Team:NYU Abu Dhabi/Hardware




Sample Preparation and Reaction Medium

Engineering product development timeline

The reaction medium forms the backbone of the diagnostic device where the processes in the diagnostic pathway take place. While in a lab setting this medium is just test tubes with pipetting, POC devices like the one we imagine require completely different approaches to make the reactions happen in the field. Hence, the team did extensive research, especially in the literature (Case Studies) to find as many hardware options that could be implemented, going beyond the most popular ones such as microfluidics. These are broadly categorized and mapped below:

Reaction Medium - Initial Options

However, this was only a part of the challenge, the other major stage in the diagnostic pathway is sample preparation/nucleic acid extraction which is an exceptionally harder task for our device since we are trying to lyse fungal cells. While we mapped out all the possible options for point of care nucleic acid extraction as described in the results section, to make the device possible, it requires us to combine the reaction mediums described above with the sample preparation methods.

This is where the team performed several rounds of brainstorming to come up with ideas of how to combine the existing reaction medium options with the sample preparation options by thinking about the process and hardware utilized in each option. Throughout the process, which is still ongoing, we have been mapping the compatible options together as given below:

Sample Prep + Reaction Medium Combinations

This process also gave us even newer ideas of reaction medium hardware adding to our initial mapping and creating a much more detailed map (the one given in results section).

While we have not been able to prototype and test these combinations due to restricted lab access, we will be doing so soon and meanwhile will be adding more feasible combinations to generate sufficient number of concepts that can provide the best hardware for the device.

Quantitative LFA Detection using Smartphones

The most important components of an LFA detection system include of simply LFA strips. It is acknowledged for not requiring complex equipment thereby making it an ideal detection method for Point Of Care systems. It also ensures sensitivity levels as low as hundreds of copies of DNA.

However, if the LFA strips are being used for quantification, further hardware sources are required. LFA quantitative system we designed for this project involves quantitative detection using image acquisition. When there is a positive result, a red band will be formed on the control test line on the LFA strip. The analyte quantity in the sample is expected to be proportional to the intensity of the red colored test line region. Hence, a weighted summation of red color in the test line region can be considered as the parameter for the detection of analyte quantity, where the weight is considered to be the color intensity of each pixel.

An image of this band would be captured using a smartphone, therefore, an important part of hardware for LFA quantitative detection would be a smartphone according to the methodology that is followed by our team. A smart phone would be ideal since it comes in smaller sizes than a professional camera, whilst giving it a similar or equal resolution. In addition, it is more lightweight and this would be more useful when it comes to implementing these onto the design of the device itself. In addition, for experimental purposes, it is readily available and is a very convenient choice.

An image of the LFA strips used (Milenia Hybridetect 1)©2009-2020 TwistDx™ Limited. Taken from:

A smart device acquiring images of the LFA strip to be analysed and used for quantification,Foysal, Kamrul H et al. “Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone.” Sensors (Basel, Switzerland) vol. 19,21 4812. 5 Nov. 2019, doi:10.3390/s19214812

Fluorescence detection prototype

We finalized a set up for fluorescence detection platform that is both cost-effective and highly sensitive. The major problem with using fluorescence detection as the measurement criteria is that most setups use filters and lenses that increase the overall costs significantly. Our team identified this and we believe that we found effective ways around these extra costs.

The function of the filters used is to separate the excited light from the sample form the Blue light that is emitted by the LED. Our team uses a combination of two simple techniques to increase the effectiveness of our system without the use of the expensive filters:

  1. We used a RGB sensor (Picture below) to sense the light and obtain distinct values for the Green and Blue light which will be used to measure the fluorescence values without a filter
  2. We have set up a barrier between the LED and the sensor to allows the light to only pass through the sample and reduce the disturbance at the sensor significantly.

Apart from the components discussed above, we will also be using a shell to block out all external light to increase the signal to noise ratio. All the components used are over the counter electronic items that are relatively cheap and easy to use. The LED used will emit light of 490 nm, which is ideal to excite our fluorophore.

For our initial prototype, the components used are as below:

  1. TCS RGB 34735: This is a Light sensor that can identify the Red, Green and Blue composition in the incident light. We would be using the Green and Blue readings to calibrate our device.
  2. Arduino Micro: This is the microcontroller that we're using to take readings from the light sensor and display it during the testing stages.
  3. 490nm Blue LED: As the excitation wavelength of our fluorophore is 495 nm. Using a highly specific LED like this one increases the efficiency of our device

    Fluorescence detection schematic

    490nm Blue LED

  4. The complete setup
  5. Inside view of the prototype with the photodiode on top, a PCR tube with the sample and the LED on the other side.

    Outside view of the fluorescence prototype showing covering to block external light.

Calibration of the sensor will be done in the following way:

  1. We will first take 20 samples that we know are positive for the target DNA. We will place these in the prototype and check the sensor readings for both the Green and Blue component.
  2. We will repeat the above steps for 20 control (negative) samples as well.
  3. Finally, we will incorporate the values we obtained in steps 1 and 2 into the code and use this to predict if an unknown sample placed in the prototype is positive or not.

Quantitative calibration using CRISPR-Cas detection will be done as follows:

  1. Preparation of multiple samples with different concentrations of the target DNA.
  2. Find the average sensor value for 20 repeats of each concentration.
  3. Plot a graph of sensor value (x axis) against concentration (y axis)
  4. Use this in the software to give a quantitative resul