Stable reference genes selected from RNAseq data do not offer any significant advantage over conventional reference genes for normalizing qPCR assays
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE178924 | GEO | 2021/12/01
REPOSITORIES: GEO
Similar Datasets
Project description:Arabidopsis thaliana has been used regularly as a model plant in gene expression studies on transcriptional reprogramming upon pathogen infection, such as that by Pseudomonas syringae pv. tomato DC3000 (Pst DC3000), or when subjected to stress hormone treatments including jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA). RT-qPCR has been extensively employed to quantitate these gene expression changes. However, the accuracy of the quantitation is largely dependent on the stability of the expressions of reference genes used for normalization. Recently, RNA-seq has been widely used to mine stably expressed genes for use as references in RT-qPCR. However, the amplification step in RNA-seq creates an intrinsic bias against those genes with relatively low expression levels, and therefore does not provide an accurate quantification of all expressed genes. In this study, we employed mass spectrometry-based label-free quantification (LFQ) in proteomic analyses to identify those proteins with abundances unaffected by Pst DC3000 infection. We verified, using RT-qPCR, that the levels of their corresponding mRNAs were also unaffected by Pst DC3000 infection. In addition, using RT-qPCR, we verified that the mRNAs were stably expressed upon stress hormone treatments including JA, SA, and ABA. Results indicated that the candidate genes identified here had stable expressions upon these stresses and are suitable to be used as reference genes for RT-qPCR. Among the 18 candidate reference genes reported in this study, many of them had greater expression stability than the commonly used reference genes, such as ACT7, in previous studies. Here, besides proposing more appropriate reference genes for Arabidopsis expression studies, we also demonstrated the capacity of mass spectrometry-based LFQ to quantify protein abundance and the possibility to extend protein expression studies to the transcript level.
2022-09-05 | PXD035677 | Pride
Project description:Background/Objective: Quantitative real-time PCR (RT-qPCR) is widely used in miRNA expression studies on cancer. To compensate for the analytical variability produced by the multiple steps of the method, relative quantification of the measured miRNAs is required, which is based on normalization to endogenous reference genes. A literature search in PubMed revealed that no study has been performed so far on reference miRNAs for normalization of miRNA expression in urothelial carcinoma. The aim of this study was to identify suitable reference genes for miRNA expression studies by RT-qPCR in urothelial carcinoma. Methods: Candidate reference miRNAs were selected from 24 papillary urothelial carcinoma and normal bladder tissue samples by miRNA microarrays. The usefulness of these candidate reference miRNAs together with the commonly for normalization purposes used small nuclear RNAs RNU6B, RNU48, and Z30 were thereafter validated by RT-qPCR in 58 tissue samples and analyzed by the algorithms geNorm, NormFinder, and BestKeeper. Principal Findings: Based on the miRNA microarray data, a total of 16 miRNAs were identified as putative reference genes. After validation by RT-qPCR, miR-101, miR-125a-5p, miR-148b, miR-151-3p, miR-151-5p, miR-181a, miR-181b miR-29c, miR-324-3p, miR-424, miR-874, RNU6B, RNU48, and Z30 were used for geNorm, NormFinder, and BestKeeper analyses that gave different combinations of recommended reference genes for normalization. Conclusions: The present study provided the first systematic analysis for identifying suitable reference miRNAs for miRNA expression studies of urothelial carcinoma by RT-qPCR. Different combinations of reference genes resulted in reliable expression data for both strongly and less strongly altered miRNAs. Notably, RNU6B, which is the most frequently used reference gene for miRNA studies, gave inaccurate normalization. The combination of four (miR-125a-5p, miR-148b, miR-151-3p, and miR-151-5p) or three (miR-148b, miR-874, miR-181b) miRNA reference genes is recommended for normalization.
2013-02-26 | GSE36121 | GEO
Project description:Genes that are constitutively expressed across multiple environmental stimuli are crucial to quantifying differentially expressed genes, particularly when employing quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) assays. However, the identification of these potential reference genes in non-model organisms is challenging and is often guided by expression patterns in distantly related organisms. Here, transcriptome datasets from the diatom Thalassiosira pseudonana grown under replete, phosphorus-limited, iron-limited, and phosphorus and iron co-limited nutrient regimes were analyzed through literature-based searches for homologous reference genes, k-means clustering, and Analysis of Sequence Counts (ASC) to identify putative reference genes. A total of 9759 genes were identified and screened for stable expression. Literature-based searches surveyed 18 generally accepted reference genes, revealing 101 homologs in T. pseudonana with variable expression and a wide range of mean tags per million. K-means analysis parsed the whole transcriptome into 15 clusters. The two most stable clusters contained 709 genes but still had distinct patterns in expression. ASC analyses identified 179 genes that were stably expressed (posterior probability < 0.1 for 1.25 fold change). Genes known to have a stable expression pattern across the test treatments, like actin, were identified in this pool of 179 candidate genes. ASC can be employed on data without biological replicates and was more robust than the k-means approach in isolating genes with stable expression. The intersection of the genes identified through ASC with commonly used reference genes from the literature suggests that actin and ubiquitin ligase may be useful reference genes for T. pseudonana and potentially other diatoms. With the wealth of transcriptome sequence data becoming available, ASC can be easily applied to transcriptome datasets from other phytoplankton to identify reference genes.
2013-01-01 | GSE40509 | GEO
Project description:The present work aimed to identify reference genes for RT-qPCR studies of hypoxia in cervical cancer. From 422 candidate reference genes selected from the literature, we used Illumina array-based expression profiles to identify 182 genes not affected by hypoxia treatment in eight cervical cancer cell lines or correlated with the hypoxia-associated dynamic contrast-enhanced magnetic resonance imaging parameter ABrix in 42 patients. Among these genes, we selected nine candidates (CHCHD1, GNB2L1, IPO8, LASP1, RPL27A, RPS12, SOD1, SRSF9, TMBIM6) that were not associated with tumor volume, stage, lymph node involvement or disease progression in array data of 150 patients, for further testing by RT-qPCR. geNorm and NormFinder analyses of RT-qPCR data of 74 patients identified CHCHD1, SRSF9 and TMBIM6 as the most suitable set of reference genes, with stable expression both overall and across patient subgroups with different hypoxia status (ABrix) and clinical parameters. The suitability of the three candidates as reference genes were validated in studies of the hypoxia-induced genes DDIT3, ERO1A, and STC2. After normalizing with CHCHD1, SRSF9 and TMBIM6, the RT-qPCR data of these genes showed a significant correlation with Illumina expression (P<0.001, n=74) and ABrix (P<0.05, n=32), and the STC2 data were associated with clinical outcome, in accordance with the Illumina data. Thus, CHCHD1, SRSF9 and TMBIM6 seem to be suitable reference genes for studying hypoxia-related gene expression in cervical cancer samples by RT-qPCR. STC2 might be a useful prognostic hypoxia biomarker in cervical cancer that warrants further investigation.
2016-03-29 | GSE75034 | GEO
Project description:Stable reference genes selected from RNAseq data do not offer any significant advantage over conventional reference genes for normalizing qPCR assays
| PRJNA741505 | ENA
Project description:Background/Objective: Quantitative real-time PCR (RT-qPCR) is widely used in miRNA expression studies on cancer. To compensate for the analytical variability produced by the multiple steps of the method, relative quantification of the measured miRNAs is required, which is based on normalization to endogenous reference genes. A literature search in PubMed revealed that no study has been performed so far on reference miRNAs for normalization of miRNA expression in urothelial carcinoma. The aim of this study was to identify suitable reference genes for miRNA expression studies by RT-qPCR in urothelial carcinoma. Methods: Candidate reference miRNAs were selected from 24 papillary urothelial carcinoma and normal bladder tissue samples by miRNA microarrays. The usefulness of these candidate reference miRNAs together with the commonly for normalization purposes used small nuclear RNAs RNU6B, RNU48, and Z30 were thereafter validated by RT-qPCR in 58 tissue samples and analyzed by the algorithms geNorm, NormFinder, and BestKeeper. Principal Findings: Based on the miRNA microarray data, a total of 16 miRNAs were identified as putative reference genes. After validation by RT-qPCR, miR-101, miR-125a-5p, miR-148b, miR-151-3p, miR-151-5p, miR-181a, miR-181b miR-29c, miR-324-3p, miR-424, miR-874, RNU6B, RNU48, and Z30 were used for geNorm, NormFinder, and BestKeeper analyses that gave different combinations of recommended reference genes for normalization. Conclusions: The present study provided the first systematic analysis for identifying suitable reference miRNAs for miRNA expression studies of urothelial carcinoma by RT-qPCR. Different combinations of reference genes resulted in reliable expression data for both strongly and less strongly altered miRNAs. Notably, RNU6B, which is the most frequently used reference gene for miRNA studies, gave inaccurate normalization. The combination of four (miR-125a-5p, miR-148b, miR-151-3p, and miR-151-5p) or three (miR-148b, miR-874, miR-181b) miRNA reference genes is recommended for normalization. Candidate reference miRNAs (for RT-qPCR analysis) were selected from 24 papillary urothelial carcinoma and normal bladder tissue samples by miRNA microarrays.
2013-02-26 | E-GEOD-36121 | biostudies-arrayexpress
Project description:Real-time quantitative PCR (RT–qPCR) is the favoured method for gene expression analysis in molecular biology due to its sensitivity, specificity, cost-effectiveness, and reproducibility. To obtain the accuracy and reliability of RT-qPCR, the use of reliable reference genes is inevitable. There were many reports about the physiological response of giant reed (Arundo donax L.) to abiotic stresses. However, there is little use in the validation of reference genes under different treatments. It still belongs to the blank that the research about selecting reference genes under salt and alkali. In this study, the expression stability of twenty-three candidate reference genes in leaves and roots were assessed under salt, drought, and alkali stresses using geNorm, NormFinder, BestKeeper, and Delta Ct algorithms. Our results showed that no one gene had an invariant expression under different conditions. For example, under drought stress, UPL3, UBC2, and APT1 were better reference genes in leaves, RPL5 and FPS2 were better in roots. Under alkali stress, GAPDH, APT1, and RPS5 were better reference genes in leaves; UPL3, ACT2, and SAMDC2 were better in roots. In addition, the expression of MSD1 was used to further confirm the validated reference genes under salt, drought, and alkali stresses. It was proved that the use of inappropriate reference genes in giant reed significantly altered the relative expression of target genes and even reversed the results. Consequently, our results provided guidelines for reference gene selection under salt, drought, and alkali stresses and a foundation for more accurate and widespread use of RT-qPCR in the giant reed.
2021-12-15 | GSE190890 | GEO
Project description:The interaction of Leishmania with BALB/c mice induces dramatic changes in transcriptome patterns in the parasite, but also in the target organs (spleen, liver…) due to its response against infection. Real-time quantitative PCR (qPCR) is an interesting approach to analyze these changes and understand the immunological pathways that lead to protection or progression of disease. However, qPCR results need to be normalized against one or more reference genes (RG) to correct for non-specific experimental variation. The development of technical platforms for high-throughput qPCR analysis, and powerful software for analysis of qPCR data, have acknowledged the problem that some reference genes widely used due to their known or suspected "housekeeping" roles, should be avoided due to high expression variability across different tissues or experimental conditions. In this paper we evaluated the stability of 112 genes using three different algorithms: geNorm, NormFinder and RefFinder in spleen samples from BALB/c mice under different experimental conditions (control and Leishmania infantum-infected mice). Despite minor discrepancies in the stability ranking shown by the three methods, most genes show very similar performance as RG (either good or poor) across this massive data set. Our results show that some of the genes traditionally used as RG in this model (i.e. B2m, Polr2a and Tbp) are clearly outperformed by others. In particular, the combination of Il2rg + Itgb2 was identified among the best scoring candidate RG for every group of mice and every algorithm used in this experimental model. Finally, we have demonstrated that using "traditional" vs rationally-selected RG for normalization of gene expression data may lead to loss of statistical significance of gene expression changes when using large-scale platforms, and therefore misinterpretation of results. Taken together, our results highlight the need for a comprehensive, high-throughput search for the most stable reference genes in each particular experimental model
2016-10-05 | GSE80709 | GEO
Project description:The human embryonic stem cells (hESCs) are a unique model system for investigating the mechanisms of human development due to their ability to replicate indefinitely while retaining the capacity to differentiate into a host of functionally distinct cell types. In addition, these cells could be potentially used as therapeutic agents in regenerative medicine. Differentiation of hESCs involves selective activation or silencing of genes, a process controlled in part by the epigenetic state of the cell. In order to gain a better understanding of the epigenetic mechanisms regulating differentiation of hESCs, and produce general reference epigenome maps of the human cells, we propose to establish an Epigenome Center in San Diego. Our center will be focused on both undifferentiated hESC and four hESC-derived early embryonic cell lineages including extraembryonic endoderm, trophoblast, mesendoderm (a common precursor to mesodermal and endodermal lineages), and mesenchymal cells (a specific mesoderm derivative). We have developed and validated high throughput technologies for mapping the state of DNA methylation and chromatin modifications throughout the genome, and will use these methods to generate high-resolution maps of the reference epigenomes. Specifically, we will grow and differentiate hESCs into multiple lineages, and map DNA methylation sites using a newly developed technology that combines bisulfite conversion and whole genome shotgun sequencing. We will also determine the histone modification status in the genome by performing both ChlP-chip and ChlP-Seq analysis. We will develop advanced statistical and algorithmic solutions to facilitate high-throughput sequencing data analysis, and establish an informatics pipeline for collecting, storage, and distribution of epigenome maps. Finally, we will perform integrated data analysis to identify new epigenetic patterns in the genome that could provide insights in mechanisms of epigenetic regulation. **************** For data usage terms and conditions, please refer to: http://www.drugabuse.gov/funding/funding-opportunities/nih-common-fund/epigenomics-data-access-policies ****************
2009-08-03 | GSE16256 | GEO