iGEM 2024 — Team dbhs-ca

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{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
{% block page\_content %}
{% endblock %}

* [CYCLE I](#cycle1)

+ [Design](#cycle1design)
+ [Cycle 2](#cycle1build)
+ [Cycle 2](#cycle1test)

* [CYCLE II](#cycle2)

+ [Design and Build](#cycle2designandbuild)
+ [Test](#cycle2test)
+ [Learn](#cycle2learn)

## Cycle 1

### Design

*Our main goal was to build a plasmid which would incorporate EHMT2, a CMV promoter, and a puromycin resistance gene (which could be used in future studies to ensure maximum transfection rate into mammalian cells) that could be produced in E-coli DH5-α.*

We first started by assessing which plasmid parts we would take, and sourced them out. We used a pre-existing primer provided to us by a professor (named GFP 3.3) which was essentially the backbone of our plasmid and contained a CMV promoter. We also sourced another plasmid similarly with a puromycin resistance gene for mammalian cells, and an EHMT2 plasmid (sourced from addgene). We planned to take the GFP 3.3 sequence which contained the neomycin resistance gene, replace it with the puromycin resistance gene, and take out the “GFP” protein and insert in “EHMT2” instead. We also designed primers using Primer Blast for each portion of the PCR.

The dark blue portion of the sequence is labeled as the CMV promoter, and the yellow portion is the EHMT2 gene, with the dark green being EHMT2 vector primer (reverse).

![](https://static.igem.wiki/teams/5535/engineering/d-1-1.webp)

Similarly, the PuroR (puromycin resistance gene) is in the deep yellow color, and its forward and reverse vector primers are in neon green.

![](https://static.igem.wiki/teams/5535/engineering/d-1-2.webp)

The ideal plasmid in construction with its sizes were:

![](https://static.igem.wiki/teams/5535/engineering/d-1-3.webp)

The full sequence of primers were:
​​PuroR vector reverse: GCGAAACGATCCTCATCCTGT
PuroR vector forward: GCGGGACTCTGGGGTTCGCGA
PuroR insert forward: ACAGGATGAGGATCGTTTCGCATGACCGAGTACAAGCCCACG
PuroR insert reverse: TCGCGAACCCCAGAGTCCCGCTCAGGCACCGGGCTTGCGGGT
EHMT2 vector forward: GCTGCCTTCTGCGGGGCTTGC
EHMT2 vector reverse: GGTGGCTCTTATATTTCTTCT
EHMT2 insert forward: AGAAGAAATATAAGAGCCACCATGGCGGCGGCGGCGGGAGCT
EHMT2 insert reverse: GCAAGCCCCGCAGAAGGCAGCCTAACCGGTACGCGTAGAATC

### Build

*We then conducted a Plasmid Extraction, PCR, Gel Electrophoresis, and Gibson Assembly in order to construct our plasmid. We first tested our PCR products, and everything matched in the gel electrophoresis stage, except for the EHMT2 band.*

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162238-1.webp)

Therefore, we tried to run another PCR with the EHMT2, and got clearer results, but still had 2 bright bands. We therefore resolved to cut out the one band that looked closer to the size we needed

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162354.webp)

Then, for Gibson Assembly, we combined the parts of the plasmid into one.

### Test

*For the first iteration of our gibson assembly, we ran a gel and did not see any favorable results, so we regrouped and tried again with new-grown EHMT2 plasmids cultured in LB overnight at 37°C. This yielded much better results which was confirmed for us after running the gel, so we grew them in culture and decided to send it in for sequencing.*

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162732.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162737.webp)

Our sequencing results for these three plasmids yielded almost 100% alignment except for a few letters in the sequence (none in parts which mattered most: EHMT2, puroR, CMV promoter), and therefore we considered the plasmid production to be a success.

Iteration 1: (Matches: 9124; Mismatches: 8; Gaps: 6)

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162748.webp)

Iteration 2: (Matches: 9125; Mismatches: 7; Gaps: 6)

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162755.webp)

Iteration 3: (Matches: 9127; Mismatches: 5; Gaps: 6)

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-162806.webp)

## Cycle 2

### Design & Build

We initially aimed to design a plasmid which was constructed to contain a CMV enhancer and the EHMT2 protein to increase production in cell lines. We sourced the plasmid’s sequence through gene synthesis, obtained the target gene, and transformed it into E-coli DH5-α. After, we transfected the said plasmid into HEK293 cell lines and ran the transfection through RT-PCR to detect the RNA levels in the cells after 4 days.

First, because we were working with cell lines and not just E-coli alone, we went through a validation process in order to confirm that our plasmid would be expressed in our cell line through The Human Protein Atlas (image shown below). We ended up settling on HEK-293 tissue cells due to its second highest expression level of our protein and was safe for us to work with.

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-173228.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-173242.webp)

We also kept in mind certain details of the plasmid which we believed would be optimal for cell production, including a CMV enhancer which is a strong driver of a gene, especially in mammalian cells.

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-173634.webp)

*\*Image sourced from Addgene*

Further, we looked into different isoforms of EHMT2 to make sure that the comparisons between our plasmid and primers/probes designed for qPCR aligned by using the NIH catalog and PrimerBlast which compared our sequenced primers and probes constructed in similarity to the plasmid made in cycle 1. (images below of NIH and PrimerBlast)

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-173821.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-173903.webp)

The primers and probes we selected are as follows for our qPCR run:
EHMT2 qPCR forward primer: TGACTGCGTGCTGTTATTCC
EHMT2 qPCR reverse primer: GAGCTTGCGGTTGAGTTGAA
EHMT2 qPCR probe: CCGCAGCTCAGGGTTGGCCCCACGT
GAPDH qPCR forward primer: TTGTCAAGCTCATTTCCTGGTAT
GAPDH qPCR reverse primer: AGGGTCTCTCTCTTCCTCTTGT
GAPDH qPCR probe: AGACCCCTGGACCACCAGCCCCAGCA

After extensive rounds of validation, we then performed a plasmid extraction protocol and sent out our plasmid for sequencing. The results came back to match our expected sequence and thus we moved on to the testing stage.

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174028.webp)

### Test

For our test, we transfected the said plasmid into HEK293T cells using Lipofectamine 3000 from Thermofisher (see experiments or notebook page for more detail about our protocol), and we compared these results after a few days with cells that were not given the treatment with the plasmids transfected through qPCR. We expected to see a higher Cq value for the cells that were not given the plasmid, as it would have taken longer for them to amplify compared to those given a plasmid meant to overexpress this protein.
*Note: The black line on the right is the line we are comparing for EHMT2. When referring to cells, we mean “[cells] per well in a 24 well plate”*

The results of our test can be shown below:

0.5 x 10^5 cells, Treatment; transfected 3 days: Cq value of 21.41

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174311.webp)

0.5 x 10^5 cells, Control; left for 3 days: Cq value of 24.88

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174425.webp)

Normalization expression graph compared to GAPDH:

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174540.webp)

2.0 x 10^5 cells, Treatment; transfected 4 days: Cq value of 19.88

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174645.webp)

*Due to the 3 day transfected well falling through, we were forced to make a comparison between transfected 4 days wells with a control of 3 days which could prove insufficient in data.*

2.0 x10^5 cells, Control; left for 3 days: Cq value of 29.68

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174751.webp)

*Note: Our 1.0 x 10^5 cells control group had not amplified, and therefore had no sufficient data for us to extrapolate from.*

Normalization expression graph compared to GAPDH:

![](https://static.igem.wiki/teams/5535/screenshot-2024-10-01-174915.webp)

### Learn

Although we do see a clear difference in the Cq value between cells transfected with the plasmid and cells without in the qPCR runs with genes that did amplify, we believe that the data is not comprehensive enough for a conclusive set of results. We are able to create a vague conclusion that our plasmid was effective in the sense of EHMT2 production, but our biggest block in fully trusting the validity was the test we ran with 1x10^5 cells which fell through as the control variable did not amplify. Another large problem we ran into was that the third transfection day’s well of 2x10^5 cells in the treatment group did not amplify when run through a qPCR test, therefore forcing us to compare results across two days: transfection day 4 and control of day 3. Therefore, our only set of data that we can chalk up as completely accurate is the 0.5x10^5 cells group.

For the next iteration, we could focus on not only trying to validate our 0.5x10^5 cells samples, but also test 1.0x10^5 cells and 2.0x10^5 cells once more with more than one well of control and treatment. Because we were doubling up this experiment with one testing our siRNA technology simultaneously, we were limited in the trials we attempted. Further, we could have tested our predesigned primers prior to running the qPCR in order to ensure maximum amplification, and kept transfection time constant throughout.

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## Plasmid Construction

###

* [Plasmid Extraction](#tab1)
* [Nanodrop](#tab2)
* [PCR](#tab3)
* [Gel Electrophoresis](#tab4)
* [Gibson Assembly](#tab5)
* [E-coli Transformation](#tab6)
* [Gel Extraction](#tab7)

- **Plasmid Extraction (DNA miniprep)**

1. Pellet bacterial overnight culture by centrifugation at a speed of 21130 rcf and temperature of 25°C.
2. Resuspend pelleted bacterial cells in 250 μl Buffer P1 and transfer to a microcentrifuge tube.
3. Add 250 μl Buffer P2 and mix thoroughly by inverting the tube 4–6 times until the solution becomes clear.
4. Add 350 μl Buffer N3 and mix immediately and thoroughly by inverting the tube 4–6 times.
5. Centrifuge for 10 min at a speed of 21130 rcf and temperature of 25°C in a microcentrifuge.
6. Apply 800 μl supernatant from step 5 to the QIAprep 2.0 spin column by pipetting.
7. Wash the QIAprep 2.0 spin column by adding 0.5 ml Buffer PB.
8. Centrifuge for 30–60 s and discard the flow-through.
9. Wash the QIAprep 2.0 spin column by adding 0.75 ml Buffer PE.
10. Centrifuge for 30–60 s and discard the flow-through.
11. Centrifuge for 1 min to remove residual wash buffer (dry spin).
12. Place the QIAprep 2.0 column in a clean 1.5 ml microcentrifuge tube. To elute DNA, add 50 μl Buffer EB.
13. Let stand for 1 min, and centrifuge for 1 min at 21130 rcf and temperature of 25°C

- **Nanodrop**

1. Turn on the nanodrop machine and click on the option for synthesis of nucleic acids.
2. Lift arm over pedestal and apply 3µl of RNase free water to the pedestal, close the arm, and hit “OK” to initialize.
3. Lift arm over pedestal, wipe water off pedestal and arm with a dry kimwipe, apply 3µl of blank solution (can be water or elution buffer).
4. Close the arm and click “Blank” to blank the machine.
5. When it displays and flat graph and a quantitation near 0, lift arm over pedestal, wipe blank off pedestal and arm with a dry kimwipe, pipette 3µl of sample 1, and gently close arm.
6. Hit “Run” to measure the concentration, noting down the final concentration in ng/µl.
7. After taking down the concentration, lift the arm over the pedestal, wipe off the first sample with a dry kimwipe.
8. Pipette 3µl of sample 2 onto the pedestal, gently close arm, and hit “Run” to measure the concentration.

- **PCR**

1. Mix the following reactions on ice
![]({{ url_for('static', filename='images/Screenshot_2024-09-29_130832.jpg') }})2. Gently mix the reaction by flicking the tube and do a quick spin
3. Transfer PCR tubes to a PCR machine and begin thermocycling.
![]({{ url_for('static', filename='images/Screenshot_2024-09-29_130839.jpg') }})
 *\*Source: NEB Protocol for Q5® High-Fidelity 2X Master Mix*

- **Gel Electrophoresis**
- Preparing a 1% agarose gel

1. Measure 70 mL of 1xTAE buffer solution into a flask
2. Weigh 0.7 grams of agarose powder and gently dump into the 70 mL of 1xTAE buffer
3. Swirl the flask to mix the buffer and powder
4. Put the flask in the microwave and heat it for 1 minute
5. Take out the flask (with gloves) and gently swirl the flask for 30 seconds
6. Heat the flask in the microwave for another minute
7. Take the flask out, and pipette in 7 μL of gel red dye into the agarose gel
8. Push a gel box into the casting tray, making sure the edges are tight and have no leak, and place a well comb on one end of the gel box
9. Pour the agarose buffer solution into the gel box with the combs
10. Let this sit in 4°C for 30 minutes to solidify

- Running the gel

1. Add 2 μL of loading dye (NEB Purple 6x B7024A) to each DNA sample of 10 μL
2. Once the agarose is solidified, reposition the gel so that the combs face the negative electrode side
3. Pour 1xTAE buffer in the casting tray until the whole gel is covered
4. Carefully load the DNA samples (mixed with the loading dye) into each well of the gel, including the 1 kb ladder and the 100 bp ladder
5. Connect the lid to the respective electrode outlets and plug in the voltage source
6. Run the gel at 120 volts for 45 minutes
7. Turn off the voltage source and scan samples

- **Gibson Assembly**

1. Set up the following reaction on ice:
![]({{ url_for('static', filename='images/Screenshot_2024-09-29_130832.jpg') }})2. Using the [NEBio Calculator](https://nebiocalculator.neb.com/#!/dsdnaamt), calculate the amount of DNA fragments

- **E-coli Transformation**

1. Take out the cells and thaw it
2. Warm carb agar plates in 37°C incubator
3. Mix 1 - 5 μl of DNA (usually 10 pg - 100 ng) into 20-50 μL of competent cells in a microcentrifuge
4. Incubate the competent cell/DNA mixture on ice for 20-30 mins.
5. Heat shock each transformation tube by placing the bottom 1/2 to 2/3 of the tube into a 42°C water bath for 30-60 secs
6. Put the tubes back on ice for 2 min.
7. Add 250-1,000 μl LB to the bacteria and grow in 37°C shaking incubator
8. Plate some or all of the transformation onto a 10 cm LB agar plate containing the appropriate antibiotic (carb in this case)
9. Incubate plates at 37°C overnight.

- **Gel Extraction**

1. Excise the DNA fragment from the agarose gel with a clean, sharp scalpel.
2. Weigh the gel slice in a tube. Add 3 volumes of Buffer QG to 1 volume gel. For >2% agarose gels, add 6 volumes Buffer QG.
3. Incubate at 50°C for 10 min. Vortex the tube every 2–3 min to help dissolve gel.
4. After the gel slice has dissolved completely, check that the color of the mixture is yellow.

1. If the color of the mixture is orange or violet, add 10 μl 3 M sodium acetate, pH 5.0, and mix. The mixture turns yellow.

5. Add 1 gel volume isopropanol to the sample and mix.
6. Place a QIAquick spin column in a provided 2 ml collection tube or into a vacuum manifold.
7. To bind DNA, apply the sample to the QIAquick column and centrifuge for 1 min until all the samples have passed through the column. Discard flow-through and place the QIAquick column back into the same tube.

1. Only load samples 800 μL max at a time into the QIAquick column.

8. If DNA will subsequently be used for sequencing, in vitro transcription, or microinjection, add 500 μl Buffer QG to the QIAquick column and centrifuge for 1 min or apply vacuum. Discard flow-through and place the QIAquick column back into the same tube.
9. To wash, add 750 μl Buffer PE to the QIAquick column and centrifuge for 1 min at max speed. Discard flow-through and place the QIAquick column back into the same tube. Note: If the DNA will be used for salt-sensitive applications (e.g., sequencing, blunt- ended ligation), let the column stand 2–5 min after addition of Buffer PE.
10. Centrifuge the QIAquick column in the provided 2 ml collection tube for 1 min to remove residual wash buffer.
11. Place the QIAquick column into a clean 1.5 ml microcentrifuge tube.
12. To elute DNA, add 50 μl Buffer EB (10 mM Tris·Cl, pH 8.5) or water to the center of the QIAquick membrane and centrifuge the column for 1 min. For increased DNA concentration, add 30 μl Buffer EB to the center of the QIAquick membrane, let the column stand for 1 min, and then centrifuge for 1 min. After the addition of Buffer EB to the QIAquick membrane, increasing the incubation time to up to 4 min can increase the yield of purified DNA.

## Cell Culture

###

* [siRNA Handling](#tab21)
* [HEK-293 Handling](#tab22)
* [Cell Plating](#tab23)
* [siRNA Transfection](#tab24)
* [RNA isolation](#tab25)
* [qPCR Prep](#tab26)
* [qPCR Calibrations](#tab27)
* [qPCR Run](#tab28)

- **siRNA Handling**
- Resuspending is used to integrate the dried siRNA into a suitable liquid solution to prepare it for use. We will be preparing a 100uM siRNA stock solution using the 1.75mL water provided.

1. Briefly centrifuge the siRNA tube so that the dried siRNA is at the bottom of the tube
2. Resuspend through pipetting up and down the 5 nmol siRNA pellets using 50uL of the nuclease free water for a concentration of 100uM

- **HEK-293 Handling**
- Unpacking & storage:

1. Check all containers for leakage or breakage.
2. Remove the frozen cells from the dry ice packaging and immediately place the cells at a temperature below, preferably in liquid nitrogen vapor, until ready for use

- Temperature: 37°C
- Atmosphere: 95% Air, 5% CO

- **Cell Plating**

1. Create Stock Solution using DMEM and FBS. Serol (optimem?). Using the fancy pump technique
2. Serological Pipette 450 ml of DMEM and 50 ml of FBS into a Vacuum Filtration System to create a stock cell culturing solution.
3. Using a multi-channel pipette, transfer x ml of stock solution every well
4. Using a serological pipette transfer xml of stock solution and x ml of cells depending on desired final concentration into a sterilized reservoir. Rock the reservoir back and forth to ensure an even mix of solutions.
5. Gently utilize a multi-channel pipette to transfer x mL the reservoir solution into x amount of wells depending on the amount of experimental groups desired. Ensure that gentle precaution is taken in order to not cause damage to cells.

- Use electronic Pipette to pump x amount of stock solution into all wells
- Use serological Pipette to pipette into a reservoir x amount of cells and x amount of stock solution

- **siRNA Transfection**

1. One day before transfection, plate 10,000 cells in 100 µl of growth medium without antibiotics. The cell density should be 30-50% confluent at the time of transfection. **For each well to be transfected, prepare RNAi duplex-Lipofectamine™ RNAiMAX complexes as follows:**
2. Dilute 1.2 pmol RNAi duplex in 50 µl Opti-MEM® I Reduced Serum Medium without serum. Mix gently.
3. Mix Lipofectamine™ RNAiMAX gently before use, then dilute 0.2 µl in 50 µl Opti-MEM® I Reduced Serum Medium. Mix gently.
4. Add the RNAi duplex-Lipofectamine™ RNAiMAX complexes to each well containing cells. This gives a final volume of **600 µl** and a final RNA concentration of 10 nM. Mix gently by rocking the plate back and forth.
5. Incubate the cells 24-48 hours at 37°C in a CO2 incubator until you are ready to assay for gene knockdown. Medium may be changed after 4-6 hours, but this is not required

- **RNA isolation**

1. Prepare Lysis/Binding Solution
2. Lyse up to 2 x 10^6 cells in 140 uL prepared Lysis/Binding Solution
3. Shake for 1 min on an orbital shaker at the maximum speed for larger volumes identified in step 2. Cells will lyse during this shaking incubation.
4. Add 20 uL Bead Mix to each sample; shake for 5 min
5. Magnetically capture the RNA Binding Beads and discard the supernatant
6. Wash with 150 L Wash Solution 1 for 1 min with shaking
7. Wash with 150 L Wash Solution 2, and prepare Diluted TURBOT™ DNase
8. Add 50 uL of Diluted TURBO DNase and shake for 10-15 min at room temp
9. Add 100 L of RNA Rebinding Solution (isopropanol added) to each sample and shake for 3 min at the maximum speed for larger volumes.
10. Magnetically capture the RNA Binding Beads and discard the supernatant
11. Wash twice with 150 L Wash Solution 2 each time
12. Dry the beads by shaking for 2 min
13. Elute the RNA in 50 uL of Elution Buffer

- **QPCR Prep**

1. Thaw all reagents on ice.
2. Vortex assays briefly to mix, then centrifuge to collect.
3. Mix thawed samples by gentle inversion or flicking 3–5 times, then briefly centrifuge to collect.
4. Ensure that there are no gel lumps remaining. Repeat steps 2 and 3, if necessary, to ensure a homogenous solution.
5. Calculate the total volume required for each reaction component:
6. Working on ice, prepare a bulk RT-qPCR reaction mixture.

1. Mix only the master mix, assay, and water that you calculated in the previous step. Exclude the sample.
2. Invert the mixture at least 10 times to mix the components thoroughly.
3. Centrifuge the mixture briefly to collect the contents.

7. Aliquot the appropriate volume of RT-qPCR reaction mix into each well of an optical reaction plate. The volume of reaction mix to add to each well is (50 µL – the volume of the sample)
8. Add a sample to each well in the optical reaction plate that contains the RT-qPCR reaction mix.
9. Cover the reaction plate with an optical adhesive cover, invert the plate 3–5 times, making sure that the contents of the wells are moving back and forth between the seal and the bottom of the wells to ensure proper mixing, then centrifuge at 150 × g for 1 minute to spin down the contents and eliminate air bubbles.

- **qPCR Calibrations**

1. Ensure that all calibration plates are securely wrapped in reflective foil to prevent light exposure and minimize any potential light pollution during processing.
2. Allow plates to thaw at room temperature for approximately 30 minutes, or until completely thawed and no visible ice remains.
3. Using a cell plate centrifuge, centrifuge the plates at 1500 RPM for 1 minute. Ensure that the centrifugation is consistent across all plates to avoid discrepancies during the qPCR calibration process.
4. After centrifugation, immediately transfer the plates to the qPCR machine and initiate the specified calibration mode according to the qPCR machine utilized.

1. ROI/Background Calibration
2. Dye Calibration

- **qPCR Run**
![](https://static.igem.wiki/teams/5535/qpcrrun.webp)

* [1st Hypothesis](#hypothesis1)
* [2nd Hypothesis](#hypothesis2)
* [3rd Hypothesis](#hypothesis3)
* [4th Hypothesis](#hypothesis4)
* [5th Hypothesis](#hypothesis5)

## 1st Hypothesis

### Difference between Transfection Days

![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-233259.webp)

B3 vs B9 → transfection Day 4 higher Cq
B4 vs B10 → transfection Day 4 higher Cq
B5 vs B11 → transfection Day 4 higher Cq
B6 vs B12 → transfection Day 4 higher Cq
C1 vs C7 → transfection Day 3 higher Cq
C2 vs C8 → transfection Day 4 higher Cq
C3 vs C9 → transfection Day 4 higher Cq
D1 vs D7 → transfection Day 3 higher Cq
D3 vs D9 → transfection Day 3 higher Cq
D4 vs D10 → transfection Day 3 higher Cq
D5 vs D11 → transfection Day 3 higher Cq
D6 vs D12 → transfection Day 3 higher Cq
E4 vs E10 → transfection Day 4 higher Cq
E5 vs E11 → transfection Day 4 higher Cq

![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-233707.webp)

**Interpretation:** In general, for row B, wells with the same concentration exhibit consistently higher Cq values on Day 4 compared to Day 3. This initially suggests that extending the siRNA transfection duration by one more day might lead to a substantial decrease in the target gene (EHMT2) expression, reflecting a more significant reduction in concentration of the target mRNA.

However, a deeper examination of the data from row D challenges this hypothesis. For these wells, the Day 3 Cq values are consistently higher than those of Day 4, contradicting the previous assumption. This inconsistency in Cq trends between the two rows raises questions about whether transfection time alone directly correlates with gene silencing efficiency, as would be expected in typical siRNA knockdown experiments.

**Potential Sources of Error:** The observed discrepancies between Cq values across different rows may stem from technical inconsistencies rather than biological factors. Since the lipofectamine reagent was pipetted into all wells within a row simultaneously using a multi-channel pipette, systematic errors during the pipetting process could have occurred. For instance, variations in concentration due to uneven pipetting or the introduction of air bubbles could explain why an entire row consistently produced higher Cq values. These technical variations may have masked the true effect of transfection duration on gene silencing efficiency.

**Conclusion:** Due to the conflicting Cq value trends between different rows, it is challenging to draw definitive conclusions regarding the optimal transfection duration based on this dataset alone. While Day 4 transfection appears to yield higher Cq values in some cases, such as row B, this observation is contradicted by the lower Cq values seen on Day 4 in row D. Consequently, further experiments with improved pipetting accuracy and more replicates will be necessary to conclusively determine the impact of transfection duration on siRNA-mediated gene silencing.

## 2nd Hypothesis

### Cells concentration

![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234003.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234102.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234201.webp)

**Interpretation:** When analyzing the Cq values across varying cell concentrations with a constant siRNA concentration, an expected decrease in Cq values between 0.5 × 10^5 and 1.0 × 10^5 cells is observed. This is consistent with the notion that higher cell numbers should require more siRNA to achieve gene silencing. However, this inverse relationship does not hold between the 1.0 × 10^5 and 2.0 × 10^5 cell concentrations. The Cq values remain inconsistent, contradicting the hypothesis that increased cell concentrations would correlate with higher siRNA demand and thus lower Cq values.

**Potential Sources of Error:** The observed discrepancies may result from technical errors during the experimental setup, rather than biological phenomena. Specifically, the preparation of stock solution for different cell concentrations could have been inaccurately prepared, leading to misrepresented cell numbers in each well. Additionally, the use of a multi-channel pipette for distributing cells might have introduced systematic errors, such as unequal pipetting or air bubbles, which could have led to inaccurate cell concentrations across wells. These technical issues may have obscured the true relationship between cell concentration and Cq values, preventing accurate conclusions regarding the effects of cell numbers on gene silencing.

**Conclusion:** The inconsistency in Cq values across different cell concentrations suggests that technical errors in the preparation of cell stocks or pipetting may have skewed the data. While the initial hypothesis predicted an inverse relationship between cell concentration and Cq values, the results indicate otherwise. This highlights the importance of rigorous quality control in experimental setup and the need for further experiments to clarify the true impact of cell concentration on siRNA-mediated gene silencing.

## 3rd Hypothesis

### Concentration of siRNA

![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234335.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234402.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234548.webp)

**Interpretation:** When analyzing the impact of varying siRNA concentrations (2.5 nmol, 5 nmol, 10 nmol) across constant cell concentrations, no significant effect was observed in the Cq values. For instance, at 0.5 × 10^5 cells, wells transfected with 2.5 nmol siRNA (B7: 25.63, C7: 20.97) displayed values similar to those transfected with 5 nmol (B8: 25.17, C8: 25.17) and 10 nmol siRNA (B9: 26.20, C9: 23.16). This lack of variation suggests that increasing siRNA concentrations did not improve gene silencing efficiency as hypothesized.

**Potential Sources of Error:** Several technical issues may have contributed to the lack of observable differences in Cq values across varying siRNA concentrations. Foremost, inconsistent transfection efficiency across wells may have masked any potential effects of increased siRNA concentration. Variability in siRNA delivery, possibly due to uneven mixing or pipetting inconsistencies, may have reduced the effectiveness of siRNA uptake across wells. Additionally, siRNA degradation could have occurred prior to transfection, limiting its effectiveness regardless of concentration.

**Conclusion:** The data suggests that increasing siRNA concentrations did not correlate with a more pronounced reduction in Cq values, indicating no significant enhancement of gene silencing. The consistency of Cq values across siRNA concentrations implies that technical issues such as pipetting errors, siRNA degradation, or transfection inefficiencies may have masked the expected effects. Future experiments should focus on improving transfection consistency, optimizing siRNA stability, and ensuring proper siRNA-to-target gene ratios to more effectively assess the relationship between siRNA dosage and gene silencing.

## 4th Hypothesis

### siRNA vs silencer

![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234734.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234747.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-234912.webp)

**Interpretation:** If siRNA is consistently showing fewer cycles (lower Cq values), it suggests that there may be higher G9a expression in the siRNA-treated cells. This is contrary to the expected outcome since siRNA should reduce G9a expression, leading to higher Cq values (more cycles).

**Potential Sources of Error:** One potential source of error in your experiment could be the ineffective siRNA knockdown. If the siRNA is not properly designed or degraded before it reaches the target gene, it might not efficiently silence G9a expression, leading to inaccurate qPCR results. Additionally, qPCR technical issues such as poorly designed primers, inconsistent amplification efficiency, or degradation of reagents like RNA, cDNA, or the qPCR mix can also affect the accuracy of the Cq values. Another potential issue is pipetting errors, where even small variations in handling can introduce significant discrepancies in qPCR cycle numbers. Furthermore, the control group, which uses a silencer expected to have no effect on G9a expression, may not be functioning properly, possibly due to contamination or unexpected impacts on gene expression. Transfection efficiency could also vary between samples, leading to inconsistent siRNA uptake and reduced gene silencing effectiveness. Lastly, off-target effects of the siRNA might cause unintended consequences on other genes or pathways, indirectly influencing G9a expression and resulting in misleading data.

**Conclusion:** In conclusion, the unexpected observation of fewer qPCR cycles (lower Cq values) in the siRNA-treated samples, which suggests higher G9a expression, indicates that something may be wrong with the experimental setup. The siRNA, designed to silence G9a, should have resulted in reduced gene expression and higher Cq values, but this was not the case. This discrepancy could be due to technical issues such as ineffective siRNA knockdown, problems with qPCR reagents or primer specificity, or errors in transfection efficiency. To resolve these issues, further optimization and verification of the siRNA's knockdown efficiency, careful review of the qPCR conditions, and validation of the control group's neutrality are necessary.

## 5th Hypothesis

### siRNA vs blank

![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-235130.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-235135.webp)
![](https://static.igem.wiki/teams/5535/screenshot-2024-09-30-235228.webp)

**Interpretation**
When comparing the effects of 5 nmol siRNA treatment to blank wells across varying cell concentrations, we hypothesize that Cq values in siRNA-treated wells should \*\*increase\*\* compared to the blank. This is because effective gene silencing would reduce the amount of target mRNA, requiring more cycles for qPCR detection. The Cq values are expected to increase proportionally to the siRNA-induced reduction of mRNA.When analyzing the results across different cell concentrations (0.5, 1.0, and 2.0 × 10^5), inconsistencies arise. For instance, comparing BC2 (5 nmol siRNA) and E4 (blank) with 0.5 × 10^5 cells, we observe no significant difference in Cq values (21.30 vs. 21.41, respectively). Similarly, BC5 vs. E5 (1.0 × 10^5 cells) shows a lower Cq value for siRNA (19.35) than the blank (24.08), which is contradictory to expectations. In theory, siRNA-treated wells should exhibit higher Cq value due to reduced mRNA levels caused by gene silencing. This trend is also seen in other examples such as BC11 (22.22) vs. E11 (24.65), and BC8 (25.17) vs. E10 (25.33), where little to no increase in Cq values is observed.

**Potential Sources of Error:**
Inconsistent pipetting may have led to varying siRNA concentrations across wells, causing discrepancies in the silencing effect and the resulting qPCR amplification cycles. Moreover, uneven distribution of cells during plating could have contributed to fluctuating gene expression levels, making it challenging to compare Cq values across different cell concentrations. Suboptimal transfection conditions, such as incorrect incubation times or temperature variations, could have also impaired siRNA uptake into the cells, reducing the overall silencing efficiency. Additionally, contamination between wells or during the qPCR process could have introduced noise into the results, masking the true effects of siRNA treatment and leading to unexpected deviations in Cq values.

**Conclusion:**
The data suggest that the application of 5 nmol siRNA across varying cell concentrations does not consistently result in the expected increase in Cq values compared to blank wells. This indicates potential inefficiencies in the siRNA transfection process or technical errors during the experiment. To confirm these findings, further optimizations in transfection efficiency and qPCR procedures should be undertaken. Additionally, repeating the experiment with controls for technical variables may provide more conclusive results regarding the impact of siRNA on gene silencing.

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