Week 6: Perfecting Techniques and Fine-Tuning My Experiment
Hasini c -
Hello everyone,
Welcome back to my blog! This week, I focused on refining my data and conducting an additional experiment. As I mentioned last week, I completed the immunofluorescence staining of mice cells targeted with GCN2. However, after examining pictures under the microscope and comparing control group and experimental group slides, I’ve found that my results are insufficient because it is hard to distinguish a clear difference. So this week, I decided to retry the experiment and use a different method: RT-qPCR.
RT-qPCR, or quantitative reverse transcription polymerase chain reaction, is a technique that measures RNA levels by amplifying DNA copies of RNA. We primarily use this method in molecular biology research and medical diagnostics. For example, in recent years, we have used PCR tests to detect COVID-19 from nose swab samples. In my experiment I am using qPCR to study gene expression and obtain quantitative data. I would like to see if knocking out GCN2 in the experimental group will reduce influenza in mice cells.
Once I collect the data, I plan to analyze my qPCR results using Double Delta Ct Analysis. This is a method used to determine the relative expression of a gene of interest between different samples, comparing it to a reference sample and a housekeeping gene. In my case, the housekeeping gene is beta-actin which is a protein typically used as a control. My reference sample included GCN2. After running it through a qPCR machine, I have to calculate my results using four steps:
- First, take the average of the Ct values for the housekeeping gene and the gene being tested in the experimental and control conditions, returning 4 values. The four values are:
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- Gene being Tested Experimental (TE)
- Gene being Tested Control (TC)
- Housekeeping Gene Experimental (HE)
- Housekeeping Gene Control (HC).
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- Next, calculate the differences between experimental values (TE – HE) and the control values (TC – HC). These will be my ΔCt values for the experimental (∆CTE) and control (∆CTC) conditions.
- Once I have the ΔCt values for both the experimental samples and reference sample (control), I can calculate the ΔΔCt value.
ΔCt experimental −ΔCt reference = ΔΔCt
4. Finally, I can calculate the relative gene expression using this formula:
Relative expression = 2−ΔΔCt
This value gives you the fold change in gene expression between the experimental group and the control group. If the result is greater than 1, that means the gene expression is upregulated. If it’s less than 1, the gene expression is downregulated. Upregulation refers to an increase in the activity/production of a gene, while downregulation means a decrease in gene activity. I will calculate these values in the next few days.
This might be a bit random, but this week I also learned how to prepare frozen sections by myself. It’s a super cool process where you use a cryostat (a freezing machine) to slice ultra-thin sections of mice tissue and place them on microscope slides. Since it was my first time using this machine, I was slightly nervous but overall the slides came out fine. This week has definitely been exciting, and I can’t wait to update you on my project next week!
Here is a picture of a cryostat for reference:
Image Source: https://burke.weill.cornell.edu/imaging-core/research/research-equipment/cryostat-leica-cm1800