For all of our Rolling Circle Amplification (RCA) measurements, we have used our software - VisualpH - as a way to quantify our sensitivity, specificity, and modelling experiments. VisualpH is not only fast and accurate, but it allows us to perform precise measurements of pH with relatively inexpensive and common tools; a computer, camera, and pH indicator. It provides the pH of a solution just from an image of it, and a video of the solution can be used to convert it into a tensor of measured pH over time. We created this tool because we did not have a pH probe small enough to measure the pH of solutions of a few microlitres, but we were able to capitalize on this opportunity to develop our own tools. At first, we considered building our own pH probe using the same technology as other off the shelf probes. However, we later realized that the pH indicators we were already using could be quantified easily with virtually no extra cost. We used the open source computer vision library opencv in python to ensure that we don’t end up reinventing the wheel. Our software helped us measure the pH precisely and use that data for further analysis, whether that is for modelling, specificity or determining improvements to be made to methodology. Its usefulness however, is not limited to our own project. It could be used to quantify data from anything colour related, such as the activation of fluorescent genes like mCherry or GFP. It could be also useful to field biologists who may want to compare the colour properties of certain animals or plants, or maybe measure the pH of natural environments with more precision than a pH strip can provide, while only requiring a electronic device that they would probably carry with them anyway, such as a phone. This tool has an extensive range of possible applications, and its ease of use and accuracy gives it many advantages over the current meta of options.
Team:TAS Taipei/Measurement
Measurement
Summary
Software - VisualpH
VisualpH provides an inexpensive, fast, accurate, and precise result for pH measurement. Common pH probes require proper storage and maintenance, and large ones cannot be used for solutions of small volume. In that case, smaller, more expensive probes are needed. Indicator solutions and pH papers are useful, but they do not provide very precise or accurate results when analyzed by eye. Current solutions require expensive specialized equipment to reach the same level of precision as pH probes. VisualpH allows the precise measurement of pH without any expensive equipment: just a computer, a camera, and pH indicator.
VisualpH works by first receiving a user defined mask that defines where the solutions are inside the image or video. It then compiles the hue data, and converts it into pH data, using pH and colored standard solutions.
Figure 1: Schematic of the VisualpH software tool operating in a simulated pH color changing experiment.
This software provides a way to quickly and easily turn qualitative, visual data into something that is quantitative and much more usable for numerical analysis.
Measurement of pH Change in the RCA Reaction
We used our software to measure the numerical pH change in the RCA reaction, and it was possible to do extremely quickly and with virtually extra costs in tooling because of it.
Table 1: RCA for Synthetic viral DNA targets
Table 2: RCA for Synthetic viral RNA targets
Video 1: RCA Sensitivity Test
In microcentrifuge tube 1, we ran a reaction with SARS-CoV-2 padlock probe and no target. In microcentrifuge tube 2, we ran a reaction with SARS-CoV-2 padlock probe and 2.5uM of SARS-CoV-2 target. In microcentrifuge tube 3, we ran a reaction with SARS-CoV-2 padlock probe and 2.5nM of SARS-CoV-2 target. In microcentrifuge tube 4, we ran a reaction with SARS-CoV-2 padlock probe and 2.5pM of SARS-CoV-2 target.
Figure 2: Sensitivity Test on C-19 target. Testing the sensitivity of the test using different concentrations of C-19
This entire sensitivity test was measured using visualpH, and the relations were easily analysable as done in experiments.
Figure 3: C-19 Specificity Test
0: No target (control)
1: SARS target
2: C-19 target
Our results show that when the C-19 padlock probe is used, the pH of the C-19 target solution has the greatest change. As expected, the solution with no target has little to no pH change. The SARS target has a slight pH change due to its similarities with the C-19 nucleotide sequence, but the color difference is still significant enough to distinguish between the two.
Figure 4: INFA Specificity Test
0: No target (control)
1: Incorrect target (INFB)
2: Correct target (INFA)
Our results highlight the fact that our padlock probe could detect the difference between the different targets. Although the color and pH of both targets change, the rate of change for the correct target was much faster than the incorrect target.
Figure 5: INFB Specificity Test
0: No target (control)
1: Incorrect target (INFA)
2: Correct target (INFB)
Our results highlight the fact that our padlock probe could detect the difference between the different targets. Although the color and pH of both targets change, the rate of change for the correct target was much faster than the incorrect target.
The graphs above were all made using VisualpH, as an example of how the quantitative data that can be retrieved from it can be extremely valuable in comparisons and analyses of different solutions.
Potential/Future Applications of VisualpH
VisualpH can be used not only with pH indicators, but also for the quantification of any data that correlates with colour and hue. For example, activation of mCherry, or GFP could be quantified using a similar technique, which could be additional data to build upon in an experiment. It could also be used in place of spectrophotometers in many cases, as it is easier, cheaper, and allows for measurements of other properties to occur at the same time. However, it does not provide the exact same measurements as a spectrophotometer does, and is not as precise, so it is not meant to completely replace it.
This tool expands a lot of possibilities for virus testing, as it could be included in a testing kit to provide analyses on testing results. Python provides a lot of cross compatibility, so the tool could be adapted to be put in a multitude of different devices, so that testing could be done entirely at home. Imagine, if someone could remotely request a test, have it delivered to them, and then self administer and confirm test results within a few hours. The user would be able to examine their test results just by taking a photo of it, and a mobile app will immediately return the details of the test, like pH and viral load. Furthermore, given biological details about the user, details such as symptoms or age, the app could also potentially predict future events and how severe the disease will be for that specific user. Machine learning networks have been able to do this in a similar way for diseases like the Influenza (Yang et al). The benefits of this prediction, could be very beneficial to the prevention and response to pandemics, as patients who will in the end only experience benign effects do not need to overload the medical response system. On the other hand, the patients who are most seriously affected could be given more attention earlier, hopefully making a recovery for them faster and less expensive. When a disease progresses, it gets harder to treat, but giving early intervention to those who may end up very ill could save a lot of medical resources and expenses.
References
BenRG. (2009). The Stockman and Sharpe (2000) 2° cone fundamentals, as found here, plotted against the most accurate sRGB spectrum I could manage. Now in SVG. Own work. https://commons.wikimedia.org/wiki/File:Cone-fundamentals-with-srgb-spectrum.svg
SharkD. (n.d.). English: The HSV color model mapped to a cylinder. POV-Ray source is available from the POV-Ray Object Collection. Own work. Source-code available at the POV-Ray Object Collection. Retrieved October 25, 2020, from https://commons.wikimedia.org/wiki/File:HSV_color_solid_cylinder.png
Yang, C.-T., Chen, Y.-A., Chan, Y.-W., Lee, C.-L., Tsan, Y.-T., Chan, W.-C., & Liu, P.-Y. (2020). Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources. The Journal of Supercomputing, 76(12), 9303–9329. https://doi.org/10.1007/s11227-020-03182-5