Difference between revisions of "Team:Munich/Results"

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           <li><a href="/Team:Munich/Cas13a">Ruling out RNase contamination from heat-lysed <i>in vivo</i> samples.</a></li>
 
           <li><a href="/Team:Munich/Cas13a">Ruling out RNase contamination from heat-lysed <i>in vivo</i> samples.</a></li>
 
           <li><a href="/Team:Munich/Targets">Detecting <i>Qbeta</i> RNA.</a></li>
 
           <li><a href="/Team:Munich/Targets">Detecting <i>Qbeta</i> RNA.</a></li>
          <li><a href="/Team:Munich/Targets">Reducing cross-talk between <i> E.coli </i> crRNA and <i> B.subtilitis </i> target RNA.</a></li>
 
 
           <li><a href="/Team:Munich/Readouts">Developing colorimetric read-outs.</a></li>
 
           <li><a href="/Team:Munich/Readouts">Developing colorimetric read-outs.</a></li>
 
           <li><a href="/Team:Munich/Detection">Optimizing the lyophilization and stability of Cas13a.</a></li>
 
           <li><a href="/Team:Munich/Detection">Optimizing the lyophilization and stability of Cas13a.</a></li>
           <li><a href="/Team:Munich/Amplification">Performing RPA and transcription on chip.</a></li>
+
           <li><a href="/Team:Munich/Amplification">Amplifying long sequences with RPA.</a></li>
 
   </ul>
 
   </ul>
 
</td>
 
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Revision as of 01:52, 2 November 2017


Final Results

Overview

We successfully designed, constructed and characterized each module of our platform: the sample processing, the readout circuit, and the detection of pathogens.
Although did not fully integrate all parts together in the time frame of our project, we could connect each unit to the next, so that we are confident that our entire platform is functional. For example, we could achieve equally sensitive bulk detection of pathogen RNA from in vitro and in vivo sources, and were later able to detect in vitro RNA with lyophilized Cas13a on paper, therefore we believe RNA from lysed cells can be detected on paper. In this overview, we list our achievements and where we faced issues, and we present a summary of the characterization of each module in the sub-pages.

What worked

Detection of E. coli 16S rRNA from Cas13a on paper in our self-built detector

What presented issues

Discussion

Our project CascAID is a universal solution for low cost, point of care diagnostics of infectious diseases. Currently, the available diagnostic tools are based on PCR, antibodies or microbiological methods which all need trained personal and lab equipment. Therefore, these methods are cost and time consuming. This gives rise to the need of developing effective, affordable and portable devices.

In our project, we first successfully replicated the Cas13a-based detection of RNA pathogens that was demonstrated by Gootenberg et al. Although this result is not novel, we thoroughly characterized the target detection limit for different bacterial and viral targets, from in vitro and in vivo sources, and proved the possibility to discriminate between viruses and bacteria with high specificity. We laid the groundwork for colorimetric read-outs that will add another layer of amplification in our cascade detection (gold nanoparticles, intein-extein and ssDNA amplification). Those readouts should allow for a practical readability of the diagnosis by the user without the need of digital analysis. Additionally, their amplification scheme should also lower the detection limit of the Cas13a without the need for pre-amplification of the target.

However, we worked in parallel on a scheme for amplifying the target using RPA and transcription. Although the reaction worked on paper, it did not work on chip due to the toxicity of the PDMS to the reaction. We built a fluorescence detector with high sensitivity to cost ratio, and used it successfully to detect Cas13a activity. However, the product itself needs to be redesigned for market distribution: in general, a fluorescence detector is not necessarily user-friendly, the extraction of the RNA on chip needs to be optimized, and the costs of the whole product must be lowered.

Nevertheless, we are glad to have created a functional platform that allows the detection of nanomolar concentrations of a pathogen's RNA within 30 minutes. With our modular approach, we have shown at least proof-of-concept results for each part, and are confident that no fundamental gap prevents our platform from being usable, only optimization.

Outlook

We still have some project sections that we need to improve in the future. We have therefore listed the following points that need to be optimized below.

  • In vivo heat lysis: During our experiments, we realized that the RNA extraction of E. coli using heat lysis is not always optimal for our experimental setup due to the fact that we have RNase contamination in the extracted RNA samples. Although our Cas13a cleavage assays are performed in presence of RNase Inhibitor to suppress the activity of the RNases that could be present, we observed that the heat lysed samples show relatively higher fluorescence activity in comparison to the phenol-chloroform extracted samples.
  • RNA extraction and amplification: The RNA extraction from Bacillus subtilis was particularly difficult in our case since B. subtilis is a gram positive, spore forming bacterium. Also, the amount and the quality of the RNA extracted from the B. subtilis and E.coli cultures were not sufficiently good. We therefore should find methods to improve either the RNA extraction protocol or use a better amplification steps after the extraction.
  • Cost of the chip: Now, the cost of our chip is less than 15$ per unit. We could still try to minimize the costs by reducing the chip size and making it fully recyclable. However, at the industrial level one could potentially reduce the cost of the chip.
  • Lyophilization of Cas13a: We also figured out that the lyophilization protocol of the Cas13a has to be improved in order to make our paper chip portable and sustainable. We also tried drying the Cas13a with the tardigrade intrinsically disordered proteins (TDPs) from Team TU Delft but still it was not that effective as expected. Therefore, we have to integrate some better methods to lyophilize the Cas13a without losing its activity.
  • Readouts with color and amplification: The colorimetric readout is also something we need to work on and improve since we only managed to partially succeed with the colorimetric assays. We however think that it is possible to realize this using more elegant ways of RNA detection and this is something we could try in future.
  • Integration of all the modules of the platform: Although all our modules parts are functional, we were only able to integrate them partially. So, with more time, we believe that we can have a fully functional and integrated module system.