Difference between revisions of "Team:Hong Kong HKU/Demonstrate"

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    <h3>Demonstrate</h3><br>
 +
    <ul class="nav nav-pills">
 +
      <li><a href="https://2016.igem.org/Team:Hong_Kong_HKU/Parts#Parts">Composite Parts</a></li>
 +
      <li><a href="https://2016.igem.org/Team:Hong_Kong_HKU/Parts#Achievements">Achievements</a></li>
 +
  <li><a href="https://2016.igem.org/Team:Hong_Kong_HKU/Results">Results</a></li>
 +
      <li class="active"><a href="#">Demonstrate our work</a></li>
 +
      <li><a href="https://2016.igem.org/Team:Hong_Kong_HKU/Proof">Proof of Concept</a></li>
 +
    </ul>
 +
    <p class="text-justify" align="left">
 +
    <br><font size="4"><b>RNA detection using DNA nanostructure</b></font><br><br>
 +
    <font size="3">
 +
    After showing that our DNA nanostructures can detect our target DNA (details can be found <a href="https://2016.igem.org/Team:Hong_Kong_HKU/Results">here</a>), we went further to detect RNA. 
 +
    This test aimed to simulate the detection of serum microRNA, which has potential real-world application to diagnose disease using microRNA disease biomarkers. 
 +
    The following table shows the sequence of input used in the assay.<br><br>
 +
    </font></p>
 +
    <table class="table">
 +
        <thead>
 +
            <tr>
 +
            <th></th>
 +
                <th>Sequence</th> 
 +
                <th>Length</th>
 +
            </tr>
 +
        </thead>
 +
        <tbody>
 +
            <tr>
 +
                <td>RNA Input</td>
 +
                <td>CAAUCAGGGUCUAACUCCACUGGGUGCCAU</td>
 +
                <td>30</td>
 +
            </tr>
 +
            <tr>
 +
                <td>RNA Mutant</td>
 +
                <td>CAGGCAGUAUCAUGCGACAUUGGGUGCAGC</td>
 +
                <td>30</td>
 +
            </tr>
 +
        </tbody>
 +
    </table>
 +
    <p class="text-justify" align="left"><font size="3">
 +
    First, we used our simplified DNA nanostructure (formed from the G-quadruplex side of O1 and O5 of the tetrahedron, 
 +
    which is the essential part of the 3D tetrahedral nanostructure) to detect RNA input.
 +
    Equimolar (100nM final) DNA nanostructure and RNA input were added in the assay. 
 +
    The following bar chart shows the absorbance at 420nm after the addition of different RNAs.<br><br>
 +
    </font></p>
 +
    <img class="img-responsive center-block" src="https://static.igem.org/mediawiki/2016/5/59/T--Hong_Kong_HKU--O1O5RNAmutant.png" alt="" width="800px" height="auto">
 +
    <p class="text-justify" align="left"><font size="3">
 +
    Fig. A: Absorbance at 420nm after the addition of different RNAs to the simplified DNA nanostructure (formed from O1's G-quadruplex side and O5 of the tetrahedron) which is termed as  "beacon" in the above graph. 
 +
    The absorbance was taken 15 minutes after the addition of ABTS and H2O2. Error bars show standard deviation from triplicates.<br><br>
 +
    Then, we repeated the experiment using our tetrahedral DNA nanostructure, which gave the following result.<br><br>
 +
    </font></p>
 +
    <img class="img-responsive center-block" src="https://static.igem.org/mediawiki/2016/7/7f/T--Hong_Kong_HKU--TetraRNAmutant.png" alt="" width="800px" height="auto">  
 +
    <p class="text-justify" align="left"><font size="3">
 +
    Fig. B: Absorbance at 420nm after the addition of different RNAs to the tetrahedral DNA nanostructure. 
 +
    The absorbance was taken 15 minutes after the addition of ABTS and H<sub>2</sub>O<sub>2</sub>. Error bars show standard deviation from triplicates.<br>
 +
    From the above two graphs, it can be seen that the addition of RNA input resulted in a higher absorbance than that without the addition of RNA input, and the addition of a random RNA sequence did not lead to a higher absorbance.
 +
    Hence, we have successfully demonstrated that our design not only can detect our desired RNA, it can also distinguish the correct RNA input from a random RNA.<br><br>
 +
    </font>
 +
    <font size="4"><b>Limit of detection</b></font><br><br>
 +
    <font size="3">
 +
    Then, we determined the limit of detection (LOD) of our detection beacon (formed from O1's G-quadruplex side and O5 of the tetrahedron, the active component of the tetrahedral nanostructure) by ABTS assay. 
 +
    Different concentrations of RNA input were added and their respective absorbance at 420nm was measured. 
 +
    A regression line obtained is shown in the following graph.
 +
    </font></p>  
 +
    <img class="img-responsive center-block" src="https://static.igem.org/mediawiki/2016/1/12/T--Hong_Kong_HKU--O1O5RNALOD.png" alt="" width="800px" height="auto">  
 +
    <p class="text-justify" align="left"><font size="3">
 +
    Fig. C: Absorbance at 420nm against the concentration of RNA input to the simplified DNA nanostructure (formed from O1's G-quadruplex side and O5 of the tetrahedron).
 +
    The absorbance was taken 15 minutes after the addition of ABTS and H<sub>2</sub>O<sub>2</sub>. Error bars show standard deviation from triplicates.
 +
    The regression line obtained is <i>y</i>=0.0009<i>x</i>+0.1298 (R<sup>2</sup>=0.9739).
 +
    The LOD is calculated as follows.<br><br>
 +
    C<sub>LOD</sub> = 3(s<sub><i>y</i>/<i>x</i></sub>)÷<i>b</i>,  where<br><br>
 +
    C<sub>LOD</sub> is the concentration LOD,<br>
 +
    s<sub><i>y</i>/<i>x</i></sub> is the standard error of regression, and<br>
 +
    <i>b</i> is the slope of regression line.<br><br>
 +
    First, the standard error of regression is determined.<br><br>
 +
    </font></p>
 +
      <table class="table">
 +
        <thead>
 +
            <tr>
 +
                <th style="text-align:center"><i>X</i></th>
 +
                <th style="text-align:center"><i>Y</i></th>
 +
                <th style="text-align:center"><i>Y'</i></th>
 +
                <th style="text-align:center"><i>Y</i>-<i>Y'</i></th>
 +
                <th style="text-align:center">(<i>Y</i>-<i>Y'</i>)<sup>2</sup></th>
 +
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 +
        </thead>
 +
        <tbody>
 +
            <tr>
 +
                <td style="text-align:center">0</td>
 +
                <td style="text-align:center">0.123333333333333</td>
 +
                <td style="text-align:center">0.1298</td>
 +
                <td style="text-align:center">-0.00646666666666666</td>
 +
                <td style="text-align:center">0.0000418177777777777</td>
 +
            </tr>
 +
            <tr>
 +
                <td style="text-align:center">20</td>
 +
                <td style="text-align:center">0.151</td>
 +
                <td style="text-align:center">0.1478</td>
 +
                <td style="text-align:center">0.00320000000000001</td>
 +
                <td style="text-align:center">0.0000102400000000001</td>
 +
            </tr>
 +
            <tr>
 +
                <td style="text-align:center">40</td>
 +
                <td style="text-align:center">0.170666666666667</td>
 +
                <td style="text-align:center">0.1658</td>
 +
                <td style="text-align:center">0.00486666666666666</td>
 +
                <td style="text-align:center">0.0000236844444444444</td>
 +
            </tr>
 +
            <tr>
 +
                <td style="text-align:center">60</td>
 +
                <td style="text-align:center">0.185666666666667</td>
 +
                <td style="text-align:center">0.1838</td>
 +
                <td style="text-align:center">0.00186666666666666</td>
 +
                <td style="text-align:center">0.0000034844444444444</td>
 +
            </tr>
 +
            <tr>
 +
                <td style="text-align:center">80</td>
 +
                <td style="text-align:center">0.208333333333333</td>
 +
                <td style="text-align:center">0.2018</td>
 +
                <td style="text-align:center">0.00653333333333336</td>
 +
                <td style="text-align:center">0.0000426844444444448</td>
 +
            </tr>
 +
            <tr>
 +
                <td style="text-align:center">100</td>
 +
                <td style="text-align:center">0.213666666666667</td>
 +
                <td style="text-align:center">0.2198</td>
 +
                <td style="text-align:center">-0.00613333333333332</td>
 +
                <td style="text-align:center">0.0000376177777777777</td>
 +
            </tr>
 +
            <tr>
 +
                <td style="text-align:center" colspan="5"> </td>
 +
            </tr>
 +
            <tr>
 +
                <th style="text-align:center" colspan="2">SSE</th>
 +
                <td style="text-align:center" colspan="3">0.000159528888888889</td>
 +
            </tr>
 +
        </tbody>
 +
      </table>
 +
    <p class="text-justify" align="left"><font size="3">
 +
    (<i>Y'</i> is the predicted value from the regression line <i>y</i>=0.0009<i>x</i>+0.1298)<br><br>
 +
    Standard error of regression = √(SSE÷no. of pairs)=√(0.0001595÷6)=0.005156<br><br>
 +
    Limit of detection<br>
 +
    C<sub>LOD</sub> = 3(s<sub><i>y</i>/<i>x</i></sub>)÷<i>b</i> = 3(0.005156)÷0.0009 = 17.19nM<br><br>
 +
    </font>
 +
    <br><font size="4"><b>Real-world application</b></font><br><br>
 +
  <font size="3">
 +
  Our DNA nanostructures can potentially be utilized as a simple diagnostic tool, where a higher absorbance in ABTS assay suggests the presence of our desired RNA target.
 +
    As microRNAs are potential disease biomarkers, our DNA nanostructures can potentially be used in disease screening by detecting the patien' s serum microRNA.
 +
    In addition, we can easily expand the application to detect different RNA sequences by modifying the sequence of two strands of our DNA nanostructure.
 +
    </font></p>
 
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Latest revision as of 14:28, 25 November 2016

Demonstrate



RNA detection using DNA nanostructure

After showing that our DNA nanostructures can detect our target DNA (details can be found here), we went further to detect RNA. This test aimed to simulate the detection of serum microRNA, which has potential real-world application to diagnose disease using microRNA disease biomarkers. The following table shows the sequence of input used in the assay.

Sequence Length
RNA Input CAAUCAGGGUCUAACUCCACUGGGUGCCAU 30
RNA Mutant CAGGCAGUAUCAUGCGACAUUGGGUGCAGC 30

First, we used our simplified DNA nanostructure (formed from the G-quadruplex side of O1 and O5 of the tetrahedron, which is the essential part of the 3D tetrahedral nanostructure) to detect RNA input. Equimolar (100nM final) DNA nanostructure and RNA input were added in the assay. The following bar chart shows the absorbance at 420nm after the addition of different RNAs.

Fig. A: Absorbance at 420nm after the addition of different RNAs to the simplified DNA nanostructure (formed from O1's G-quadruplex side and O5 of the tetrahedron) which is termed as "beacon" in the above graph. The absorbance was taken 15 minutes after the addition of ABTS and H2O2. Error bars show standard deviation from triplicates.

Then, we repeated the experiment using our tetrahedral DNA nanostructure, which gave the following result.

Fig. B: Absorbance at 420nm after the addition of different RNAs to the tetrahedral DNA nanostructure. The absorbance was taken 15 minutes after the addition of ABTS and H2O2. Error bars show standard deviation from triplicates.
From the above two graphs, it can be seen that the addition of RNA input resulted in a higher absorbance than that without the addition of RNA input, and the addition of a random RNA sequence did not lead to a higher absorbance. Hence, we have successfully demonstrated that our design not only can detect our desired RNA, it can also distinguish the correct RNA input from a random RNA.

 Limit of detection

Then, we determined the limit of detection (LOD) of our detection beacon (formed from O1's G-quadruplex side and O5 of the tetrahedron, the active component of the tetrahedral nanostructure) by ABTS assay. Different concentrations of RNA input were added and their respective absorbance at 420nm was measured. A regression line obtained is shown in the following graph.

Fig. C: Absorbance at 420nm against the concentration of RNA input to the simplified DNA nanostructure (formed from O1's G-quadruplex side and O5 of the tetrahedron). The absorbance was taken 15 minutes after the addition of ABTS and H2O2. Error bars show standard deviation from triplicates. The regression line obtained is y=0.0009x+0.1298 (R2=0.9739). The LOD is calculated as follows.

CLOD = 3(sy/x)÷b, where

CLOD is the concentration LOD,
sy/x is the standard error of regression, and
b is the slope of regression line.

First, the standard error of regression is determined.

X Y Y' Y-Y' (Y-Y')2
0 0.123333333333333 0.1298 -0.00646666666666666 0.0000418177777777777
20 0.151 0.1478 0.00320000000000001 0.0000102400000000001
40 0.170666666666667 0.1658 0.00486666666666666 0.0000236844444444444
60 0.185666666666667 0.1838 0.00186666666666666 0.0000034844444444444
80 0.208333333333333 0.2018 0.00653333333333336 0.0000426844444444448
100 0.213666666666667 0.2198 -0.00613333333333332 0.0000376177777777777
SSE 0.000159528888888889

(Y' is the predicted value from the regression line y=0.0009x+0.1298)

Standard error of regression = √(SSE÷no. of pairs)=√(0.0001595÷6)=0.005156

Limit of detection
CLOD = 3(sy/x)÷b = 3(0.005156)÷0.0009 = 17.19nM
Real-world application

Our DNA nanostructures can potentially be utilized as a simple diagnostic tool, where a higher absorbance in ABTS assay suggests the presence of our desired RNA target. As microRNAs are potential disease biomarkers, our DNA nanostructures can potentially be used in disease screening by detecting the patien' s serum microRNA. In addition, we can easily expand the application to detect different RNA sequences by modifying the sequence of two strands of our DNA nanostructure.


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