RT-qPCR analysis of non-protein-coding RNA biomarker expression

RT-qPCR analysis of non-protein-coding RNA biomarker expression

Non-protein-coding RNAs (ncRNAs) are expressed in viruses, archaea, prokaryotes and eukaryotes. They fulfil vital roles in the regulation of chromatin architecture, epigenetic memory, transcription, RNA splicing, editing and turnover [1, 2]. To date, at least 18 distinct types of ncRNA have been identified, which are classified according to their size, function, subcellular localization and target specificity [3, 4].

A role for ncRNAs as biomarkers?
Any ncRNA that undergoes a quantifiable change in its expression profile, and accompanies a change in cellular physiology or tissue homeostasis, is a potential biomarker [5].  The challenge is to demonstrate a meaningful association between the two.  A growing body of literature suggests that ncRNA expression is modulated in a variety of diseases and disorders [4-11].  Indeed, ncRNA biomarker analysis is now in clinical use as a tool to evaluate cancer risk, diagnosis and prognosis (Table 1).  

Table 1 – ncRNA cancer biomarkers (adapted from [5]).

BIOMARKER

ncRNA CLASS†

TYPE OF CANCER

LOCALIZATION

UP / DOWN

APPLICATION

REF

PCA3

lncRNA

Prostate

Tissue, Extracellular

Up

Diagnosis

12

HULC

lncRNA

Pancreatic

Tissue

Up

Prognosis

13

HOTAIR

lncRNA

Nasopharyngeal

Tissue

Up

Prognosis

14

miR-21

miRNA

Pancreatic

 

 

 

Glioblastoma and brain mets

Tissue

 

 

 

Extracellular

Down

 

 

 

Up

Clinical outcome prediction, potential therapeutic target

 

Diagnosis

 

15

 

 

 

16

piR-651

piRNA

Lymphoma

 

Gastric

Tissue

 

Extracellular

Down

 

Down

Prognosis

 

Diagnosis

17

 

18

SNORD33, SNORD66, SNORD76

snoRNA

Non-small-cell lung

Tissue, Extra-cellular

Up

Up

Diagnosis

Diagnosis

19

piR-823

piwiRNA

Gastric

Extracellular

Down

Diagnosis

18

miR-125a

miRNA

Oral

Extracellular

Down

Diagnosis

20

miR-200a

miRNA

Oral

Extracellular

Down

Diagnosis

20

miR-18a

miRNA

Liver

Extracellular

Up

Diagnosis

21

miR-221

miRNA

Liver

Extracellular

Up

Diagnosis

21

miR-222

miRNA

Liver

Extracellular

Up

Diagnosis

21

miR-224

miRNA

Liver

Extracellular

Up

Diagnosis

21

miR-193b, miR-301, miR-131, miR-200b

miRNA

Non-small-cell lung

Extracellular

Up

Diagnosis

22

miR-10b

miRNA

Glioblastoma and brain metastasis

Extracellular

Up

Diagnosis

16

†lncRNA long non-coding RNA; miRNA microRNA; piRNA PIWI-interacting RNA; snoRNA small nucleolar RNA

Assays of ncRNA biomarker expression
RT-qPCR > amplification is a sensitive and convenient way to quantify expression of certain classes of ncRNAs.  It may be used as a stand-alone assay, or to validate other PCR-based platform technologies, such as high-throughput RT-qPCR [23], RNA-Sequencing (RNA-Seq) [8, 24], two-tailed RT-qPCR [25], TaqMan® ncRNA assays [26], and drop digital PCR (ddPCR) [27, 28). RT-qPCR > may also complement protein-based and non-PCR-based assays of gene expression, such as in-situ hybridization [29], RNA fluorescence in-situ hybridization (RNA-FISH) [30], locked nucleic acid and flow cytometry FISH (LNA-FISH) [31], isothermal amplification [32-34], and peptide-nucleic acid probe-based single molecule arrays [35].

Considerations for selecting RT-qPCR for ncRNA biomarker analysis
Informed experimental design, execution and analysis, are key to obtaining meaningful RT-qPCR data.  However, there are some caveats to its use for ncRNA analysis:

. Biological considerations

MicroRNA (miRNA) analysis  – Unlike long non-coding RNAs (lncRNAs), mature miRNAs (19-24 nt) can be a challenge to quantify by RT-qPCR, for several reasons:  
•    Their small size (19-24 nucleotides) may put them at the threshold of detection.
•    The variable GC content among miRNAs may complicate assay design and optimization, especially when a common protocol is used for multiple, or all, miRNA targets.  
•    The RT-qPCR assay may not be sensitive or specific enough to detect single-base differences among miRNAs within the same family.  
•    miRNAs are subject to various post-transcriptional modifications, and differences in their sequence and nucleotide composition at either or both ends may hamper quantification.
•    The protocols that are adopted for miRNA isolation and purification, will likely vary because of the different niches that miRNAs occupy, and because of the different treatments used to extract miRNAs: in some cases, inhibitors of the reverse transcriptase and polymerase enzymes may be carried over into the RT-qPCR reaction mix, despite extensive purification.
•    The assay must be specific and sensitive enough to detect and quantify different members of the same miRNA family [25].
•    Use the Genaxxon miRNA Purification Mini Spin Kit > for your rapid and unbiased, phenol-free extraction of highly enriched small RNA.

. Experimental considerations

1. Scope and Purpose - RT-qPCR is unsuited to biomarker discovery, because primer design and cycling conditions are deduced from a known ncRNA sequence.  Secondly, RT-qPCR cannot interrogate the entire ncRNA transcriptome at once, but instead, caters for the analysis of small numbers of known ncRNAs.
2. Know your starting material - Unless you know the actual number of ncRNA molecules that are present in your sample at the outset, it will be a challenge to deduce its function, and to understand the effectors that modulate its level of expression.  For example, a ncRNA that is present in 1 copy per 100 cells may be considered as transcriptional noise or artefact; a transcript that is present in 2 copies per cell and is associated with chromatin, might be interpreted as acting in cis, while a transcript present in 1000 copies per cell might be thought to be acting in trans. Discordant replicates underpin problems with reproducibility and false positive or negative data.  Missing data may be caused by sub-threshold levels of ncRNA in the sample, or by technical errors.  In summary, knowing the actual number of transcripts that is present in the sample, informs functional hypotheses [36, 37].   
3. Primer efficiency may be negatively affected if the sample consists of heterogeneous miRNAs [36].
4. Know your metrics - RT-qPCR relies upon an increase in fluorescence signal that is proportional to the amount of amplicon that is produced.  This is represented by the cycle threshold (CT) value.  The CT is defined as the number of thermal cycles required for the fluorescence signal to rise above background noise, but it is often taken to be the point at which amplification enters log phase.  It is therefore, a rate-based measurement, and a powerful guide to relative concentration.  Furthermore, cognate and partially-cognate sequences may compete for primer binding; and native secondary structures may be present that are not in the synthetic template, which is often used to generate the standard curve.  Together, these differences may confound the results [36].
5. Use Appropriate references - Housekeeping genes (e.g. glyceraldehyde-3-phosphate dehydrogenase (GAPDH)), ribosomal RNA or ribosomal protein (RPL30), are commonly used to normalize CT values across samples.  However, their expression levels can vary among cell populations, cellular compartments, tissue types and samples [36].

. Computational considerations

1. Not all algorithms are equal - Most methods are based on the principle that the amount of ncRNA at the start of the qPCR amplification, is a function of the position of the amplification curve, with respect to the cycle-axis: the later the curve, the lower the quantity of ncRNA.  However, different algorithms are used (a) to determine the position of the curve, and (b) to determine the efficiency of the PCR reaction (i.e., the fold increase of product per cycle).  Furthermore, there is disagreement whether the PCR efficiency remains constant or decreases steadily.  An independent assessment of several different algorithms used, concluded that technical performance varies greatly, but the precision, bias and resolution of biomarker identification are reasonably consistent [38].
2. Handling missing data - RT-qPCR-based quantitation of circulating miRNAs presents many challenges.  On average, miRNA molecules are 19-24 nucleotides in length, and present in liquid biopsies in vanishingly small numbers.  Also, they are frequently missed on qPCR, because of off-target amplification, nonsense amplification curves, or discordance among replicates.  Strategies to minimize, and/or to understand the basis for missing data include:  
•    Confirm that the quantification cycle (Cq) and starting concentration (N0) values are valid (i.e., perform melting curve analysis or gel electrophoresis, to confirm the correct product has been amplified).
•    Set the Cq or N0 value to “missing”, when an off-target product is amplified.
•    Ensure that the quality of the amplification curves is sufficiently adequate to allow analysis (i.e., the curves should at least consist of an exponential phase and a plateau phase, otherwise the curve analysis software will treat these data as missing).
•    Check the starting template concentration: the absence of amplification or of a plateau phase most often indicates that the template concentration was below the detection limit of the qPCR assay.
•    If amplifications deviate at high concentrations, it may be necessary to design a missing data algorithm for those conditions, because deviating amplifications do not only occur at low concentrations.
•    A large difference in Cq values among replicates could be due to technical variation or pipetting errors, or to the Poisson effect that occurs by chance when pipetting from the cDNA stock to the reaction plate.  A rule of thumb is that when the variation between replicate Cq values is greater than 0.5 cycles, the data should not be trusted, and both replicates should be discarded.
•    Missing values due to low concentrations can be distinguished from missing data due to technical errors, by setting the Cq at a critical value of 35.  Reactions with a Cq value equal to zero, or above 35, are considered to contain an undetectable amount of template [37].
•    For reliable qPCR, use Genaxxon bioscience qPCR master mixes with different levels of ROX, e.g. GreenMastermix with high ROX >, GreenMastermix with low ROX >, ProbeMastermix with high ROX >, Probe Mastermix with low ROX > or Probe Mastermix w/o ROX >.

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