Contribution

Micro-Q

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 & 2): 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.

Figure 1. Micro-Q and its mobile app side-by-side.

Parts List

Design

Micro-Q utilizes two modular components to quantify different types of fluorescent signals: the laser and the emission filter (see Fig. 2). Users can swap the emission filter to block excess light from the laser to direct the sample’s fluorescence to the photoresistor; moreover, they can swap different wavelength lasers to excite the sample and allow for the quantification of their various fluorescence outputs. This photoresistor measures the light intensity, sends it to the ESP32, and updates a database. Micro-Q results can then be visualized with our mobile phone app that depicts the raw fluorescence value (RFU) and the converted microRNA (miRNA) concentration; using this method, miRNA ranges can detect the progression of coronary artery disease (CAD).

Schematic

Figure 2. Schematic of Micro-Q.

Users can assemble Micro-Q by 3D printing the STL files for the 2 caps and main body found on this GitLab Repository (see Fig. 3). Attach the 2 buttons into the side of the body and slide the photoresistor in the back, twisting the prongs so that these components wrap around into the 2 side channels of the device. Since this may require trial and error, users can use a small screwdriver or object to guide the prongs. Next, solder the connections following the schematic shown in Figure 4. After, screw the laser module into place by fitting the cap through the hole and mounting the module from the other side of the wall. Fit the larger lid into place to cover all of the wiring. Slide the plastic filter into place behind the holder for the PCR tube.

Figure 3. Image carousel displaying STL renders of Micro-Q from several angles.

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Figure 4. Schematic of Micro-Q wiring.

Usage

To use Micro-Q, first insert a blank PCR tube in the designated holder, close the fluorometer, plug the laser into the power supply, and press Button 1 to read the blank. Then, open the lid and remove the sample. Pipette your sample into a PCR tube and insert it into the designated holder. Close the lid, and press Button 2 to get a reading (see Fig. 5).

Figure 5. Image showing PCR Tube Holder and buttons of Micro-Q.

Software Pipeline

The photoresistor reads the 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 7 (see Modeling). Figure 8 below depicts this entire software pipeline in a flowchart. The files for the mobile app and Micro-Q hardware/CAD can be found on the Software Tools README.

Figure 7. Screenshot from Micro-Q app displaying read value, connection status, and blank status.

Figure 8. Software pipeline of Micro-Q, depicting how the signal translates to miRNA concentration.

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. F_t is the brightness of a fluorescent sample, and F₀ is the brightness of a blank (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. 9). 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 to our data, since we found this is the best function to apply when using relative units as it achieved the highest R² value when linearized (see Fig. 11). The results are shown below.

\( RFU = 0.00144(F_{t}-F_{0})^{1.75} \)

Figure 9. Nonlinear relationship between concentration and RFU measured by Micro-Q.

Figure 10. Power series regression to linearize Micro-Q values to plate reader values.

Figure 11. Linear relationship between concentration and RFU measured by Micro-Q after processing the data with a power series function.

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 Relative Fluorescence Units (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. 12).

Figure 12. Scatter plot showing measurements from plate reader and Micro-Q at different concentrations of fluorescein.

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%

BBa_J428112

Lambert iGEM transformed the InterLab Experiment 1 Test Device 1 (BBa_J428112) into two different strains of E. coli (BL21 and DH5-alpha) to characterize the differences in fluorescence. BBa_J428112 is a composite part that constitutively produces green fluorescent protein (GFP). The construct consists of a UNSX-UP (BBa_J428202), constitutive promoter (BBa_J23101), RBS (BBa_B0032), GFP reporter (BBa_K2656022), stop codon (BBa_M36117), and double terminator (BBa_B0015) (see Fig. 13).

Figure 13. Diagram of BBa_J428112 construct.

BL21 is used for protein expression, whereas DH5-alpha is utilized for plasmid transformation. Therefore, we expect the test device transformed into the BL21 cells to produce more fluorescence than the test device transformed into the DH5-alpha cells.

Protocol

Lambert iGEM characterized fluorescence expression of BBa_J428112 in BL21 and DH5-alpha using the iGEM InterLab Experiment 1 Protocol. The InterLab study instructs to quantify the sample at a 0 hour and 6 hour time mark. To better align with members’ schedules, we only quantified fluorescence at a 0 hour time mark.

Results

As expected, BL21 exhibited statistically greater amounts of fluorescence than DH5-alpha (see Fig. 14). Moreover, to evaluate the accuracy of our hardware component, we quantified the same samples in both plate reader and Micro-Q. The values gathered from Micro-Q were scaled up by a factor of 10⁴ to be comparable with those of the plate reader. 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. 14). Therefore, the outputs of Micro-Q and plate reader are not significantly different, validating the fluorescence measurement of our hardware device.

Figure 14. Characterization of BBa_J428112 in DH5-alpha and BL21 quantified in both Micro-Q and plate reader. The error bars between DH5-alpha and BL21 do not overlap, suggesting there is a statistically significant difference. Although the error bars are rather large, they do suggest a statistically significant difference in protein expression. The readings from Micro-Q and plate reader show similar trends, indicating similar performance.

References