells

2015 ◽  
Vol 47 (6) ◽  
pp. 232-239 ◽  
Author(s):  
Gustav Holmgren ◽  
Nidal Ghosheh ◽  
Xianmin Zeng ◽  
Yalda Bogestål ◽  
Peter Sartipy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Data ◽  
Pluripotent Stem Cells ◽  
Reference Genes ◽  
Cell Types ◽  
Human Pluripotent Stem Cells ◽  
Expression Data ◽  
Large Variability ◽  
The Stability

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.

scholarly journals Selection and validation of reference genes for gene expression studies in Klebsiella pneumoniae using Reverse Transcription Quantitative real-time PCR

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ana Érika Inácio Gomes ◽  
Leonardo Prado Stuchi ◽  
Nathália Maria Gonçalves Siqueira ◽  
João Batista Henrique ◽  
Renato Vicentini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Klebsiella Pneumoniae ◽  
Real Time ◽  
Reverse Transcription ◽  
Real Time Pcr ◽  
Reference Genes ◽  
Quantitative Real Time Pcr ◽  
Expression Studies ◽  
Gene Expression Studies

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

Sign in / Sign up

Export Citation Format

Share Document