Results

Results Pipelines

Below are the results of our project split into different pipelines. Click the green buttons to see specific results.


Protein Expression


To express our His-tagged proteins, we transformed our plasmids into both E.coli BL21(DE3) and Rosetta, and then expressed with the addition of IPTG. As a positive control for protein expression we also transformed a plasmid coding for His-Tagged mCherry. As a negative control for protein expression we transformed an empty plasmid pX1900. The result of this was inconsistent induction of both Cas proteins, and the plasmid coding for Cas12a did not even transform into Rosetta. [Figure 1]

Learning from this we continued forward with BL21(DE3), as both protein coding plasmids transformed. However we wanted to increase expression so tried autoinduction media (see Experiments) instead of IPTG induction. The result of this was much better expression of both proteins. [Figure 2]

This meant through trial and error, we used BL21(DE3) and autoinduction media as our final way to express our Cas proteins, and the next step was then to improve the purification.

^Red box indicates the presence of desired protein (Cas12a/Cas13a or mCherry to show positive expression).

image of western blot (refer to figure 1)

[Figure 1] Western Blot of the IPTG induction in E.coli BL21(DE3) and Rosetta.

image of 2 western blot (refer to figure 2)

[Figure 2] Western Blots of Autoinduction media expression of the Cas12a and Cas13a and positive and negative controls.

Protein Purification


We tried several methods for protein purification including: Gravity Nickel Affinity column only, Gravity Ni column + buffer exchange PD10 column, Gravity Ni column + 100kDa MW cutoff spin column. None of these methods resulted in pure protein.

After this, we tried a more sophisticated technique using an automated AKTA system. After cell lysis, high speed centrifugation and syringe filtration (0.8 and 0.2 um), supernatant was loaded onto a His-Trap FF crude 1mL column (Cytiva) and eluted across a gradient of 20mM to 250mM Imidazole in 15 column volumes. Protein containing fractions were identified by SDS-PAGE [Figure 1], pooled and concentrated to 2mL with a 100kDa MW cutoff spin column. This was loaded onto a Superdex 200 10/300 120mL (Cytiva) and eluted with 1 column volume of 20mM Tris-HCl, 500mM NaCl, 5% glycerol, pH 7.5. Protein containing fractions were identified by western blot [Figure 2], and pooled to give our final usable samples of Cas12a and Cas13a.

^Red box indicates the presence of desired protein (Cas12a/Cas13a).

image of 2 western blots (refer to figure 1)

[Figure 1] SDS-PAGE gels of the fractions collected from the His-Trap columns, with the most concentrated protein containing fractions collected and pooled.

image of 2 western blot (refer to figure 2)

[Figure 2] Western Blots of the fractions collected from the Superdex Size Exclusion Gel column, with the most concentrated protein containing fractions collected and pooled.

image of 2 western blot (refer to figure 3)

[Figure 3] SDS-PAGE gels and Western Blots of the different stages of the purification process.

The resulting SDS-Page gel was photographed and analysed to determine the success of purification following each technique. To quantify purity of Cas13a, the following analysis was used:

  • The image was cropped to the relevant lanes, (shown in yellow on Figure 4a) with the band containing the Cas13a protein also separately cropped (shown in red on Figure 4a).
  • These images were saved as .png files named img1-4.
  • A custom Matlab script shown below was used to import each image, convert to a numerical matrix of RGB intensities, and count blue pixels given a certain threshold (Red <=100, Green <=100, Blue >= 100). Final values were plotted [Figure 4b]
  • Code

      A=imread('SEC.png');

      B=imread('SECcas.png');

      C=imread('HisTrap.png');

      D=imread('HisTrapcas.png');


      % %


      imshow(A)

      imshow(B)

      imshow(C)

      imshow(D)


      % %

      bluePixelsA = A(:,:,1) <= 100 & A(:,:,2) <= 100 & A(:,:,3) >= 100;

      numBluePixelsA = sum(bluePixelsA(:));


      % %

      bluePixelsB = bluePixelsB(:,:,1) <= 100 & B(:,:,2) <= 100 & B(:,:,3) >= 100;

      numBluePixels = sum(bluePixelsB(:));


      % %

      bluePixelsC = C(:,:,1) <= 100 & C(:,:,2) <= 100 & C(:,:,3) >= 100;

      numBluePixelsC = sum(bluePixelsC(:));


      % %

      bluePixelsD = D(:,:,1) <= 100 & D(:,:,2) <= 100 & D(:,:,3) >= 100;

      numBluePixelsD = sum(bluePixelsD(:));


      % %


      HisTrapPurity=100/(numBluePixelsC/numBluePixelsD)

      SecPurity=100/(numBluePixelsA/numBluePixelsB)

 SDS-PAGE showing lanes after the His-Trap and SEC and graph showing percentage of Cas13a protein after the His-Trap and after the SEC. There is a significant increase in the percentage of Cas13a in the overall protein content

[Figure 4] A - SDS-PAGE showing lanes after the His-Trap and SEC. B - graph showing percentage of Cas13a protein after the His-Trap and after the SEC. There is a significant increase in the percentage of Cas13a in the overall protein content.

Probe Validity


Throughout our project, we attempted to validate both IDT DNaseAlert ™ and RNaseAlert ™ probes. This page contains the validation of RNaseAlert ™ probes for our system. The IDT DNaseAlert™ probes failed to produce consistently significant experimental data, so alongside the stakeholder-informed move to focus on the Cas13a diagnostic system and budget constraints we decided not to pursue validation.


1) RNase A Concentration Experiments

(The effect of varying concentrations of RNase A has on levels of fluorescence intensity and speed kinetics (Vmax and Kmax) measured when RNAseAlert™ is cleaved)


As shown [Figure 1], increasing the concentration of RNase A enzyme increases both the fluorescence intensity and rate of reaction [figure 2]. The high R-squared value [figure 2] shows that our data closely follows our Michaelis-Menten modelling, suggesting it can act as a positive control to show that the RNase component of our system (the cas13a enzyme) works successfully. This can then be used to assess how accurately it works in the system. Furthermore, these results highlight the sensitivity of IDT RNaseAlert™ probes to minimal levels of RNase contamination.

 A graph showing the change in fluorescein concentration equivalent (nM) of IDT RNaseAlert™ probe over time for a series of 10x RNase A dilutions

[Figure 1] A graph showing the change in fluorescein concentration equivalent (nM) of IDT RNaseAlert™ probe over time for a series of 10x RNase A dilutions. The negative control (blank) consists of QIAGEN nuclease free water (200 µL). Error bars show 5 biological repeats.

A graph showing the changing Vmax (nM min-1) of different RNase A concentrations (nM)

[Figure 2] A graph showing the changing Vmax (nM min-1) of different RNase A concentrations (nM).

2) Fluorescent Probe Concentration Experiment

(The relationship between IDT RNaseAlert™ probe concentration and fluorescence)


As shown [Figure 3], both the fluorescence intensity and rate of reaction are proportional to the concentration of IDT RNaseAlert™ probe used. All concentrations of probe except for 5nM produce a significant R-squared value, showing close adherence to our Michaelis-Menten modelling.

A graph showing the changing Vmax (nM min-1) of different RNase A concentrations (nM)

[Figure 3] A graph showing the change in fluorescein concentration equivalent (nM) of decreasing concentrations of IDT RNaseAlert™ probes exposed to 2 µL of RNase A over time (minutes). Dotted lines show the data produced, whereas solid lines show the Michaelis-Menten model.

3) Environmental RNase Contamination Experiments

(Measuring the presence of RNase contamination in experimental buffers, and the effect of various environmental conditions on the level of RNase contamination)


A graph showing the changing Vmax (nM min-1) of different RNase A concentrations (nM)

[Figure 4] A graph showing the change in fluorescein concentration equivalent (nM) of IDT RNaseAlert™ probes suspended in nuclease-free water, MilliQ water, elution buffer and equilibration buffer left in both the laminar flow hood and on the bench over time (minutes).

As shown [Figure 4] both nuclease-free water and MilliQ (in both the laminar flow hood and on the lab bench) show low levels of contamination, with a negligible increase over 900 minutes. However, both non-sterile elution buffer and equilibration buffer show significant levels of environmental RNase contamination in both the laminal flow hood and lab bench environments. Elution buffer in both environments shows an increase in contamination over time- notably, despite significant differences in starting fluorescein concentration equivalents, both points intersect at 900 minutes. Given the low effect of environmental exposure on nuclease-free water and MilliQ, it is possible both samples of elution buffer contain a similar level of endogenous RNase but an unknown external factor led to an inhibition of the RNase in the sample left on the bench environment. This would require repeat experiments to explore, but due to budget and time constraints this was not possible in our laboratory. Equilibration buffer showed a slight but similar decrease across both samples, possibly due to background noise on the plate reader. As above, this would require further repeat experiments to explore. Ideally, this experiment would have been conducted in a third environment (outside) to best mimic the environment our test would be conducted in, but due to ethical consideration and stringent adherence to iGEM guidelines, this was not possible.

Preparation of Blood Samples


To ensure the opacity of a blood sample will not prevent a positive result from displaying fluorescence, we explored different methods of reducing its opacity (see also Design Engineer Cycle 1). To begin we measured the fluorescence of synthetic blood when mixed with varying concentrations of fluorescein and compared this with a control sample of PBS [Figure 1]. We found that the synthetic blood in its original form does not fluoresce, even when a large concentration of fluorescein was added. Our team interviewed representatives from Attomarker (see Integrated Human Practices page), a testing company that work at an industrial scale. They suggested extracting serum from our blood samples to combat this problem.


A graph showing the fluorescent signal received in nM of PBS and synthetic blood when combined with varying concentrations of fluorescein.

[Figure 1] A graph showing the fluorescent signal received in nM of PBS and synthetic blood when combined with varying concentrations of fluorescein.


We attempted to decrease opacity of our blood sample through the following techniques, comparing fluorescence to our original results [Figure 1]:

After each alteration to the pigs’ blood, a sample from the top of each tube was taken and an 100-fold dilution of fluorescein was added before taking the following readings:


a) Coagulation of Blood


Eppendorfs containing 1 mL pigs’ blood were left at room temperature and on ice for an hour. For the samples of pigs’ blood, at room temperature this led to no change in fluorescence, and when on ice a decrease in fluorescence was observed.

A graph showing the percentage change in fluorescein equivalents of blood when coagulated.

[Figure 2] A graph showing the percentage change in fluorescein equivalents of blood when coagulated.


b) Centrifugation of Blood


1 mL samples of pigs’ blood were added to 2 mL Eppendorfs and three of each were spun for one, five and ten minutes at the following speeds:

A significant increase of fluorescence in hand centrifugation would have been ideal, as we wish to create a testing system that is accessible for those in developing countries that does not require specialist equipment. This was not the case, the greatest increase that was observed was from centrifugation at 2000 xG for 5 minutes at 40%. Hand centrifugation can not be done fast enough and does not guarantee a consistent speed.


A graph showing percentage change of fluorescence in pigs’ blood and PBS, when centrifuged at different speeds and for different times.

[Figure 3] A graph showing percentage change of fluorescence in pigs’ blood and PBS, when centrifuged at different speeds and for different times.


A graph showing percentage change of fluorescence in pigs’ blood and PBS, when hand centrifuged at different speeds and for different times.

[Figure 4] A graph showing percentage change of fluorescence in pigs’ blood and PBS, when hand centrifuged at different speeds and for different times.


c) Filtration of Blood


Pigs’ blood and PBS samples were filtered three times with four different filters:

When the pigs’ blood was filtered more, the decrease in fluorescence became larger, which was not as expected. This means the blood serum was being filtered out and that filtering blood samples has the opposite effect than what is required.


A graph showing percentage change of fluorescence in pigs’ blood when filtered in varying ways.

[Figure 5] A graph showing percentage change of fluorescence in pigs’ blood when filtered in varying ways.


c) Heating of Blood


After adding diluted fluorescein to 1 mL of pigs’ blood, the samples were placed into heat blocks at different temperatures for different amounts of time:

Treatment B resulted in a decrease in fluorescence, this was due to the blood solidifying at these solutions and meaning after the treatment the fluorescence could not be measured. Treatment A resulted in an increase in fluorescence of over 50%. This means when testing our blood samples for bTB on site, they will have less background noise. This change to our testing process will be easy for farmers to implement into their process.

Treatment A and B were inspired from treatments from [1] and [2] respectively.

For these experiments, absorbance and fluorescence readings were converted to fluorescein equivalents and used to calculate a standardised percentage change. This allows for our experiments to be replicated and links to our Measurement award.


A graph showing difference in fluorescence of pigs’ blood samples when heat treated for 20 minutes at 50 0C then 10 minutes at 95 0C (treatment A) and 5 minutes at 40 0C then 5 minutes at 70 0C (treatment B).

[Figure 6] A graph showing difference in fluorescence of pigs' blood samples when heat treated for 20 minutes at 50 0C then 10 minutes at 95 0C (treatment B) and 5 minutes at 40 0C then 5 minutes at 70 0C (treatment A).


Click for References

    [1]Arizti-Sanz J, Freije CA, Stanton AC, Boehm CK, Petros BA, Siddiqui S, Shaw BM, Adams G, Kosoko-Thoroddsen TS, Kemball ME, Gross R. Integrated sample inactivation, amplification, and Cas13-based detection of SARS-CoV-2. BioRxiv. 2020 May 28.

    [2]Arizti-Sanz J, Freije CA, Stanton AC, Petros BA, Boehm CK, Siddiqui S, Shaw BM, Adams G, Kosoko-Thoroddsen TS, Kemball ME, Uwanibe JN. Streamlined inactivation, amplification, and Cas13-based detection of SARS-CoV-2. Nature communications. 2020 Nov 20;11(1):5921.


sgRNA Results


Discovery and Design


We used the Bos taurus genome sequence from a Hereford cow (BioProject accession number PRJNA450837) as a starting point to design our spacer sequences for Cas13a. The DNA sequences that the literature suggested were nucleic acid biomarkers of bTB infection in cattle detectable in both blood[1] and tissue samples[2], were located and using the associated annotations the introns were removed using the splicing function in SnapGene. The resulting mRNA sequences were inputted into a Python script that screened for potential spacer sequences.

Cas13a Spacer Sequence Finder Python Script code script for finding the Cas13a spacer sequence

The Cas13a-CRISPR system requires that the protospacer flanking sequence (PFS), the adjacent nucleotide to the 3’ end of the target site, must be a non-guanine[3]. Therefore, the Python script looked for sequences 25-nucleotides long where the 25th nucleotide was non-G. The resulting 24-nucleotide spacer sequences that included either a CCCC or GGGG repeats were filtered out, as the presence of these would cause misfolding with the crRNA hairpin loop. In addition, sequences with more than one uracil base were filtered out, as uracil bases easily bind to other RNA nucleotides. The Cas13a crRNA sequence was appended to the 5’ end of each spacer sequence which were then analysed for secondary structure using the on-line software IPknot++[4]. Final spacer sequences were chosen by the highest minimum free energy, denoting least intra-sequence binding within the spacer region, and minimum inter-sequence binding between the spacer and crRNA sequences.

After designing the spacer sequences we decided to focus on the following genes as RNA biomarkers:


Gene Description[1]
CXCL8 Chemokine ligand 8, involved in infection response and tissue injury.
FOSB FBJ murine osteosarcoma viral oncogene homologue B, plays a role in regulating cell proliferation, differentiation and transformation.
NR4A1 Nuclear receptor subfamily 4, group A, member 1, plays a role in inflammation and apoptosis.
PLAUR Plasminogen activator, urokinase receptor, a biomarker of inflammation.
RGS16 Regulator of G-protein signalling 16, linked to many different disease states.

[Table 1]


For Cas12a the DNA sequence coding for each respective gene we looked to target was downloaded and inputted into a Python script that screened for potential spacer sequences.

Cas12a Spacer Sequence Finder Python Script code script for finding the Cas12a spacer sequence

The Cas12a-CRISPR system requires that the protospacer-adjacent motif (PAM), the four nucleotides 3’ to the end of the target site, must be TTTV. Therefore, the Python script looked for sequences 24-nucleotides long where final four nucleotides were either TTTA, TTTC or TTTG[5]. The protentual spacer sequences were then filtered to again remove; any sequences with CCCC or GGGG repeates (which interfiear with the crRNA hairpin bonding); any sequences with more than one uracil base (since uracil easily binds to other RNA nucleotides). The Cas12a crRNA sequence was appended to the 5’ end of each spacer sequence which were then analysed for secondary structure using the on-line software IPknot++[3]. Final spacer sequences were chosen by the highest minimum free energy, denoting least intra-sequence binding within the spacer region, and minimum inter-sequence binding between the spacer and crRNA sequences.



PCR Attempts and Initial Results



The selected Spacer sequences (appended to the crRNA sequences) were ordered from IDT as DNA, in sequences containing a T7 promoter to allow in vitro T7 transcription. Target sequences (complementary to the spacer sequences), that would be required for testing our final Cas12a and Cas13a systems, were also ordered from IDT [Figure 1].

abstract representation of DNA sequences synthesised by IDT

[Figure 1] Abstract representation of DNA sequences synthesised by IDT.

A) Template DNA to be used in transcription of Cas13a and Cas12a sgRNA. The sequence contains a T7 promoter, however unlike the target sequences does not contain a forward M13 primer. Since transcription needs to end abruptly after the spacer sequence, to ensure specific folding of the RNA, requried to form a complex with the Cas13a and Cas12a proteins.

B) Template DNA for Cas13a target, including a T7 promoter and M13 primer binding sites for amplification, via the polymerase chain reaction.

C) DNA sequence to be used as the Cas12a target direclty, including M13 primers binding sites for amplification, via the polymerase chain reaction.

Since the T7 in vitro transcription protocol required ~1 μg of template DNA (Link to Protocol), however synthesising such yields would have quickly exhausted our IDT budget. Therefore, we ordered 250 ng of each oligo. The target sequences contained M13 primer sites, so that single direction polymerase chain reaction could be used to amplify the Cas13a target DNA for use in transcription. Amplification via polymerase chain reaction was carried out, with an agarose gel to confirm DNA of the right length was produced. Subsequent gel extraction and qubit quantification showed yields were low, with all tests yielding below > 0.01 ug mL on a HS DNA qubit. (link to Protocols) Nevertheless T7 in vitro transcription was attempted on IDT stocks, however no RNA was detectable on the HSRNA tape (Agilent Tapestation 4200) most likely because the DNA template concentration was too low.

Therefore in order to increase the concentrated of the DNA template, both targets and sgRNA sequences were re-sythesised, with the design changed to incorporate Type IIS restriction sites allowing for them to be cloned into high copy number plasmids; transformed into DH5α e.coli; plasmid DNA extraction and yield quantified with a qubit, by our PI. [Table 2] All plasmids were successfully extracted, apart from the RD4_a target (#17). (Engineering cycle)

Table denotes concentrations of plasmid templates for; Cas12a sgRNA, Cas13a sgRNA and Cas13a targets.

[Table 2] Table denotes concentrations of plasmid templates for; Cas12a sgRNA, Cas13a sgRNA and Cas13a targets. The Cas12a target plasmids concentration are also noted. All concentrations are within a reasonable range of eachother, so 14 μL of plasmid can universally be used in the full transcription protocol.

Since DNA templates for the sgRNA and Cas13a targets are now in a plasmid, a Type IIS endonuclease now needs cleave and linearise the DNA so that transcription ends abruptly, without the need for a terminator sequence that would interfere with the specific RNA folding.

Abstract representation of DNA sequences synthasised by IDT and cloned into pX1800 plasmids by PI.

[Figure 2] Abstract representation of DNA sequences synthasised by IDT and cloned into pX1800 plasmids by PI.

A) The sgRNA template is to be cleaved with a Type IIS endonuclease, to linearise the plasmid template. The plasmid template needs to be linearised to cause transcription to end abruptly, without the need for a termination sequence that would interfere with the specific RNA folding. Since the endonuclease would cleave into the spacer sequence, Klenow fragment is also used to blunt the end of the template DNA, to ensure the entire spacer sequence is transcribed.

B) The target sequence for Cas13a also needs to be transcribed, so is also linearised. Although the type IIS endonuclease does not cleave into the target sequence, the Klenow fragment is still used to ensure the plasmid does not reform.

C) Since Cas12a requires a DNA target, the cloned plasmid containing the spacer sequence is used as the target direclty, thus requires no modifications.


Since endonuclease would cleave into the spacer regions on the sgRNA templates, a blunting reaction was carried out with the Klenow fragment. (Link to Protocol) A HSRNA tape – using the Agilent Tapestation 4200 showed promising initial results. [Figure 3] There were some unexpected peaks, caused by the degraded lower marker from the sample buffer, and plasmids that did not cleave, meaning RNA was transcribed all the way to a T1 terminator sequence further around the plasmid backbone.

First successful transcription results, of Cas13a targets and Cas12a sgRNA, measured on a HSRNA tape (with Agilent Tapestation 4200)

[Figure 3] First successful transcription results, of Cas13a targets and Cas12a sgRNA, measured on a HSRNA tape (with Agilent Tapestation 4200)

A) Shows tape columns: B1- #8 CXCL8 target, C1 - #9 RGS16 target, D1 – #11 EthA_b sgRNA, E1 – #15 RD4_c. Blue box surrounds bands where desired peaks are observed.

B) Shows the normalised sample intensity graph of #9 RGS16 target. Due to a slightly degraded lower marker, a peak at 45 bp is observable. A 219 bp peak, close to our desired 153 bp (and due to the unreliability of the degraded sample buffer, this is likely our desired product).

C) Shows the normalised sample intensity graph of #15 RD4_c. Due to the degraded lower marker, and the small size of the transcribed fragment, the desired product peak at 38 bp (44 bp expected) is adjoined to the lower marker. The 124 bp peak is likely from the transcription of a plasmid templates that has not been cleaved properly (127 bp expected).


Testing DNA Cleanup:


The DNA template had to be removed in order to get readings on the Agilent Tapestation 4200, achieved by adding a DNase (and equal volume of 50 mM MgCl). However, there were concerns that DNase could interfere with our Cas12a test, as DNase remaining would cleave the probes, leading to a false positive. Thus DNase was compared to a DNase treatment then a AMPure bead nucleotide extraction, thus removing the DNase after all the DNA is digested. (Link to Protocol) [Figure 4]

First successful transcription results, of Cas13a targets and Cas12a sgRNA, measured on a HSRNA tape (with Agilent Tapestation 4200)

[Figure 4] Comparing DNase treatment and DNase treatment then using AMPure beads, as methods of removing DNA.

A & C) Compare Cas13a sgRNA transcription clean-up of both methods. Both had a peak at around the desired length, with a peak caused by transcription of plasmids that had not cleaved properly.

B & C) compare Cas13a target transcription clean-up of both methods. Again, with a desired peak in both, and a peak caused by transcription of a plasmid that had not been cleaved properly.

DNA needed to be removed from all transcribed samples, in order to be analysed in the Tapestation. And Cas12a sgRNA samples need to have plasmid templates removed, as these would trigger the Cas system.

Overall, both methods are successful and interchangeable, thus AMPure bead extraction was discarded due to high cost of reactants.


As a DNase treatment, followed by a AMPure bead nucleotide extraction, gave similar results (apart from having a higher concentration) to just a DNase treatment; a DNase treatment, followed by a 15 minute denaturing cycle after incubation, was decided on due to the significant cost of the AMPure beads.


Final Results:


Once our cleanup/purification method had been decided, all sgRNA sequences (apart from #3 - CXCL8 sgRNA, which had already been transcribed when testing the AMPure bead extraction) and Cas13a target sequences were transcribed. [Figure 5]

First successful transcription results, of Cas13a targets and Cas12a sgRNA, measured on a HSRNA tape (with Agilent Tapestation 4200)

[Figure 5] Shows the Agilent Tapestation 4200 RNA ScreenTape results from final T7 in vitro transcription. Where; Cas13a sgRNA sequences (1,2,4,5), Cas13a target sequences (6,7,8,9,10) and Cas12a sgRNA sequences (11,12,13,14,15) were tested. Against a contorl, where the T7 polymerase was not included in the reaction mixture. This figure includes an image of the RNA ScreenTape and a representative normalised sample intensity sample graph.

A)Shows the tape gel – showing uniformity among varients of each RNA component.

B)Shows the PLAUR (Cas13a) sgRNA sequence (#1) normalised sample intensity graph. With a desired RNA sequence peak – at 41 bp (a 52 bp peak was expected) – and a 119 bp peak, indicating transcription of template DNA that had not been cleaved. There is also remains of a degraded upper marker from the BR sample buffer.

C)Shows PLAUR Target sequence (#6) normalised sample intensity graph. With a desired RNA sequence peak – at 70 bp (a 153 bp peak was expected) – and a 177 bp peak, indicating transcription of template DNA that had not been cleaved. There is also a large board peak, above 2000 bp, which is likely from environmental contamination.

D)Shows EthA_c sgRNA sequence (#12) normalised sample intensity graph. With a desired RNA sequence peak – at 96 bp (a 44 bp peak was expected) – with a low level of background noise across too 600 bp.


All columns on the RNA tape showed there was RNA present at our around the expected length for sgRNA, and target RNA. Since the sample buffer from the Agilent Tapestation 4200 was out of date, with a degraded upper marker, the size of RNA at each peak is not completely accurate. But, consistently there are strong peaks within the expected range. There are also signs of transcribed RNA, from plasmid templates that did not cleave properly, which terminated further through the pX1800 plasmid at a terminator. There is also signs of environmental contamination in all but one Cas13a target’s, however this was not expected to effect results.

The Agilent Tapestation 4200 gave the following concentrations:

Table denoting concentration of sgRNA and target RNA transcribed in the final scaled up T7 vitro transcription protocol, measured by the Agilent Tapestation 4200 and a qubit HSRNA per RNA class.

[Table 3] Table denotes concentration of sgRNA and target RNA transcribed in the final scaled up T7 vitro transcription protocol, measured by the Agilent Tapestation 4200 and a qubit HSRNA per RNA class. Cas13a sgRNA ranges between 18.76 – 26.80 ng/μL, with the upper limit being similar to the qubit reading of 30.67 ng/μL. The Cas13a targets have a significantly higher concentration, between 42.20 – 149.60 (with the qubit reading >200 ng/μL), the high levels of enviromental contamination in some of the targets are properly contributing to this. The Cas12a sgRNA has the lowest concentration, ranging between 8.30- 14.00 ng/μL, this could be due to a low amount of DNA template present with DNA contamination causing incorrect extracted plasmid concentration readings.


Again since the sample buffer (with the upper and lower markers) is out of date, the concentrations given were confirmed against a HS RNA qubit, under expert instruction.

Click for References

    [1]McLoughlin KE, Correia CN, Browne JA, Magee DA, Nalpas NC, Rue-Albrecht K, et al. RNA-Seq Transcriptome Analysis of Peripheral Blood From Cattle Infected With Mycobacterium bovis Across an Experimental Time Course. Frontiers in Veterinary Science. 2021; 8:662002.

    [2]Taylor GM, Worth DR, Palmer S, Jahans K, Hewinson RG. Rapid detection of Mycobacterium bovis DNA in cattle lymph nodes with visible lesions using PCR. BMC Vet Res. 2007 Jun 13; 3:12.

    [3]Kellner MJ, Koob JG, Gootenberg JS, Abudayyeh OO, Zhang F. SHERLOCK: nucleic acid detection with CRISPR nucleases. Nat Protoc. 2019 Oct; 14(10):2986-3012.

    [4]Sato K, Kato Y. Prediction of RNA secondary structure including pseudoknots for long sequences. Brief Bioinform. 2022 Jan 17; 23(1).

    [5]Chen JS, Ma E, Harrington LB, Da Costa M, Tian X, Palefsky JM, et al. CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science. 2018 Apr 27; 360(6387):436-9.

Final Test


Introduction


The diagnostic device that we have designed and built is based on four key components:

1) The Cas13a enzyme, whereby the non-specific RNA cleavage activity of Cas13a means that when the activated Cas13a/sgRNA complex associates with the complementary RNA target sequence the fluorophore-RNA-quencher probe is de-quenched and a fluorescent signal is produced.

2) The spacer sequence, bioinformatically selected from literature [1,2]. This selection of RNA biomarkers to be detected represent a variety of immune responses that have been directly sequenced from infected cattle at various stages of infection. By targeting multiple nucleic acid biomarkers we hope to make a robust diagnostic that can increase sensitivity and specificity, whilst hopefully also elucidating the stage of infection of the cattle. This is potentially possible as literature suggests that biomarker transcripts vary in concentration at different timepoints of infection [1,2]. However, a great number of targets, and further modelling to set stringent thresholds would be required before being able to confidently make that claim.

3) The target RNA sequences, complementary to the spacer sequence contained within the sgRNA. Sensitivity to RNA in biological samples is highly important, and this diagnostic has been demonstrated to have picomolar detection ability (Figure 2).

4) The hardware and biological sample preparation required to reliably and reproducibly detect the probe fluorescence.

Main Result


A graph showing the the fluorescence response when an activated Cas13a/sgRNA complex associates with various target RNA seqeunces. Dotted line represents the 10-fold threshold for a target being considered viable.

[Figure 1]This shows the targets (PLAUR, CXCL8, RGS16, NR4A1 and FOSB) in the Cas13a system compared to the negative control where Cas13a has been removed, with a threshold at 10 nM fluorescein concentration equivalents (nM) (dotted black line), units given in fluorescein concentration equivalents (nM) and R squared values for the Michaelis-Menten model the data is around. PLAUR and NR4A1 only shows the top error bars and CXCL8 shows only the bottom to make the data more clear to read. Only 2 repeats were able to be done and this is due to time and budget limitations.

Figure 1 shows that of the five targets that were tested, four of them were detected and fluorescnce statiscally higher than the control was seen. The final target (FOSB) was complementary to a mis-folded sgRNA lacking a crRNA loop, and as hypothesised, no fluorescence was detected. All targets were assayed at a concentration of 55mM, however a greater degree of sensitivity may be required. Therefore a final experiment was devised to determine the limit of sensitivity of this system.

Method


A stock solution of Cas13a (550 nM) was prepared and a 10x serial dilution was performed in an experimental buffer master mix, as according to table 1. All experiments were performed in 100μl volumes in a 96 well plate, with fluorescence read using a Tecan Infinite m200 Pro. Protocol can be found here

Well Number 1 2 3
Nuclease-Free water (µL) 61 61 61
HEPES Buffer (pH 6.8) (Filtered) (µL) 2 2 2
MgCl2 (Filtered) (µL) 19 19 19
NaCl (Filtered) (µL) 4 4 4
Cas13a (µL) 4 (55 nM concentration) 4 (5.5 nM concentration) 4 (550 pM concentration)
Ribolock (µL) 1 1 1
crRNA (µL) 1 1 1
PLAUR target (µL) 1 1 1
IDT RNaseAlert™ probe (µL) 7 7 7

[Table 1] A Table showing volume of substrates used in the trigger sensitivity experiment (Nuclease-Free water, HEPES buffer, MgCl2, NaCl, Cas13a serial dilutions, Ribolock, crRNA, PLAUR target and IDT RNaseAlert™ probe) and their arrangement in the 96-well plate used.

Results


The results of this experiment demonstrate that Ccas13a is sensitive RNA target concentrations as low as 500pM. Although the negative control and 550 pM dilutions are significantly different it remains important to remember that the a result may be statistically significant without having real-world application. Stakeholder engagement consistently reinforced that false positives are devasting to trust in bTB diagnostics, and therefore a higher threshold of difference between positive and negative results may be required.

A graph showing the change in fluorescein concentration equivalent (nM) of IDT RNaseAlert™ probes suspended in a 10x serial dilution of cas13a enzyme over time (minutes). Dotted lines show the data produced, whereas solid lines show the Michaelis-Menten model.

[Figure 2] A graph showing the change in fluorescein concentration equivalent (nM) of IDT RNaseAlert™ probes suspended in a 10x serial dilution of cas13a enzyme over time (minutes). Dotted lines show the data produced, whereas solid lines show the Michaelis-Menten model.

Future Work


To further advance this work an expansion of the target selection would be advantageous; using the Cas12a DETECTR system would allow for direct bTB DNA to be detected in blood or sputum, increasing the specificity of the diagnostic and reducing false-positives, a vital facet of the test for our stakeholders. Equally important would be quantifying the sensitivity of this proof-of-principle device first using blood samples spikes with synthetic targets, and finally using blood or sputum samples directly from infected cattle. It is the hope of the BoviTect team that this work will be continued by future iGEM teams, either to further our endeavours in eradicating bTB, or by modifying our validated parts collection. By changing the spacer sequence in order to build a diagnostic for any disease that has nucleic acid targets available. We believe that the protocols, parts and calibrated measurements provided within this wiki and the associated registry pages will be of great help to future iGEM teams developing rapid field diagnostics.


Summary

We have demonstrated reliable expression and purification of Cas13a from an E. coli BL21 (DE3) host. We have also successfully performed in vitro transcription of the sgRNA, comprised of the crRNA and spacer sequences for five targets. We have spiked buffer solutions with synthetic (in vitro transcribed DNA oligo) RNA targets and shown that our system is capable of detecting picomolar amounts of target RNA (Figure 2). Finally we have made the first steps towards developing a piece of hardware that will provide end users with a simple and robust data collection and collation tool for better manging cattle herd health for our stakeholders.

Click for References

    [1] McLoughlin KE, Coreia CN, Browne JA, Magee DA, Nalpas NC, Rue-Albrecht K, et al. RNA-Seq Transcriptome Analysis of Peripheral Blood From Cattle Infected With Mycobacterium bovis Across an Experimental Time Course. Frontiers in Veterinary Science. 2021; 8:662002.

    [2] Taylor GM, Worth DR, Palmer S, Jahans K, Hewinson RG. Rapid detection of Mycobacterium bovis DNA in cattle lymph nodes with visible lesions using PCR. BMC Vet Res. 2007 Jun 13; 3:12.

Glossary


crRNA: The specific RNA sequence that folds into a hairpin loop, which binds to Cas12a or Cas13a.

Spacer sequence: The RNA sequence which is complementary to the intended target of the Cas12a or Cas13a system.

sgRNA: Is the combined spacer and respective crRNA sequence.

simple diagram of sgrna structure

Target: The sequence that the spacer sequence is complementary too, these were synthasised to test the final Cas12a and Cas13a systems. 

Probe: A DNA or RNA oligomer bond to a fluorophore and quencher. When cleaved, by a DNase or RNase respectively (in our case Cas12a or Cas13a), the quencher is separated from the fluorophore. This causes a recordable fluoresce signal.



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