Difference between revisions of "Team:Munich/Cas13a"

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<ul class="menuList" id="menu">
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  <li><a href="/Team:Munich/Results">Overview</a></li>
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  <li><a href="/Team:Munich/Cas13a">Cas13a</a></li>
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  <li><a href="/Team:Munich/Readouts">Readouts</a></li>
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  <li><a href="/Team:Munich/Targets">Targets</a></li>
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  <li><a href="/Team:Munich/DetectionOnChip">Detection Chip</a></li>
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<font size=7 color=#51a7f9><b style="color: #51a7f9">Description</b></font>
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<div style="margin-top: 40px;"><font size=7 color=#51a7f9><b style="color: #51a7f9; margin-top: 40px;">Results: Cas13a</b></font></div>
 
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<p class="introduction">
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<h3>What worked:</h3>
Thanks to advances in molecular biology and biochemistry, scientists have been able to consistently detect lower and lower concentration of molecules<sup><a class="myLink" href="#ref_1">1</a></sup>, to the point that single molecules can be reliably recognized with methods such as polymerase chain reaction (PCR)<sup><a class="myLink" href="#ref_2">2</a></sup>, fluorescence in situ hybridization (FISH)<sup><a class="myLink" href="#ref_3">3</a></sup> and enzyme-linked immunosorbent assays (ELISA)<sup><a class="myLink" href="#ref_4">4</a></sup>. This has opened doors for synthetic biology to create better and more accurate diagnostic tests that use biomarkers like nucleic acids and proteins as targets<sup><a class="myLink" href="#ref_5">5</a>,<a class="myLink" href="#ref_6">6</a></sup>. Through such advances, the field of molecular diagnostics developed. Unfortunately, current standard methods require expensive equipment or trained personnel, which generally limits their usability to hospitals or laboratories. Recently, there has been a push to develop new tests that fuse the reliability of standard methods with affordable platforms such as lab-on-a-chip or paper strips  to overcome this restrictions<sup><a class="myLink" href="#ref_7">7-9</a></sup>. We wanted to help close this gap and set out to engineer a diagnosis principle for the detection of a wide array of targets that could be used without difficult-to-meet technical requirements.
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  <ul class="listResults">
                </p>
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          <li>We characterized Cas13a and its detection limit with native and <a class="myLink" href="https://2017.igem.org/Team:Munich/DetectionOnChip#lyophi">lyophilized</a> protein, with
 
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<a class="myLink" href="https://2017.igem.org/Team:Munich/Cas13a#Figure_1">in vitro</a> and <a class="myLink" href="https://2017.igem.org/Team:Munich/Cas13a#vivo">in vivo</a> sources of RNA, in bulk and <a class="myLink" href="https://2017.igem.org/Team:Munich/DetectionOnChip#onpaper2">on paper</a>. </li>
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<h3>What presented issues:</h3>
 +
  <ul class="listResults">
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          <li>Optimizing the purification protocol for Cas13a.</li>
 +
<li>Demonstrating functionality of Lsh Cas13a.</li>
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<li>Ruling out RNase contamination from heat-lysed in vivo samples.</li>
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  </ul>
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<h3>Applications and reasons for a field-use detection device</h3>
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<h3>Protein Cloning, Expression and Purification</h3>
 
<p>   
 
<p>   
In times of increasing antibiotic resistances and the accompanying problems to fight bacterial infections against multi-resistant bacterial strains, it is of especial need to conserve antibiotic use to reasonable utilization, to avert a post-antibiotic era and its fatal aftermath. Freely available antibiotics for big parts of the world population leads to lots of misuse and wrong practice in combination with antibiotics, which will not change without an easy entry point for the public, when and what antibiotic is of need.</p>
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We decided to compare three versions of Cas13a that were previously characterized in the literature<sup><a class="myLink" href="#ref_1">1</a></sup>: Lbu, Lsh, and Lwa. We ordered the Lbu and Lsh plasmids from Addgene, and we cloned Lwa using Golden Gate assembly (sequence was taken from Gootenberg et al., 2017). Lbu and Lsh were expressed in <i>E.coli</i> Rosetta2, as the sequences were not codon-optimized, and Lwa was expressed in <i>E.coli</i> BL21 (DE3) star. We created three BioBricks from the Lwa sequence: <a class="myLink" href="http://parts.igem.org/Part:BBa_K2323000">BBa_K2323000</a> (containing the Lwa coding sequence and a Tphi terminator), <a class="myLink" href="http://parts.igem.org/Part:BBa_K2323001">BBa_K2323001</a> (where a 6xHis/Twin strep tag and a SUMO tag are added to the N-terminal end of BBa_K2323000), and <a href="http://parts.igem.org/Part:BBa_K2323004">BBa_K2323004</a> (where BBa_K2323001 is preceded by the T7 promoter and the Elowitz RBS). We improved the TEV-protease <a class="myLink" href="http://parts.igem.org/Part:BBa_K1319008">BBa_K1319008</a> by tagging it with a 6xHis tag, purified it and successfully used it for the TEV cleavage of our Cas13a proteins. </p>
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<div  class="captionPicture">
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<a href="#Lbu_Popup"><img width=220 src="https://static.igem.org/mediawiki/2017/e/ee/T--Munich--Cas13a_Lbu_PAGE_graph.png"></a>
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<a href="#Lsh_Popup"><img width=220 src="https://static.igem.org/mediawiki/2017/2/22/T--Munich--Cas13a_Lsh_PAGE_graph.png"></a>
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<a href="#Lwa_Popup"><img width=220 src="https://static.igem.org/mediawiki/2017/1/19/T--Munich--Cas13a_Lwa_PAGE.png"></a>
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<a href="#TEV_Popup"><img width=220 src="https://static.igem.org/mediawiki/2017/8/8d/T--Munich--Cas13a_TEV_PAGE_graph.png"></a>
 +
<p>Gel pictures of the final steps in the purification of our four proteins</p>
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</div>
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<p>
 +
We followed the purification protocols from literature, and found that although the His-purification and the tag cleavage steps worked as expected, the cation-exchange purification step failed, and we systematically lost our proteins. We still completed the size-exclusion purification, and our proteins with some amount of contamination. Protein purification took most of the first month of our project, due to the failure of the cation-exchange chromatography, but we eventually purified functional, if not perfectly clean, proteins.
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<h3>Key parts a home diagnostic method must fulfill</h3>
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<h3 id="Figure_1">Proof-of-Concept</h3>
 
<p>   
 
<p>   
Therefore, an easy to use home diagnostic tool must work based on the public standard state of knowledge. What is commonly known by the public, is that antibiotics only have effect against bacterial infections. Working of this knowledge state a low cost, easy to use, reliable home diagnostic tool, which could determine the reason of an infection could give the public a straightforward way to reduce the previously stated misuse of antibiotics. They could determine their etiology by themselves without the entry barriers of visiting their general practitioner or a hospital. From the test result on, the path to find the right treatment for their disease would be extremely simplified. By the switch of diagnostic treatment to everyone’s home step, an extremely synergistic effect for the whole population could be generated.</p>
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To prove the functionality of Cas13a, we used the 16S rRNA sequence from <i>E.coli</i> as a target sequence, given that is highly conserved in all bacterial species and can be easily extracted from bacterial cultures in large concentrations. For our first experiments, we used only 130 nucleotides of the 16S rRNA sequence and transcribed <i>in vitro</i> from a DNA template (since the whole 16S rRNA is 1500 nucleotides, therefore too large to be transcribed). Our crRNA DNA template was designed so that the target-binding region could easily be changed to detect <a class="myLink" href=https://2017.igem.org/Team:Munich/Targets>new targets</a>. We found that both Lbu and Lwa were functional and degraded the read-out RNase Alert in presence of both the target and the crRNA. An example time plot is shown in <b>Figure 1</b>, where the specific activity of Lbu was controlled by taking out the crRNA and Lbu, alternatively. 
 +
</p>
 +
<p>
 +
Lbu showed higher cleaving efficiency at equal concentrations compared to Lwa (contradicting what was shown in Gootenberg et al., 2017), and Lsh was not functional (we assume that the purification process inactivated the protein), see <b>Figure 2</b>. We therefore decided to use Lbu for the rest of our experiments.
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<div class="captionPicture">
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<a href="#Lbu_Experiment_Popup"><img width=440 src="https://static.igem.org/mediawiki/2017/f/f1/T--Munich--Cas13a_Experiment_with_lbu.png" alt="Lbu experiment"></a>
 +
<p><b>Figure 1</b>: Plot of a typical experiment with 10 nM Lbu Cas13a, 100 nM crRNA, 50 nM target, 185 nM RNase Alert and 1U/µL RNase inhibitor. For analysis, we typically considered the fluorescence intensity of samples after 30 minutes, and normalized it to obtain the ratio of cleaved RNase Alert, assuming that our negative control (with neither crRNA and Cas13a) had 0% cleavage and our positive control (with RNaseA) had 100% cleavage. We should note that we occasionally found that high target concentrations led to above positive control signals (which could be due to the degradation and lesser activity of RNaseA) and that low target concentrations led to below negative control signals (which could be due to noise at low fluorescence intensities).</p>
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<h3>Problem definition for a reliable home diagnostic</h3>
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<div class="captionPicture">
 +
<img width=900 src="https://static.igem.org/mediawiki/2017/5/57/T--Munich--Cas13a_Protein_Comparision.png" alt="Protein comparision">
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<p><b>Figure 2:</b> Target RNA concentration was screened for all three Cas13a proteins and their matching crRNA. A conservative cut-off of at least 15% of the RNaseAlert cleavage was chosen to determine the detection limit of our system.</p>
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<td id="Figure_3" colspan=3 align=center valign=center>
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<div class="captionPicture">
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<a href="#Lwa_Experiment_Popup"><img width=440 src="https://static.igem.org/mediawiki/2017/3/3f/T--Munich--Cas13a_Lwa_activity.png" alt="Lwa experiment"></a>
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<p><b>Figure 3</b>: The activity of Lwa Cas13a was found to be similar before and after His purification.</p>
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</div>
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<td class="verticalColumn" colspan=3 align=center valign=center>
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<p>
 +
Interestingly, we found that Lwa was active even without purification: after lysing cells expressing the Cas13a, we used the supernatant in our detection system, and found similar activity as after purification, see <b>Figure 3</b>. This result, along with further characterization, showed us that Cas13a is a relatively robust enzyme that works in a variety of contexts.
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<p>   
 
<p>   
Today, pathogens are discriminated by cell culture or PCR-based methods, requiring expensive equipment, trained personal, and time. Point-of-care tests currently on the market, like pregnancy tests, target certain metabolites and are therefore restricted to one specific application and one detection target. For a reliable home diagnostic test it would be of need to combine the portability, affordability, and usability of a smartphone like gadget with the universality and sensitivity of typical laboratory experimental setup of PCR-based nucleic acid detection methods and their accompanying equipment.</p>
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We screened the cleavage efficiency dependence on Cas13a and target concentrations, and found that for high Cas13a concentration, the background activity of Cas13a was overlaying with the target [plot of ratio vs Cas13a concentration] specific activation <b>(Figure 4)</b>. As our device should detect low target RNA concentrations in less than 30 minutes, we optimized the concentration of Cas13a: at high concentrations of the enzyme, the background activity hid the target-dependent signal; at low concentrations, the enzyme was too slow and a detectable signal could not be obtained in 30 mins unless large amounts of target RNA were added. A compromise was found at 10nM of Cas13a, and in these conditions, we found our target detection limit to be around 10nM <b>(Figure 1)</b>.
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</p>
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<div class="captionPicture">
 +
<img width=900 src="https://static.igem.org/mediawiki/2017/1/1e/T--Munich--Cas13a_Lbu_concentration_graph.png" alt="Lbu graph">
 +
<p><b>Figure 4:</b> At constant target RNA concentration, as Lbu Cas13a concentration increases, the background activity of the enzyme reduces the on/off ratio of activation by the target RNA.
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</p>
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<h3>Solution statement</h3>
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<h3>Cell lysis and RNA extraction</h3>
 
<p>   
 
<p>   
Based on these stated problems the solution seems only accomplishable by the combination of the transfer of all diagnostic reaction parts into the already previously used paper strip format and a readout and sample processing based around a portable processing unit with an integrated sensor with a high sensitivity/cost ratio.</p>
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For RNA extraction from our bacterial targets, we looked at several possible lysis methods. We tried and abandoned Guanidine-salts as lysis agent, since its strong chaotropic power makes extensive purification necessary. For the same reason regarding the need for purification, we used detergent/ heat lysis only in our lab work. While we investigated RNA-silica binding properties (see labbook Sept. 1st to 5th, section "other") and tested commercial silica-based kits for such purifications, we decided against adding unnecessary complexity for our prototype.
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<div class="captionPicture">
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<a href="#methods_Popup"><img width=450 src="https://static.igem.org/mediawiki/2017/e/e3/T--Munich--pic--lysis_rnaconc_methods.png" alt="lysis Rnaconc"></a>
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<p><b>Figure 5</b>: Lysis-RNA yield of detergent/heat and alkaline lysis.</p>
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</div>
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<td colspan=3 align=center valign=center>
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<div class="captionPicture">
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<a href="#degradation_Popup"><img width=350 src="https://static.igem.org/mediawiki/2017/4/48/T--Munich--pic--lysis_alkaline_degradation.png" alt="Alkaline degradation"></a>
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<p><b>Figure 6</b>: Degradation of RNA due alkaline lysis with different incubation times.</p>
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<h3>CascAID<sup>+</sup></h3>
 
 
<p>   
 
<p>   
Our project, which we named Cas13a controlled assay for infectious diseases (CascAID), features the recently identified CRISPR/Cas effector Cas13a<sup><a class="myLink" href="#ref_10">10</a></sup>. Unlike other proteins in the familiy, Cas13a has the unique ability to bind and cleave specific RNA targets rather than DNA ones.  Moreover, after cleaving its target, Cas13a is able to unspecifically cleave RNA molecules. By using this collateral activity from Cas13a, our system is capable of detecting virtually any RNA target. This is done by changing the crRNA in the protein, that is a short RNA sequence that determines what is recognized as target.</p>
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Alkaline lysis is well-known for DNA-, but not for RNA-extraction due to the rapid hydrolysis of RNA under alkaline conditions. Since our protein responds to a very short part of our target sequence (<30 bp), compared to the resulting RNA fragments (most >300 pb, see <b>Figure 6</b>), it should work none the less and with better efficiency <b>(Figure 5)</b> and superior speed (seconds) compared to detergent/ heat lysis.
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<p> 
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Since microfluidic mixing of liquids is a rather complicated process, we settled for an isothermal PCR-based approach (RPA). With the exceptional sensitivity of PCR, we can even use an inefficient heat-only lysis (5-10 times less efficient than detergent/ heat) and still detect RNA with an amount of 100 cells in the PCR reaction volume.
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</p>
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<td colspan=3 align=center valign=center>
 
<div class="captionPicture">
 
<div class="captionPicture">
<img src="https://static.igem.org/mediawiki/2017/0/04/T--Munich--Description_Cas13a_Mechanism.svg" alt="Diagram for Cas13a's function">
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<a href="#Lysis_PCR_Popup"><img width=220 src="https://static.igem.org/mediawiki/2017/e/e0/T--Munich--pic--lysis_pcr_deltacells.png" alt="PCR lysis">
<p>Cas13a binds specific target RNA depending on the crRNA sequence. After activation, Cas13a cleaves RNA indiscriminately.</p>
+
<p>
 +
<b>Figure 7</b>: PCR with varying cell density [1] log2 DNA ladder, [2] 10<sup>6</sup> cells/ml, [3] 10<sup>5</sup> cells/ml, [4] 5*10<sup>4</sup> cells/ml, [5] 10<sup>4</sup> cells/ml,[6] 5*10<sup>3</sup> cells/ml, [7] 10<sup>3</sup> cells/ml, [8] 10<sup>2</sup> cells/ml,[9] 10<sup>6</sup> cells/ml (no heat-lysis step. only PCR at 37 °C)
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<h3 id="vivo">Detection of Pathogenic RNA from <i>in vivo</i> Source</h3>
 
<p>   
 
<p>   
Our project is divided in the following 3 general parts:</p>
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We then set out to detect RNA from <i>in vivo</i> samples rather than from <i>in vitro</i> transcribed RNA. As we had chosen the 16S rRNA sequence of <i>E. coli</i> as a target, we used <i>E. coli</i> DH5α cultures as <i>in vivo</i> samples. We performed two kinds of treatment on the cells (from an overnight culture):
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<h4>Sample processing unit</h4>
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<p>
<img src="">
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We lysed the cells with 10% SDS and heated them between 80°C or 95°C for 10 minutes, and then extracted the RNA with phenol-chloroform extraction. We used this purified RNA to perform the detection tests.
 +
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<td class="verticalColumn" colspan=3 align=center valign=center>
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<p>
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We lysed the cells with heat (80°C or 95°C for 10 minutes) and used this directly for our detection tests. As the sample was not purified, we expect to have some amount of RNase present here, and it is unclear whether the RNase inhibitor we used was enough to prevent activity from the <i>E. coli</i> native RNases.
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</p>
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<p>   
 
<p>   
Tackling the challenge of sample pre-processing in field, we started developing a portable fluidic system featuring a temperature control unit for lysis and isothermal PCR (RPA). Conceiving a platform independent of lab infrastructure, we demonstrate the feasibility of controlling fluid flow control with the simplest tools possible using bike tires and air balloons.</p>
+
To have an estimation for the 16S rRNA concentration for our first extraction method, we did the following calculations. We assumed that a concentration of 10 fM of 16S rRNA would be equivalent to a cell concentration of 100 CFU/mL, which is the conservative end of the range given by Esfandiari et al<sup><a class="myLink" href="#ref_2">2</a></sup>.  We then assumed that our overnight culture would have an O.D. 600 nm of 2, corresponding to 1,6 * 10<sup>9</sup> CFU/mL. We assumed no loss of RNA during phenol-chloroform extraction (which is again, a conservative estimation of the concentration), and considered a concentrating factor of 40, as we extracted the RNA from a 2 mL culture and resuspended it in 50 µL. We estimated that our extracted RNA would have a concentration of 6,4 µM of 16S rRNA, and tested our detection circuit with dilutions from this source, see <b>Figure 8</b>. We found that we had a higher detection limit for our <i>in vivo</i> source, which could be caused by our conservative calculation of the extracted RNA concentration.
 +
</p>
 +
<div class="captionPicture">
 +
<img width=900 src="https://static.igem.org/mediawiki/2017/e/e5/T--Munich--Cas13a_Lbu_Titration_graph.png" alt="Tritation">
 +
<p><b>Figure 8:</b> Titration curve for the detection of the 16S rRNA from <i>E.coli</i>, from an <i>in vitro</i> or an <i>in vivo</i> source.
 +
</p>
 +
</div>
 +
<p>
 +
Our second extraction method is closest to what we want to achieve on our chip: the cells are lysed and the target is amplified. As we did not manage to bring together our amplification module with our <i>in vivo</i> extraction module (due to lack of time), we set out to directly detect the RNA from the lysed cells. Assuming the same O.D. as for our first extraction method, the concentration of 16S rRNA in a saturated culture would be around 160 nM. In this experiment, we found that the fluorescence was maximum for an intermediate concentration of the lysed cells (equivalent to an estimated 48 nM of 16S rRNA). As expected, the fluorescence was lower as the lysed cells concentration decreased <b>(Figure 9)</b>, but we could not explain why the signal also went down for the higher concentration (equivalent to 80 nM 16S rRNA). In all samples with cells, the fluorescence was higher than the positive control, which could indicate that the fluorescence is not due to Cas13a activity but rather to RNAse activity. However, the positive control was significantly lower here than in our first <i>in vivo</i> experiment (around 3*10<sup>4</sup> a.u. of fluorescence compared to 6*10<sup>4</sup> a.u. for the same gain), which could be due to a loss of activity of RNaseA. Besides, our Lwa experiments have shown a similar activity for the enzyme directly pipetted from lysed cells as for a His-purified enzyme. We therefore think that there is good indication that we can directly detect the 16S rRNA from heat-lysed cells. However, it is clear that this experiment should be reproduced and confirmed. A control experiment could consist of an unnatural target that will be added to <i>E.coli</i> via a plasmid. We could then compare cells with and without the plasmid, i.e. with and without the target, but where the RNase contamination from cell lysis should be identical.
 +
</p>
 +
<div id="Figure_6" class="captionPicture">
 +
<img width=900 src="https://static.igem.org/mediawiki/2017/3/37/T--Munich--Cas13a_invivo.png" alt="In vivo">
 +
<p><b>Figure 9:</b> Direct detection of 16S rRNA from heat-lysed cells led to a peak response depending on concentration.</p>
 +
</div>
 
</td>
 
</td>
 
</tr>
 
</tr>
  
 
<tr><td colspan=6 align=center valign=center>
 
<tr><td colspan=6 align=center valign=center>
<h4>Paper strip reaction unit</h4>
+
<h3>Reproducibility</h3>
<img src="">
+
 
<p>   
 
<p>   
After pre-processing, the idea was to combine all diagnostic reactions into an easy to use format. We chose to imbed all the reactions into the format of a paper strip of the size of a typical post-it. Our full readout producing reaction chain takes place on this small paper strip. This enables us to freeze-dry all reaction agents in a small proximity and further provides also long-time storage possibility. In addition, the advantages of the paper format are the low sample volumes needed for a reaction asset. To enable transport of the sample-containing fluid to the areas containing the detection mixture, we chose to use the paper-fluidics technology. The whole printing mechanism of the paper fluidics is based around a regular office printer to pattern the paper with hydrophobic wax channels. The detection circuit is first assessed in bulk, the Cas13a is characterized using the RNaseAlert standard, its detection limit is determined, and the differentiation between viral and bacterial targets is verified; before the mechanism is transferred into a paper strip application. Three advanced readout methods are designed and explored, all of which propose an amplification cascade following Cas13a target detection. Those readout methods, combined with the fluidics, should give us the possibility to lower the detection limit and improve the on-field use.</p>
+
As we characterized the Cas13a thoroughly, we found that the enzyme was extremely robust in its activity. It showed reproducible cleaving activity through batches of purification of both the enzyme and the purified RNAs, with different target concentrations, and especially when handled by different experimenters, more or less trained. However, we did find that as the kinetics of Cas13a in these conditions are relatively fast, the signal had already reached saturation when the slower experimenter was done assembling the reaction into the reading plate. We recommend that all parts of the sample be assembled, and that the target RNA (or its source) be added at the very last minute, just before starting fluorescence acquisition, so that the kinetics can be properly followed. In the context of our diagnosis device, this would not cause a problem, as we want the fastest possible result reading by the patient or doctor. </p>
 
</td>
 
</td>
 
</tr>
 
</tr>
  
 
<tr><td colspan=6 align=center valign=center>
 
<tr><td colspan=6 align=center valign=center>
<h4>Detector unit</h4>
+
<h3>Discussion and Conclusion</h3>
<img src="">
+
 
<p>   
 
<p>   
Starting from the fact that suitable measurement instruments with high enough sensitivity for field use are too expensive for mass production, we constructed a portable low-cost fluorescence detector, which can be easily assembled with a few standard worldwide available electronic parts and a 3D-printer. Driving the development even further, we pushed the sensitivity of our detector into the range of commercial plate reader, while conserving an assembly cost of around 15 $. A detailed documentation of the detector development and sensitivity determination can be found under <a class="myLink" href="/Team:Munich/Measurement">Measurement</a> & <a class="myLink" href="/Team:Munich/Hardware/Detector">Detector</a>
+
We purified and proved the functionality of the Cas13a enzyme, chose Lbu for its better activity, optimized the concentrations in our detection scheme and found the detection limit to be in the range of 10 nM target RNA. We found that we could detect RNA from <i>in vivo</i> sources, with full RNA extraction, but possibly also from simply lysed cells. This makes this module (the Cas13a detection circuit) the best characterized and most promising module of our platform. It gives fast, high fluorescence signals for low target RNA concentration, and can be combined with our amplification module, which would use heat lysis (80°C) followed by reverse transcription, RPA and transcription (room temperature).</p>
Furthermore, with our hardware technology we provide a software for a crRNA databank, secondary structure verification of crRNAs and off target verification of designed crRNAs. In combination with the detector unit, we supply a program code for data evaluation of data acquired with our detector.</p>
+
 
</td>
 
</td>
 
</tr>
 
</tr>
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<p>  
 
<p>  
 
     <ol style="text-align: left">
 
     <ol style="text-align: left">
       <li id="ref_1">Cohen, Limor, and David R. Walt. "Single-Molecule Arrays for Protein and Nucleic Acid Analysis." Annual Review of Analytical Chemistry 0 (2017).</li>
+
       <li id="ref_1">Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy, A. J., Joung, J., ... & Myhrvold, C. (2017). Nucleic acid detection with CRISPR-Cas13a/C2c2. Science, eaam9321.</li>
      <li id="ref_2">Nakano, Michihiko, et al. "Single-molecule PCR using water-in-oil emulsion." Journal of biotechnology 102.2 (2003): 117-124.</li>
+
       <li id="ref_2">Esfandiari, L., Wang, S., Wang, S., Banda, A., Lorenzini, M., Kocharyan, G., ... & Schmidt, J. J. (2016). PCR-Independent Detection of Bacterial Species-Specific 16S rRNA at 10 fM by a Pore-Blockage Sensor. Biosensors, 6(3), 37.</li>
      <li id="ref_3">Taniguchi, Yuichi, et al. "Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells." science 329.5991 (2010): 533-538.</li>
+
      <li id="ref_4">Rissin, David M., et al. "Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations." Nature biotechnology 28.6 (2010): 595-599.</li>
+
      <li id="ref_5">Pardee, Keith, et al. "Rapid, low-cost detection of Zika virus using programmable biomolecular components." Cell 165.5 (2016): 1255-1266.</li>
+
       <li id="ref_6">Slomovic, Shimyn, Keith Pardee, and James J. Collins. "Synthetic biology devices for in vitro and in vivo diagnostics." Proceedings of the National Academy of Sciences 112.47 (2015): 14429-14435.</li>
+
      <li id="ref_7">Tang, Ruihua, et al. "A fully disposable and integrated paper-based device for nucleic acid extraction, amplification and detection." Lab on a Chip 17.7 (2017): 1270-1279.</li>
+
      <li id="ref_8">Vashist, Sandeep Kumar, et al. "Emerging technologies for next-generation point-of-care testing." Trends in biotechnology 33.11 (2015): 692-705.</li>
+
      <li id="ref_9">Gubala, Vladimir, et al. "Point of care diagnostics: status and future." Analytical chemistry 84.2 (2011): 487-515.</li>
+
      <li id="ref_10">Abudayyeh, Omar O., et al. "C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector." Science 353.6299 (2016): aaf5573.</li>
+
 
     </ol>  
 
     </ol>  
 
</p>
 
</p>
 
</td>
 
</td>
 
</tr>
 
</tr>
 
  
  

Latest revision as of 03:51, 2 November 2017


Results: Cas13a

What worked:

What presented issues:

  • Optimizing the purification protocol for Cas13a.
  • Demonstrating functionality of Lsh Cas13a.
  • Ruling out RNase contamination from heat-lysed in vivo samples.

Protein Cloning, Expression and Purification

We decided to compare three versions of Cas13a that were previously characterized in the literature1: Lbu, Lsh, and Lwa. We ordered the Lbu and Lsh plasmids from Addgene, and we cloned Lwa using Golden Gate assembly (sequence was taken from Gootenberg et al., 2017). Lbu and Lsh were expressed in E.coli Rosetta2, as the sequences were not codon-optimized, and Lwa was expressed in E.coli BL21 (DE3) star. We created three BioBricks from the Lwa sequence: BBa_K2323000 (containing the Lwa coding sequence and a Tphi terminator), BBa_K2323001 (where a 6xHis/Twin strep tag and a SUMO tag are added to the N-terminal end of BBa_K2323000), and BBa_K2323004 (where BBa_K2323001 is preceded by the T7 promoter and the Elowitz RBS). We improved the TEV-protease BBa_K1319008 by tagging it with a 6xHis tag, purified it and successfully used it for the TEV cleavage of our Cas13a proteins.

Gel pictures of the final steps in the purification of our four proteins

We followed the purification protocols from literature, and found that although the His-purification and the tag cleavage steps worked as expected, the cation-exchange purification step failed, and we systematically lost our proteins. We still completed the size-exclusion purification, and our proteins with some amount of contamination. Protein purification took most of the first month of our project, due to the failure of the cation-exchange chromatography, but we eventually purified functional, if not perfectly clean, proteins.

Proof-of-Concept

To prove the functionality of Cas13a, we used the 16S rRNA sequence from E.coli as a target sequence, given that is highly conserved in all bacterial species and can be easily extracted from bacterial cultures in large concentrations. For our first experiments, we used only 130 nucleotides of the 16S rRNA sequence and transcribed in vitro from a DNA template (since the whole 16S rRNA is 1500 nucleotides, therefore too large to be transcribed). Our crRNA DNA template was designed so that the target-binding region could easily be changed to detect new targets. We found that both Lbu and Lwa were functional and degraded the read-out RNase Alert in presence of both the target and the crRNA. An example time plot is shown in Figure 1, where the specific activity of Lbu was controlled by taking out the crRNA and Lbu, alternatively.

Lbu showed higher cleaving efficiency at equal concentrations compared to Lwa (contradicting what was shown in Gootenberg et al., 2017), and Lsh was not functional (we assume that the purification process inactivated the protein), see Figure 2. We therefore decided to use Lbu for the rest of our experiments.

Lbu experiment

Figure 1: Plot of a typical experiment with 10 nM Lbu Cas13a, 100 nM crRNA, 50 nM target, 185 nM RNase Alert and 1U/µL RNase inhibitor. For analysis, we typically considered the fluorescence intensity of samples after 30 minutes, and normalized it to obtain the ratio of cleaved RNase Alert, assuming that our negative control (with neither crRNA and Cas13a) had 0% cleavage and our positive control (with RNaseA) had 100% cleavage. We should note that we occasionally found that high target concentrations led to above positive control signals (which could be due to the degradation and lesser activity of RNaseA) and that low target concentrations led to below negative control signals (which could be due to noise at low fluorescence intensities).

Protein comparision

Figure 2: Target RNA concentration was screened for all three Cas13a proteins and their matching crRNA. A conservative cut-off of at least 15% of the RNaseAlert cleavage was chosen to determine the detection limit of our system.

Lwa experiment

Figure 3: The activity of Lwa Cas13a was found to be similar before and after His purification.

Interestingly, we found that Lwa was active even without purification: after lysing cells expressing the Cas13a, we used the supernatant in our detection system, and found similar activity as after purification, see Figure 3. This result, along with further characterization, showed us that Cas13a is a relatively robust enzyme that works in a variety of contexts.

We screened the cleavage efficiency dependence on Cas13a and target concentrations, and found that for high Cas13a concentration, the background activity of Cas13a was overlaying with the target [plot of ratio vs Cas13a concentration] specific activation (Figure 4). As our device should detect low target RNA concentrations in less than 30 minutes, we optimized the concentration of Cas13a: at high concentrations of the enzyme, the background activity hid the target-dependent signal; at low concentrations, the enzyme was too slow and a detectable signal could not be obtained in 30 mins unless large amounts of target RNA were added. A compromise was found at 10nM of Cas13a, and in these conditions, we found our target detection limit to be around 10nM (Figure 1).

Lbu graph

Figure 4: At constant target RNA concentration, as Lbu Cas13a concentration increases, the background activity of the enzyme reduces the on/off ratio of activation by the target RNA.

Cell lysis and RNA extraction

For RNA extraction from our bacterial targets, we looked at several possible lysis methods. We tried and abandoned Guanidine-salts as lysis agent, since its strong chaotropic power makes extensive purification necessary. For the same reason regarding the need for purification, we used detergent/ heat lysis only in our lab work. While we investigated RNA-silica binding properties (see labbook Sept. 1st to 5th, section "other") and tested commercial silica-based kits for such purifications, we decided against adding unnecessary complexity for our prototype.

lysis Rnaconc

Figure 5: Lysis-RNA yield of detergent/heat and alkaline lysis.

Alkaline degradation

Figure 6: Degradation of RNA due alkaline lysis with different incubation times.

Alkaline lysis is well-known for DNA-, but not for RNA-extraction due to the rapid hydrolysis of RNA under alkaline conditions. Since our protein responds to a very short part of our target sequence (<30 bp), compared to the resulting RNA fragments (most >300 pb, see Figure 6), it should work none the less and with better efficiency (Figure 5) and superior speed (seconds) compared to detergent/ heat lysis.

Since microfluidic mixing of liquids is a rather complicated process, we settled for an isothermal PCR-based approach (RPA). With the exceptional sensitivity of PCR, we can even use an inefficient heat-only lysis (5-10 times less efficient than detergent/ heat) and still detect RNA with an amount of 100 cells in the PCR reaction volume.

Detection of Pathogenic RNA from in vivo Source

We then set out to detect RNA from in vivo samples rather than from in vitro transcribed RNA. As we had chosen the 16S rRNA sequence of E. coli as a target, we used E. coli DH5α cultures as in vivo samples. We performed two kinds of treatment on the cells (from an overnight culture):

We lysed the cells with 10% SDS and heated them between 80°C or 95°C for 10 minutes, and then extracted the RNA with phenol-chloroform extraction. We used this purified RNA to perform the detection tests.

We lysed the cells with heat (80°C or 95°C for 10 minutes) and used this directly for our detection tests. As the sample was not purified, we expect to have some amount of RNase present here, and it is unclear whether the RNase inhibitor we used was enough to prevent activity from the E. coli native RNases.

To have an estimation for the 16S rRNA concentration for our first extraction method, we did the following calculations. We assumed that a concentration of 10 fM of 16S rRNA would be equivalent to a cell concentration of 100 CFU/mL, which is the conservative end of the range given by Esfandiari et al2. We then assumed that our overnight culture would have an O.D. 600 nm of 2, corresponding to 1,6 * 109 CFU/mL. We assumed no loss of RNA during phenol-chloroform extraction (which is again, a conservative estimation of the concentration), and considered a concentrating factor of 40, as we extracted the RNA from a 2 mL culture and resuspended it in 50 µL. We estimated that our extracted RNA would have a concentration of 6,4 µM of 16S rRNA, and tested our detection circuit with dilutions from this source, see Figure 8. We found that we had a higher detection limit for our in vivo source, which could be caused by our conservative calculation of the extracted RNA concentration.

Tritation

Figure 8: Titration curve for the detection of the 16S rRNA from E.coli, from an in vitro or an in vivo source.

Our second extraction method is closest to what we want to achieve on our chip: the cells are lysed and the target is amplified. As we did not manage to bring together our amplification module with our in vivo extraction module (due to lack of time), we set out to directly detect the RNA from the lysed cells. Assuming the same O.D. as for our first extraction method, the concentration of 16S rRNA in a saturated culture would be around 160 nM. In this experiment, we found that the fluorescence was maximum for an intermediate concentration of the lysed cells (equivalent to an estimated 48 nM of 16S rRNA). As expected, the fluorescence was lower as the lysed cells concentration decreased (Figure 9), but we could not explain why the signal also went down for the higher concentration (equivalent to 80 nM 16S rRNA). In all samples with cells, the fluorescence was higher than the positive control, which could indicate that the fluorescence is not due to Cas13a activity but rather to RNAse activity. However, the positive control was significantly lower here than in our first in vivo experiment (around 3*104 a.u. of fluorescence compared to 6*104 a.u. for the same gain), which could be due to a loss of activity of RNaseA. Besides, our Lwa experiments have shown a similar activity for the enzyme directly pipetted from lysed cells as for a His-purified enzyme. We therefore think that there is good indication that we can directly detect the 16S rRNA from heat-lysed cells. However, it is clear that this experiment should be reproduced and confirmed. A control experiment could consist of an unnatural target that will be added to E.coli via a plasmid. We could then compare cells with and without the plasmid, i.e. with and without the target, but where the RNase contamination from cell lysis should be identical.

In vivo

Figure 9: Direct detection of 16S rRNA from heat-lysed cells led to a peak response depending on concentration.

Reproducibility

As we characterized the Cas13a thoroughly, we found that the enzyme was extremely robust in its activity. It showed reproducible cleaving activity through batches of purification of both the enzyme and the purified RNAs, with different target concentrations, and especially when handled by different experimenters, more or less trained. However, we did find that as the kinetics of Cas13a in these conditions are relatively fast, the signal had already reached saturation when the slower experimenter was done assembling the reaction into the reading plate. We recommend that all parts of the sample be assembled, and that the target RNA (or its source) be added at the very last minute, just before starting fluorescence acquisition, so that the kinetics can be properly followed. In the context of our diagnosis device, this would not cause a problem, as we want the fastest possible result reading by the patient or doctor.

Discussion and Conclusion

We purified and proved the functionality of the Cas13a enzyme, chose Lbu for its better activity, optimized the concentrations in our detection scheme and found the detection limit to be in the range of 10 nM target RNA. We found that we could detect RNA from in vivo sources, with full RNA extraction, but possibly also from simply lysed cells. This makes this module (the Cas13a detection circuit) the best characterized and most promising module of our platform. It gives fast, high fluorescence signals for low target RNA concentration, and can be combined with our amplification module, which would use heat lysis (80°C) followed by reverse transcription, RPA and transcription (room temperature).

References

  1. Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy, A. J., Joung, J., ... & Myhrvold, C. (2017). Nucleic acid detection with CRISPR-Cas13a/C2c2. Science, eaam9321.
  2. Esfandiari, L., Wang, S., Wang, S., Banda, A., Lorenzini, M., Kocharyan, G., ... & Schmidt, J. J. (2016). PCR-Independent Detection of Bacterial Species-Specific 16S rRNA at 10 fM by a Pore-Blockage Sensor. Biosensors, 6(3), 37.