RNA Integrity Number (RIN) – Standardization of RNA Quality Control Application Odilo Mueller Samar Lightfoot Andreas Schroeder Abstract The assessment of RNA integrity is a critical first step in obtaining meaningful gene expression data. Using intact RNA is a key element for successful microarray or RT-PCR analyses. The Agilent 2100 Bioanalyzer System and RNA kits play an important role in assisting researchers in the determination of RNA quality. Profiles generated on the Agilent 2100 Bioanalyzer System yield information on concentration, allow a visual inspection of RNA integrity, and generate ribosomal ratios. This Applica- tion Note describes a new software algorithm that has been developed to extract information about RNA sample integrity from a Bioanalyzer electrophoretic trace. Introduction ribosomal peaks and the lower eukaryotic total RNA, based on a marker. The Bioanalyzer software numbering system from 1 to 10, Determining the integrity of RNA automatically generates the ratio with 1 being the most degraded starting materials is a critical step of the 18S to 28S ribosomal profile and 10 being the most in gene expression analysis. The subunits. Although ribosomal intact. In this way, interpretation Agilent 2100 Bioanalyzer System ratios play an important role in of an electropherogram is facilitat- and associated RNA 6000 Nano determining the level of sample ed, comparison of samples is and Pico kits have become the degradation in gel electrophoresis, enabled and repeatability of standard in RNA quality assess- the more detailed analysis on the experiments is ensured. ment and quantitation1,2. Using Agilent 2100 Bioanalyzer System electrophoretic separation on reveals that it inadequately Development of the RIN tool microfabricated chips, RNA sam- describes sample integrity. The RIN software algorithm was ples are separated and subse- developed for samples acquired quently detected via laser induced In order to standardize the process with the Eukaryote Total RNA fluorescence detection. The Bioan- of RNA integrity interpretation, Nano assay on the Agilent 2100 alyzer software generates an elec- Agilent Technologies has Bioanalyzer System. Input data tropherogram and gel-like image introduced a new tool for RNA included approximately 1300 total and displays results such as sam- quality assessment. The RNA RNA samples from various ple concentration and the so-called Integrity Number (RIN), was tissues, three mammalian species ribosomal ratio. The electro- developed to remove individual (human, mouse and rat), all with pherogram provides a detailed interpretation in RNA quality varying levels of integrity. visual assessment of the quality of control. It takes the entire elec- Categorization of the RNA an RNA sample. However, meth- trophoretic trace into account. samples was done manually by ods that rely on human visual The RIN software algorithm application specialists who interpretation of data are intrinsi- allows for the classification of classified each total RNA cally flawed. Previously, researchers have used the ribosomal ratio in both slab gel analysis and as a fea- ture within the Bioanalyzer soft- ware to characterize the state of RNA intactness. Slab gel analysis of total RNA samples using riboso- mal ratios often results in an inac- curate assessment of the RNA integrity3. The Agilent 2100 Bioan- alyzer System provides a better assess-ment of RNA intactness by show-ing a detailed picture of the size distribution of RNA fragments. RNA degradation is a gradual process. As degradation proceeds (Figure 1), there is a decrease in the 18S to 28S ribosomal band ratio and an increase in the line signal between the two base Figure 1 A total RNA sample was degraded for varying times and the resulting samples were analyzed on the Agilent 2100 Bioanalyzer System using the Eukaryote Total RNA Nano assay. A shift towards shorter fragment sizes can be observed with progressing degradation. 2 sample within a predefined numeric system from 1 through 10. 90 2.00 80 Figure 2 shows representative 70 RIN 10 1.75 RIN 6 1.50 electropherograms for different 60 50 1.25 RIN classes (10, 6, 3, 2, respectively). 40 1.00 Fluorescence 30 0.75 20 Fluorescence 0.50 For development of the RIN 10 28S 0.25 0 18S algorithm, adaptive learning tools, 0.00 18S 28S such as neural networks, were 19 24 29 34 39 44 49 54 59 64 69 24 29 34 39 44 49 54 59 64 69 employed (tools provided by Time (seconds) Time (seconds) quantiom bioinformatics). They 0.8 3.5 allowed the determination of 0.7 RIN 33.0 RIN 2 critical features that can be 0.6 2.5 0.5 extracted from an electrophoretic 0.4 2.0 0.3 1.5 trace. These features are parts of Fluorescence 0.2 Fluorescence 1.0 an electropherogram that can be 0.1 0.5 analyzed using an appropriate 0.0 0.0 integrator. They can be signal 24 29 34 39 44 49 54 59 64 69 19 24 29 34 39 44 49 54 59 64 69 areas, intensities, ratios etc. Time (seconds) Time (seconds) Important elements of an electro- Figure 2 pherogram are listed in figure 3. Sample electropherograms used to train the RNA Integrity Number (RIN) software. Samples They include different regions range from intact (RIN 10), to degraded (RIN 2). (pre-, 5S-, fast-, inter-, precursor-, post-region) and peaks (marker, 18S, 28S). 4.5 Inter Region 28S Fragment Pre Region 18S Fragment 4.0 RIN visualization Marker RIN will be part of the Agilent 3.5 2100 expert software. Data found 3.0 in previous versions of the biosiz- 2.5 Fast Region Precursor Region ing software can also be found in 2.0 5S Region the next expert software version, for example, RNA area, RNA con- Fluorescence 1.5 Post Region centration, rRNA ratios. The RIN 1.0 software includes the RIN number 0.5 (Figure 4), which can be 0.0 expressed either as a decimal or integer. The RIN value can be 19 24 29 34 39 44 49 54 59 64 69 changed from a decimal to an Time (seconds) integer in the Assay Properties tab, in the Set Point Explorer under Figure 3 Global Advanced settings. RIN Electropherogram detailing the regions that are indicative of RNA quality. values may not be computed if the software finds an unexpected peak or signal in certain regions. This will result in an error message indicating that an 3 anomaly has been detected (listed in the error tab of the software). Anomalies include genomic DNA contamination, ghost peaks, spikes, and wavy baselines. Anomalies can be divided into two classes: critical and non-critical. Non-critical anomalies, for example, a spike in the post region, will result in the computation of a RIN number while critical anomalies, for exam- ple, spikes in the fast region, will result in no RIN computation. If an anomaly is not deemed to be Figure 4 Figure 5 RIN visualization in the Agilent 2100 Bioanalyzer Changing anomaly thresholds and single decimal critical (such as genomic contami- System expert software. RIN numbers are RIN representation. If critical anomalies have nation, where a DNase digest found in the results tab, while the error tab been detected during the analysis, in many should be performed to obtain will contain useful information in cases where cases RIN values can still be computed by meaningful data), a RIN value can the RIN was not computed. increasing the thresholds (max. = 1). Information still be computed by increasing regarding anomalies can be found in the error tab. the anomaly threshold settings found in the advanced settings 45 in the Set Point Explorer for the 40 sample that has been flagged 35 (Figure 5). The maximum value for 30 Intact RNA: RIN 10 anomaly threshold detection is 1. 25 20 Description of the error message Fluorescence 15 will correspond to an appropriate 10 28S threshold number. 5 18S 0 Results obtained with RIN 8 The RIN software was developed 7 6 Partially degraded to remove user-dependent inter- 5 RNA: RIN 5 pretation of RNA quality. Charac- 4 3 Fluorescence terization of total RNA samples is 2 largely independent of the instru- 1 18S 0 28S ment, sample concentration, and the operator allowing for the com- 3.0 parison of samples across differ- 2.5 ent laboratories. 2.0 Strongly Degraded 1.5 RNA: RIN 3 1.0 RNA integrity Fluorescence 0.5 Figure 6 shows three RNA sam- 0.0 ples at varying stages of intact- ness. The RIN tool gave three dif- 19 24 29 34 39 44 49 54 59 64 69 Time (seconds) ferent designations representing their respective intactness. During Figure 6 a large validation study using dif- The RNA integrity number was tested on samples of varying levels of intactness. The RIN soft- ferent samples from the ones that ware algorithm was able to accurately classify the samples. 4 were used to train the algorithm, 36 samples 15.0 reliable sample classification was 3 instruments obtained. 12.5 CV RIN: 1.4 % CV ribosomal ratio: 5.1 % Ribosomal ratios 10.0 Figure 7 shows the same sample 7.5 human brain (Ambion, Inc.) total Fluorescence RNA that was run on three differ- 5.0 Inst. 1: RIN 8 ent instruments and representa- 2.5 tive electropherograms of instru- 0.0 28S ment 1 and 3 are shown. Riboso- 18S mal ratios as generated with the Bioanalyzer software are com- 20 pared with RIN values. For the 36 samples there is a larger degree of 15 variability when using ribosomal Inst. 3: RIN 8 ratios as compared to RIN values. 10 Calculated RIN values were at Fluorescence 1.4 % coefficient of variance while 5 ribosomal ratios had a 5.1 % CV. Keep in mind that these CV values 0 28S refer to identical samples.