Hallmarks of good RT-qPCR measurements in a successful knockdown experiment

Hallmarks of good RT-qPCR measurements drive Cq-value troubleshooting in RNAi knockdown experiments

Dharmacon’s siRNA and shRNA technology has a broad spectrum of applications in single assay as well as for RNAi screening. An extensively characterized advanced design algorithm, proprietary chemical modifications, and optimized shRNA backbones are the basis of the knockdown guarantees for all predesigned siGENOME, ON-TARGETplus, and Accell siRNAs as well as SMARTvector shRNAs. To ensure optimized workflows these guarantees are based on demonstrating delivery and detection using a positive control and measuring the knockdown at the mRNA level. Because RNAi acts directly on mRNA, the RT-qPCR technique is highly recommended to quantify the gene knockdown. When the measured gene knockdown does not exceed the guaranteed 75%, insights from the qPCR results can be used to troubleshoot experiments. This article will present some of these too often ignored red flags and will propose potential solutions.

In this blog two different scenarios are distinguished to help pinpoint the causes of unexpected results: insufficient (<75%) knockdown is measured, or an overexpression rather than a knockdown is measured.

Table 1 shows a knockdown experiment using the ΔΔCq calculation method of an experiment where the Gene of Interest (GOI) is successfully knocked down. Table 2 highlights the indicators associated with the most common potential root causes for each scenario.

Please select the proper scenario and then consider each suggested cause (associated with the letters) to find our recommended corrective actions.

 

cq calculation methods

Table 1: Example of detailed ΔΔCq calculation method. NTC: Non-Targeting Control; GOI: Gene of Interest.

 

cq calculation method red flag

Table 2: ΔΔCq calculations and proposed red flags. Colors/letter codes are associated with different troubleshooting suggestions; Cq: quantification cycle measured by qPCR instrument. NTC : Non-targeting control; GOI: Gene of Interest; No RT control: No Reverse Transcription control.

 

Scenario 1: RT-qPCR measures a knockdown lower than 75% for the gene of interest

There are 6 indicators suggesting the following optimization:

a) A positive control should lead to > 75% knockdown. If not, have the following points been verified:

  • Appropriate timing of the analysis. For instance, with siRNA transfection, are the cells harvested 24h to 48h after delivery?
  • Optimal siRNA delivery conditions obtained (siRNA concentration, volume of transfection reagent, cell confluency)?
  • In the case of shRNA, low knockdown levels could be caused by the degradation of the lentiviral particles, low functional MOI, or selection of a promoter which is not compatible with the cell type (please contact us for more detailed troubleshooting).

If a positive control has not been included with the data, our control siRNAs and shRNAs can help verify optimal delivery and detection conditions.

b) A significant amplification in the “no RT” wells (<35 Cq) could be due to traces of genomic DNA. This amplification can mask situations where knockdown has been achieved. Improving DNase treatment or using qPCR primer that span exon junctions or flank a long intron could solve this issue.

c) No change in ΔCq values for the GOI suggests that the qPCR primers may not target the proper gene. Verification of primer specificity with an in silico PCR tool and testing a different qPCR primer pair could resolve the issue.

d) Very high Cq values (>30 Cq) could be caused by several reasons:

  • Low integrity of the RNA preparation which may result from low extraction yield or non-ideal storage conditions. A new RNA extraction may solve the issue.
  • Low quality of cDNA preparation which may result from issues with the RT step. A new cDNA preparation may solve the issue.
  • Primers which are either not specific to the targeted gene, or non-efficient. Testing a different pair of primers may be helpful.
  • Very low or no expression of the GOI in the cell line. In such cases, a gain-of-function study based on CRISPR activation could be more relevant.

e) With a significant difference of Cq between the GOI and the internal control (ΔCq>10 or ΔCq<-10) the control cannot properly account for variations of cDNA, potentially masking the knockdown (see "Guideline to reference gene selection for quantitative real-time PCR”1). Consider different internal control genes with expression levels more similar to those of the GOI.

f) The control for the baseline should be the non-targeting control (NTC) to account for the impact of delivery. Non-normalized data might mask the knockdown. We do offer various options for Non-Targeting controls.

 

Scenario 2: RT-qPCR suggests gene overexpression rather than knockdown

There are 3 main indicators for which we suggest the following solutions:

g) Technical Cq duplicates might not identify potential outliers. Use at least technical triplicates to ensure that data is reproducible and outliers can be excluded.

h) A high standard deviation (>0.2) indicates excessive variations between samples. Consider technical issues caused by inconsistent pipetting (e.g. amount of cDNA template, primer and qPCR reagent concentrations) or excessive cell death due to non fully optimized delivery conditions.

d) Very high Cq values (>30 Cq) could be caused by several reasons:

  • Low integrity of the RNA preparation which may result from low extraction yield or non-ideal storage conditions. A new RNA extraction may solve the issue.

  • Low quality of cDNA preparation which may result from issues with the RT step. A new cDNA preparation may solve the issue.

  • Primers which are either not specific to the targeted gene or non-efficient. Testing a different pair of primers may be helpful.
  • Very low or no expression of the GOI in the cell line. In such cases, a gain-of-function study based on CRISPR activation could be more relevant.

 

Issues with PCR reagents are best resolved with the supplier.

If you would like to discuss your knockdown troubleshooting with us, feel free to contact our Scientific Support team .

References
  1. Radonić, A. et al. Guideline to reference gene selection for quantitative real-time PCR. Biochemical and Biophysical Research Communications 313, 856–862 (2004).
RNAi troubleshooting resources
Jan Korte headshot
Written by Jan Korte, Ph.D. Scientific Support Specialist 2
Jan has been part of the Scientific Support team since 2017. He is driven by good scientific practice and energized to see strong data sets supported by a broad spectrum of orthogonal validation strategies. Fascinated by Dharmacon´s RNAi and CRISPR gene editing reagents, as well as the recently developed CRISPRmod family of gene modulation tools (CRISPRa and CRISPRi), he enjoys guiding customers in the combined usage of these different techniques. 
Written By Sébastien Muller, Ph.D. Scientific Support Specialist
Sébastien has been part of the scientific support team since January 2017. Prior to that he has got 14 years’ experience at the bench for academic research, and has accumulated a broad knowledge in Molecular Biology, especially in the field of non-coding RNAs. He enjoys guiding scientists with their challenging troubleshooting questions in RNAi or CRISPR related fields.
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