Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces

Abstract

While it is universally accepted that intact RNA constitutes the best representation of the steady-state of transcription, there is no gold standard to define RNA quality prior to gene expression analysis. In this report, we evaluated the reliability of conventional methods for RNA quality assessment including UV spectroscopy and 28S:18S area ratios, and demonstrated their inconsistency. We then used two new freely available classifiers, the Degradometer and RIN systems, to produce user-independent RNA quality metrics, based on analysis of microcapillary electrophoresis traces. Both provided highly informative and valuable data and the results were found highly correlated, while the RIN system gave more reliable data. The relevance of the RNA quality metrics for assessment of gene expression differences was tested by Q-PCR, revealing a significant decline of the relative expression of genes in RNA samples of disparate quality, while samples of similar, even poor integrity were found highly comparable. We discuss the consequences of these observations to minimize artifactual detection of false positive and negative differential expression due to RNA integrity differences, and propose a scheme for the development of a standard operational procedure, with optional registration of RNA integrity metrics in public repositories of gene expression data.

Figure 1

Chromatograms of microcapillary electrophoresis from four RNA samples showing different degrees of degradation. (A) Typical electropherogram of high-quality RNA including a clearly visible 28S:18S rRNA peak ratio of 2.0. (B) Partially degraded sample as indicated by a shift in the electropherogram to shorter fragment sizes, 28S:18S rRNA ratio of 1.0. (C) Partially sheared genomic DNA contamination; and (D) RNA judged intact but 28S:18S rRNA ratio <2.0 (ratio equals 1.5).

Figure 2

Conventional RNA integrity definition. (A and B) A₂₆₀:A₂₈₀ and 28S:18S ratio distributions were checked for 399 RNA sample profiles. Predefined classes and number of observations are, respectively, indicated in the x- and y-axes. Lines refer to mean distribution. (A) The A₂₆₀:A₂₈₀ ratio distribution from normal (in black) and cancerous (in gray) sample types is shown. (B) rRNA area ratios were successfully computed from 348 RNA sample profiles by the Agilent biosizing software based on a routine baseline detection and definition of ribosomals peak areas. Distribution of cell lines (in black) and tissue type (in gray) samples is shown. (C) Human categorization was done from Agilent electropherograms by trained operators who indexed 379 RNA sample profiles into 5 discrete classes (Human Categorization-level, HC-level). The distribution is represented by the number of records in each class. The remaining 20 samples could not be allocated.

Figure 3

RNA degradation characterization. Integrity of 399 RNA sample profiles was scored using the degradometer software. (A) A total of 334 RNA profiles were successfully categorized into 5 predefined alert classes using a mathematical model that quantifies RNA degradation and computes a degradation factor (DegFact). Four classes (White, Yellow, Orange and Red) are associated with different levels of degradation. A fifth class, Black alert corresponds to samples that the system was not able to qualify with accuracy (n.d.). The distribution is represented by the number of records in each class. (B) Comparative analysis was done using human evaluation (x-axis) based on electrophoresis analysis as a reference for RNA integrity classification; observations of rRNA peak heights and DegFact values were taken at each of the 5 HC levels. Histograms refer to the mean 28S and 18S rRNA peak heights and 95% confidence intervals (fluorescence intensities; left scale). Mean DegFact values and 95% confidence intervals (arbitrary unit, right scale) are plotted with the means joined.

Figure 4

RIN categorization. Integrity of 399 RNA sample profiles was scored using the 2100 RIN expert software. (A) Classification was done based on a numbering metrics, from 1 to 10, with 1 being the most degraded profile and 10 being the most intact. RIN numbers were successfully computed for 363 RNA profiles; number integer category is indicated in the x-axis, N/A refers to remaining failed computation. The distribution is represented by the number of records in each class. (B) Comparative analysis was done using human evaluation (x-axis) based on electrophoresis analysis as a reference for RNA integrity classification; observations of RIN number and DegFact values were taken at each of the 5 HC levels. Mean RIN numbers (right scale) or DegFact values (left scale) and 95% confidence intervals are plotted with the means joined.

Figure 5

Relative expression between quality metrics categories. Q-PCR collected on 16 aliquots of unique batch of RNA of various RNA integrities (cf. Table 1) and 3 housekeeping genes (GUSB, TFRC and GAPD). (A and B) Representation of the mean fold change (2^−ΔΔCt) of the RNA aliquots grouped by quality metrics categories, including (A) RIN number and (B) DegFact values, with expected 95% confidence intervals. (C) Distribution of the threshold cycle (Ct) measured on the 16 RNA aliquots using GUSB-5′ (x-axis) and GUSB-3′ (y-axis) Taqman probes. The linear regression curve is indicated.

Figure 6

Workflow of operational procedure for RNA quality assessment. Integrity of the RNA, once extracted and purified from cell lines, clinical or biological tissues samples, is controlled from the widely used bioanalyzer electrophoretic traces. As standard part of the Agilent analysis software (25), a RIN metrics is first calculated, scoring each RNA sample into 10 numerically predefined categories of integrity (RIN, from 1 to 10; N is a threshold value). As an independent control, a degradation factor metrics (DegFact, from 1 to ∞; N′ is a threshold value) may optionally be allocated to each RNA sample using the bioanalyzer-independent degradometer software (24). In a standard operating procedure, RIN and/or DegFact metrics will first be used as a standard exchange language to document RNA integrity and degradation, second to classify the RNA in homogeneous groups, and finally to select samples of comparable RNA integrity to improve the scheme of meaningful downstream experiments. The standard operating procedure will benefit from feedback information that will help users to define threshold integrity metrics values based on the results of RNA-based analyses.