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- Working prototype
- Aptamers characterization
- Develop more different aptamers/electrodes with a SELEX autonomous machine
- Increase aptamers stability by chemical modifications
- Deep learning
The next step would be to order automated components to incorporate them in the millifluidic system we designed. After combining all the different pieces (PEEK millifluidic system with pump, arduino card, LCD screen), we would let physicians and potential users test the prototype in their hospitals to see if this device works well in simulated real conditions.
We have proven, through electrochemical measurements that aptamers bind to their targeted bacteria in a fluid sample. We can detect a signal in the presence of bacteria that is specific to this strain. The binding affinity and the specificity of the aptamers need to be further characterized by flow-cytometry.
Moreover, additional tests of the aptamers on the electrodes will be necessary in order to confirm our results (other bacterial strains, at different concentrations). Depending on the results we will obtain, the electrodes should be tested in the actual device in order to adapt it to the millifluidic system which may show different results from the ones we obtained during our proof of concept.
In order to develop a large number of electrodes specific to different pathogens, we need to find more aptamers. These new aptamers can be specific to other bacteria, viruses, parasites. However, the SELEX is a very long procedure. Fortunately, it is an iterative procedure so we can imagine improving the SELEX experiment by using an autonomous machine (Opentron for instance) to save time.
Our device was designed to be used at room temperature. Hopefully DNA aptamer are pretty stable, even at 25°C. But high temperature can decrease the stability of our aptamers. However, an integrated cooling system can make our device more fragile and less portable.
A solution is to stabilise our aptamers thanks to chemical modifications. Modification of aptamers can be achieved either by supplementing random libraries with functional groups directly in the SELEX protocol or by post-SELEX solid-phase chemical synthesis. Moreover, the post-SELEX strategy is particularly efficient for the introduction of phosphorothioate units to improve the resistance against nuclease digestion [1].
Currently, some deep learning researchers are developing tools to find, in silico, aptamers sequences selective of a target protein. This kind of tool can be very useful to save time [2].
References
[1] P. Rothlisberger, M. Hollenstein, Aptamer chemistry, Adv. Drug Deliv. Rev.(2018) 134 3-21
[2] B. Alipanahi, A. Delong, M.T. Weirauch, B.J. Frey, Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning, Nat. Bio-technol. 33 (2015) 831-838