Team:TU Eindhoven/Project/Characterization/Antifouling Properties

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                   <h2>Antifouling Assay</h2>
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<img id='Fig1' src="https://static.igem.org/mediawiki/2014/b/be/TU_Eindhoven_Modeling4.png" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;">
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<figcaption style="font-size:18px;color:#CCCCCC;">Figuur 1. Used sample image.</figcaption>
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                   <h2>Bacterial Cell Counter</h2>
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                  <p>When photos of the microfluidics results are taken, the droplets and the cells in the droplets have to be counted. In order to have reliable results this has to be done on a large scale. To make it possible to analyses large numbers of photos a computer program has been made to do the counting. To give a better visualization of the process, a step by step analysis of an image (<a href="#Fig1">Figure 1</a>) is given below.</p>
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<p>The program has the following steps: find and count the droplets, then find and count the cells in the droplets and finally create a histogram of the results (number of droplets with 0 cells, with 1 cell, with 2 et cetera). By adding up the histograms of multiple images you get a view of how the cells are divided over the droplets.</p>
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<img id='Fig2' src="https://static.igem.org/mediawiki/2014/a/a1/TU_Eindhoven_Modeling5.png" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;">
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<figcaption style="font-size:18px;color:#CCCCCC;">Figuur 2. Results after EdgeDetect</figcaption>
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<h3>Droplet Detection</h3>
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<p>To detect the cells <i>EdgeDetect</i> (a function of Mathematica using gradient methods) is used followed by a dilation to make the edges clearer (<a href="#Fig2">Figure 2</a>).</p>
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<p>This results in clusters of pixels, all the pixels that are connected with only black pixels. The program then looks for the clusters bigger than 2000 pixels and smaller than 6000 pixels (<a href="#Fig2">Figure 3</a>).</p>
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<h3>Parameters of Droplets Detection</h3>
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<p>The parameters of this function are chosen so they have a very low false positive, because a false positive means a non-existing droplet and thus false data.  A false negative only lowers the sample size, which can be increased by analyzing more images.</p>
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<img id='Fig3' src="https://static.igem.org/mediawiki/2014/a/aa/TU_Eindhoven_Modeling6.png" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;">
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<img id='Fig1' src="https://static.igem.org/mediawiki/2014/4/46/TU_Eindhoven_Antifouling.jpg" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;">
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<figcaption style="font-size:18px;color:#CCCCCC;">Figuur 3. Results of selecting clusters</figcaption>
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<figcaption style="font-size:18px;color:#CCCCCC;">Figure 1: Anti-fouling assay COMPx.</figcaption>
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<h3>Bacterial Cell Detection</h3>
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                  <p>One of the applications for the click coli system was to evade the immune system. This was attempted by clicking large PEG tails to the bacteria. The PEG tails create a hydrophilic environment, blocking possible interactions between the bacteria and anti-bodies.  
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<p>The program is now at the point where it has to count the number of cells in the droplets. Using the droplets from the edgedetect picture, erosion with value 1 is performed (resulting with the deletion of before in closing of the image). This makes sure the cells connected to the borders of the droplet are loose, the program deletes the border components so it only finds cells. The program uses <i>EnclosingComponentCount</i> instead of <i>count</i> now because a lot of cells became empty circles to get the results.
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The results for this image are 9 false positives and 11 false negatives on a total of 93 cell in 126 droplets. Because some droplets had multiple false positives or false negatives the next step to improve this number is selecting to droplets better.
 
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First PEG 10kDa was clicked to the membrane of the bacteria. The bacteria were then incubated in a solution with fluorescently labelled Anti-HA anti-bodies (316 nM), COMPx (membrane protein) contains a HA-tag which is targeted by the antibodies, and after incubation the bacteria were washed. There were two types of control groups used, positive and negative. For negative control one with only bacteria and another one with bacteria and DBCO. For the positive control bacteria with anti-HA antibody and no DBCO were used.</p>
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<img id='Fig4' src="https://static.igem.org/mediawiki/2014/8/87/TU_Eindhoven_Modeling_Histogram.jpg" width="500" style="display: inline-block; border: 4px solid #00BAC6; padding: 4px; background: #222; margin-bottom: 10px;">
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<figcaption style="font-size:18px;color:#CCCCCC;">Figuur 4. Histogram of the example image</figcaption>
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<h4>Bibliography</h4>
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<p>Results show that there is a decrease in fluorescence of the bacteria that have a PEG 10kDa. This means that there are less Anti-HA antibodies bound to the HA-tag on the membrane of the bacteria coated with PEG 10kDA. The number of antibodies bound to these bacteria is still too high to say it evades an immune response. This was not unexpected because PEG tails do not form a network; they just stick out of the membrane. To get a further decrease in antibodies bound usage of a polymer that can also crosslink to form a network is a good possibility. Possible future improvements are discussed in <a href="">Future Perspectives</a></p>
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Latest revision as of 20:04, 13 October 2014

iGEM Team TU Eindhoven 2014

iGEM Team TU Eindhoven 2014

Antifouling Assay

Figure 1: Anti-fouling assay COMPx.

One of the applications for the click coli system was to evade the immune system. This was attempted by clicking large PEG tails to the bacteria. The PEG tails create a hydrophilic environment, blocking possible interactions between the bacteria and anti-bodies.

First PEG 10kDa was clicked to the membrane of the bacteria. The bacteria were then incubated in a solution with fluorescently labelled Anti-HA anti-bodies (316 nM), COMPx (membrane protein) contains a HA-tag which is targeted by the antibodies, and after incubation the bacteria were washed. There were two types of control groups used, positive and negative. For negative control one with only bacteria and another one with bacteria and DBCO. For the positive control bacteria with anti-HA antibody and no DBCO were used.

Results show that there is a decrease in fluorescence of the bacteria that have a PEG 10kDa. This means that there are less Anti-HA antibodies bound to the HA-tag on the membrane of the bacteria coated with PEG 10kDA. The number of antibodies bound to these bacteria is still too high to say it evades an immune response. This was not unexpected because PEG tails do not form a network; they just stick out of the membrane. To get a further decrease in antibodies bound usage of a polymer that can also crosslink to form a network is a good possibility. Possible future improvements are discussed in Future Perspectives

iGEM Team TU Eindhoven 2014

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