• Cas13a
• Readouts
• Targets
• Detection Chip
• Amplification
• Biobrick

Overview

We demonstrated that each of the modules of our platform (extraction, amplification and detection of pathogenic RNA) is functional, although we did not yet fully integrate all the modules into a final product.

What worked

• Demonstrated functionality of Lbu and Lwa Cas13a.
• Modelled the detection limit of our circuit and confirmed it experimentally (~10 nM RNA).
• Detected pathogen RNA sequence from in vitro and in vivo sources.
• Differentiated viral sequences from bacterial sequences.
• Used RNase Alert and Spinach aptamer read-out circuits.
• Used gold nanoparticles to detect general RNase activity.
• Detected RNA in bulk, on paper, and from lyophilized Cas13a.
• Constructed a functional fluorescence detector with high sensitivity and low production cost.
• Detected RNA in bulk, on paper, and from lyophilized Cas13a.
• Amplified target with RPA and transcription on paper.
• Improved the biobrick BBa_K1319008 by adding a 6x His-tag and provided Cas13a Lwa as three different composite biobricks.
• Characterized the GFP degradation tags and sent them as a part collection.

What presented issues

• Optimizing the purification protocol for Cas13a.
• Demonstrating functionality of Lsh Cas13a.
• Ruling out RNase contamination from heat-lysed in vivo samples.
• Detecting Q5 beta RNA.
• Reducing cross-talk between E.coli crRNA and B.subtilitis target RNA.
• Developing colorimetric read-outs.
• Optimizing the lyophilization and stability of Cas13a.
• Performing RPA and transcription on chip.

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 equipments. 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 pathogens 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 RNase Inhibitor to suppress the activity of the RNases that could be present, we saw 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 the Bacillus subtilis was particularly difficult in our case since B. subtilis is a gram positive, spore forming bacteria. Also the amount and the quality of the RNA extracted from the B. subtilis and E.coli cultures were 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 dollars per chip. We could still try to minimize the costs by reducing the chip size and making it fully recyclable. However, at 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 Delft but still it wasn’t 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.