The overall goal of the following experiment is to test whether a particular DNA variant such as one associated with the disease has an effect on the expression of a particular gene. This is achieved by selecting cell lines that are heterozygous for both this variant and a coding snip in the gene of interest. As a second step, DNA and RNA are extracted from the cells and then used as template for an allele specific primary extension reaction.
Next primary extension products are analyzed by mass spectrometry in order to quantify the relative abundance of the two alleles. Results are obtained that show whether the original DNA variant associated with a disease is also associated with a difference in gene expression based on quantitative allele specific measurements by mass spectrometry. The main advantages of this technique are that unlike with a luciferase reported gene assay, it doesn't require molecular manipulation and it measures endogenous levels of gene expression.
Moreover, we're able to compare levels of expression between the two alleles in an internally controlled way, which removes some of the experimental variability. This method helps us answer key questions in the field of human genetics such as the molecular mechanisms underlying genetic associations. We first set the idea of this method when we identify genetic association spanning three genes and could not refine a signal to a single one.
By farther genetic mapping. The cells used for this assay should express the gene of interest as well as meet two fundamental genotypic requirements. They have to be heterozygous both for the DNA sequences associated with disease and for DNA markers, which can distinguish transcripts based on their chromosomal origin In the cell line selection strategy shown here, cell lines A and B, A heterozygous for a transcribed coding marker represented by triangles.
However, Omnicell line A is heterozygous for the DNA sequence associated with disease called the risk DNA variant represented by circles. The blue allele of the risk variant is associated with a lower level of transcription After selecting the cell line culture, the cells in a flask according to the supplier's specifications, we'll use fibroblasts in this demonstration. Although this assay is equally applicable to other cell types, including primary cells, prepare two 10 centimeter petri dishes for DNA and RNA extractions respectively.
When cells reach 80%confluence, they can be harvested for DNA and RNA extractions using standard methods including commercially available kits that are appropriate to the cell type selected. I Prior to performing this assay designed two pairs of PCR primers for each DNA marker, one pair for genomic DNA amplification and the other for CDNA amplification place forward and reverse primers for CDNA amplification in different exons to avoid genomic contamination design primers to obtain a PCR product in the range of 100 to 500 base pairs in length for each cell line prepare eight he microliter PCR reactions. Four, independent reactions for genomic DNA amplification and four independent reactions for CDNA amplification each time.
Micro related to PCR reaction contains MLA's tack dn, NTPs primers, magnesium chloride, and either genomic DNA or CD NA.Run the PCR reactions under the conditions optimized for each primer pair, keeping the cycle number in the linear phase of amplification. Once the PCR reactions are complete. Remove PCR primers and excess DN TPS by adding exonuclease one and shrimp alkaline phosphatase SAP to each PCR product and incubating at 37 degrees Celsius for 20 minutes, followed by 15 minutes at 85 degrees Celsius.
Spot each PCR product twice onto a 384 well plate, so that eight measurements will be available for each sample. 16 spots in total for the primary extension assay. Design the extension primer with the assay design software so that the same primer can be used for both genomic and C-D-N-A-P-C-R products.
Specify primer length in the range of 14 to 18 base pairs. Carry out the primary extension reaction with the appropriate mix of three deoxy nucleotide terminators and one deoxy nucleotide. A typical primary extension reaction includes a starting step at 94 degrees Celsius for two minutes, followed by 40 cycles of 94 degrees Celsius for five seconds, 52 degrees Celsius for five seconds and 72 degrees Celsius for five seconds.
Finally, the extended oligonucleotide are quantified by matrix assisted laser desorption ionization time of flight mass spectrometry using a spectro reader mass spectrometer. A key point of this protocol is to use several strategies to control experimental noise to roll out artifacts. It's really important to repeat experiment at least three times.
The best control for the experiment is using a cell line heterozygous for the coating snip assay for primary extension, but that does not carry the disease associated allele. Additionally, design assays for alternative DNA transcribed coding markers. If these are not available, an additional control can be provided by alternative PCR primer pairs targeting different exons for the primary extension assay.
An internal control can be provided by the design of extension primers and kneeling to both forward and reverse strands. An example of allele specific expression analysis is shown here with examples of mass spectrometry traces. This figure shows the results from the analysis of four allele specific primary extension products carried out on four different templates.
The top two graphs represent the results obtained from the analysis of genomic DNA of two different cell lines, both of which are heterozygous for a transcribed coding polymorphism. However, only cell line A is heterozygous for the risk DNA variance. These lower two graphs represent the results obtained from the analysis of the CDNA generated from the same cell lines as a result of experimental artifact.
The first peak is always higher than the second peak, even in the genomic analysis. Therefore, the results from the genomic analysis are used to normalize the CDNA data. Statistical analysis is conducted by extracting the all ELO typing report within the mass array type of software.
This report provides data about the peak area, and we show here an example relative to two cell lines, cell A and cell B heterozygous for a coding snip, but where only cell A is heterozygous for a disease associated marker. The columns lay wood in gray are directly generated by the software while columns lay wood in green are derived manually for each cell. There are eight measurements for both the CDNA and the genomic DNA.
The software will calculate the area underneath each spectrum peak, which represents the abundance of each of the two alleles. The allele ratio is calculated by dividing the area of allele one by the area of the allele two for each CDNA and genomic sample. A mean and standard error are calculated across the eight measurements.
A high standard error greater than 0.1 indicates that measurements are not consistent and should be disregarded. Lastly, the CDNA mean ratio is normalized using the genomic DNA mean ratio to estimate the deviation from a ratio of one, which is expected when there is no difference in allele specific expression. The normalized ratio is different from one in cell A, but close to one in cell B, the data suggests that cell line A carries a DNA variant in phased with the allele measured by the second peak, which reduces the expression of the gene under analysis.
Cell line B provides a very convenient negative control. While attempting this procedure is really important to define informative genetic variants and to define appropriate experimental controls Following this procedure. Other methods can be used such as haplotype, specific chromatin immunoprecipitation, which help you answer questions such as where there are genetic variant for which there are no transcribed markers to use to analyze allele specific gene expression.
After watching this video, you should have a good understanding on how to analyze RNA in an allele specific manner and how to use this to understand genetically important functional variants.
