Overview

Our project began with research on biomarkers and their diagnostic assays. Initially, microfluidics seemed to be a promising approach to streamlining the diagnostic process. However, as we progressed, it became evident that there was also a pressing need for a hardware solution capable of fluorescence detection and quantification. We recognized the need to keep it an accessible, affordable and practical device for widespread use, in comparison to traditional fluorimeters. In an effort to provide a more reliable and objective diagnostic tool, we have attempted to develop a novel FRET-based microfluidic device to diagnose clinical depression.

Our main goal is to introduce an affordable and user-friendly biomarker-based diagnostic tool for Major Depressive Disorder (MDD). To achieve this, we’ve designed a simple and accessible system. It consists of a microfluidic chip that is inserted into the slot of a 3D printed box equipped with an LCD screen, an Arduino UNO setup, and the necessary accessories.

Here’s how it works: when the microfluidic chip is inserted into the device, the internal blue LED excites the sample, and the fluorescence emitted is detected by a photodarlington sensor. The intensity of fluorescence measured is then converted into the biomarker concentration levels and displayed on the LCD screen,with all data readings being managed by the Arduino UNO microcontroller. This prototype is designed to show the functionality of the system, using the most basic components and with the least complexities.

Design

The development of a device capable of detecting biological samples requires the integration of various components. This device has a unique design that allows it to accurately identify fluorescence in a sample.The outer shell of the device is being 3D printed using PLA(Polylactic acid) a thermoplastic monomer derived from renewable, organic sources such as corn starch or sugar cane. The main components of the device include a photodarlington ( known as a photodarlington transistor or photodarlington sensor, operates based on the principle of photoelectric effect and amplification.), Customized LED of 480 nm, ARDUINO UNO(It is a microcontroller board based on the Microchip ATmega328P), 16-bit ADC converter(this will make the measurements more precise), and an LCD screen. These components work together to create an ultra-sensitive device that can detect fluorescence which is not generally visible to the naked eye. The device's inside is pitch black to ensure maximum absorption of blue light and avoid any interference.
When the microfluidic chip is inserted into the slot of the device, the 480 nm specific LED will excite the sample, and the photodarlington placed directly above the sample, will detect the most feeble fluorescence. The device's design is such that it can screen the noise created by blue light, ensuring that the fluorescence value is not affected.

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Basic principle

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The basic principle of this method uses Forster resonance energy transfer (FRET) to quantify the biomarker.

samples containing the biomarker and the respective quantifier were exposed to blue light of a specific wavelength. Our quantifier has a fluorophore and quencher, which move apart in the presence of a biomarker, and the fluorophores give out the fluorescence. In this case, the Photodarlington sensor detected the fluorescence emission. The sensor measures the intensity of fluorescence corresponding to the presence or concentration of the biomarker in the sample. The recorded fluorescence value is then displayed on a screen, providing information about the amount of the biomarker.

In Förster Resonance Energy Transfer (FRET), a fluorophore molecule absorbs light and emits lower-energy photons as fluorescence. When a quencher molecule is in close proximity to the fluorophore, it can absorb the emitted photons, preventing them from being observed as fluorescence. This energy transfer from the fluorophore to the quencher effectively "quenches" the fluorescence.

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Testing and calibration

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Which sensor is the best?

To determine which sensor would detect the lowest fluorescence, we conducted experimental trials with three different sensors: a photodiode, a phototransistor, and a Darlington transistor. We ultimately chose to use the Darlington transistor in our hardware design.

Why not the other two?
Even though Photodiodes are very specific to the wavelength of our interest it can't detect the lowest intensity of them hence both photodiodes and phototransistors require an external amplifier to boost the signal, which increases the cost and complexity of the circuit.


The flow for our experiments

To determine the sensitivity of the photodarlington sensor, we conducted a series of experiments:

Firstly,we measured the output voltage of the sensor at different light intensities from a green LED. We then plotted the output voltage versus the light intensity to create a calibration curve.

In the second, we moved on with a real lab sample, where we measured the output voltage of the sensor when exposed to different concentrations of PTE(phenyl tetraene) in DCM(dichloromethane). We also plotted the output voltage versus the PTE concentration to create a calibration curve.

Lastly, we immobilized the biomarker on an apta sensor s well as on our nanoprobe and then measured the output voltage of the sensor when exposed to different concentrations of the Biomarker bound with the aptasensor. We again plotted the output voltage versus the biomarker concentration to create a calibration curve.

1.Nanoprobe

2.Aptasensor

Aptasensor with Serotonin

Aptasensor with Cortisol

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Future of the device

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Our current prototype uses basic components to showcase the concept, but we plan to use more efficient and affordable components in the future.
We are considering upgrading to a microprocessor like the ESP32 series, which has more processing power and memory, built-in WiFi and Bluetooth modules, and can run Python and TensorFlow. This would allow us to add more photo sensors and modules, improve the results display system, and run machine learning algorithms on the device. We will also design a well-organized circuit board layout to make the hardware small and compact. One possibility is to change the measurement from a voltage measurement to a capacitor discharge time measurement. This would allow us to measure even very low currents accurately.

Picture of our solid 3D printed hardware device, which is in the process of development

Finally, Please refer our PI page for more information about the future of our hardware device.

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References

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1. Ultrasensitive fluorescence detection of Fe3+ ions using fluorescein isothiocyanate functionalized Ag/SiO2/SiO2 core–shell nanocomposites - Journal of Materials Science: Materials in Electronics Rajbongshi et al.
https://link.springer.com/article/10.1007/s10854-019-00852-w#Abs1

2. Isothermal real time DNA amplification instrument Terrijärvi
https://lutpub.lut.fi/handle/10024/159386

3. Zhang, X., Li, L., & Liu, H. (2022). A review of the recent advances in the synthesis and applications of coumarins. MDPI Sensors, 22(20), 7916.
https://www.mdpi.com/1424-8220/22/20/7916

4. Sharma, S., Kumar, N., & Kumar, M. (2012). Synthesis and biological evaluation of new coumarin derivatives as anti-cancer agents. Journal of Materials Chemistry, 22(28), 14078-14088.
https://pubs.rsc.org/en/content/articlelanding/2012/lc/c2lc21226a

5. Demirbas, N., Yildiz, S., Karaoglu, S. A., & Demirbas, A. (2016). Synthesis and characterization of novel coumarin derivatives as potential anti-inflammatory agents. European Journal of Medicinal Chemistry, 113, 1-12.
https://europepmc.org/article/pmc/6919979

6. Ye, J., Chen, H., Zhu, W., & Jiang, Y. (2016). Synthesis and characterization of coumarin-based fluorescent probes for the detection of reactive oxygen species. Journal of the American Chemical Society, 138(51), 16630-16637.
https://iopscience.iop.org/article/10.1149/1945-7111/abd494