Digital-PCR for gene expression: impact from inherent tissue RNA degradation.
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ABSTRACT:
SUBMITTER: Millier MJ
PROVIDER: S-EPMC5722939 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGene expression profiling is a highly sensitive technique which is used for profiling tumor samples for medical prognosis. RNA quality and degradation influence the analysis results of gene expression profiles. The impact of this influence on the profiles and its medical impact is not fully understood. As patient samples are very valuable for clinical studies, it is necessary to establish criteria for the RNA quality to be able to use these samples in later analysis.MethodsTo investigate the effects of RNA integrity on gene expression profiling, whole genome expression arrays were used. We used tumor biopsies from patients diagnosed with locally advanced rectal cancer. To simulate degradation, the isolated total RNA of all patients was subjected to heat-induced degradation in a time-dependent manner. Expression profiling was then performed and data were analyzed bioinformatically to assess the differences.ResultsThe differences introduced by RNA degradation were largely outweighed by the biological differences between the patients. Only a relatively small number of probes (275 out of 41,000) show a significant effect due to degradation. The genes that show the strongest effect due to RNA degradation were, especially, those with short mRNAs and probe positions near the 5' end.ConclusionsDegraded RNA from tumor samples (RIN > 5) can still be used to perform gene expression analysis. A much higher biological variance between patients is observed compared to the effect that is imposed by degradation of RNA. Nevertheless there are genes, very short ones and those with the probe binding side close to the 5' end that should be excluded from gene expression analysis when working with degraded RNA. These results are limited to the Agilent 44 k microarray platform and should be carefully interpreted when transferring to other settings.
| S-EPMC2927474 | biostudies-literature
Project description:We developed a digital RNA allelotyping method for quantitatively interrogating allele-specific gene expression. This method involves ultra-deep sequencing of padlock-captured single-nucleotide polymorphisms (SNPs) from the transcriptome. We characterized four cell lines established from two human subjects in the Personal Genome Project. Approximately 11-22% of the heterozygous mRNA-associated SNPs showed allele-specific expression in each cell line and 4.3-8.5% were tissue-specific, suggesting the presence of tissue-specific cis regulation. When we applied allelotyping to two pairs of sibling human embryonic stem cell lines, the sibling lines were more similar in allele-specific expression than were the genetically unrelated lines. We found that the variation of allelic ratios in gene expression among different cell lines was primarily explained by genetic variations, much more so than by specific tissue types or growth conditions. Comparison of expressed SNPs on the sense and antisense transcripts suggested that allelic ratios are primarily determined by cis-regulatory mechanisms on the sense transcripts.
| S-EPMC2742772 | biostudies-literature
Project description:Compromised RNA quality is suggested to lead to unreliable results in gene expression studies. Therefore, assessment of RNA integrity and purity is deemed essential prior to including samples in the analytical pipeline. This may be of particular importance when diagnostic, prognostic or therapeutic conclusions depend on such analyses. In this study, the comparative value of six RNA quality parameters was determined using a large panel of 740 primary tumour samples for which real-time quantitative PCR gene expression results were available. The tested parameters comprise of microfluidic capillary electrophoresis based 18S/28S rRNA ratio and RNA Quality Index value, HPRT1 5'-3' difference in quantification cycle (Cq) and HPRT1 3' Cq value based on a 5'/3' ratio mRNA integrity assay, the Cq value of expressed Alu repeat sequences and a normalization factor based on the mean expression level of four reference genes. Upon establishment of an innovative analytical framework to assess impact of RNA quality, we observed a measurable impact of RNA quality on the variation of the reference genes, on the significance of differential expression of prognostic marker genes between two cancer patient risk groups, and on risk classification performance using a multigene signature. This study forms the basis for further rational assessment of reverse transcription quantitative PCR based results in relation to RNA quality.
| S-EPMC3089491 | biostudies-literature
Project description:1.) Background: Gene expression profiling is a highly sensitive technique which is used for profiling of tumor samples for medical prognosis. RNA quality and degradation influences the analysis results of gene expression profiles. The impact of this influence on the profiles and its medical impact is not fully understood. As patient samples are very valuable for clinical studies, it is necessary to establish criteria for the RNA quality to use these samples in later analysis. 2.) Methods: To investigate the effects of RNA integrity on gene expression profiling whole genome expression arrays were used. We therefore used tumor biopsies from patients diagnosed with locally advanced rectal cancer. To simulate degradation, the isolated total RNA of all patients was subjected to heat-induced degradation in a time-dependent manner. Expression profiling was then performed and data were analyzed bioinformatically to assess the differences. 3.) Results: The biological differences between the patients largely over-weighed the differences introduced by RNA degradation. Only a relatively small number of probes (275 out of 41000) show a significant effect due to degradation. The genes that show the strongest effect due to RNA degradation were especially those with short mRNAs and probe positions near the 5' end. 4.) Conclusions: RNA from tumor samples with differing realistic qualities (> 5) can still be used to perform gene expression analysis. A much higher biological variance between patients is observed compared to the effect that is imposed by degradation of RNA. Nevertheless there are genes that are prone to degradation, especially very short ones and those with the probe binding side close to the 5´-end. Therefore, these genes should be excluded from gene expression analysis when working with degradated RNA. Biopsies were taken from 3 different patients with 4 time-points of degradation per patient. The resulting 12 samples were separately hybridised (OneColor Array Design).
2010-08-18 | E-GEOD-17753 | biostudies-arrayexpress
Project description:Gene expression measurements detailing mRNA quantities are widely employed in molecular biology and are increasingly important in diagnostic fields. Reverse transcription (RT), necessary for generating complementary DNA, can be both inefficient and imprecise, but remains a quintessential RNA analysis tool using qPCR. This study developed a Transcriptomic Calibration Material and assessed the RT reaction using digital (d)PCR for RNA measurement. While many studies characterise dPCR capabilities for DNA quantification, less work has been performed investigating similar parameters using RT-dPCR for RNA analysis. RT-dPCR measurement using three, one-step RT-qPCR kits was evaluated using single and multiplex formats when measuring endogenous and synthetic RNAs. The best performing kit was compared to UV quantification and sensitivity and technical reproducibility investigated. Our results demonstrate assay and kit dependent RT-dPCR measurements differed significantly compared to UV quantification. Different values were reported by different kits for each target, despite evaluation of identical samples using the same instrument. RT-dPCR did not display the strong inter-assay agreement previously described when analysing DNA. This study demonstrates that, as with DNA measurement, RT-dPCR is capable of accurate quantification of low copy RNA targets, but the results are both kit and target dependent supporting the need for calibration controls.
| S-EPMC3779174 | biostudies-literature
Project description:1.) Background: Gene expression profiling is a highly sensitive technique which is used for profiling of tumor samples for medical prognosis. RNA quality and degradation influences the analysis results of gene expression profiles. The impact of this influence on the profiles and its medical impact is not fully understood. As patient samples are very valuable for clinical studies, it is necessary to establish criteria for the RNA quality to use these samples in later analysis. 2.) Methods: To investigate the effects of RNA integrity on gene expression profiling whole genome expression arrays were used. We therefore used tumor biopsies from patients diagnosed with locally advanced rectal cancer. To simulate degradation, the isolated total RNA of all patients was subjected to heat-induced degradation in a time-dependent manner. Expression profiling was then performed and data were analyzed bioinformatically to assess the differences. 3.) Results: The biological differences between the patients largely over-weighed the differences introduced by RNA degradation. Only a relatively small number of probes (275 out of 41000) show a significant effect due to degradation. The genes that show the strongest effect due to RNA degradation were especially those with short mRNAs and probe positions near the 5' end. 4.) Conclusions: RNA from tumor samples with differing realistic qualities (> 5) can still be used to perform gene expression analysis. A much higher biological variance between patients is observed compared to the effect that is imposed by degradation of RNA. Nevertheless there are genes that are prone to degradation, especially very short ones and those with the probe binding side close to the 5´-end. Therefore, these genes should be excluded from gene expression analysis when working with degradated RNA.
2010-08-19 | GSE17753 | GEO
Project description:The human rhinovirus (RV) is a positive-stranded RNA virus that causes respiratory tract diseases affecting both the upper and lower halves of the respiratory system. RV enhances its replication by concentrating RNA synthesis within a modified host membrane in an intracellular compartment. RV infections often occur alongside infections caused by other respiratory viruses, and the RV virus may remain asymptomatic for extended periods. Alongside qualitative detection, it is essential to accurately quantify RV RNA from clinical samples to explore the relationships between RV viral load, infections caused by the virus, and the resulting symptoms observed in patients. A reference material (RM) is required for quality evaluation, the performance evaluation of molecular diagnostic products, and evaluation of antiviral agents in the laboratory. The preparation process for the RM involves creating an RV RNA mixture by combining RV viral RNA with RNA storage solution and matrix. The resulting RV RNA mixture is scaled up to a volume of 25 mL, then dispensed at 100 µL per vial and stored at -80 °C. The process of measuring the stability and homogeneity of RV RMs was conducted by employing reverse transcription droplet digital polymerase chain reaction (RT-ddPCR). Digital PCR is useful for the analysis of standards and can help to improve measurement compatibility: it represents the equivalence of a series of outcomes for reference materials and samples being analyzed when a few measurement procedures are employed, enabling objective comparisons between quantitative findings obtained through various experiments. The number of copies value represents a measured result of approximately 1.6 × 105 copies/μL. The RM has about an 11% bottle-to-bottle homogeneity and shows stable results for 1 week at temperatures of 4 °C and -20 °C and for 12 months at a temperature of -80 °C. The developed RM can enhance the dependability of RV molecular tests by providing a precise reference value for the absolute copy number of a viral target gene. Additionally, it can serve as a reference for diverse studies.
| S-EPMC10742479 | biostudies-literature
Project description:Quantitative viral load assays have transformed our understanding of viral diseases. They hold similar potential to advance COVID-19 control and prevention, but SARS-CoV-2 viral load tests are not yet widely available. SARS-CoV-2 molecular diagnostic tests, which typically employ real-time RT-PCR, yield semiquantitative results only. Droplet digital RT-PCR (RT-ddPCR) offers an attractive platform for SARS-CoV-2 RNA quantification. Eight primer/probe sets originally developed for real-time RT-PCR-based SARS-CoV-2 diagnostic tests were evaluated for use in RT-ddPCR; three were identified as the most efficient, precise, and sensitive for RT-ddPCR-based SARS-CoV-2 RNA quantification. For example, the analytical efficiency for the E-Sarbeco primer/probe set was approximately 83%, whereas assay precision, measured as the coefficient of variation, was approximately 2% at 1000 input copies/reaction. Lower limits of quantification and detection for this primer/probe set were 18.6 and 4.4 input SARS-CoV-2 RNA copies/reaction, respectively. SARS-CoV-2 RNA viral loads in a convenience panel of 48 COVID-19-positive diagnostic specimens spanned a 6.2log10 range, confirming substantial viral load variation in vivo. RT-ddPCR-derived SARS-CoV-2 E gene copy numbers were further calibrated against cycle threshold values from a commercial real-time RT-PCR diagnostic platform. This log-linear relationship can be used to mathematically derive SARS-CoV-2 RNA copy numbers from cycle threshold values, allowing the wealth of available diagnostic test data to be harnessed to address foundational questions in SARS-CoV-2 biology.
| S-EPMC8164350 | biostudies-literature
Project description:Cell cycle progression depends on phase-specific gene expression. Here we show that the nuclear RNA degradation machinery plays a lead role in promoting cell cycle-dependent gene expression by triggering promoter-dependent co-transcriptional RNA degradation. Single molecule quantification of RNA abundance in different phases of the cell cycle indicates that relative curtailment of gene expression in certain phases is attained even when transcription is not completely inhibited. When nuclear ribonucleases are deleted, transcription of the Saccharomyces cerevisiae G1-specific axial budding gene AXL2 is detected throughout the cell cycle and its phase-specific expression is lost. Promoter replacement abolished cell cycle-dependent RNA degradation and rendered the RNA insensitive to the deletion of nuclear ribonucleases. Together the data reveal a model of gene regulation whereby RNA abundance is controlled by promoter-dependent induction of RNA degradation.
| S-EPMC6572803 | biostudies-literature
Project description:BackgroundThe use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples (for example, samples collected in the course of fieldwork) are at times the sole means of addressing specific questions.ResultsWe sought to quantify the impact of variation in RNA quality on estimates of gene expression levels based on RNA-seq data. To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. The RNA samples we collected spanned the entire range of RNA Integrity Number (RIN) values (a metric commonly used to assess RNA quality). We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples.ConclusionsWhile standard normalizations failed to account for the effects of degradation, we found that by explicitly controlling for the effects of RIN using a linear model framework we can correct for the majority of these effects. We conclude that in instances in which RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples.
| S-EPMC4071332 | biostudies-literature