Dataset Information

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.

ABSTRACT: qPCR is still the gold standard for gene expression quantification. However, its accuracy is highly dependent on the normalization procedure. The conventional method involves using the geometric mean of multiple study-specific reference genes (RGs) expression for cross-sample normalization. While research on selecting stably expressed RGs is extensive, scant literature exists regarding the optimal approach for aggregating multiple RGs into a unified RG. In this paper, we introduce a family of scale-invariant functions as an alternative to the geometric mean aggregation. Our candidate method (weighted geometric mean minimizing standard deviation) demonstrated significantly better results compared to other proposed methods. We provide theoretical and experimental support for this finding using real data from solid tumors and liquid biopsies. Moreover, the closed form and regression-based solution enable efficient computation and straightforward adoption on various platforms. All the proposed methods have been implemented within an easy-to-use R package with graphics processing unit (GPU) acceleration.

SUBMITTER: Salimi A

PROVIDER: S-EPMC10565776 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.

Salimi Adel A Rahmani Saeid S Sharifi-Zarchi Ali A

iScience 20230920 10

qPCR is still the gold standard for gene expression quantification. However, its accuracy is highly dependent on the normalization procedure. The conventional method involves using the geometric mean of multiple study-specific reference genes (RGs) expression for cross-sample normalization. While research on selecting stably expressed RGs is extensive, scant literature exists regarding the optimal approach for aggregating multiple RGs into a unified RG. In this paper, we introduce a family of sc ...[more]

PMID: 37829204

Similar Datasets

Project description:BackgroundRT-qPCR is a common tool for quantification of gene expression, but its accuracy is dependent on the choice and stability (steady state expression levels) of the reference gene/s used for normalization. To date, in the bone field, there have been few studies to determine the most stable reference genes and, usually, RT-qPCR data is normalised to non-validated reference genes, most commonly GAPDH, ACTB and 18 S rRNA. Here we draw attention to the potential deleterious impact of using classical reference genes to normalise expression data for bone studies without prior validation of their stability.ResultsUsing the geNorm and Normfinder programs, panels of mouse and human genes were assessed for their stability under three different experimental conditions: 1) disease progression of Crouzon syndrome (craniosynostosis) in a mouse model, 2) proliferative culture of cranial suture cells isolated from craniosynostosis patients and 3) osteogenesis of a mouse bone marrow stromal cell line. We demonstrate that classical reference genes are not always the most 'stable' genes and that gene 'stability' is highly dependent on experimental conditions. Selected stable genes, individually or in combination, were then used to normalise osteocalcin and alkaline phosphatase gene expression data during cranial suture fusion in the craniosynostosis mouse model and strategies compared. Strikingly, the expression trends of alkaline phosphatase and osteocalcin varied significantly when normalised to the least stable, the most stable or the three most stable genes.ConclusionTo minimise errors in evaluating gene expression levels, analysis of a reference panel and subsequent normalization to several stable genes is strongly recommended over normalization to a single gene. In particular, we conclude that use of single, non-validated "housekeeping" genes such as GAPDH, ACTB and 18 S rRNA, currently a widespread practice by researchers in the bone field, is likely to produce data of questionable reliability when changes are 2 fold or less, and such data should be interpreted with due caution.

Project description:BackgroundAnalysis of gene expression at the mRNA level, using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR), mandatorily requires reference genes (RGs) as internal controls. However, increasing evidences have shown that RG expression may vary considerably under experimental conditions. We sought for an appropriate panel of RGs to be used in the 3T3-L1 cell line model during their terminal differentiation into adipocytes. To this end, the expression levels of a panel of seven widely used RG mRNAs were measured by qRT-PCR. The 7 RGs evaluated were ß-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hypoxanthine phosphoribosyl-transferase I (HPRT), ATP synthase H+ transporting mitochondrial F1 complex beta subunit (ATP-5b), tyrosine 3-monooxygenase/tryptophan 5- monooxygenase activation protein, zeta polypeptide (Ywhaz), Non-POU-domain containing octamer binding protein (NoNo), and large ribosomal protein L13a (RPL).Methodology/principal findingsUsing three Excel applications, GeNorm, NormFinder and BestKeeper, we observed that the number and the stability of potential RGs vary significantly during differentiation of 3T3-L1 cells into adipocytes. mRNA expression analyses using qRT-PCR revealed that during the entire differentiation program, only NoNo expression is relatively stable. Moreover, the RG sets that were acceptably stable were different depending on the phase of the overall differentiation process (i.e. mitotic clonal expansion versus the terminal differentiation phase). RPL, ACTB, and Ywhaz, are suitable for terminal differentiation, whereas ATP-5b and HPRT, are suitable during mitotic clonal expansion.ConclusionOur results demonstrate that special attention must be given to the choice of suitable RGs during the various well defined phases of adipogenesis to ensure accurate data analysis and that the use of several RGs is absolutely required. Consequently, our data show for the first time, that during mitotic clonal expansion, the most suitable RGs are ATP-5b, NoNo and HPRT, while during terminal differentiation the most suitable RGs are, NoNo, RPL, ACTB and Ywhaz.

Dataset Information

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.

Publications

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets