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, 18 (4), 334-9

High-throughput RNA Interference Screening: Tricks of the Trade

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High-throughput RNA Interference Screening: Tricks of the Trade

N Miranda Nebane et al. J Lab Autom.

Abstract

The process of validating an assay for high-throughput screening (HTS) involves identifying sources of variability and developing procedures that minimize the variability at each step in the protocol. The goal is to produce a robust and reproducible assay with good metrics. In all good cell-based assays, this means coefficient of variation (CV) values of less than 10% and a signal window of fivefold or greater. HTS assays are usually evaluated using Z' factor, which incorporates both standard deviation and signal window. A Z' factor value of 0.5 or higher is acceptable for HTS. We used a standard HTS validation procedure in developing small interfering RNA (siRNA) screening technology at the HTS center at Southern Research. Initially, our assay performance was similar to published screens, with CV values greater than 10% and Z' factor values of 0.51 ± 0.16 (average ± standard deviation). After optimizing the siRNA assay, we got CV values averaging 7.2% and a robust Z' factor value of 0.78 ± 0.06 (average ± standard deviation). We present an overview of the problems encountered in developing this whole-genome siRNA screening program at Southern Research and how equipment optimization led to improved data quality.

Keywords: RNA interference; coefficient of variation; high throughput screening; small interfering RNA.

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
Z′ factor values from a 320 siRNA plate before and after troubleshooting and assay optimization. (A) Z′ analysis before troubleshooting with Z′ factor value of 0.51 ± 0.16 (average ± standard deviation), n = 43. (B) Z′ data after troubleshooting showing a robust Z′ factor value of 0.78 ± 0.06 (average ± standard deviation), n = 43.
Figure 2
Figure 2
Plate format showing layout of controls before troubleshooting. (A) Plate template showing controls on only one side of the plate. (B) Heat map of raw values from a 384-well plate showing edge effect.
Figure 3
Figure 3
Plate format showing layout of controls after extensive troubleshooting. (A) Plate template with controls on both sides of the plate, increasing the number of control wells. Negative control (scrambled siRNA) is strategically placed in the center wells of the column to minimize edge or corner effect. (B) Heat map of raw values from a 384-well plate showing eradication of edge effect following troubleshooting and change of plate format.
Figure 4
Figure 4
Reproducibility of RNAi high-throughput screening. Pearson correlation plots of percentage inhibition values across three replicates of one master plate (320 siRNAs) showing excellent reproducibility after troubleshooting.

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