Team:Purdue/Engineering
The biologics of our device are based off the PAND or PfAgo mediated nucleic acid system from literature[1]. We decided to use the argonaute TtAgo for our detection based on its availability and its reaction temperature between 65C-85C. The basic principle of our biological detection system is summarized in the figure from literature[1] below:
In order to easily detect the presence of our RNA fragment of interest, we needed to use a probe that will emit a visible signal with high specificity to the molecule of interest. For our device cArgo, we decided to use a molecular beacon probe specific to the TtAgo guide DNA produced after Argonaute cleavage. Currently available software for the design of molecular beacons are often expensive and inaccessible, hence we decided to develop our own molecular beacon design software. Literature [2] suggested the G-C content of the stem be between 70-80% and to avoid sequences with a 5’ guanine.
Given that our initial approach required a considerable amount of tedious data entry into various websites by the user, in order to construct a more robust and scalable code, we implemented web scraping in Python by using the Selenium library. Through this library, we were able to automate the interaction of primer3 and Quikfold to eliminate the user’s need to act as a middle point.
The first code generated a list of best molecular beacons but it was limited in that it could only do so for beacons with a stem length of 5 bp. As an input, the code took a DNA sequence (5’ to 3’). Example input below:
Its output was a list of sequences of molecular beacons 5’->3’.
After further literature review, it was clear the stem length needed some flexibility as it could vary between 4-7 nucleotides [3,4,5]. Hence, we optimized our code to be even broader by allowing stem lengths between 4-7 nt. We also reformatted it to store information as classes improving computational efficiency; We determined a more objective measurement of adequacy of the beacon to be Gibbs free energy and hence we decided to sort the best beacons by increasing ∆G. An example output is shown below for the same sequence:
In order to determine which sequence in the genome lend itself better to hybridization by the TtAgo protein, we designed guide nucleotides (gt, gr, gn, gf) which met the following characteristics as outlined in literature [6,7] (for all positions, genome is being read 5’ to 3’):
We designed RPA primers following recommendations from TwistDx and utilizing the open-source software Primer3 under the following conditions:
Initially, we searched current literature and used Basic Local Alignment Search Tool (BLAST) to identify unique sequences specific to Sars-CoV-2. However, further automation was pursued to identify a specific target compatible for Argonaut detection assays.
We combined all three essential aspects of our design: identification of a target and generation of appropriate guide DNA sequences, design of primers suitable for Recombinase Polymerase Amplification of the previously identified target, and design and classification of compatible molecular beacon probes. We found the use of .txt files saved computational power considerably from other existing software [5] and prevented having to run the same sequence multiple times. Our final code takes as an input a .txt file containing a whole-genome sequence of an organism of interest (entered in 5’ to 3’ direction) and returns: the most suitable target for a probe sequence, a .txt file containing RPA amplification targets sorted based on GC% of their gn and their respective primers according to the Primer3 design parameters and another .txt file with optimal beacons for the selected probe sequence sorted according to increasing ΔG.
The PDF below contains documentation and analysis of the various iterations of molecular beacon designs produced from the output of the combined code.
The wet lab notebook we used when validating the biological parts designed to identify a characteristic sequence of SARS-CoV-2 for the proposed molecular diagnostic assay is linked below. All protocols, methodology, and results are present. We thank Benchling for letting us use their platform for free as students! After determining the biologics of the chip extensive research was done to develop a microfluidic device for the biologics to occur in. The following design was the first iteration of our Microfluidic Device:
After consulting with various experiments we decided to change up the chip size, channel dimensions, channel pattern, increase the number of outlets, and develop space in our chip design for a control reaction along with many other modifications.
The following design is the 2nd iteration of our Microfluidic Device:
The following design is the 3rd iteration of our device. We decided to try a radial design due to the difficulty associated with aliquoting the correct amount of saliva, manually actuating flow at appropriate times without costly pumps, and starting reactions. The image below shows the three cross sections of this radial device, each corresponding to the initiation of a separate reaction as the device is turned by the user, allowing the sample and reagents to travel through the device. An cross-sectional view showing the suggested arrangement of reaction chambers is also included.
Alongside the development of our microfluidic chip, our team set out to engineer a portable heating system for the rapid heating and cooling of our microfluidic chip. The biologics of our device are temperature and time-dependent. Thus, using feed-back control systems and electrical circuit designs an economical and efficient portable heating circuit was designed and tested. See the PDF below for full documentation of the circuit design iterations, analysis, and results.
Engineering Design
Biologics
The Basis of cArgo's Biological System
Molecular Beacon Design
Our initial approach was taking a specific probe sequence from the COVID genome. It was then screened for all possible stems according to the guidelines outlined above. The probe and stem combined sequence was uploaded to Quikfold to determine sequences which folded into the correct hairpin structures. From the sequences that did fold into hairpin structures, we manually screened the results to identify any inconsistencies with the outlined parameter.
Scaling Up
First Iteration
Further Optimization
Target And Guide DNA Identification
Example guide DNA sequences:
Primer Design
Integrated Code
Wet Lab Protocols
Hardware
Microfluidic Device Design 1
Microfluidic Device Design 2
Device Design 3 - Radial
Microfluidic Circuit Heater Engineering Design
References
