Overview
Traditionally, the diagnosis of coronary artery disease (CAD) is time-consuming. In the period between tests and results, patients experience anxiety and stress which can lead to patient unhappiness and health decline (Alizadehsani et al., 2012). To address this issue, Lambert iGEM created Micro-Q (see Fig. 1): a frugal PCR tube fluorometer with minimal set-up, capable of quantifying fluorescent biosensor samples within seconds. With a cost of under $20, this device conveniently quantifies various fluorescent signals and sends the results to a mobile app by utilizing a 405nm laser as an excitation light source, a photoresistor sensor to measure fluorescence intensity from PCR tube samples (20-200 µL), and an ESP32 Wi-Fi Microcontroller to process sensor input and transmit data. Micro-Q is entirely open-source, so users can customize the design to fit other reaction containers and measure other fluorescent signals by replacing the light source and filter for different wavelengths. As a result, this fluorometer enables users worldwide to cost-effectively and rapidly measure their risk of CAD and quantify fluorescence signals.
Parts List
| Item | Price |
|---|---|
| ESP32 Microcontroller | $5 |
| 3D-printed Micro-Q Shell: 14.48g ($0.05 per g) | $0.73 |
| 5mm Photoresistor | $0.17 |
| 3 10k Ω Resistor ($0.01 each) | $0.03 |
| Wires: 4 wires ($0.057 each) | $0.23 |
| Buttons: 2 buttons ($0.13 each) | $0.26 |
| E010 Medium Yellow Emission Filter | $0.15 |
| 405nm Laser | $9.80 |
| Total: | $16.37 |
Software Pipeline
The photoresistor reads the relative fluorescence units (RFU) after a user inserts a PCR tube and takes a measurement. The microcontroller passes this value through a pre-trained linear regression mode, producing similar values to a commercial spectrophotometer and sending it to the Google Firestore Database (via wifi) to store in a document. This value is displayed on the mobile app by authenticating with the specific Micro-Q reader; the mobile app shows the raw fluorescence intensity and the converted microRNA (miRNA) concentration through a pre-trained model as shown in Figure 2 (see Modeling). Figure 3 below depicts this entire software pipeline in a flowchart.
Quantification Algorithm
Micro-Q uses a photoresistor to measure the fluorescence of a sample inserted into the device. The photoresistor outputs a current that reflects the light intensity that it observes; since an emission filter is placed directly in front of the photoresistor, the only light that will hit this sensor is fluorescence from the sample. When users press Button 1, the ESP32 microcontroller records a brightness value of a blank, which is used to calculate fluorescence when other samples are measured. Upon pressing Button 2, the ESP32 records a value from the photoresistor and uses the following formula to calculate a brightness value. Ft is the brightness of a fluorescent sample, and F0 is the brightness of a blank (Photon Systems Instruments [PSI], n.d.).
\( F_{t}-F_{0}\)
We tested Micro-Q with different concentrations of fluorescein and noticed a logarithmic curve in the Fluorescence vs. Concentration graph shown below (see Fig. 4). Upon further research, we discovered that the photoresistor output is logarithmic, meaning that the output from the photoresistor must be processed so that fluorescence is linearly correlated to concentration. To linearize our data, we applied a power series regression using the curve shown in Figure 10, since we found this is the best function to apply as it achieved the highest R2 value when linearized (see Fig. 6). The results are shown below.
\( 0.00144(F_{0}-F_{0})^{}{1.75} \)
The ESP32 uses an API to transmit this brightness value to a Firestore Database, also recording a key unique to the specific Micro-Q device. The corresponding mobile app obtains the value from the Database using this unique key and displays it to the user.
Results
Micro-Q was tested using fluorescein from concentrations 0 - 500 µM. To determine its efficacy compared to a commercial fluorometer, we measured triplicates of several concentrations with sample sizes of 100µL in a plate reader and in Micro-Q. Since RFU are relative, we scaled the output of Micro-Q to match the scale of the output from the plate reader so that they can be compared (see Fig. 7).
In order to have an accurate comparison of the data from the plate reader and Micro-Q, we added points at the origin to our data set and calculated a slope from a linear regression for each measurement device, assuming a y-intercept of 0. The slopes of the data are ~0.9473 and ~0.9194 from Micro-Q, respectively, achieving a percent error of -2.952%
Micro-Q was also compared against a plate reader in the quantification of BBa_J428112 in DH5-alpha and BL21. The fluorescence/OD600 values are consistent between Micro-Q and plate reader at 6766.657 and 7405.837 for DH5-alpha, and 16916.56 and 21328.17 for BL21. When comparing fluorescence in each cell strain individually, the error bars for Micro-Q and plate reader overlap (see Fig. 8). Therefore, the outputs of Micro-Q and plate reader are not significantly different, validating the fluorescence measurement of our hardware device.
Micro-Q was given to an alumni iGEM member who was successfully able to use the device and provide feedback. We took this feedback into consideration, finding that the excitation laser decreases in strength over time due to heat. In the future, we aim to operate the laser programatically as well. Until then, however, we recommend that users disconnect the laser between trials and reconnect the laser only when needed.