RNA Integrity Number (RIN) –
Standardization of RNA Quality Control
Application
Abstract
The asssment 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 analys. The Agilent 2100 bioanalyzer and RNA LabChip ®kits play an important role in assisting rearchers in the determination of RNA quality. Profiles generated on the Agilent 2100 bioanalyzer 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.
Odilo Mueller Samar Lightfoot Andreas Schroeder
somal peaks and the lower mark-er. The bioanalyzer software auto-matically generates the ratio of the 18S to 28S ribosomal subunits.Although ribosomal ratios play an important role in determining the level of sample degradation in gel electrophoresis, the more detailed analysis on the Agilent 2100 bioan-alyzer reveals that it inadequately describes sample integrity. In order to standardize the
process of RNA integrity interpre-tation, Agilent Technologies has introduced a new tool for RNA quality asssment. The RNA Integrity Number (RIN), was developed to remove individual interpretation in RNA quality con-trol. It takes the entire elec-trophoretic trace into account.The RIN software algorithm allows for the classification of
Introduction
Determining the integrity of RNA starting materials is a critical step in gene expression analysis. The Agilent 2100 bioanalyzer and asso-ciated RNA 6000 Nano and Pico LabChip kits have become the standard in RNA quality asss-ment and quantitation 1,2. Using electrophoretic paration on microfabricated chips, RNA sam-ples are parated and sub-quently detected via lar induced flu
orescence detection. The bioan-alyzer software generates an elec-tropherogram and gel-like image and displays results such as sam-ple concentration and the so-called ribosomal ratio. The elec-tropherogram provides a detailed visual asssment of the quality of an RNA sample. However, meth-ods that rely on human visual interpretation of data are intrinsi-cally flawed. Previously, rearchers have ud 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 asssment of the RNA integrity 3. The Agilent 2100 bioan-alyzer provides a better asss-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 decrea in the 18S to 28S ribosomal band ratio and an increa in the ba-line signal between the two ribo-
eukaryotic total RNA, bad on a numbering system from 1 to 10,with 1 being the most degraded profile and 10 being the most intact. In this way, interpretation of an electropherogram is facilitat-ed, comparison of samples is enabled and repeatability of experiments is ensured.
Development of the RIN tool
The RIN software algorithm was developed for samples acquired with the Eukaryote Total RNA Nano assay on the Agilent 2100bioanalyzer. Input data included approximately 1300 total RNA samples from various tissues,
three mammalian species (human,mou and rat), all with varying levels of integrity. Categorization of the RNA samples was done manually by application special-ists who classified each total RNA
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Figure 1
A total RNA sample was degraded for varying times and the resulting samples were analyzed on the Agilent 2100 bioanalyzer using the Eukaryote Total RNA Nano assay. A shift towards shorter fragment sizes can be obrved with progressing degradation.
sample within a predefined Array numeric system from 1 through 10.
Figure 2 shows reprentative
electropherograms for different
RIN class (10, 6, 3, 2, respectively).
For development of the RIN
algorithm, adaptive learning tools,
such as neural networks, were
employed (tools provided by
quantiom bioinformatics). They
allowed the determination of
critical features that can be
extracted from an electrophoretic大腿内侧抽筋应急处理
trace. The features are parts of
an electropherogram that can be
analyzed using an appropriate
integrator. They can be signal
areas, intensities, ratios etc.
Important elements of an electro-
pherogram are listed in figure 3.
They include different regions
(pre-, 5S-, fast-, inter-, precursor-,
post-region) and peaks (marker,
18S, 28S).
RIN visualization
婚庆策划RIN will be part of the Agilent
2100 expert software. Data found
in previous versions of the biosiz-
ing software can also be found in
the next expert software version,
for example, RNA area, RNA con-
centration, rRNA ratios. The RIN
software includes the RIN number
(figure 4), which can be expresd
either as a decimal or integer. The佚名的名言名句
RIN value can be changed from a
decimal to an integer in the Assay
Properties tab, in the Set Point
犬瘟病
Explorer under Global Advanced
ttings. RIN values may not be
computed if the software finds an
unexpected peak or signal in cer-
tain regions. This will result in an
error message indicating that an
3
Figure 4
RIN visualization in the Agilent 2100 bioanalyzer expert software. RIN numbers are found in the results tab, while the error tab will contain uful information in cas where the RIN
was not computed.
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Figure 5
Changing anomaly thresholds and single decimal RIN reprentation. If critical anomalies have been detected during the analysis, in many cas RIN values can still be computed by increasing the thresholds (max. = 1). Information regarding anomalies can be found in the error tab.酸根离子
anomaly has been detected (listed in the error tab of the software).Anomalies include genomic DNA contamination, ghost peaks, spikes,and wavy balines. Anomalies can be divided into two class:critical and non-critical. Non-critical anomalies, for example, a spike in the post region, will re
王之涣《凉州词》sult 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 critical (such as genomic contami-nation, where a DNa digest should be performed to obtain meaningful data), a RIN value can still be computed by increasing the anomaly threshold ttings found in the advanced ttings in the Set Point Explorer for the sample that has been flagged (figure 5). The maximum value for anomaly threshold detection is 1. Description of the error message will correspond to an appropriate threshold number.Results obtained with RIN
The RIN software was developed to remove ur-dependent inter-pretation of RNA quality. Charac-terization of total RNA samples is largely independent of the instru-ment, sample concentration, and the operator allowing for the com-parison of samples across differ-ent laboratories.
RNA integrity
Figure 6 shows three RNA sam-ples at varying stages of intact-ness. The RIN tool gave three dif-ferent designations reprenting their respective intactness. During a large validation study using dif-ferent samples from the ones that
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Figure 6
The RNA integrity number was tested on samples of varying levels of intactness. The RIN soft-ware algorithm was able to accurately classify the samples.
were ud to train the algorithm, reliable sample classification was obtained.
Ribosomal ratios
Figure 7 shows the same sample human brain (Ambion, Inc.) total RNA that was run on three differ-ent instruments and reprenta-tive electropherograms of instru-ment 1 and 3 are shown. Riboso-mal ratios as generated with the bioanalyzer software are com-pared with RIN values. For the
36 samples there is a larger degree of variability when using riboso-mal ratios as compared to RIN values. Calculated RIN values were at 1.4 % coefficient of vari-ance while ribosomal ratios had a 5.1 % CV. Keep in mind that the CV values refer to identical sam-ples. When including samples from different species and tissues, significantly larger CV values are found for the ribosomal ratio. When analyzing a sample at vari-ous dilutions a similar picture is found (figure 8). Mou brain total RNA was diluted into three differ-ent concentrations, 25 ng/µL,
100 ng/µL and 500 ng/µL. For the 108 samples tested it is apparent that the RIN value outperforms the ribosomal ratio by a large mar-gin. RIN CVs were at 3 % versus 22 % for the ribosomal ratio.
It should be noted that below
25 ng/µL, no accurate RIN values can be obtained. Best results are obtained for concentration values above 50 ng/µL.
Figure 7
36 total RNA samples were analyzed on three different instruments. The RIN was compared with the ribosomal ratio value. CV’s for the RIN tool were significantly lower than for the ribosomal ratios.
Figure 8
When testing an identical RNA sample in various dilutions, identical RINs are
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obtained, within narrow limits, whereas ribosomal ratios show a much lesr
degree of reproducibility.
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