- Short Report
- Open Access
Evaluation of a novel approach for the measurement of RNA quality
© Wilkes et al; licensee BioMed Central Ltd. 2010
- Received: 22 December 2009
- Accepted: 1 April 2010
- Published: 1 April 2010
Microarray data interpretation can be affected by sample RNA integrity. The ScreenTape Degradation Value (SDV) is a novel RNA integrity metric specific to the ScreenTape® platform (Lab901). To characterise the performance of the ScreenTape® platform for RNA analysis and determine the robustness of the SDV metric, a panel of intentionally degraded RNA samples was prepared. These samples were used to evaluate the ScreenTape® platform against an alternative approach for measuring RNA integrity (Agilent Bioanalyzer RIN value). The samples were also subjected to microarray analysis and the resulting data correlated to the RNA integrity metrics.
Measurement of SDV for a panel of intentionally degraded RNA samples ranged from 0 for intact RNA to 37 for degraded RNA, with corresponding RIN values ranging from 10 to 4 for the same set of samples. SDV and RIN scales both demonstrated comparable discrimination between differently treated samples (RIN 10 to 7, SDV 0 to 15), with the SDV exhibiting better discrimination at higher degradation levels. Increasing SDV values correlated with a decrease in microarray sample labelling efficiency and an increase in numbers of differentially expressed genes.
The ScreenTape® platform is comparable to the Bioanalyzer platform in terms of reproducibility and discrimination between different levels of RNA degradation. The robust nature of the SDV metric qualifies it as an alternative metric for RNA sample quality control, and a useful predictor of downstream microarray performance.
- HepG2 Cell
- Differentially Express Gene
- Intraclass Correlation Coefficient
- Good Classification Performance
- Linear Discrimination Analysis
The use of microarray technology has revolutionised the fields of molecular biology and genetics. However, concerns have been raised over the numerous potential sources of variation that can affect assay consistency and data quality [1, 2]. Previous studies have highlighted RNA integrity as one source that has a major effect on microarray data quality [3–5].
To date, no single RNA integrity metric has been adopted universally by the research community. RNA quality is commonly determined by several different techniques, including the ribosomal peak ratio (eukaryotic 28s/18s rRNA peak intensity ratio) , RIN , 5'/3' transcript signal intensity ratio determined by qRT-PCR or microarray analysis  and other quality indices .
RNA purity is assessed routinely by measuring the OD260 nm/OD280 nm ratio [6, 10, 11] of a sample. However, this metric yields no information about RNA integrity. Molecular biologists have therefore relied on the technique of gel electrophoresis, which provides a reproducible separation of ribosomal RNA (rRNA) molecules to derive overall sample RNA integrity. Currently, such conventional methods are being replaced by microfluidic-based platforms, such as that developed by Agilent Technologies (2100 Bioanalyzer).
More recently, Lab901 have developed a novel electrophoretic ScreenTape® platform that employs precast multilane gels and microfluidics enabling semi-automated operation, simplifying sample handling and reducing assay times. In this study we have compared the performance characteristics of the ScreenTape® R6K platform (Lab901) and corresponding RNA quality metric, SDV  with the 2100 Bioanalyzer and associated quality metric, RIN .
We report here on the broad correlation observed for these quality metric values and associated microarray data.
HepG2 cells (passage = 84) were grown to confluence in T175 vented culture flasks, using Eagles Minimum Essential Medium (EMEM, ATCC) plus 10% Foetal Calf Serum (FCS, Invitrogen) in a humid 37°C incubator supplemented with 5% CO2. Cells were then exposed for 24 h to EMEM exposure media supplemented with 0.5% (v/v) DMSO vehicle (Sigma Aldrich) and 4 mM ACAP (Paracetamol, Sigma Aldrich). Following treatment, the cells were washed with an excess of 1 × Phosphate Buffered Saline (Gibco) before being lysed in situ by the application of ice cold TRIzol® LS Reagent (Invitrogen). Total RNA was then isolated according to manufacturer's instructions (Invitrogen). RNA quantity was determined using a NanoDrop 1000 spectrophotometer.
For comparative analysis of RNA integrity, the TapeStation® (Lab901) was used in conjunction with ScreenTape® R6K, and the 2100 Bioanalyzer (Agilent Technologies) with the RNA 6000 series II Nano LabChip analysis kit. Total RNA samples were prepared for analysis according to manufacturer's recommendations. Results were compared between the platforms for six levels of RNA sample integrity and at a single RNA concentration of 25 ng/μl.
RNA from the treated HepG2 cells was diluted to a concentration of 1 μg/μl with nuclease-free water (Ambion) and aliquoted into volumes of 50 μl in 0.2 ml thin wall PCR tubes. The tubes were placed in the block of an MJ Research PTC-200 DNA Engine Thermal Cycler PCR machine which was then heated to 90°C. Tubes were removed in batches of three at 3-minute intervals. The RNA was then diluted to 25 ng/μl with nuclease-free water, and RNA integrity for each sample determined on both platforms.
Microarray experimental design
A single-colour labelling approach was adopted for the microarray hybridisation scheme. Duplicate biological samples of those employed for the Lab901/Bioanalyser platform evaluation were used, with two technical replicates (arrays) for each sample. Samples were hybridized to Agilent Homo sapiens 4 × 44K whole genome gene expression arrays according to manufacturer's instructions. The hybridised arrays were scanned using an Agilent G2505B Scanner and expression data extracted using Feature Extraction software, version 10.5 (Agilent Technologies). Data was exported to the Genespring GX (Agilent) software package, normalised to the 75th percentile of the data set and base-lined to the median signal intensity of all chips. The data were quality controlled by filtering on flags (features present and marginal), before the number of differentially expressed genes (DEG) were determined using the combination of an analysis of variance (ANOVA) and relative fold change (FC = > 1.5).
All statistical analysis used R version 2.9.2 .
RNA Integrity Measurements
Comparison with RNA Integrity Number (RIN)
Both RIN and SDV show good discrimination between samples in general.
SDV appears to show generally better within-group precision.
There is evidence to suggest that RIN and SDV do not share a linear relationship.
The within-group dispersion does not appear to be constant across treatment groups for either of the metrics.
The assessment of relative platform performance was determined by the use of three different statistical indicators, namely rank order correlation, intraclass correlation and classification performance
i) Rank order correlation
Rank correlations (observed vs. treatment level)
ii) Intraclass correlation
Intraclass correlation coefficients
95% Confidence interval**
The ICC for both metrics are high, which is indicative of good performance. The observed ICC for SDV is marginally higher but the confidence intervals show that the difference is not significant at the 95% level of confidence.
iii) Classification performance
It can be seen that the relative performance of the two RNA degradation metrics is consistent across all four classification methods. However, more misclassifications were observed when using the RIN metric. In addition, the difference in misclassification was found to be significant at the 95% level for two of the four classification methods. This would indicate that a significant difference in performance exists between the SDV and RIN metrics.
RNA integrity and labelling efficiency
In terms of microarray assay performance, reduced sample labelling efficiency may have an impact on the robustness of microarray measurement and data reliability. RNA integrity measurement, whether by SDV or RIN, may provide a valuable tool for early prediction of RNA labelling efficiency and ultimately of overall microarray performance.
The number of differentially expressed genes and associated additional gene discovery rate at each level of RNA integrity
Treatment Time (min)
Median number of genes detected*
Additional Discovery Rate
Differential expression was defined as those genes which demonstrated a statistically significant difference of expression (p ≤ 0.05) while employing a Benjamini and Hochberg  false discovery rate correction factor, and a fold change ≥ 1.5 compared to the control. The additional gene discovery rate was determined by subtracting the median number of genes determined for three technical replicates of an intact RNA sample away from the median number of genes derived from three technical replicates at each level of degraded RNA sample.
It has been reported previously that gene expression profiling using Affymetrix GeneChip arrays is relatively tolerant to moderate RNA degradation as well as to 5' truncation occurring as a consequence of successive rounds of in vitro transcription . However, with progressively decreasing RNA integrity, a substantial increase in the rate of detection of additional positives is reported here, particularly with RIN values < 7 . Comparing RIN or SDV with the ADR reveals a progressive increase in the ADR with decreasing RNA integrity. The exact nature of this increase is uncertain at this stage, but these findings indicate that a shift in assay specificity is occurring as a consequence of reduced RNA integrity, which can be accurately measured and predicted with SDV and RIN.
The measurement of gene expression is based on the assumption that an analysed RNA sample accurately represents the population of transcripts present in vivo. Many transcripts demonstrate stability differences of several orders of magnitude in vivo, raising the possibility that partial sample degradation could cause variable bias in transcript quantification. The adoption of a suitable RNA quality metric with the capacity to accurately determine RNA integrity is therefore an essential prerequisite for robust data generation in any expression profiling experiment.
In this paper we have compared both the performance of the novel Lab901 ScreenTape® platform with that of the Agilent 2100 Bioanalyzer and also compared the SDV metric with the RIN metric for the determination of RNA integrity when applied to microarray data analysis.
Both metrics performed well when using the samples employed in this study, with the data highlighting the difficulty associated with unambiguously assigning samples to a definitive integrity level when they have ether a RIN value ≤ 6 or an SDV value ≥ 15.
In conclusion, the ScreenTape® system was demonstrated to be a reliable and robust means of determining RNA integrity with SDV estimates, correlating well with the RIN values generated by the Agilent Bioanalyzer platform and with a better classification performance in this study. In addition, the RIN and SDV metrics both performed well in terms of distinguishing different levels of RNA degradation treatment. For microarray data, Rank correlations with treatment and intraclass correlations are high. Classification methods show that the majority of observations were classified into appropriate treatment groups by both RIN and SDV, with the slightly better classification performance of the SDV metric being significant at the P ≤ 0.05 level for two out of the four classification methods used.
The ScreenTape platform and SDV therefore offer an alternative to currently available systems for RNA integrity analysis and provide a performance comparable to that of the Bioanalyzer 2100. The rapid assay time and medium through put capacity may favour its use in laboratories which process large numbers of samples. Future studies with this platform could enable the development of an SDV based RNA quality threshold to be established for use with downstream applications.
The work described in this paper was undertaken by LGC in its capacity as the designated National Measurement Institute. This work was funded by the UK National Measurement System under the Measurement for Innovators Programme and, in part, by a financial contribution made by Lab901, as required by the Programme.
- Hessner MJ, Meyer L, Tackes J, Muheisen S, Wang X: Immobilised probe and glass surface chemistry as variables in microarray fabrication. BMC Genomics. 2004, 5: 53-10.1186/1471-2164-5-53.PubMed CentralPubMedView ArticleGoogle Scholar
- Jarvinen AK, Hautaniemi S, Edgren H, Auvinen P, Sarrela J, Kallioiemi OP, Monni O: Are data from different gene expression microarrays comparable?. Genomics. 2004, 83: 1164-8. 10.1016/j.ygeno.2004.01.004.PubMedView ArticleGoogle Scholar
- Thompson KL, Scott Pine P, Rosenzweig BA, Turpaz Y, Retief J: Characterization of the effect of sample quality on high density oligonucleotide microarray data using progressively degraded rat liver RNA. BMC Biotechnology. 2007, 7: 57-10.1186/1472-6750-7-57.PubMed CentralPubMedView ArticleGoogle Scholar
- Copois V, bibeau F, Bascoul-Mollevi C, Salvetat N, Chabos P, Bareli C, Candeil L, Fraslon C, Conseiller E, Granci V, Maziere P, Kramar A, Ychou M, Pau B, Martineau P, Molina F, Del Rio M: Impact of RNA degradation on gene expression profiles: Assessment of different methods of reliability determines RNA quality. Journal of Biotechnology. 2007, 127: 549-559. 10.1016/j.jbiotec.2006.07.032.PubMedView ArticleGoogle Scholar
- Grissom SF, Lobenhofer EK, Tucker CJ: A qualitative assessment of direct labelled cDNA products prior to microarray analysis. BMC Genomics. 2005, 6: 36-10.1186/1471-2164-6-36.PubMed CentralPubMedView ArticleGoogle Scholar
- Sambrook J, Fritsch EF, Maniatis T: Molecular Cloning: A Laboratory Manual. 1989, Cold Spring Harbour, 2Google Scholar
- Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassman M, Lightfoot S, Menzel W, Granzow M, Ragg R: The RIN: An RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology. 2006, 7: 3-10.1186/1471-2199-7-3.PubMed CentralPubMedView ArticleGoogle Scholar
- Finkelstein DB: Trends in the Quality of Data from 5168 Oligonucleotide Microarrays from a Single Facility. Journal of Biomolecular Techniques. 2005, 16: 143-153.PubMed CentralPubMedGoogle Scholar
- Auer H, Lyianarachchi S, Newsom D, Kilsovic MI, Marcucci G, Kornacker K: Chipping away at the chip bias: RNA degradation in microarray analysis. Nat Genet. 2003, 35 (4): 292-293. 10.1038/ng1203-292.PubMedView ArticleGoogle Scholar
- Manchester KL: Value of A260/A280 ratios for measurement of purity of nucleic acids. Biotechniques. 1995, 19: 208-210.PubMedGoogle Scholar
- Manchester KL: Use of UV methods for measurement of protein and nucleic acid concentrations. Biotechniques. 1996, 20: 968-970.PubMedGoogle Scholar
- R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2008, ISBN 3-900051-07-0, [http://www.R-project.org]Google Scholar
- Davison AC, Hinkley DV: Bootstrap methods and their application. 1997, Cambridge University Press, chapter 5:View ArticleGoogle Scholar
- Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, Zettel M, Coleman P: Analysis of gene expression in single live neurons. PNAS. 1992, 89 (7): 3010-3014. 10.1073/pnas.89.7.3010.PubMed CentralPubMedView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B. 1995, 57: 289-300.Google Scholar
- Schoor O, Weinschehenk T, Hennenlotter J, Corvin S, Stenzl A, Rammensee HG, Stevenovic S: Moderate degradation does not preclude microarray analysis of small amounts of RNA. Biotechniques. 2003, 35: 1192-1201.PubMedGoogle Scholar
- Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995, 270 (5235): 467-70. 10.1126/science.270.5235.467.PubMedView ArticleGoogle Scholar
- Berger SL, Cooper LS: Very Short-Lived and Stable mRNAs from Resting Human Lymphocytes. PNAS. 1975, 71 (10): 3873-3877. 10.1073/pnas.72.10.3873.View ArticleGoogle Scholar
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