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Thinking of a novel qPCR normalization method that could make it more precise

Image credit: Taken from Dheda et al (https://doi.org/10.2144/04371RR03)

jnikola Mar 10, 2021
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The question

Can we think of a better universal reference/housekeeping control or normalization method for the qPCR, which is preferably cheap and easily implementable and would allow us to determine the gene expression between the samples more precisely?

In Western Blot Assay, along with housekeeping proteins, we have Ponceau Red or Amido Black dyes, which bind to the total protein content in the samples and help the normalization. The qPCR is more sensitive to small differences and, in my opinion, requires better normalization controls than the widely used ones that rely on reference genes.

Basic Introduction

Reverse transcription-quantitative Polymerase Chain Reaction (RT-qPCR) is a quantitative laboratory technique used to detect specific nucleic acids in the sample . In short, the amplification of cDNA that was reversely transcribed from the original mRNA molecule is being monitored by measuring the fluorescence. So, if we are interested in how active is BRCA1 gene, we will isolate the total RNA, apply the same concentration to the reverse transcription (RT) protocol to get the cDNA and do the qPCR. The cDNA is amplified in 30-40 heating and cooling cycles in which the amount of the DNA doubles (ideally) every cycle.
Example: If we detect the fluorescent signal of a BRCA1 cDNA in the 20th cycle in one sample, and in the 22nd cycle in the other, it means that we approximately have 4x more BRCA1 mRNA in the first sample.

There are the two most used detection methods, and consequently, qPCR types:
1) non-specific fluorescent dyes that intercalate with any double-stranded DNA (etc. SYBR)
2) sequence-specific DNA probes with a fluorescent reporter (etc. Taq-man)

Introduction of the problem

What I am interested in is the normalization of the samples. Not the quality control, but the normalization of an expression of a single gene between two or more samples.

Let's say we have 20 samples and a BRCA1 gene that we want to analyze. We can simply put triplicates of every sample on the plate, add primers and SYBR mix and proceed to the qPCR. We will get the information in which cycle does the signal appears, but is that enough to say that we have e.g more BRCA1 mRNA in sample 1, compared to sample 2? No, because we could have simply had more cDNA in sample 1. Why? Well, small mistakes and imperfect procedures of RNA isolation, RNA concentration measurement and RNA quality assessment could have affected the equality and efficiency of the RT and which would then result in different amounts of synthesized cDNA. Therefore, to compare sample on the same or on different plates, we need the so-called "housekeeping control".

Up-to-date solutions

  • Reference/housekeeping genes
To overcome this problem, scientists started analyzing certain genes (Figure 1) which are believed or proven to have their expression unchanged in the condition of interest.

Problem 1: How can we be sure that our control genes are not changing? Every cell is different and thus, responds in a different way to stimuli. In addition, the same cell will probably not respond in a completely identical way when treated/stimulated again . Therefore, how can the activity of certain genes, that are the constituent parts of the same highly dynamic biological system as the gene of interest, be used as normalisation controls?
Problem 2: What reference genes should we use when exploring the novel condition which was not researched before and no signaling pathway or gene expression information is available?

Figure 1. Widely used reference genes. Taken and adapted from Dheda et al. .

  • Softwares that find the best reference genes
There are some software or add-on features developed which try to overcome the normalization problems of the qPCR. Some of them, like geNorm, calculate the ratios between the expressions of multiple reference genes and define the "normalization factor", which means that the normalization of the RT-PCR data is based on two or three reference genes. BestKeeper assesses the genes by pair-wise correlations based on raw crossing points (CP) or cycle threshold (Ct) values. Although the approach is different from the above-mentioned geNorm, the result of the BestKeeper is also to identify the best housekeeping gene. The NormFinder uses a bit different approach to achieve the same goal as the tools above.

  • Softwares that do the normalization without the reference genes
On the other hand, some software try to avoid using the reference genes and calculate mean expression values for each replicate across the studied target genes and subsequently estimating a normalization factor that estimates and reduces the systematic bias of a replicate across all genes. In short, they try to minimize the variances that are results of technical performance without the use of reference genes. The biggest flaws are that their precision depends proportionally on the number of replicates and target genes and the user is required to have moderate programming skills.

The new concept criteria

  • cheap
  • easy implementable on current qPCR systems
  • does not affect the handling or running time
  • does not affect the efficiency of the qPCR
  • does not interfere with fluorescence dyes or probes
  • minimal room for a technical mistake







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