iGEM 2020 — Team NYU_Abu_Dhabi

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# Team:NYU Abu Dhabi/Notebook

Results

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# Notebook

To read the detailed protocols of each day, please refer to the protocols to each corresponding day through this [pdf file](https://static.igem.org/mediawiki/2020/1/17/T--NYU_Abu_Dhabi--Notebook.pdf).

# Wednesday, 30/09/2020

First day of experiments!

* Prepared LB+Ampicilin Agar plates for E.coli cultures.
* Prepared Agar Broth
* Heat Shock Transformation practice run

# Sunday, 04/10/2020

* Bd and Bsal gBlock resuspended into solution
* PCR Primers 1 and 2 For Bd were resuspended
* PCR Primers 3 and 4 for Bsal were resuspended
* Ran 4 Q5 High Fidelity PCR Reactions on Bd and Bsal with primer sets 1-4

# Monday, 05/10/2020

* Ligated each PCR Products onto 4 separate pJET vectors
* Transformed our pJET vector+PCR products into competent E.coli through heat shock. Competent E.coli was spread across separate agar plates.

# Tuesday, 06/10/2020

* Inoculated E.coli containing Bd and Bsal genes
![](https://static.igem.org/mediawiki/2020/b/be/T--NYU_Abu_Dhabi--notebook--pc1.png)

*Transformed Product with Bacteria Colonies before Inculation*

# Wednesday, 07/10/2020

* Cultures that were inoculated on Tuesday, 06/10/2020 were miniprepped.
* Respective DNA concentrations were measured using a nanodrop
* Miniprepped DNA was used to run a PCR reaction as a quality control for Thursday, 08/10/2020

# Thursday, 08/10/2020

* Gels were prepared for DNA Gel electrophoresis
* Gels were ran on Bd and Bsal DNA from all primer sets

+ Bsal Gel ran perfectly
+ Bd Gel had issues with the PCR reaction ran on Wednesday, 07/10/2020.

![](https://static.igem.org/mediawiki/2020/9/93/T--NYU_Abu_Dhabi--notebook--pc2.png)

*UV Bsal Gel Quality Control 8/10/2020*

![](https://static.igem.org/mediawiki/2020/5/56/T--NYU_Abu_Dhabi--notebook--pc3.png)

*UV Bd Gel Quality Control 8/10/2020.png*

#

# Sunday, 11/10/2020

* Bd DNA from the miniprep prepared on Wednesday, 07/10/2020 was used again to run a PCR reaction
* More E.coli that had Bsal were inoculated.
![](https://static.igem.org/mediawiki/2020/8/8a/T--NYU_Abu_Dhabi--notebook--pc4.png)

*Bsal 4 Agar Plate*

![](https://static.igem.org/mediawiki/2020/9/94/T--NYU_Abu_Dhabi--notebook--pc5.png)

*Bd 2 Agar Plate*

# Monday, 12/10/2020

* E.coli that was inoculated on Sunday, 11/10/2020 were miniprepped to have larger stock of Bsal DNA. The DNA concentrations were measured using a nanodrop
* PCR reaction from Sunday, 11/10/2020 was ran on a gel.
* PCR reaction failed, once again. Suspected that the primers stock was contaminated.
![](https://static.igem.org/mediawiki/2020/2/2b/T--NYU_Abu_Dhabi--notebook--pc6.png)

*Bd Miniprep PCR 12/10/2020*

# Tuesday, 13/10/2020

* New Bd primers were resuspended and were used to run another PCR reaction on Bd
* RPA reactions were ran on Bsal
* Reaction results were observed on a gel.

+ RPA reaction on Bsal worked!
+ Bd PCR finally worked on primer set 2. We can proceed with amplification on Bd

![](https://static.igem.org/mediawiki/2020/b/b7/T--NYU_Abu_Dhabi--notebook--pc7.png)

*Bsal RPA Gel Results 13/10/2020*

![](https://static.igem.org/mediawiki/2020/d/d8/T--NYU_Abu_Dhabi--notebook--pc8.png)

*Bd Miniprep PCR Gel 13/10/2020*

# Wednesday, 14/10/2020

* 8 RPA reactions were ran on Bsal to check for specificity
* More E.coli containing Bd DNA was innoculated

# Thursday, 15/10/2020

* Miniprepped Bd 2 cultures that were inoculated on Wednesday, 14/10/2020. The DNA concentrations were measured through a nanodrop

# Monday, 19/10/2020

* RPA reactions were ran on Bd to check for specificity
* Results were not ideal. Bands were too faint for Bd and two out of three RPA reactions for Bsal failed. Have to re-try tomorrow
![](https://static.igem.org/mediawiki/2020/2/25/T--NYU_Abu_Dhabi--notebook--pc9.png)

*RPA Specificity test on Bd DNA*

![](https://static.igem.org/mediawiki/2020/d/d5/T--NYU_Abu_Dhabi--notebook--pc10.png)

*RPA Specificity test on Bsal DNA*

# Tuesday, 20/10/2020

* 8 RPA reactions for both Bd and Bsal each were ran to check for specificity
* Gels were ran for all RPA reactions
* Gel results were perfect! RPA for Bd and Bsal are specific to their own primers!
![](https://static.igem.org/mediawiki/2020/6/67/T--NYU_Abu_Dhabi--notebook--pc11.png)

*RPA Specificity test on Bsal DNA*

![](https://static.igem.org/mediawiki/2020/8/8f/T--NYU_Abu_Dhabi--notebook--pc12.png)

*RPA Specificity test on Bd DNA*

# Wednesday, 21/10/2020

* CRISPR Experiments were ran on RPA reactions done on Tuesday, 20/10/2020
* CRISPR Experiments failed, no fluorescence was observed by the FQ reporters.

# Thursday, 22/10/2020

* Miniprepped Bd 2 and Bsal 4 cultures. DNA concentration was measured using nanodrop.
* Used Nanodrop to determine concentration of all 8 DNAs
* Optimized CRISPR protocol and reran the experiment on Bd and Bsal

+ DETECTR Protocol on Bd worked
+ DETECTR Protocol did not function on Bsal.

![](https://static.igem.org/mediawiki/2020/f/f0/T--NYU_Abu_Dhabi--notebook--pc13.jpg)

*CRISPR DETECTR worked on Bd, producing fluorescence*

![](https://static.igem.org/mediawiki/2020/5/57/T--NYU_Abu_Dhabi--notebook--pc14.jpg)

*Troubleshooting required for Bsal CRISPR. Potential issue with sgRNA*

# Tuesday, 27/10/2020

* Proof of concept experiment for DETECTR on Bd
* Successful detection of Bd from RPA to CRISPR/Cas12a to fluorescence by FQ reporter cleavage (DETECTR).
![](https://static.igem.org/mediawiki/2020/7/72/T--NYU_Abu_Dhabi--notebook--pc15.jpg)

*DETECTR Proof of Concept for Bd. Ran Triplicates and a negative control.*

* [Wednesday, 30/09/2020](#first)
* [Sunday, 04/10/2020](#sec)
* [Monday, 05/10/2020](#th)
* [Tuesday, 06/10/2020](#th1)
* [Wednesday, 07/10/2020](#th2)
* [Thursday, 08/10/2020](#th3)
* [Sunday, 11/10/2020](#th4)
* [Monday, 12/10/2020](#th5)
* [Tuesday, 13/10/2020](#th6)
* [Wednesday, 14/10/2020](#th7)
* [Thursday, 15/10/2020](#th8)
* [Monday, 19/10/2020](#th9)
* [Tuesday, 20/10/2020](#th10)
* [Wednesday, 21/10/2020](#th11)
* [Thursday, 22/10/2020](#th12)
* [Tuesday, 27/10/2020](#th13)

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nyuad.igem@nyu.edu

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* [Description

  Detailed description of the project](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Description)
* [Result

  Timeline, results, and future plans](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Results)
* [Docs

  Separate comprehensive documentation](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation)
* [About

  Learn more about the NYUAD iGEM Group](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Team)
* + Project Pages*arrow\_drop\_down*

    - [Proposed Implementation](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Implementation)
    - [Proof of Concept](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Proof_Of_Concept)
    - [Notebook](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Notebook)
    - [Experiments](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Experiments)
    - [Design](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Design)
* + Competition Pages*arrow\_drop\_down*

    - [Parts](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Parts)
    - [Attributions](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Attributions)
    - [Contribution](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Contribution)
    - [Human Practices](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Human_Practices)
    - [Collaboration](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Collaborations)
    - [Engineering Success](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Engineering)
    - [Science Communication](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Education)
    - [Hardware](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Hardware)

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# Team:NYU Abu Dhabi/Engineering

Results

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![](https://static.igem.org/mediawiki/2020/0/0c/T--NYU_Abu_Dhabi--eng--header.jpg)

# Engineering Success

# Parts Creation and Success

Detection relies on identification, amplification, and reporting of a specific DNA region of the organism's genome. For our purposes, we searched for a highly conserved and nonpathogenic region of the Bd and Bsal genomes and found the ITS region. The ITS (internal transcribed spacer) region is highly conserved and widely used as the DNA barcode region in fungi such as Bd and Bsal. It is a spacer DNA that is nonpathogenic, adding an additional layer of safety; this will be the region amplified in downstream reactions such as RPA or LAMP.

We created a gblock part for the Bd ITS region and Bsal ITS region. Our experiment relies upon DNA extraction, amplification, and detection. For the latter two, we designed primer parts within our gblock region. These included those for PCR, qPCR, RPA, LAMP, and CRISPR.
In the three weeks we had access to a biology lab space, we were able to confirm success of our gblocks and PCR and RPA primers, along with CRISPR guide RNA for Bd. See our [results page](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Results) for a more detailed description of how we confirmed success of these parts!

# Diagnostic Device Engineering Design Process

The development of the diagnostic device has been following the engineering design cycle very closely. The team first started out with a very extensive research phase where we compiled all the existing literature regarding each component and process of the device and documented on the  [Notion platform](https://www.notion.so/Documentation-02327ead32044ffca7a939a66af069a9) The complete biological [diagnostic pathway](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Engineering_8297e0cf88af4f8fbfe00ad598c6a52f/Bio-Requirements_a0409b5140a944dc8b04a55669a38016) was also determined to help the engineering team divide the device into distinct components, as can be seen in the diagrams below:

![](https://static.igem.org/mediawiki/2020/d/de/T--NYU_Abu_Dhabi--eng--pc1.jpg)

*Pipeline diagram of device pathway*

![](https://static.igem.org/mediawiki/2020/1/1e/T--NYU_Abu_Dhabi--eng--pc2.jpg)

*Mapping diagram of device pathway*

Meanwhile, we also reached out to stakeholders and experts to set constraints on the device [Questions for Stakeholders from Engineering Side](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Engineering_8297e0cf88af4f8fbfe00ad598c6a52f/Questions_for_Stakeholders_from_Engineering_Side_66e735d774a54f0bb33edcbb73241cf8), [Metrics and Requirement](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Engineering_8297e0cf88af4f8fbfe00ad598c6a52f/Metrics_and_Requirement_0480a077c4fc4ec388599dc6f7567220) ). Using this knowledge and keeping the constraints in mind, for each process, both in biology and engineering, we have been researching which options could be implemented in the device. Based on these options, we have been also brainstorming alternate options, such as in case of [reaction medium](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Results#ReactionMedium), and coming up with innovative ways to put the processes together as in case of [sample preparation and reaction medium.](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Hardware#SamplePrep)

Using our selected options, which are still increasing as we brainstorm further, we are designing experiments using standard and proven methodologies, keeping factors such as errors, need of replicates, and experimental controls in mind. While our lab access has been very limited so far due to COVID-19, we have reached the initial prototyping stage for some processes such as fluorescence, have experimental results for RPA amplification, and we plan to prototype and test other options soon. Using the results from these tests, we will be improving or discarding the options to develop concepts which will be then combined under the constraints to provide a complete device design. Finally, this design will be prototyped, tested, and improved to become field deployable.

The complete engineering product development timeline can be seen here:

![](https://static.igem.org/mediawiki/2020/6/6f/T--NYU_Abu_Dhabi--eng--pc3.jpg)

*Engineering product development timeline*

Given below are some brief examples of components of our diagnostic process that are meant to further explain the use of engineering design cycle and the engineering success in our methodology:

# Engineering Success- LFA sensing Mechanism

**Research and Imagine**

This process involved us conducting research in order to implement an experimental analysis design that can then be tested and improved accordingly. This involved studying articles that used LFA as a sensing mechanism (especially those that used LFA with CRISPR based reactions).

After comparing articles and the methods they used, we implemented methods that seemed to be the most feasible in terms of Point of Care testing, and the methods that seemed to be most cost-effective and time-efficient

**Why should we use Milenia HybriDetect?**

When deciding on the brand of LFA strips to be used, we found that the Milenia Hybridetect 1 strips were highly popular among researchers and users and has been cited in a series of high quality publications. A list of all the publications can be found [here](https://www.milenia-biotec.com/en/landingpage-hybridetect-general/#publication) . The products were suitable since we could detect products derived from amplification methods including RPA, PCR and LAMP. The strips are of reliable quality and are readily available.

For quantitative analysis, we tried to look for equipment such as those produced by Milenia Biotech, that would grant quantification of these results however, these were not proven to have reliable results. Therefore, we began looking at ways through which we could engineer a method to quantify these LFA strips.

After intensive research, we decided to follow the technique outlined in "Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone," by the authors Foysal, Kamrul H et al. In this article, the writers carefully explain developing an image processing software which was used to quantify LFA results.

Image-processing software was then created and designed using Python, replicating the method explained in the article. Some adjustments were made depending on the requirements of out experiment and the expectation of the results. Therefore, the research that went into preparing the experimental analysis for quantitative LFA detection involved a lot of interdisciplinary collaboration between computer science studies and bio technology. This collaboration helped us derive an experimental design that can be used to successfully quantify the DNA. The development of the experimental design for LFA quantification involved research in both computer science and bio technology.

**Design and Build**

We designed the experiments using data from our research explained above.
Experimental design of the qualitative and quantitative analysis is as follows:

**Experimental Design for Qualitative Analysis**

1) After completion of the pre-amplification step, there will be a 20 μL of reaction mix. This will be added to a 2-mL Eppendorf tube after which 80 μL of Hybridetect 1 Assay Buffer should be added into it. A nuclease-free 2-mL 96- well block can be used instead of Eppendorf tubes.

2) A lateral flow strip (Milenia HybriDetect 1, TwistDx) should then be added to the reaction tube and a result should be visualized after approximately 2 min. The lateral flow strip must be placed in an upright position and must be incubated at room temperature for at least 5 minutes. (unto 15 minutes). Results must then be analysed.
A single band, close to the sample application pad (the control band) will indicate a negative result, whereas two bands close to the top of the strip (test band) and the control band should indicate a positive result. The bands are clearly formed red bands. In case of very high concentrations of hybridisation product, control band’s intensity may be affected (during a positive test) Nevertheless, the control band should be still visible clearly.

3) After completion, remove the lateral flow strip and place it on a white background for visual inspection.

**Experimental Design for Quantitative Analysis**

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. The intensity of the red could then be quantified by image acquisition followed by an image processing software to measure the band intensity and to determine a positive detection against a positive-control dilution series.

To develop the software to be ready for quantification, data points have to be obtained for a calibration curve to be formulated. Once this has been fed into the system, images to be tested will be studied against the calibration curve to determine a quantified result for the test.

The method below refers to the creation of the calibration curve. Firstly, data points have to be obtained to formulate the curve.

1) Firstly, we need to get known quantities of the target analyte. We hope to consider 5 different concentrations, such as: 10 ng/mL, 1 ng/mL, 100 pg/mL, 10 pg/mL, and 1 pg/mL.

2) Three sets of samples must be obtained for this, with each set containing 5 LFA strips of different concentrations. 10 μL of the sample should be prepared. The quantities we could study are therefore : 100pg, 10pg, 1pg, 100fg, 10fg. 15 LFA strips will be used. Each reading should be taken 5 times to reduce error. 75 readings should be obtained overall. The sets of readings will be used to train the machine learning model.

3) Using the grid view of the camera, the region of interest (ROI) should be positioned inside the center box. The image will then be captured, and the ROI obtained. The data acquisition procedure will be performed with a smartphone. Which brand has not been anticipated yet, but it should be kept constant. The flashlight would be turned off during the data acquisition procedure. Images of the LFA strips will be captured under different ambient lighting conditions in the laboratory. The illumination power of the room must be noted. The quantification method will be evaluated based on acquired data using MATLAB.

Next, we analyse the results using the method below, to obtain the regression and classification of our points.

**METHOD**

Feature extraction- The number of red pixels accumulated on the test line is considered the feature. Assuming that the cumulative sum of red pixels’ intensities in the test line varied proportionally with analyte quantity under a specific lighting environment, we carried out the test. In order to do this:

The testing procedure is as follows:
(1) A test strip is cut according to consistent measurements with only the test line and control line regions visible. It is placed under the smartphone on a white background. Using the grid view of the camera, the ROI was positioned inside the center box.

(2) The image was captured. The application then created a mask using the preprocessing technique mentioned in the proposed algorithm (Otsu's method).

(3) In the masked region, the weighted sums of red pixels’ intensities of the test and the control line regions were calculated.

(4) T/C ratio values that corresponded to the quantity of the analyte were calculated.

(5) A regression curve is plotted which will be used as the calibration curve.
 **Citation: 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**

**Test, Learn and Improve**

We plan on testing our experimental designs explained above, and repeat with any alterations that may be required after detailed observation of the results.

# Engineering Success - Fluorescence

To start our process, we started researching different possible methods for fluorescence detection in Point of Care devices. This detection mechanism was one of our options as we plan on taking advantage of the fluorescence quenching-based reporting that can be easily combined with CRISPR-Cas reactions. We mapped all possible methods that included different types of excitation sources, sensors and setups using different case studies. A full list of this can be found on the [Fluorescence — page](https://www.notion.so/Fluorescence-67252b6f698b4eec878cfa75943d6516). We then classified these methods based on their sensitivity and cost as the time taken in this method is negligible for all setups.

![](https://static.igem.org/mediawiki/2020/4/46/T--NYU_Abu_Dhabi--eng--pc4.jpg)

*Mapping of fluorescence options*

**Imagine**

After the research was completed, we concluded on two possible methods, 1 of which involved filters and 1 that did not. The dichroic mirrors that are used as filters are expensive but increase the overall sensitivity of the device. Therefore instead of just picking one between cost effectivity and sensitivity, we decided to test both setups once we got lab access. The final components we decided can be found on the [Hardware page](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Hardware).

**Design and Build**

We designed a shell to block the light out of the detection medium to increase the signal to noise ratio. Finally, we developed a code to run initial tests and check the sensor that we plan on using.

![](https://static.igem.org/mediawiki/2020/9/9d/T--NYU_Abu_Dhabi--eng--pc5.jpg)

*Fluorescence detection schematic*

**Test**

We plan on testing this initial setup with amplified samples and control samples using the SYBR green fluorescence dye provided by the bio team to calibrate the device. The calibratrion would be done as follows:

The device should then be able to detect whether an unknown sample is positive or not using the above data

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.

**Learn and Improve**

We will then make the necessary changes and recalibrate it to sense quantitatively for a positive sample using CRISPR. The recalibration process will involve the following steps:

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 the average sensor value (x axis) against concentration (y axis)

4. Use this in the code to give a quantitative result

Once the above processes are completed successfully for both our methods, we will compare them based on biology and engineering requirements and make a decision on which method to move forward with. We hypothesize that the dichroic mirror method will be more sensitive but significantly more expensive while other parameters such as size and time taken would be similar.

We will then repeat the above steps and try and make the device more sensitive and durable.

* [Parts Creation and Success](#parts)
* [Diagnostic Device Engineering Design Process](#diag)
* [Engineering Success- LFA sensing Mechanism](#lfa)
* [Engineering Success - Fluorescence](#fluo)

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nyuad.igem@nyu.edu

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All Rights Reserved 2020

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* [Description

  Detailed description of the project](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Description)
* [Result

  Timeline, results, and future plans](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Results)
* [Docs

  Separate comprehensive documentation](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation)
* [About

  Learn more about the NYUAD iGEM Group](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Team)
* + Project Pages*arrow\_drop\_down*

    - [Proposed Implementation](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Implementation)
    - [Proof of Concept](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Proof_Of_Concept)
    - [Notebook](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Notebook)
    - [Experiments](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Experiments)
    - [Design](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Design)
* + Competition Pages*arrow\_drop\_down*

    - [Parts](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Parts)
    - [Attributions](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Attributions)
    - [Contribution](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Contribution)
    - [Human Practices](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Human_Practices)
    - [Collaboration](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Collaborations)
    - [Engineering Success](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Engineering)
    - [Science Communication](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Education)
    - [Hardware](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Hardware)

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# Team:NYU Abu Dhabi/Experiments

Experiments

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# Experiments

# LB [Ampicillin] Agar Preparation

1. 35g of LB agar was added into 1L of distilled water in a 2L flask.
2. Solution was autoclaved for 15 minutes at 121 °C.
3. 1000µL of ampicillin (100mg/mL) was added to reach a final concentration of 100µg/mL

# LB Broth Preparation

1. 25g of LB broth was added into 1L of distilled water in a 2L flask.
2. Solution was autoclaved for 15 minutes at 121 °C.
3. 1000µL of ampicillin (100mg/mL) was added to reach a final concentration of 100µg/mL.

# Inoculation

1. Pipette 6 mL of LB Broth into each tube.
2. Use an inoculation loop to take a colony from the agar plate and transfer the colony to the tubes containing LB Broth.
3. Incubate the tubes at a temperature of 37°C in a shaker incubator at 225 RPM for 18-24hrs.

# Preparation of gBlock (IDT DNA protocol for gBlock)

1. Prior to opening, centrifuge the tube at a minimum of 13.3 rpm for 1 minute to ensure that the material is at the bottom of the tube.
2. Add appropriate volume of TE buffer to the lyophilized gBlock to reach a final concentration of 100ng/µl or 50ng/µl.
3. Vortex the sample for 5-10 seconds.
4. Incubate in the Eppendorf heat block at 50°C for 20 minutes.
5. Vortex the sample for 5-10 seconds.
6. Centrifuge the sample for 1 minute.
7. Store at -20˚C.

# Preparation of PCR Primers

1. Prior to opening, centrifuge the tube at 13.3 rpm for 1 minute to ensure that the material is at the bottom of the tube.
2. Add
   *x* volume of TE buffer to reach a final concentration of 100µM for each primer according to manufacturer specification.
3. Mix the tubes by pipetting and place on ice.
4. Dilute the 100µM stock solution to a working solution of 10µM at a final volume of 50µL.

# PCR Protocol

1. Preheat the thermocycler to 95 °C.
2. Allow the reagents to thaw in ice.
3. Prepare the PCR tubes with the following mixture composition:

* - 12.5µL of NEB Q5 High-Fidelity 2X Master Mix
* - 1.25µL of forward primer (10μM)
* - 1.25µL of reverse primer (10μM)
* - 2µL of DNA template
* - 8µL of nuclease-free water

4. Spin the tubes for a few seconds.
5. Transfer the tubes to the thermocycler and set the following temperatures and settings:

* - Initial denaturation: 98°C for 30 seconds
* - 30 cycles:

1. Denaturation: 98°C for 10 seconds
2. Annealing: 50-72˚C for 30 seconds (Annealing temperature selected based on Tm of primer set; typically 3-5˚C below the Tm)
3. Extension: 72°C for 15 seconds

* - Final Extension: 72°C for 2 minutes
* - Hold: 4°C

6. Tubes stored at -20˚C or ran on gel electrophoresis immediately post PCR-amplfiication.

# Ligation (Thermofisher Blunt-end Cloning Protocol)

1. Prepare the ligation reaction in a PCR tube on ice.
2. Add 10µL of 2X reaction buffer to the PCR tube.
3. Add 1µL of non-purified PCR product.
4. Add 1µL of pJET1.2/blunt cloning vector.
5. Add 7µL of nuclease free water.
6. Add 1µL of T4 DNA ligase.
7. Vortex for 5 seconds.
8. Centrifuge for 5 seconds.
9. Incubate the ligation mixture in the PCR machine for 5 minutes at 22˚C.
10. . Use mixture directly after step 9 for transformation.

# Transformation (NEB 5-alpha Competent E. coli)

1. Thaw a tube of NEB 5-alpha Competent \*E. coli\* cells on ice for 10 minutes.
2. Add 1-5µL containing 1pg - 100ng of plasmid DNA to the cell mixture.
3. Carefully flick the tube 4-5 times to mix cells and DNA (do not vortex).
4. Place the mixture on ice for 30 minutes.
5. Heat shock at exactly 42˚C for exactly 30 seconds.
6. Place on ice for 5 minutes.
7. Pipette 950µL of room temperature SOC into the mixture.
8. Place at 37˚C for 60 minutes.
9. Shake vigorously (225 rpm) or rotate.
10. . Warm selection plates to 37˚C during the 60 minute incubation from steps 8 & 9.
11. . Centrifuge the tubes at 13.3 rpm for 1 minute.
12. . Remove 850µL of the supernatant and re-suspend the cells with the remaining supernatant in order to concentrate them.
13. . Spread the remaining 55µL concentrated cells on appropriately labeled agar plates along with the respective antibiotic.
14. . Incubate at 37˚C for 18-24hrs.

# Miniprep

1. Pellet 1-5mL bacterial overnight culture by centrifugation at >8000 rpm for 3 min at room temperature.
2. Discard the supernatant.
3. Resuspend pelleted bacterial cells in 250µL resuspension buffer and transfer to a microcentrifuge tube.
4. Add 250µL Buffer lysis buffer and mix thoroughly by inverting the tube 4-6 times until the solution becomes clear (do not allow the lysis reaction to proceed for more than 5 min).
5. Add 350µL Buffer neutralization buffer and mix immediately and thoroughly by inverting the tube 4-6 times.
6. Centrifuge for 10 min at 13,000 rmp.
7. Apply 800µL supernatant to the QIAprep 2.0 spin column by pipetting.
8. Centrifuge for 30-60 seconds and discard the flow-through.
9. Wash the QIAprep 2.0 spin column by adding 0.5mL binding buffer.
10. . Centrifuge for 30-60 seconds and discard the flow-through.
11. . Wash the QIAprep 2.0 spin column by adding 0.75mL wash buffer.
12. . Centrifuge for 30-60 seconds and discard the flow-through.
13. . Centrifuge for 1 minute to remove residual wash buffer.
14. . Place the QIAprep 2.0 column in a clean and appropriately labelled 1.5mL microcentrifuge tube in order to elute the plasmid.
15. . Add 50µL elution buffer to the center of the QIAprep 2.0 spin column.
16. . Let stand for 1 minute and centrifuge for 1 minute.

# Nanodrop

1. Blank the nanodrop spectrophotometer using water or elution buffer according to the solution used to elute plasmid DNA from the miniprep protocol.
2. Measure DNA concentration using the nanodrop by taking a 2µL sample of the plasmid.
3. Wipe the sample from both the upper and lower pedestal after each measurement using Kim Wipes and repeat for additional samples.

# Making gels

1. Mix 40mL of 50X TAE buffer with 1960mL of MilliQ water to make 2L of 1X TAE buffer.
2. Prepare 1% or 3% agarose gels by weighing out appropriate amount of agarose according to concentration of gel required in a final volume of 50mL of 1X TAE buffer (e.g. 0.5grams used for 1% agarose gel).
3. Microwave mixtures in increments until boiling to dissolve the Agarose.
4. Add 3µl of Gel green (final concentration of 5x) into the 50mL agarose mixture.
5. Pour the solution into the casting tray and place the well combs.
6. Let the gel solidify and remove comb prior to loading.

# Loading Gels

1. Add the appropriate amount of 5x loading dye to the set volume of DNA to obtain a 1x concentration of the loading dye (e.g. 1 µL of loading dye to 5 µL of DNA).
2. Place set gel into electrophoresis chamber.
3. Load 5µl of the ladder and each of the samples into their appropriate wells.
4. Pour 1X TAE buffer carefully into the chamber to submerge the gel.
5. Connect the electrophoresis chamber to MyRun electrophoresis.
6. Run gel on 135 V for 20 minutes.
7. Visualize the gel using UV light or E-gel imager using Blue Light base.

# RPA Protocol

1. Resuspend RPA primers to final concentration of 100µM.
2. Make a 5µM dilution for each primer for RPA reactions.
3. Prepare the reaction mix in 1.5mL tube:

* - Primer Forward (5µM) - 2.4µL
* - Primer Reverse (5µM) - 2.4µL
* - Rehydration Buffer - 29.5µL
* - dH2O - 8.2µL

4. Add the reaction mix (42.5µL) to 1 freeze dried RPA provided tube, and mix by pipetting up and down.
5. Split the reaction into 2 volumes of 15µL into 2 separate PCR tubes.
6. Add 1µL of 280mM magnesium acetate to each tube and mix well to start the reaction.
7. Add 5µL of DNA to one tube and 5µL of nuclease-free water to the negative control tube.
8. Incubate 20 minutes at 37°C.

# DETECTR RPA CRISPR Cas12a Protocol

1. Add the following reagents in this order at the room temperature:

* - 2µL NEBuffer 2.1 Reaction Buffer (10x)
* - 0.5µL 5µM gRNA
* - 1.5µL 1 µM EnGen Lba Cas12a (Cpf1)

2. Make the RPA reactions on target gene to obtain a total volume of 21µL.
3. Incubate both CRISPR complex for 10 minutes at 37°C.
4. Add 1µL of 50µM FQ quencher to the CRISPR complex.
5. Add CRISPR complex mix (5µL) with quencher to RPA reaction mix.
6. Incubate 30 minutes at 37°C.
7. Check under the blue light based E-gel imager.

* [LB [Ampicillin] Agar Preparation](#lbagar)
* [LB Broth Preparation](#lbbroth)
* [Inoculation](#innoc)
* [Preparation of gBlock](#gblock)
* [Preparation of PCR Primers](#pcrprimer)
* [PCR Protocol](#pcrprotocol)
* [Ligation](#Ligation)
* [Transformation](#Transformation)
* [Miniprep](#Miniprep)
* [Nanodrop](#Nanodrop)
* [Making gels](#gels)
* [Loading gels](#lgels)
* [RPA Protocol](#rpa)
* [DETECTR RPA CRISPR Cas12a Protocol](#detectr)

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# Team:NYU Abu Dhabi/Results

Results

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![](https://static.igem.org/mediawiki/2020/c/ce/T--NYU_Abu_Dhabi--results--header.jpg)

# Results

# Biology Experimental Results

**Stage 1 - gBlock cloning, Transformation and Quality Control**

![](https://static.igem.org/mediawiki/2020/9/9b/T--NYU_Abu_Dhabi--results--pc1.jpg)

**Goal:**At the inception of our experimental period, our first goal is to generate stock solutions of plasmids containing either our Bd gBlock or Bsal gBlock to be used for all our downstream experiments for amplification and CRISPR detection. To give an experimental overview, this was achieved by:

1. Amplifying our gBlock by PCR
2. Ligating the amplified PCR product into a pJET vector
3. Transforming the ligated vector into competent E. coli cells
4. Inoculating the successfully transformed bacteria into Lb Borth in order to purify the large quantities of the plasmid using the miniprep protocol
5. PCR was performed on the purified plasmids to ensure our product was successfully ligated

*Note: When our PCR primers were designed, two sets for each Bd gBlock and Bsal gBlock were created. Considering we only had 3 weeks to experiment, it is crucial that this stage is completed as soon as possible to ensure that we can start testing the the main aspects of our project: amplification and CRISPR detection. To ensure that this stage happens successfully, we used two sets of primers for each gBlock in the case that one set of primers did not work.*

**Results**

Quality Control Check

![](https://static.igem.org/mediawiki/2020/f/fb/T--NYU_Abu_Dhabi--results--pc2.png)

*Quality control check on the production of purified Bd plasmid. Bd1 refers to the Bd gBlock amplified by our first Bd PCR primer set, and Bd2 refers to the Bd gBlock amplified by our second Bd PCR primer set. Bd1 Primers amplicon length: 364 bp. Bd2 Primers amplicon length: 373 bp.*

![](https://static.igem.org/mediawiki/2020/f/fc/T--NYU_Abu_Dhabi--results--pc3.png)

*Quality control check on the production of purified Bsal plasmid. Bsal3 refers to the Bsal gBlock amplified by our third Bsal PCR primer set. Bsal4 refers to the Bsal gBlock amplified by our fourth Bsal PCR primer set. Bsal3 Primers amplicon length: 476 bp. Bsal4 Primers amplicon length: 486 bp.*

The first set of PCR reactions were successful. The bands observed were in agreement with the amplicon length for each corresponding primer set. pJET was used as a reference for the bands of the miniprep. For each miniprep, the bands were indicative of successful ligation, transformation, inoculation, and miniprep. This is true because for each miniprep, a heavily concentrated band that travelled faster than the pJET band is observed. This is indicative of our circular plasmid containing the corresponding gBlocks. In addition, a fainter band that travelled slower than the pJET band is observed. This is indicative of our linear plasmid containing the corresponding gBlocks. To ensure that our plasmid clones contained the correct gBlocks, we ran another PCR test on the minipreps. For the Bsal plasmids, we were able to get successful amplification. Bands that were observed matched that of the initial bands from the first PCR reaction. There were also another set of bands that matched that of our plasmid. Since a band was observed in the negative control of primer set 3, which was most probably a primer dimer, we decided that all our downstream reactions will strictly work with Bsal plasmid amplified by primer set 4. While on the other hand, for the Bd plasmids, we were not able to get conclusive results and required us to troubleshoot our PCR reaction for Bd.

**Troubleshooting**

![](https://static.igem.org/mediawiki/2020/3/30/T--NYU_Abu_Dhabi--results--pc4.png)

*PCR Reaction of minipreps after making a new working stock of Bd primers. Bd1 refers to the Bd gBlock amplified by our first Bd PCR primer set, and Bd2 refers to the Bd gBlock amplified by our second Bd PCR primer set. Bd1 Primers amplicon length: 364 bp. Bd2 Primers amplicon length: 373 bp.*

Suspected to be due to primer contamination, the Bd PCR reaction after the miniprep was repeated with a new set of diluted primers. Contamination in Bd1 was observed again, but successful results were observed for Bd 2. Two bands were observed: a 370 bp band, which is the same length as the amplicon length of Bd Primer set 2, and a band at the top, which is the plasmid that was used to amplify the DNA. That said, all downstream reactions worked with minipreps that were produced from Bd Primer set 2.

# Stage 2 - Amplification with RPA

![](https://static.igem.org/mediawiki/2020/7/73/T--NYU_Abu_Dhabi--results--pc5.jpg)

Now that we had confirmed successfully inserting our gblock into our plasmid, transforming it into E. coli, and miniprepped our Bsal and Bd plasmids, it was time to move on to amplifying our product.

Goal: Amplify the Bd or Bsal product to then be used for CRISPR detection.

Due to limited time in the lab, we decided to test amplification using RPA and later test and compare amplification techniques with LAMP. We began with RPA specificity tests against Bd and Bsal primers that simultaneously confirmed that our RPA functioned as intended to amplify exclusively our region of interest. We initially ran into problems with one Bd RPA primer set and so switched to another set. We then confirmed RPA specificity. Going forward, we plan on testing for RPA sensitivity and testing LAMP as an amplification technique. See results below:

**Results**

![](https://static.igem.org/mediawiki/2020/7/72/T--NYU_Abu_Dhabi--results--pc6.png)

Protocol needs adjustment: there should be no band seen when Bd primers are used with Bsal DNA. As it is a similar length to the Bd primer length, we decided to use our alternate Bd RPA primer set. The right-most Bsal primer with Bsal DNA served as an initial proof of concept that RPA worked.

![](https://static.igem.org/mediawiki/2020/f/f0/T--NYU_Abu_Dhabi--results--pc7.png)

*Protocol needs adjustment: Bands were faint. We optimized by using higher concentration miniprep plasmids.*

Initial protocol needed adjustment. The primary issue seemed to be the Bd RPA primer set, so we substituted it for an alternate Bd RPA primer set

![](https://static.igem.org/mediawiki/2020/1/1e/T--NYU_Abu_Dhabi--results--pc8.png)

*Positive result: RPA showed specificity by amplifying only the Bsal DNA that matched the Bsal primers. No amplification detected when Bd primers were used against Bsal DNA.*

![](https://static.igem.org/mediawiki/2020/c/cb/T--NYU_Abu_Dhabi--results--pc9.png)

*Positive result: RPA showed specificity by amplifying only the Bsal DNA that matched the Bsal primers. No amplification detected when Bd primers were used against Bsal DNA.*

Adjusted protocol showed optimal results.

![](https://static.igem.org/mediawiki/2020/4/4d/T--NYU_Abu_Dhabi--results--pc10.png)

*SYBR green (5X) detection of RPA reactions*

As another method to detect RPA amplification, SYBR green was used. We found an optimal concentration of SYBR green to be 5X. At this level, there was minimal noise from the negative control and sufficient fluorescence from the experimental.

# Stage 3 - CRISPR Detection

![](https://static.igem.org/mediawiki/2020/7/7a/T--NYU_Abu_Dhabi--results--pc11.jpg)

After evidence of successful RPA amplification of our genes, including our specificity experiments, we moved on to CRISPR to add a second layer of specificity and in order to observe the fluorescence using our reporter. The optimization and use of CRISPR and RPA compared to the traditional PCR and gel is that in our method, we have reduced the time from 2 hours (PCR) plus 30 minutes (loading gel + running gel) to 20 min (RPA) and 20 min (CRISPR).

**Goal: Confirming proof of concept for CRISPR Cas12a.**

To give an experimental overview, this was achieved by:

1. Performing RPA to amplify our gene of interest
2. Performing DETECTR RPA CRISPR Cas12a protocol on the RPA product
3. Repeating and testing to troubleshoot and optimize protocol

**Troubleshooting CRISPR:**

The first CRISPR experiment did not produce florescence for either Bd or Bsal samples, even when concentrations of reagents were tripled and left to incubate overnight. However, we saw detection using SYBR green on the RPA, indicating it was the CRISPR protocol that needed optimizing.

We then ran CRISPR using the optimized protocol for our last year’s experiments that had slightly varied the reagents’ concentrations. Results were successful for our Bd target gene; however, Bsal did not fluoresce. Future experiments will investigate the issue further. Both RPA samples were detected using an optimized 5X concentration of SYBR green. See results below:

![](https://static.igem.org/mediawiki/2020/2/27/T--NYU_Abu_Dhabi--results--pc12.jpg)

*CRISPR optimization required: Fluorescence of CRISPR products of Bd. CRISPR had slight fluorescence indicating the method works but there may be an error in the CRISPR protocol section*

![](https://static.igem.org/mediawiki/2020/7/7a/T--NYU_Abu_Dhabi--results--pc13.jpg)

*CRISPR optimization required: No fluorescence was detected.*

The first initial set of CRISPR experiments did not provide successful results; thereby, our team decided to re-asses the protocol and test out the optimized version our 2019 NYUAD iGEM team performed. When ran again, Bsal again did not fluoresce and will require further analysis and troubleshooting. However, our Bd target gene provided successful results displaying successful amplification and detection. Further troubleshooting and protocol optimization is our next step for our Bsal target gene.

![](https://static.igem.org/mediawiki/2020/a/a8/T--NYU_Abu_Dhabi--results--pc14.jpg)

*Triplicate run of CRISPR DETECTR on Bd. Fluorescence in the Bd CRISPR of the reaction works as intended.*

RPA amplification of Bd target gene in addition to CRISPR DETECTR protocol displayed successful detection under UV light. This suggests that using this method of detection can be applied to our point-of-care device to be used in the field. Future work will be done to test sensitivity, optimize protocols, and compare the best methods, such as LAMP versus RPA, that will integrate well into our final product.

# Future Biology Work

Our future work will work in the following general path: testing and optimizing amplification and detection techniques, testing sensitivity and specificity, comparing different techniques, comparing results to the golden standards, and working throughout the process to integrate our chosen biological techniques into the engineering design. The work we have done up until this point is a humble beginning in showing our project's promise and potential. The work we have ahead of us is to verify, optimize, and integrate.

Refer to the [design page](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Design) for the overview of our project's timeline and how far we have come!

# Engineering Research Progress

![](https://static.igem.org/mediawiki/2020/9/9e/T--NYU_Abu_Dhabi--results--pc15.jpg)

*Engineering product development timeline*

**Sample Collection**

Sample collection is most commonly performed in the field on adult amphibians via the swabbing method. Swabbing the infection sites (hands, feet, thighs, and venter) minimizes the occurrence of false-negatives. **The swabbing method is thus far the golden standard in non-lethal sample collection of Chytrid fungi from infected amphibians.** Several other methods have been developed in recent years, providing alternative routes to obtaining the fungi samples. Mouthparts swabbing of tadpoles is less sensitive than lethal sampling (extraction of mouthparts) but does not cause mortality, unlike ordinary excision methods done on tadpoles. Non-lethal sampling is also performed via biopsy punches from toe webbing from live amphibians. This method does not decrease survival after one year and has been validated via microscopy. Due to the difficult circumstances, field samples could not be obtained for this project, but if they were, they would be obtained via the swab method.

![](https://static.igem.org/mediawiki/2020/3/32/T--NYU_Abu_Dhabi--results--pc16.jpg)

*Sample collection via swabbing.*

![](https://static.igem.org/mediawiki/2020/7/75/T--NYU_Abu_Dhabi--results--pc17.png)

*Sample collection via biopsy punch*

Another recently emerging sample collection that the team looked at is eDNA, a brief description of which is given below:

**eDNA**

Currently, the team is focusing on making the device compatible with the standard swabbing method, however, we plan to work more on eDNA in the future.

# Sample Preparation

Sample preparation, which is nucleic acid extraction in our device, is one of the most crucial stages in the diagnostic pathway for nucleic acid detection based point of care devices [(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4943866/)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4943866/). It is critical that we get high quality DNA to be used for detection. As our device deals with fungal cells which are generally harder to lyse, this stage is even more important and challenging. We started out with compiling research about all the available nucleic acid extraction options , and then upon researching the feasibility of these options of POC Fungal Cell lysis, we mapped the filtered options as below:

![](https://static.igem.org/mediawiki/2020/e/ed/T--NYU_Abu_Dhabi--results--pc18.jpg)

*Mapping of sample preparation/nucleic acid extraction options (note: the options we are trialing are marked green, the ones we need to research more marked yellow, and the ones we will not be proceeding with red)jpg*

A brief overview of these filtered options is given below:

**Silica Matrices**

Using silica matrices is a well known method for purifying DNA by exploiting its negatively charged backbone. Positively-charged silica acts as a selective filter to DNA by tightly binding to it. Sodium cations in the solution can break the hydrogen bonds between the hydrogen in water and the negatively charged oxygen ions in silica under high salt conditions (pH ≤ 7). The DNA is tightly bound, and extensive washing removes all contaminations. The purified DNA molecules can be eluted under low ionic strength (pH ≥ 7) later by using TE buffer or distilled water. [Tan. 2009](https://www.hindawi.com/journals/bmri/2009/574398/)

**Whatman FTA cards**

Whatman FTA cards are fibrous cards impregnated with chelators and denaturants that would lyse and inactivate most microorganism. It is used for sample collection, long-term storage, and purification of the nucleic acid. Once the eukaryotic cells are in contact with the card, it lyses the cell and the DNA is bound to the matrix of the card and protected from the environmental and enzymatic damages. Trinh et al., 2019 uses Whatman FTA card accompanied with LAMP in POC device. The research team applied the FTA card in the bottom layer of the microdevice and use FTA paper for DNA extraction and purification. It took 30 minutes for the extraction.
Reference: https://link.springer.com/article/10.1007/s12686-018-1018-z

**Bead Beating**

Bead beating is a mechanical method for the lysis of thick-walled organisms. As opposed to chemical methods such as enzymatic treatment or detergents, bead beating uses high-energy cell disruption in order to release nucleic acids.

The most common method of implementing bead beating is through high-power, benchtop devices such as the gold standard Biospec [Mini-BeadBeater](https://biospec.com/product/mii-beadbeater-16). However, for the purposes of our application, we intended to incorporate bead beating in a more portable, low-energy device that was also inexpensive and required little expertise to operate.
As such, our research focused on the options mapped below:

![](https://static.igem.org/mediawiki/2020/7/77/T--NYU_Abu_Dhabi--results--pc19.png)

*Bead Beating options mapping*

OmniLyse and AudioLyse, are low-power, low-cost alternatives to benchtop bead beating devices. They devices are also disposable, more suited for commercial production and significantly less expensive. OmniLyse is a battery-powered bead blender while AudioLyse is an audio-powered mechanical lysis device. In terms of other options, we also considered fabricating something on-Chip according to the papers.

As per the color coding in the map above, however, we have decided to follow OmniLyse and AudioLyse instead of fabricating a lab-on-a-chip bead beating device. The factors we kept in mind while making a decision between these beat beating options included viability of the method, time taken and lysis efficiency.

**Magnetic Extraction**

We have also looked at Magnetic bead extraction as an option for nucleic acid extraction and narrowed down our research to the methods in the map below. The factors we kept in mind while deciding include input volume, time taken and lysis efficiency.

![](https://static.igem.org/mediawiki/2020/7/7d/T--NYU_Abu_Dhabi--results--pc20.png)

*Magnetic extraction options mapping*

Each of these methods is listed in [Nucleic Acid Extraction.](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Biology_93d1eff7b0cd4d6ca8529879e773d615/Nucleic_Acid_Extraction_a64230b736354a338d2bbaaf96efce14)

As can be seen from the mapping, we are in the process of making decisions on the options we will be prototyping and testing in tandem with the reaction medium options to develop our final concepts. On the options we have marked green, i.e. the ones where we have made the decision to prototype, we expect to start the process very soon.

# Reaction Medium

We define reaction medium as the platform where the processes in the diagnostic device such as sample preparation and sensing happen, such as microfluidic chips, paper-based bio sensors etc. The constraint of the device being usable in the field plays a great role in reaction medium. By researching numerous case studies of literature about POC diagnostic devices compiled in  [Case Studies](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Engineering_8297e0cf88af4f8fbfe00ad598c6a52f/Case_Studies_b2be0b8bd13548ac99d3f41e1124dbb2), we were able to map out the possible options for the reaction medium of our device:

![](https://static.igem.org/mediawiki/2020/c/c6/T--NYU_Abu_Dhabi--results--pc21.jpg)

*Mapping of reaction medium options*

![](https://static.igem.org/mediawiki/2020/d/d0/T--NYU_Abu_Dhabi--results--pc22.png)

*PMP Droplet Cartridge*

![](https://static.igem.org/mediawiki/2020/8/8f/T--NYU_Abu_Dhabi--results--pc23.png)

*Paper-based sensor*

![](https://static.igem.org/mediawiki/2020/3/3f/T--NYU_Abu_Dhabi--results--pc24.gif)

*LabOnAChip Integrated Method*

![](https://static.igem.org/mediawiki/2020/d/d7/T--NYU_Abu_Dhabi--results--pc25.jpg)

*Microfluidic Chip*

For reaction mediums, currently, we are in the process of ideation and concept development where we will be researching more options for the reaction medium type based on the compatibility with sample preparation options and will be prototyping and testing the already formed options.

# Sensing

The engineering-based research regarding sensing is compiled in the  [Measurement](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Engineering_8297e0cf88af4f8fbfe00ad598c6a52f/Measurement_86375b01cc9340d8a7ed01121b1aa428) section of our Notion space. However, a brief overview is given here.

Upon deciding the objective of creating a point of care diagnostic device, the engineering team started the research with a broad overview of all the reporting and sensing techniques that have been successfully implemented in point of care devices by mapping them as below:

![](https://static.igem.org/mediawiki/2020/a/aa/T--NYU_Abu_Dhabi--results--pc26.jpg)

*Sensing Methods Mapping Board*

As the mechanism of the device was narrowed to a nucleic acid detection based diagnostic device based on the CRISPR-Cas systems, we focused our research on sensing methods compatible with CRISPR-Cas which are mapped with detailed options as below:

![](https://static.igem.org/mediawiki/2020/d/dc/T--NYU_Abu_Dhabi--results--pc27.jpg)

*CRISPR-Cas compatible Sensing Methods (note that the options we are trialing are marked as green)*

We decided not to follow E-CRISPR after comparing it with the other two methods and discussing with faculty experts in POC devices who pointed out the demerits of the techniques such as fragile and expensive electrodes.
More information about our further research regarding the other two methods is given as below:

1. #### Lateral Flow Assays

   LFA is the use of immunoassay technology using nitrocellulose membrane, coloured nanoparticles (or labels), and typically antibodies to detect the presence of a target substance in a liquid sample without the need for specialized and costly equipment. For our device, we are testing CRISPR-Cas based LFA detection where the technique is used to detect a cleaved single stranded DNA (ssDNA) reporter.

   **How does it work?**

   The expected results for LFA is the successful detection and analysis (quantification) of the CRISPR-Cas reaction product through the reporter. This is indicated by the control and test line of the LFA strip showing coloration, and this is illustrated by the mechanism below.

   **Mechanism**

   ![](https://static.igem.org/mediawiki/2020/1/1c/T--NYU_Abu_Dhabi--results--pc28.png)

   *LFA Mechanism - Due to the recognition and presence of The target analyte, the sgRNA/Cas- complex unleashes collateral activity. This activates the Cas complex therefore, the Cas protein cleaves the dual labelled FAM or FITC/Biotin reporter. The complexed DNA analyte, labeled with FITC/FAM and biotin, binds first to the gold-labeled FITC/FAM-specific antibodies in the sample application area of the dipstick. The gold complexes travel through the membrane, driven by capillary forces. Only the analyte captured gold particles will bind when they pass the line with the immobilized biotin-ligand molecules and generate a red-blue band over the time. Unbound gold particles migrate over the control band and will be captured by species-specific antibodies. With prolonged incubation time, the formation of an intensely colored control band appears. Taken from: https://www.milenia-biotec.com/en/tips-lateral-flow-readouts-crispr-cas-strategies/*

   **What have we worked on?**

   Having performed extensive research, we decided to quantify the results as well as perform qualitative analysis (which is what is primarily used for LFA). The following description is an extensive assessment on the experimental design which was formulated for both quantitative and qualitative assessment.

   For qualitative results, the following methodology will be conducted:

   **Methodology Qualitative results**

   1. After completion of the pre-amplification step, there will be a 20 μL of reaction mix which is a result of the CRISPR reaction. This also included the FAM/Biotin reporter. This will be added to a 2-mL Eppendorf tube after which 80 μL of Hybridetect 1 Assay Buffer should be added into it. A nuclease-free 2-mL 96- well block can be used instead of Eppendorf tubes.
   2. A lateral flow strip (Milenia HybriDetect 1, TwistDx) should then be added to the reaction tube and a result should be visualized after approximately 2 min. The lateral flow strip must be placed in an upright position and must be incubated at room temperature for at least 5 minutes. (unto 15 minutes). Results must then be analysed.
   3. single band, close to the sample application pad (the control band) will indicate a negative result, whereas a two bands close to the top of the strip (test band) and the control band should indicate a positive result. The bands are clearly formed red bands. In case of very high concentrations of hybridisation product, control band’s intensity may be affected (during a positive test) Nevertheless, the control band should be still visible clearly.
   4. After completion, remove the lateral flow strip and place it on a white background for visual inspection. View Figure 1 and 2 for qualitative assessment.

   For quantitative results, we created a software that will be able to analyse images of the control test band. 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. The intensity of the red could then be quantified by image acquisition followed by an image processing software to measure the band intensity and to determine a positive detection against a positive-control dilution series. Image-processing software was then created and designed using Python.

   **Methodology Quantitative results**

   The article "Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone," by the authors Foysal, Kamrul H et al. was carefully studied and a similar procedure was followed in the design of the image processing software. The software was altered to suit the aims of this study accordingly.

   In order to feed calibration data into the software, a series of experimental results will have to be calculated. This would allow accurate quantification of the data during the test, with our method for experimentation. The following method was used for the data acquisition required to build the calibration curve which is to be implemented in the software.

   Feature extraction- The number of red pixels accumulated on the test line is considered the feature. Assuming that the cumulative sum of red pixels’ intensities in the test line varied proportionally with analyte quantity under a specific lighting environment, we carried out the test. In order to do this:

   The testing procedure is as follows:

   * - A test strip is cut according to consistent measurements with only the test line and control line regions visible. It is placed under the smartphone on a white background. Using the grid view of the camera, the ROI was positioned inside the center box.
   * - The image was captured. The application then created a mask using the preprocessing technique mentioned in the proposed algorithm (Otsu's method).
   * - In the masked region, the weighted sums of red pixels’ intensities of the test and the control line regions were calculated.
   * - T/C ratio values that corresponded to the quantity of the analyte were calculated.
   * - A regression curve is plotted which will be used as the calibration curve.

   **What next?**

   Currently, the future plans for the project include successfully detecting the CRISPR-Cas reactions, using LFA and ssDNA reporter. We hope to conduct the above qualitative and quantitative assessments. For quantitative assessment, we hope to develop the quantitative detection method by creating the software using the above proposed method. We then hope to use this software to conduct quantitative analysis in the future, with high accuracy.

   Further plans for the project include developing the quantitative detection method even further. For example, instead of having to take an accurately fixed image of both regions A and B that have been identified, we could simply take a photo of the whole LFA strip. The software could then go on to crop the photo itself, to define regions A and B independently. The current method involves us feeding the images of the software independently.

   Furthermore, currently, we try to maintain a specific lighting and background of the LFA strip. However, when it comes to point-of-care testing, it may be difficult to ensure this sense of consistency in the lighting and background atmosphere. Therefore, a plan for this project could involve training the software to ignore background noise thereby training it to overlook changes in lightning and adapt to the changes.

   Citation: **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**
2. #### Fluorescence Detection

   A basic fluorescence detection platform has the following components :

   1. An excitation source
   2. A sensor
   3. Microcontroller to relay output
   4. Occasionally has filters to increase effectiveness![](https://static.igem.org/mediawiki/2020/f/f6/T--NYU_Abu_Dhabi--results--pc29.jpg)

   *Fluorescence detection schematic*

   Over the course of the few months with limited lab access we researched different components that could be used for each of the above units. Talking to experts in the field and research of our own, we made a comprehensive list taking into account multiple factors like sejpnsitivity, cost effectiveness and the time taken for the entire process to occur. This full list can be found on the [Fluorescence with CRISPR page](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Documentation/DOCS_20ee279bfcdc46b09c4fb108851b2757/Engineering_8297e0cf88af4f8fbfe00ad598c6a52f/Measurement_86375b01cc9340d8a7ed01121b1aa428/Fluorescence_with_CRISPR_f26d02d7b6a442ec839ea6608d8a7e8c) along with the advantages and disadvantages of each method.

   **What did we decide**
   After looking at all our available options, we decided that we will be trying 2 possible methods:

   1. No filters
   * - Uses a Blue LED or Laser for exciting the fluorophore
   * - Photodiode for detection
   * - We use statistical models to calculate the change in the fluorescence detected by the photodiode2. Dichroic mirror
   * - Uses a Blue LED or laser for exciting the fluorophore
   * - Photodiode for detection
   * - Uses a dichroic filter to filter out all wavelengths except that emitted by the excitation of the fluorophore

These are the methods we decided to test because the No filters method is extremely cost effective while the Dichroic mirror method is expensive but effective. We finalized on testing both these methods to check if the results are comparable.

**Where are we now?**

After getting access to our lab spaces, we developed a rough prototype to test our No Filters method. Please refer to [Hardware](https://2020.igem.org/Team%3ANYU_Abu_Dhabi/Hardware) for more details about our current setup. We ran some initial tests to check if the sensor can give us different readings for Blue and Green light successfully and are now preparing to test our samples.

The code we used to test the sensor can be found [here](https://github.com/Abhay736/Fluorescence-Detection)

**What next?**

Now that we have a working prototype, we plan on using SYBR green as the fluorophore and qualitatively test our samples. For this, we must first calibrate our device using statistical models to sense the difference between a positive and negative sample. We will then use the samples with CRISPR and calibrate it to give a quantitative result.

Once the steps mentioned above are completed and we have a fully functioning model for the No Filters method we will create a similar prototype to check the effectiveness of the dichroic mirror method. Finally, we intend to compare the results and close in on the method that we will be using in our final model.

* [Stage 1 - gBlock cloning, Transformation and Quality Control](#bio)
* [Stage 2 - Amplification with RPA](#stage2)
* [Stage 3 - CRISPR Detection](#stage3)
* [Future Biology Work](#future)
* [Engineering Research Progress](#eng)
* [Sample Preparation](#sampleprep)
* [Reaction Medium](#reactionmed)
* [Sensing](#sensing)

![](https://static.igem.org/mediawiki/2020/7/79/T--NYU_Abu_Dhabi--small_logo.jpg)

nyuad.igem@nyu.edu

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