Validating Reference Gene Expression Stability in Human Ovarian Follicles, Oocytes, Cumulus Cells, Ovarian Medulla, and Ovarian Cortex Tissue

Human ovarian cells are phenotypically very different and are often only available in limited amounts. Despite the fact that reference gene (RG) expression stability has been validated in oocytes and other ovarian cells from several animal species, the suitability of a single universal RG in the different human ovarian cells and tissues has not been determined. The present study aimed to validate the expression stability of five of the most used RGs in human oocytes, cumulus cells, preantral follicles, ovarian medulla, and ovarian cortex tissue. The selected genes were glyceraldehyde 3-phosphate dehydrogenase (GAPDH), beta-2-microglobulin (B2M), large ribosomal protein P0 (RPLP0), beta-actin (ACTB), and peptidylprolyl isomerase A (PPIA). Overall, the stability of all RGs differed among ovarian cell types and tissues. NormFinder identified ACTB as the best RG for oocytes and cumulus cells, and B2M for medulla tissue and isolated follicles. The combination of two RGs only marginally increased the stability, indicating that using a single validated RG would be sufficient when the available testing material is limited. For the ovarian cortex, depending on culture conditions, GAPDH or ACTB were found to be the most stable genes. Our results highlight the importance of assessing RGs for each cell type or tissue when performing RT-qPCR analysis.

Selecting suitable reference genes for qPCR normalization: a comprehensive analysis in MCF-7 breast cancer cell line

Background MCF-7 breast cancer cell line is undoubtedly amongst the most extensively studied patient-derived research models, providing pivotal results that have over the decades translated to constantly improving patient care. Many research groups, have previously identified suitable reference genes for qPCR normalization in MCF-7 cell line. However, over the course of identification of suitable reference genes, a comparative analysis comprising these genes together in a single study has not been reported. Furthermore, the expression dynamics of these reference genes within sub-clones cultured over multiple passages (p) has attracted limited attention from research groups. Therefore, we investigated the expression dynamics of 12 previously suggested reference genes within two sub-clones (culture A1 and A2) cultured identically over multiple passages. Additionally, the effect of nutrient stress on reference gene expression was examined to postulate an evidence-based recommendation of the least variable reference genes that could be employed in future gene expression studies. Results The analysis revealed the presence of differential reference gene expression within the sub-clones of MCF-7. In culture A1, GAPDH-CCSER2 were identified as the least variable reference genes while for culture A2, GAPDH-RNA28S were identified. However, upon validation using genes of interest, both these pairs were found to be unsuitable control pairs. Normalization of AURKA and KRT19 with triplet pair GAPDH-CCSER2-PCBP1 yielded successful results. The triplet also proved its capability to handle variations arising from nutrient stress. Conclusions The variance in expression behavior amongst sub-clones highlights the potential need for exercising caution while selecting reference genes for MCF-7. GAPDH-CCSER2-PCBP1 triplet offers a reliable alternative to otherwise traditionally used internal controls for optimizing intra- and inter-assay gene expression differences. Furthermore, we suggest avoiding the use of ACTB, GAPDH and PGK1 as single internal controls.

Selecting Suitable Reference Genes for qPCR Normalization: A Comprehensive Analysis in MCF-7 Breast Cancer Cell Line

BackgroundMCF-7 breast cancer cell line is undoubtedly amongst the most extensively studied patient-derived research models, providing pivotal results that have over the decades translated to constantly improving patient care. Many research groups, have previously identified suitable reference genes for qPCR normalization in MCF-7 cell line. However, over the course of identification of suitable reference genes, a comparative analysis comprising these genes together in a single study have not been reported. Furthermore, the expression dynamics of these reference genes within sub-clones cultured over multiple passages (p) has attracted limited attention from research groups. Therefore, we investigated the expression dynamics of 12 previously suggested reference genes within two sub-clones (culture A1 and A2) cultured identically over multiple passages. Additionally, the effect of nutrient stress on reference gene expression was examined to devise an evidence-based recommendation of the least variable reference genes that could be employed in future gene expression studies.ResultsThe analysis revealed the presence of differential reference gene expression within the sub-clones of MCF-7. In culture A1, GAPDH-CCSER2 were identified as the least variable reference gene pair while for culture A2, GAPDH-RNA28S was identified. However, upon validation using genes of interest, both these pairs were found to be unsuitable control pairs. Normalization of AURKA and KRT19 with triplet pair GAPDH-PCBP1-CCSER2 yielded successful results. The triplet also proved its capability to handle variations arising from nutrient stress.ConclusionsThe variance in expression behavior amongst sub-clones highlights the potential need for exercising caution while selecting reference genes for MCF-7. GAPDH-PCBP1-CCSER2 triplet offers a reliable alternative to otherwise traditionally used internal controls for optimizing intra- and inter-assay gene expression differences. Furthermore, we suggest avoiding the use of ACTB, GAPDH and PGK1 as single internal controls.

The Influence of Melatonin on the Daily 24-h Rhythm of Putative Reference Gene Expression in White Adipose Tissues

In adipose tissue, the expression of hundreds of genes exhibits circadian oscillation, which may or may not be affected by circulating melatonin levels. Using control and pinealectomized rats, we investigated the daily expression profile of Actb, Hprt-1, B2m, and Rpl37a, genes that are commonly used as reference genes for reverse transcription quantitative polymerase chain reaction (RT-qPCR), in epididymal (EP), retroperitoneal (RP), and subcutaneous (SC) adipose tissues. In control rats, Actb expression presented a daily oscillation in all adipose tissues investigated, Hprt-1 showed 24-h fluctuations in only RP and SC depots, B2m was stable over 24 h for EP and RP but oscillated over 24 h in SC adipose tissue, and Rpl37a presented a daily oscillation in only RP fat. In the absence of melatonin, the rhythmicity of Actb in all adipose depots was abolished, the daily rhythmicity of Hprt-1 and B2m was disrupted in SC fat, the peak expression of Rpl37a and Hprt-1 was delayed, and the amplitude of Rpl37a was reduced in RP adipose tissue. Collectively, our results demonstrate that the expression of putative reference genes displays a daily rhythm influenced by melatonin levels in a manner specific to the adipose depot. Thus, the proper standardization and daily profile expression of reference genes should be performed carefully in temporal studies using RT-qPCR analysis.

Selection of reference genes for quantitative PCR: identifying reference genes for airway epithelial cells exposed to Pseudomonas aeruginosa

Most quantitative PCR (qPCR) experiments report differential expression relative to the expression of one or more reference genes. Therefore, when experimental conditions alter reference gene expression, qPCR results may be compromised. Little is known about the magnitude of this problem in practice. We found that reference gene responses are common and hard to predict and that their stability should be demonstrated in each experiment. Our reanalysis of 15 airway epithelia microarray data sets retrieved from the National Center for Biotechnology Information (NCBI) identified no common reference gene that was reliable in all 15 studies. Reanalysis of published RNA sequencing (RNA-seq) data in which human bronchial epithelial cells (HBEC) were exposed to Pseudomonas aeruginosa revealed that minor experimental details, including bacterial strain, may alter reference gene responses. Direct measurement of 32 TaqMan reference genes in primary cultures of HBEC exposed to P. aeruginosa (strain PA14) demonstrated that choosing an unstable reference gene could make it impossible to observe statistically significant changes in IL8 gene expression. We found that reference gene instability is a general phenomenon and not limited to studies of airway epithelial cells. In a diverse compendium of 986 human microarray experiments retrieved from the NCBI, reference genes were differentially expressed in 42% of studies. Experimentally induced changes in reference gene expression ranged from 21% to 212%. These results highlight the importance of identifying adequate reference genes for each experimental system and documenting their response to treatment in each experiment. This will enhance experimental rigor and reproducibility in qPCR studies.

Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells

Background To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. Methods Our deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. Results In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. Conclusions We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.