Systematic selection of suitable reference genes for quantitative real-time PCR normalization studies of gene expression in Lutjanus erythropterus.

Quantitative real-time PCR (qRT-PCR) has been widely employed for the study of gene expression in fish, and accurate normalization is crucial. In this study, we aimed to identify the most stably expressed genes in various tissues, different developmental stages, and within astaxanthin treatment groups in Lutjanus erythropterus. Twelve candidate genes (EEF1A, CYB5R3, DLD, IDH3A, MRPL17, MRPL43, NDUFS7, PABPC1, PAGR1, PFDN2, PSMC3, and RAB10) were examined via qRT-PCR. We employed geNorm and NormFinder to assess their stability. The results revealed that RAB10 and PFDN2 exhibited relatively stable expression patterns across different tissue and astaxanthin treatment groups, while NDUFS7 and MRPL17 proved to be the most reliable reference gene combinations across various developmental stages. The stability of these selected genes was further validated by assessing the expression of two target genes, CRADD and CAPNS1, across developmental stages, reinforcing the reliability of NDUFS7 as it closely aligned with transcriptome-wide expression patterns at these stages. The present results will help researchers to obtain more accurate results in future qRT-PCR analysis in L. erythropterus.

Evaluation and validation of suitable reference genes for quantitative real-time PCR analysis in lotus (Nelumbo nucifera Gaertn.).

The qRT-PCR technique has been regarded as an important tool for assessing gene expression diversity. Selection of appropriate reference genes is essential for validating deviation and obtaining reliable and accurate results. Lotus (Nelumbo nucifera Gaertn) is a common aquatic plant with important aesthetic, commercial, and cultural values. Twelve candidate genes, which are typically used as reference genes for qRT-PCR in other plants, were selected for this study. These candidate reference genes were cloned with, specific primers designed based on published sequences. In particular, the expression level of each gene was examined in different tissues and growth stages of Lotus. Notably, the expression stability of these candidate genes was assessed using the software programs geNorm and NormFinder. As a result, the most efficient reference genes for rootstock expansion were TBP and UBQ. In addition, TBP and EF-1α were the most efficient reference genes in various floral tissues, while ACT and GAPDH were the most stable genes at all developmental stages of the seed. CYP and GAPDH were the best reference genes at different stages of leaf development, but TUA was the least stable. Meanwhile, the gene expression profile of NnEXPA was analyzed to confirm the validity of the findings. It was concluded that, TBP and GAPDH were identified as the best reference genes. The results of this study may help researchers to select appropriate reference genes and thus obtain credible results for further quantitative RT-qPCR gene expression analyses in Lotus.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

A web-based integrative transcriptome analysis, RNAseqChef, uncovers the cell/tissue type-dependent action of sulforaphane.

RNA sequencing (RNA-seq) is a powerful technique for understanding cellular state and dynamics. However, comprehensive transcriptomic characterization of multiple RNA-seq datasets is laborious without bioinformatics training and skills. To remove the barriers to sequence data analysis in the research community, we have developed "RNAseqChef" (RNA-seq data controller highlighting expression features), a web-based platform of systematic transcriptome analysis that can automatically detect, integrate, and visualize differentially expressed genes and their biological functions. To validate its versatile performance, we examined the pharmacological action of sulforaphane (SFN), a natural isothiocyanate, on various types of cells and mouse tissues using multiple datasets in vitro and in vivo. Notably, SFN treatment upregulated the ATF6-mediated unfolded protein response in the liver and the NRF2-mediated antioxidant response in the skeletal muscle of diet-induced obese mice. In contrast, the commonly downregulated pathways included collagen synthesis and circadian rhythms in the tissues tested. On the server of RNAseqChef, we simply evaluated and visualized all analyzing data and discovered the NRF2-independent action of SFN. Collectively, RNAseqChef provides an easy-to-use open resource that identifies context-dependent transcriptomic features and standardizes data assessment.

Live-seq enables temporal transcriptomic recording of single cells.

Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell's ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell's trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.

Selection of reference genes for quantitative analysis of microRNA expression in three different types of cancer.

MicroRNAs (miRNAs) are promising biomarkers in cancer research. Quantitative PCR (qPCR), also known as real-time PCR, is the most frequently used technique for measuring miRNA expression levels. The use of this technique, however, requires that expression data be normalized against reference genes. The problem is that a universal internal control for quantitative analysis of miRNA expression by qPCR has yet to be known. The aim of this work was to find the miRNAs with stable expression in the thyroid gland, brain and bone marrow according to NanoString nCounter miRNA quantification data. As a results, the most stably expressed miRNAs were as follows: miR-361-3p, -151a-3p and -29b-3p in the thyroid gland; miR-15a-5p, -194-5p and -532-5p in the brain; miR-140-5p, -148b-3p and -362-5p in bone marrow; and miR-423-5p, -28-5p and -532-5p, no matter what tissue type. These miRNAs represent promising reference genes for miRNA quantification by qPCR.

Selection and Validation of Reliable Reference Genes for Gene Expression Studies in Different Genotypes and TRV-Infected Fruits of Peach (Prunus persica L. Batsch) during Ripening.

Real-time quantitative PCR (RT-qPCR) is a powerful tool to detect and quantify transcription abundance, and the stability of the reference gene determines its success. However, the most suitable reference gene for different genotypes and tobacco rattle virus (TRV) infected fruits was unclear in peach (Prunus persica L. Batsch). In this study, 10 reference genes were selected and gene expression was characterized by RT-qPCR across all samples, including different genotypes and TRV-infected fruits during ripening. Four statistical algorithms (geNorm, NormFinder, BestKeeper, and RefFinder) were used to calculate the stability of 10 reference genes. The geNorm analysis indicated that two suitable reference genes should be used for gene expression normalization. In general, the best combination of reference genes was CYP2 and Tua5 for TRV-infected fruits and CYP2 and Tub1 for different genotypes. In 18S, GADPH, and TEF2, there is an unacceptable variability of gene expression in all experimental conditions. Furthermore, to confirm the validity of the reference genes, the expression levels of PpACO1, PpEIN2, and PpPL were normalized at different fruit storage periods. In summary, our results provide guidelines for selecting reliable reference genes in different genotypes and TRV-infected fruits and lay the foundation for accurate evaluation of gene expression for RT-qPCR analysis in peach.

Comparative transcriptomic analysis reveals translationally relevant processes in mouse models of malaria.

Recent initiatives to improve translation of findings from animal models to human disease have focussed on reproducibility but quantifying the relevance of animal models remains a challenge. Here, we use comparative transcriptomics of blood to evaluate the systemic host response and its concordance between humans with different clinical manifestations of malaria and five commonly used mouse models. Plasmodium yoelii 17XL infection of mice most closely reproduces the profile of gene expression changes seen in the major human severe malaria syndromes, accompanied by high parasite biomass, severe anemia, hyperlactatemia, and cerebral microvascular pathology. However, there is also considerable discordance of changes in gene expression between the different host species and across all models, indicating that the relevance of biological mechanisms of interest in each model should be assessed before conducting experiments. These data will aid the selection of appropriate models for translational malaria research, and the approach is generalizable to other disease models.

scMRMA: single cell multiresolution marker-based annotation.

Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Screening and stability analysis of reference genes in fasting caecotrophy model in rabbits.

BACKGROUND: The selection and validation of stably expressed reference genes is key for accurately quantifying the mRNA abundance of genes under different treatments. In the rabbit model of fasting caecotrophy, reports about the selection of stable reference genes are not available. METHODS AND RESULTS: This study aims to screen suitable reference genes in different tissues (including uterus, cecum, and liver) of rabbits between control and fasting caecotrophy groups. RT-qPCR was used to analyze the expression levels of eight commonly used reference genes (including GAPDH, 18S rRNA, B2M, CYP, HPRT1, ß-actin, H2afz, Ywhaz), and RefFinder (including geNorm, NormFinder, and BestKeeper) was used to analyze the expression stability of these reference genes. Our results showed that the most stable reference genes were different in different tissues and treatments. In the control and fasting caecotrophy groups, CYP, GAPDH and HPRT1 were proven to be the top stable reference genes in the uterus, cecum, and liver tissues, respectively. GAPDH and Ywhaz were proven to be the top two stable reference genes among uterus, cecum, and liver in both control and fasting caecotrophy groups. CONCLUSIONS: Our results indicated that the combined analysis of three or more reference genes (GAPDH, HPRT1, and Ywhaz) are recommended to be used for RT-qPCR normalization in the rabbit model of fasting caecotrophy, and that GAPDH is a better choice than the other reference genes for normalizing the relative expression of target genes in different tissues of fasting caecotrophy rabbits.

Transcriptomic signatures of tumors undergoing T cell attack.

BACKGROUND: Studying tumor cell-T cell interactions in the tumor microenvironment (TME) can elucidate tumor immune escape mechanisms and help predict responses to cancer immunotherapy. METHODS: We selected 14 pairs of highly tumor-reactive tumor-infiltrating lymphocytes (TILs) and autologous short-term cultured cell lines, covering four distinct tumor types, and co-cultured TILs and tumors at sub-lethal ratios in vitro to mimic the interactions occurring in the TME. We extracted gene signatures associated with a tumor-directed T cell attack based on transcriptomic data of tumor cells. RESULTS: An autologous T cell attack induced pronounced transcriptomic changes in the attacked tumor cells, partially independent of IFN-γ signaling. Transcriptomic changes were mostly independent of the tumor histological type and allowed identifying common gene expression changes, including a shared gene set of 55 transcripts influenced by T cell recognition (Tumors undergoing T cell attack, or TuTack, focused gene set). TuTack scores, calculated from tumor biopsies, predicted the clinical outcome after anti-PD-1/anti-PD-L1 therapy in multiple tumor histologies. Notably, the TuTack scores did not correlate to the tumor mutational burden, indicating that these two biomarkers measure distinct biological phenomena. CONCLUSIONS: The TuTack scores measure the effects on tumor cells of an anti-tumor immune response and represent a comprehensive method to identify immunologically responsive tumors. Our findings suggest that TuTack may allow patient selection in immunotherapy clinical trials and warrant its application in multimodal biomarker strategies.

Selection and stability validation of reference gene candidates for transcriptional analysis in Rousettus aegyptiacus.

Bats are the only mammals capable of powered flight and their body temperature can reach up to 42 °C during flight. Additionally, bats display robust type I IFN interferon (IFN-I) responses and some species constitutively express IFN-α. Reference genes with stable expression under temperature oscillations and IFN-I release are therefore critical for normalization of quantitative reverse-transcription polymerase chain reaction (qRT-PCR) data in bats. The expression stability of reference genes in Rousettus aegyptiacus remains elusive, although this species is frequently used in the infection research. We selected ACTB, EEF1A1, GAPDH and PGK1 as candidate reference genes and evaluated their expression stability in various tissues and cells from this model bat species upon IFN-I treatment at 35 °C, 37 °C and 40 °C by qRT-PCR. We employed two statistical algorithms, BestKeeper and NormFinder, and found that EEF1A1 exhibited the highest expression stability under all tested conditions. ACTB and GAPDH displayed unstable expression upon temperature change and IFN-I treatment, respectively. By normalizing to EEF1A1, we uncovered that GAPDH expression was significantly induced by IFN-I in R. aegyptiacus. Our study identifies EEF1A1 as the most suitable reference gene for qRT-PCR studies upon temperature changes and IFN-I treatment and unveils the induction of GAPDH expression by IFN-I in R. aegyptiacus. These findings are pertinent to other bat species and may be relevant for non-volant mammals that show physiological fluctuations of core body temperature.

Standardizing workflows in imaging transcriptomics with the abagen toolbox.

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

Identification of suitable reference genes for expression profiling studies using qRT-PCR in an important insect pest, Maruca vitrata.

BACKGROUND: Maruca vitrata is one of the potential insect pests that cause devastating losses to legume cultivation worldwide. Gene functional studies facilitate dissecting the molecular mechanisms underlying the infection process and enable devising appropriate molecular strategies to control this insect pest. Expression profiling using quantitative real-time PCR (qRT-PCR) provides insights into the functional characterization of target genes; however, ideal reference genes should be deployed in such studies to nullify the background variation and improve the accuracy of target gene expression. An ideal reference gene should have a stable expression across developmental stages, biological conditions, tissues, or experimental conditions. METHODS AND RESULTS: Given this, the stability of eight candidate reference genes was evaluated in M. vitrata at different developmental stages, diets, and sexes by qRT-PCR method, and the data was analyzed using four independent algorithms, namely GeNorm, NormFinder, BestKeeper, and ΔCt, and one comprehensive algorithm, RefFinder. CONCLUSION: The analysis showed that RP49 and RPL13 were the best suitable reference genes for studying target gene expression at different developmental stages. Further, the study identified RP49 and RPL24, and GAPDH and RPL24 as the ideal reference genes in M. vitrata fed with different diets and sexes, respectively. The reference genes reported in the present study will ensure the accuracy of target gene expression, and thus, will serve as an important resource for gene functional studies in M. vitrata.

Selection and Validation of Candidate Reference Genes for Gene Expression Analysis by RT-qPCR in Rubus.

Due to the lack of effective and stable reference genes, studies on functional genes in Rubus, a genus of economically important small berry crops, have been greatly limited. To select the best internal reference genes of different types, we selected four representative cultivars of blackberry and raspberry (red raspberry, yellow raspberry, and black raspberry) as the research material and used RT-qPCR technology combined with three internal stability analysis software programs (geNorm, NormFinder, and BestKeeper) to analyze 12 candidate reference genes for the stability of their expression. The number of most suitable internal reference genes for different cultivars, tissues, and fruit developmental stages of Rubus was calculated by geNorm software to be two. Based on the results obtained with the three software programs, the most stable genes in the different cultivars were RuEEF1A and Ru18S. Finally, to validate the reliability of selected reference genes, the expression pattern of the RuCYP73A gene was analyzed, and the results highlighted the importance of appropriate reference gene selection. RuEEF1A and Ru18S were screened as reference genes for their relatively stable expression, providing a reference for the further study of key functional genes in blackberry and raspberry and an effective tool for the analysis of differential gene expression.

RPS13, a potential universal reference gene for normalisation of gene expression in multiple human normal and cancer tissue samples.

BACKGROUND: Reference genes are considered stable genes and are used for normalizing the gene expression profile across different cell types; as well as, in normal and diseased samples. However, these gene associates with different biological processes, and hence expression vary in different pathological conditions. Therefore, in the present study, eight different reference genes were used and compared to identify common reference gene usable for an array of different cell types and human cancers. METHODS AND RESULTS: The expression stability of the eight reference genes across eleven normal and cancerous tissues was confirmed through real time-qPCR. Ribosomal protein S13 (RPS13) was found to be a common and stable reference gene across intra- and inter-comparison between various normal and tumor tissue types. Further, TCGA data analysis across and between normal and tumor tissue types also showed minimum deviation in expression of RPS13 gene out of eight routinely used reference genes. CONCLUSION: RPS13 is the common stable reference gene in normalization for gene expression based analysis in cancer research.

Tutorial: Guidelines for Single-Cell RT-qPCR.

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

Identification of new reference genes with stable expression patterns for gene expression studies using human cancer and normal cell lines.

Reverse transcription-quantitative real-time PCR (RT-qPCR) is a ubiquitously used method in biological research, however, finding appropriate reference genes for normalization is challenging. We aimed to identify genes characterized with low expression variability among human cancer and normal cell lines. For this purpose, we investigated the expression of 12 candidate reference genes in 13 widely used human cancer cell lines (HeLa, MCF-7, A-549, K-562, HL-60(TB), HT-29, MDA-MB-231, HCT 116, U-937, SH-SY5Y, U-251MG, MOLT-4 and RPMI-8226) and, in addition, 7 normal cell lines (HEK293, MRC-5, HUVEC/TERT2, HMEC, HFF-1, HUES 9, XCL-1). In our set of genes, we included SNW1 and CNOT4 as novel candidate reference genes based on the RNA HPA cell line gene data from The Human Protein Atlas. HNRNPL and PCBP1 were also included along with the "classical" reference genes ACTB, GAPDH, IPO8, PPIA, PUM1, RPL30, TBP and UBC. Results were evaluated using GeNorm, NormFiner, BestKeeper and the Comparative ΔCt methods. In conclusion, we propose IPO8, PUM1, HNRNPL, SNW1 and CNOT4 as stable reference genes for comparing gene expression between different cell lines. CNOT4 was also the most stable gene upon serum starvation.

Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction.

National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction. METHODS: An integrated 31-GEP (i31-GEP) neural network algorithm incorporating clinicopathologic features with the continuous 31-GEP score was developed using a previously reported patient cohort (n = 1,398) and validated using an independent cohort (n = 1,674). RESULTS: Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G2 = 91.3, P < .001) for predicting SLN positivity. The i31-GEP demonstrated high concordance between predicted and observed SLN positivity rates (linear regression slope = 0.999). The i31-GEP increased the percentage of patients with T1-T4 tumors predicted to have < 5% SLN-positive likelihood from 8.5% to 27.7% with a negative predictive value of 98%. Importantly, for patients with T1 tumors originally classified with a likelihood of SLN positivity of 5%-10%, the i31-GEP reclassified 63% of cases as having < 5% or > 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. CONCLUSION: These data suggest the i31-GEP could reduce the number of SLNBs performed by identifying patients with likelihood under the 5% threshold for performance of SLNB and improve the yield of positive SLNBs by identifying patients more likely to have a positive SLNB.

Evaluation of Angelica decursiva reference genes under various stimuli for RT-qPCR data normalization.

Angelica decursiva is one of the lending traditional Chinese medicinal plants producing coumarins. Notably, several studies have focused on the biosynthesis and not the RT-qPCR (quantitative real-time reverse transcription polymerase chain reaction) study of coumarins. This RT-qPCR technique has been extensively used to investigate gene expression levels in plants and the selection of reference genes which plays a crucial role in standardizing the data form the RT-qPCR analysis. In our study, 11 candidate reference genes were selected from the existing transcriptome data of Angelica decursiva. Here, four different types of statistical algorithms (geNorm, NormFinder, BestKeeper, and Delta Ct) were used to calculate and evaluate the stability of gene expression under different external treatments. Subsequently, RefFinder analysis was used to determine the geometric average of each candidate gene ranking, and to perform comprehensive index ranking. The obtained results showed that among all the 11 candidate reference genes, SAND family protein (SAND), protein phosphatase 2A gene (PP2A), and polypyrimidine tract-binding protein (PTBP) were the most stable reference genes, where Nuclear cap binding protein 2 (NCBP2), TIP41-like protein (TIP41), and Beta-6-tubulin (TUBA) were the least stable genes. To the best of our knowledge, this work is the first to evaluate the stability of reference genes in the Angelica decursiva which has provided an important foundation on the use of RT-qPCR for an accurate and far-reaching gene expression analysis in this medicinal plant.