A Map to More Reliable RT-qPCR Results Using MIQE Guidelines

Thursday, November 7, 2013

RT-qPCR (reverse transcription-quantitative polymerase chain reaction) is used routinely by a broad spectrum of researchers to assess the absolute or relative quantity of genomic DNA (gDNA) or complementary DNA (cDNA) in or between samples. Unfortunately, the many steps required for a successful experimental outcome have resulted in the adoption of a wide variation in methodologies, even within the same lab. This has led to publication of potentially misleading and even artifactual results that can slow the pace of research.1 The publication of the MIQE (Minimum Information for the publication of Quantitative real-time PCR Experiments) guidelines in 20092 and subsequent articles3–9 provided the scientific community with criteria to help achieve high-quality RT-qPCR results with valid conclusions.3–9 However, the majority of labs favor using their historical methods of experimentation over these highly recommended best practices.10 In an attempt to help remedy this ongoing challenge of adoption, a simplified roadmap based on the MIQE guidelines has been produced (see Figure 1).

A roadmap to excellence in RT-qPCR

The most challenging hurdle in designing an experiment is to ensure that the results are not a consequence of poor execution of one or more critical step(s). For RT-qPCR, this can be an overwhelming task due to the multiple interdependent steps, kits, and techniques required to produce the final results, including:

Figure 1 – A roadmap to solid data for qPCR.
  1. Experimental design (e.g., technical and biological replicates, number of samples, downstream statistical analyses)
  2. Reproducible treatment of the organisms or cells
  3. Sample extraction, collection, and lysis for RNA or gDNA isolation and purification
  4. Purity and quality control of the RNA or gDNA
  5. Validation of the reverse transcription reaction (e.g., dynamic range, sensitivity, linearity)
  6. Bioinformatics of assay design (e.g., unique sequence ID, secondary structure, primer interactions)
  7. Validation of the assay to evaluate numerous performance attributes (e.g., PCR efficiency, dynamic range, sensitivity, specificity, linearity)
  8. Reference gene validation to ensure that stable target(s) are chosen for normalization.

Most labs complete some, but not all, of these steps. Without a consistent and thorough approach, the quality of resulting data varies widely, and can have potentially misleading interpretations.11,12 In such instances, researchers may not be fully aware that they have omitted a step or executed a step poorly.

Step 1: Design experiments with care

Careful experimental design is necessary for good results, and that is certainly the case for RT-qPCR, in which interrogating the transcriptome is ultrasensitive to even small changes in parameters. Researchers should take extra care to ensure that the differences in gene expression reflect the different treatments or underlying biological differences and are not a direct result of flawed execution. A good way to begin the design process is to create an experimental “recipe” that includes the following five items: targets, treatments, time points, biological replicates, and technical replicates. The sum of these items will provide the total number of wells required to perform a given experiment revealing the amount of reagents, compound(s), media, animals, etc. This allows one to not only calculate the study’s total cost but also to order compounds, media, and other reagents to cover the entire experiment, avoiding lot-to-lot variability of reagents used in the experiment.

Step 2: Achieve consistent sample treatment

Treating cells, tissue, or animals with drugs is a common source of error due to the varying post-treatment incubation times.13 Transcription of mRNA is dynamic such that a treatment-induced transcriptional effect can be observed only during a particular time frame. Thus, sampling a series of time points may be the difference between valid results, less than optimal data, or no data at all.

Many researchers are aware of the dynamic nature of the transcriptome and plan experiments to sample different time points; however, some may fail during implementation. A good example is the addition of a compound to several plates of cells that will be used in a time-course study in which all the plates are treated at precisely the same initial time. Under this circumstance, it would be difficult, or impossible, to stop the treatment for several replicates at each time point due to the time required to manipulate each replicate plate of cells. A more accurate approach is to stagger the treatments between each replicate to allow enough time to stop treatment at precisely the same time for each plate in a replicate group.

Step 3: Stop transcription quickly and reproducibly

The surgical removal of tissue, or the collection of cells from a plate, can be stressful when the researcher has not had adequate training and practice. Bearing in mind that the transcriptome is dynamic13 and highly sensitive to environmental factors14 (for example, tissue removal from an animal, washing cells on a plate, or tissue-handling methods), the methodology to harvest the sample must be highly reproducible in the shortest possible time frame. This will minimize transcriptional changes induced by the manipulation of the samples, and can dramatically reduce the variability in expression between biological replicates in which statistical significance may be key to a successful publication. Flash freezing of tissue samples in liquid nitrogen immediately upon isolation is typically recommended. For cell-based assays, the initial RNA extraction buffer should be added from a kit directly to the washed cells on the plate with scraping and mixing to form a stable homogenate that can then be frozen at –20 ºC or –80 ºC.

Step 4: Check the Purity and Quality of RNA and gDNA

The extraction and purification of RNA and gDNA from a sample is another common source of error. This type of error is invariably inherent because every lab has its own preferred extraction and/or purification method. Kit-based extraction methods that include RNase-free reagents with on-column DNase I digestion and purification, such as the Aurum™ RNA extraction kit (Bio-Rad  Laboratories, Hercules, CA), are highly recommended for pure and intact total RNA.

The purity of RNA can be the cause of variable Cq values from contaminants that can affect reaction efficiency,15 and can be verified using a calibrated spectrophotometer to determine the OD readings and ratios for A260 nm/A280 nm and A260 nm/A230 nm; both should yield a ratio of 1.8 to 2.0. The quality of RNA or gDNA can be evaluated by running a gel or using an Experion™ automated electrophoresis system (Bio-Rad) to visualize ribosomal bands for RNA and a single band for gDNA to ensure the samples pass minimum standards for reverse transcription or direct qPCR (for gDNA).2,5

Step 5: Attain consistent and complete coverage of the transcribed genome

An important aspect of the RT step is to obtain consistent and complete unbiased coverage of the transcribed genome in the extracted RNA sample. Not all reverse transcription kits are developed equally, and it is vital to evaluate several key performance attributes prior to commencing the RT-qPCR project. These include dynamic range, sensitivity, and linearity. The iScript™ cDNA synthesis kits (Bio-Rad) synthesize fuller-length, unbiased cDNA through the use of an RNase H+ MMLV RT enzyme, a proprietary blend of oligo(dT) and random primers, a potent blend of RNase inhibitors, and a patented RT inhibition reducer. To ensure complete reverse transcription of the lowest expressing targets and/or limited amounts of RNA, these kits enable an RNA dynamic range from 100 fg to 1 μg.

Step 6: Design primers with bioinformatics in mind

All self-designed assays must be vetted through an in-depth bioinformatics pipeline to ensure optimal performance. Although details will not be given here, the pipeline should, at a minimum, include the following:

  • Biological significance (correct isoform/splice variant)
  • Sequence quality/secondary structure (m-fold)
  • Sequence length (~80–500 bp)
  • Masking the sequence (repeat masker/SNP [single-nucleotide polymorphism] masker)
  • Uniqueness of the sequence (BLAST/BLAT)
  • Use of a preferred assay design tool
  • Uniqueness of the assay (in silico PCR).

Step 7: Validate the assay

An integral part to this step is the choice of qPCR reagents. Guanine-cytosine (GC)- and/or adenine-thymine (AT)-rich targets as well as templates with high secondary structure, for example, are very challenging to amplify with standard Taq-based reagents. Therefore, it is critical to use an alternate enzyme designed for such situations to ensure the enzyme remains bound to the template and maintains processivity. Sso7d fusion enzymes are designed to maintain optimal binding to ensure complete PCR reactions. SsoAdvanced™ supermixes from Bio-Rad provide Sso7d fusion enzymes in addition to a variety of other high-performance features, enabling optimal qPCR on virtually all sample types and target sequences.

Wet-lab validation of the primer annealing temperature (Ta) is important to ensure optimal PCR reactions because the assay used for a given experiment may not reflect the parameters used by primer design software that give a “predicted” Ta. Gradient-enabled real-time PCR instruments such as the CFX96 Touch™ and CFX384 Touch real-time PCR systems (Bio-Rad) allow for the rapid testing of multiple primer pairs for annealing temperature. Too high a Ta results in less than optimal primer binding; too low a Ta results in mispriming events.

Using an appropriate Ta, it is important to assess reaction efficiency with standard curves to reveal the dynamic range, sensitivity, specificity, and linearity of DNA concentrations that can be used in each reaction. Simply stated, a minimum of two targets—reference and low expressing—can be used to evaluate all of the key performance attributes. For a full evaluation, a reference, high, medium, and low expressing target can be used. Each target should be evaluated using a standard curve. Depending on the expression level, a five- to tenfold, 5–6 point serial dilution is strongly recommended.

To learn more about these key performance attributes and how to perform these experiments in a simple step-by-step approach, see www.bio-rad.com/supermixes_tutorial. To avoid most of the primer design and validation steps required to produce reliable data, PrimePCR™ assays and panels from Bio-Rad offer SYBR® Green (Molecular Probes, Eugene, OR) and probe-based qPCR assays that have undergone rigorous bioinformatics followed by wet-bench efficiency and sequence validation, ensuring 99.9% accuracy.

Step 8: Determine the appropriate reference genes

Many published papers discuss reference gene selection for normalization and the potential negative effects on data interpretation.16–21 Selection of an inappropriate reference gene can dramatically change data interpretation. Many journal reviewers now request supporting data for the chosen reference gene and often require the use of multiple targets for normalization. Use of predesigned reference panels enables quick screening of a representative sample set across numerous potential reference genes. PrimePCR reference gene panels are a good option. To assess target stability, software programs such as GeNorm (Biogazelle, Zwijnaarde, Belgium) have become the gold standard in analyzing the relative change in gene expression across the samples, providing an automated, unbiased approach to reference gene selection.

Conclusion

RT-qPCR is a highly sensitive experimental tool that can yield quantitative, accurate, and statistically significant data. However, without proper experimental design and wet-bench validation, it is prone to producing misleading results that give researchers a false sense of security.9 From sample preparation to assay design, the determination of reverse transcription and PCR reaction efficiency, and the selection of valid reference genes, following the steps mapped here and in the MIQE guidelines will ensure that the shortest path is followed to optimal performance and valid qPCR data for publication.

For more information on MIQE, see https://www.americanlaboratory.com/Blog/154106-The-MIQE-Bedtime-Story-A-Tale-of-Two-qPCR-Experiments/.

References

  1. Williams, S.C.P. How reliable is real-time PCR? Biotechniques  2012.
  2. Bustin, S.A.; Benes, V. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem.  2009, 55, 611–22.
  3. Bustin, S.A.; Vandesompele, J. et al. Standardization of qPCR and RTqPCR. Gen. Eng. Biotechnol. News  2009, 29(14), 1–3.
  4. Bustin, S.A. Why the need for qPCR publication guidelines? The case for MIQE. Methods  2010, 50(4), 217–26.
  5. Taylor, S.; Wakem, M. et al. A practical approach to RT-qPCR—publishing data according to the MIQE guidelines. Methods  2010, 50(4), S1–5.
  6. De Keyser, E.; Desmet, L. et al. How to perform RT-qPCR accurately in plant species? A case study on flower colour gene expression in an azalea (Rhododendron simsii hybrids) mapping population. BMC Mol. Biol.  Jun 24, 2013, 14(1), 13.
  7. Dooms, M.; Chango, A. et al. Improving biological relevancy of transcriptional biomarkers experiments by applying the MIQE guidelines to pre-clinical and clinical trials. Methods  Jan 2013, 59(1), 147–53.
  8. Lanoix, D.; Lacasse, A.A. et al. Quantitative PCR pitfalls: the case of the human placenta. Mol. Biotechnol.  Apr 17, 2012.
  9. Taylor, S.; Buchanan, M. et al. A MIQE case study—effect of RNA sample quality and reference gene stability on gene expression data. Bio-Rad Laboratories Tech Note 6245, Dec 2011, 1–6.
  10. Google Scholar search for “qPCR” after 2010 related to total citations for the MIQE Guidelines paper (see Ref. 2).
  11. Bohlenius, H.; Eriksson, S. et al. Retraction—the mRNA of the Arabidopsis gene FT moves from leaf to shoot apex and induces flowering. Science  2007, 316(5823), 367.
  12. Editors of Lancet. Retraction—ileal-lymphoid-nodular hyperplasia, nonspecific colitis, and pervasive developmental disorder in children. Lancet  2010, 375(9713), 445.
  13. Bar-Joseph, Z.; Gitter, A. et al. Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet.  2012, 13(8), 552–64.
  14. Ma, Y.; Dai, H.L. et al. Impact of warm ischemia on gene expression analysis in surgically removed biosamples. Anal. Biochem.  2012, 423(2), 229–35.
  15. Opel, K.L.; Chung, D. et al. A study of PCR inhibition mechanisms using real time PCR. J. Forens. Sci.  2010, 55(1), 25–33.
  16. Barber, R.D.; Harmer, D.W. et al. GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol. Genomics  2005, 21, 389–95.
  17. Jacob, F.; Guertler, R. et al. Careful selection of reference genes is required for reliable performance of RT-qPCR in human normal and cancer cell lines. PLoS One  2013, 8(3).
  18. Rhinn, H.; Marchand-Leroux, C. et al. Housekeeping while brain’s storming: validation of normalizing factors for gene expression studies in a murine model of traumatic brain injury. BMC Mol. Biol. Jul 8, 2008, 9, 62.
  19. Schmittgen, T.D.; Zakrajsek, B.A. Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J. Biochem. Biophys. Methods  Nov 20, 2000, 46(1–2), 69–81.
  20. Thellin, O.; Zorzi, W. et al. Housekeeping genes as internal standards: use and limits. J. Biotechnol.  Oct 8, 1999, 75(2–3), 291–5.
  21. Yang, X.; Hatfield, J.T. et al. Bone to pick: the importance of evaluating reference genes for RT-qPCR quantification of gene expression in craniosynostosis and bone-related tissues and cells. BMC Res. Notes  May 8, 2012, 5, 222; additional file 1.

Sean Taylor, Ph.D., is Field Application Specialist, Bio-Rad Laboratories (Canada) Ltd., 1329 Meyerside Dr., Mississauga, Ontario L5T 1C9, Canada; tel.: 905-364-3435; fax: 905-364-3434; e-mail: [email protected].

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