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== EPFSens 2019 ==
EPFSens is a team from École Polytechnique Fédérale de Lausanne competing in the SensUs 2019 event. For SensUs 2019, EPFSens investigated the possibilities for creating a biosensor which is able to measure the concentration of [[Adalimumab]]. The full TRD can be found [ via this link]
== Method ==
optical technique uses a chip functionalized with capture antibodies and gold nanoparticles covered with detection
antibodies. Both antibodies target Humira. When the drug is captured by a gold nanoparticle close to the array, this
induces a local change in refractive index. In practice, the intensity of the transmitted light is reduced at these locations.
This variation of light intensity can be easily measured using a camera and used to quantify the drug concentration.
==Molecular Recognition ==
The EPFSens bioassay, is designed as a 1-step ELISA-like sandwich assay. It uses two monoclonal antibodies, each
recognizing a different part of the target analyte. We decided to use monoclonal antibodies to ensure high specificity
and little background because of their inability to bind more than one antigen through their epitope. We selected two
anti-adalamimuab (anti-ADA) antibodies from GenScript, the capture antibody (clone 15C7) is the one at the surface of
the chip. The detection antibody (clone 3C2) is the one bound to the signal generating particle [1] [2].
The detection antibody is attached onto gold nanoparticles (Au-NPs) coated with N-hydroxysuccinimide (NHS) [3].
Those Au-NPs are commercially available (by Cytodiagnostics) and the attachment is performed following manufacturer
instructions.
In order to conduct the assay, additional reagents are required and mixed in two specific buffers. First, a blocking buffer
composed of bovine serum albumin (BSA) diluted a hundred times in phosphate buffer saline (PBS). This buffer is used
to activate the capture antibodies located at the surface of the chip. Secondly, we prepare a reaction buffer composed
of NaOH (50 mM), BSA (1%) and Tween 20 (2.5%) in PBS. The role of this reaction buffer is to ensure an appropriate
basic pH of approximately 9, to limit non-specific interactions, to maximize the binding and to generate a signal.
Note that for the plasma samples, we are still optimizing the blocking buffer. We tried with PBS and 5% of nonfat dry
milk (Sigma).
== Physical Transduction ==
The detection principle used in this project is based on the variation of extraordinary optical transmission (EOT) intensity
that a gold nanohole array (Au-NHA) presents when a gold nanoparticle (Au-NP) of 100nm diameter is located in close
vicinity of a nanohole. Our Au-NHA chip is functionalized with anti-ADA antibodies (clone 15C7) on specific spots as in
Figure 1.a and illuminated with 660nm light to generate the EOT via a plasmonic effect at the gold surface. This effect
is enhanced by the nanohole array found on our chip. Without any sample on it, the chip has a uniform transmission. A
sample solution containing the target analyte (adalimumab), activated Au-NPs functionalized with detection antibodies
(clone 3C2) and reaction buffer is put on the chip 1.b. The adalimumab protein will eventually bind to the capture
antibodies fixed on the Au-NHA surface. The bounded adalimumab alone induce only very little change in intensity of
the extraordinary plasmonic transmission. A small frequency peak shift is known to happen, but cannot be detected
with a standard camera (it would be the scheme of a label-free detection). What triggers the detection is the binding
of a Au-NP on top of the adalimumab, as in Figure 1.c. The vicinity of this NP to the NHA induces a local change
in refractive index, which strongly affects the plasmonic resonance peak present on the surface of the Au-NHA. The
intensity of transmitted light at the NP locations will be reduced, thus making it possible to digitally measure variation of
intensity on a far-field image (black dots will appear, representing a NP).
== Cartridge ==
In our assay, the capture antibodies are deposited with a spotting machine (Scienion sci-FLEXARRAYER S3) on the
gold nanohole array (Au-NHA). The spots have a diameter of 100 μm and are spaced every 300 μm. The machine is
programmed to spot both the capture antibody used for the assay and a mouse antibody that is used as a negative
control for reference. On the chip, a silicon rubber is placed to form a well. The sample is added inside and then a
round cover slip is used preventing the evaporation of the sample. The volume of the sample is around 20 μm. At the
moment, the mixes are prepared by the user through pipetting steps but we fully intend to automatize the process in
future iterations of our prototype. Furthermore, to facilitate the insertion of the chip in our device, a single use holder as
been cut in PMMA.
== Reader Instrument ==
The reader’s dimensions (Figure 2) are estimated at 42x30x24 cm. It is composed mainly of two parts. The hexagonal
tower contains the optical setup presented in Figure 2 as well as a slot for inserting the chip and a z-axis translation
mount accessible to the user for focus adjustment. As for the base support, it contains the switching power supply for
the LED and the raspberry pi used for user-machine interaction.
3
EPFSens
Figure 2: Our biosensor (left) and its optical system (right).
In order to provide an accessible device, we implemented an intuitive user interface for our touchscreen. The user
chooses to refer back to previously recorded results or make a measurement. The measurement process is simplified
by a provided tutorial displayed with instructions on the handling of the cartridge and the result acquisition. The user is
also guided during the focus adjustment before starting the measurement in order to ensure the best image quality for
the analysis. The implemented software is continuously printing the image on the screen, as the user is changing the
z-axis translation mount for focus adjustment. As part of the development of our prototype, we intend to implement an
autofocus algorithm so as to further simplify the user interaction.
The main job of the software consists in acquiring the images of the assay over a certain time period and then it uses
image analysis algorithms to detect capture antibody spots locations, where the detection takes place. For the moment,
as our device is still at a prototype stage, we added the possibility for the user to visually check the position of the spots
and, if needed, to adjust the detected circle position, but this step would ultimately only take place in the background
without any user input.
After spot detection, the camera takes regular captures of the spots, showing the increased binding of adalimumab on
the nanohole gold array over time, which is translated by the increasing number of black dots on the captured image.
The software measures the variation of the image intensity in time and outputs the detected adalimumab concentration,
based on a well established calibration curve. The result is saved and printed on the screen.
== EPFSens 2019 ==
EPFSens is a team from École Polytechnique Fédérale de Lausanne competing in the SensUs 2019 event. For SensUs 2019, EPFSens investigated the possibilities for creating a biosensor which is able to measure the concentration of [[Adalimumab]]. The full TRD can be found [ via this link]
== Method ==
optical technique uses a chip functionalized with capture antibodies and gold nanoparticles covered with detection
antibodies. Both antibodies target Humira. When the drug is captured by a gold nanoparticle close to the array, this
induces a local change in refractive index. In practice, the intensity of the transmitted light is reduced at these locations.
This variation of light intensity can be easily measured using a camera and used to quantify the drug concentration.
==Molecular Recognition ==
The EPFSens bioassay, is designed as a 1-step ELISA-like sandwich assay. It uses two monoclonal antibodies, each
recognizing a different part of the target analyte. We decided to use monoclonal antibodies to ensure high specificity
and little background because of their inability to bind more than one antigen through their epitope. We selected two
anti-adalamimuab (anti-ADA) antibodies from GenScript, the capture antibody (clone 15C7) is the one at the surface of
the chip. The detection antibody (clone 3C2) is the one bound to the signal generating particle [1] [2].
The detection antibody is attached onto gold nanoparticles (Au-NPs) coated with N-hydroxysuccinimide (NHS) [3].
Those Au-NPs are commercially available (by Cytodiagnostics) and the attachment is performed following manufacturer
instructions.
In order to conduct the assay, additional reagents are required and mixed in two specific buffers. First, a blocking buffer
composed of bovine serum albumin (BSA) diluted a hundred times in phosphate buffer saline (PBS). This buffer is used
to activate the capture antibodies located at the surface of the chip. Secondly, we prepare a reaction buffer composed
of NaOH (50 mM), BSA (1%) and Tween 20 (2.5%) in PBS. The role of this reaction buffer is to ensure an appropriate
basic pH of approximately 9, to limit non-specific interactions, to maximize the binding and to generate a signal.
Note that for the plasma samples, we are still optimizing the blocking buffer. We tried with PBS and 5% of nonfat dry
milk (Sigma).
== Physical Transduction ==
The detection principle used in this project is based on the variation of extraordinary optical transmission (EOT) intensity
that a gold nanohole array (Au-NHA) presents when a gold nanoparticle (Au-NP) of 100nm diameter is located in close
vicinity of a nanohole. Our Au-NHA chip is functionalized with anti-ADA antibodies (clone 15C7) on specific spots as in
Figure 1.a and illuminated with 660nm light to generate the EOT via a plasmonic effect at the gold surface. This effect
is enhanced by the nanohole array found on our chip. Without any sample on it, the chip has a uniform transmission. A
sample solution containing the target analyte (adalimumab), activated Au-NPs functionalized with detection antibodies
(clone 3C2) and reaction buffer is put on the chip 1.b. The adalimumab protein will eventually bind to the capture
antibodies fixed on the Au-NHA surface. The bounded adalimumab alone induce only very little change in intensity of
the extraordinary plasmonic transmission. A small frequency peak shift is known to happen, but cannot be detected
with a standard camera (it would be the scheme of a label-free detection). What triggers the detection is the binding
of a Au-NP on top of the adalimumab, as in Figure 1.c. The vicinity of this NP to the NHA induces a local change
in refractive index, which strongly affects the plasmonic resonance peak present on the surface of the Au-NHA. The
intensity of transmitted light at the NP locations will be reduced, thus making it possible to digitally measure variation of
intensity on a far-field image (black dots will appear, representing a NP).
== Cartridge ==
In our assay, the capture antibodies are deposited with a spotting machine (Scienion sci-FLEXARRAYER S3) on the
gold nanohole array (Au-NHA). The spots have a diameter of 100 μm and are spaced every 300 μm. The machine is
programmed to spot both the capture antibody used for the assay and a mouse antibody that is used as a negative
control for reference. On the chip, a silicon rubber is placed to form a well. The sample is added inside and then a
round cover slip is used preventing the evaporation of the sample. The volume of the sample is around 20 μm. At the
moment, the mixes are prepared by the user through pipetting steps but we fully intend to automatize the process in
future iterations of our prototype. Furthermore, to facilitate the insertion of the chip in our device, a single use holder as
been cut in PMMA.
== Reader Instrument ==
The reader’s dimensions (Figure 2) are estimated at 42x30x24 cm. It is composed mainly of two parts. The hexagonal
tower contains the optical setup presented in Figure 2 as well as a slot for inserting the chip and a z-axis translation
mount accessible to the user for focus adjustment. As for the base support, it contains the switching power supply for
the LED and the raspberry pi used for user-machine interaction.
3
EPFSens
Figure 2: Our biosensor (left) and its optical system (right).
In order to provide an accessible device, we implemented an intuitive user interface for our touchscreen. The user
chooses to refer back to previously recorded results or make a measurement. The measurement process is simplified
by a provided tutorial displayed with instructions on the handling of the cartridge and the result acquisition. The user is
also guided during the focus adjustment before starting the measurement in order to ensure the best image quality for
the analysis. The implemented software is continuously printing the image on the screen, as the user is changing the
z-axis translation mount for focus adjustment. As part of the development of our prototype, we intend to implement an
autofocus algorithm so as to further simplify the user interaction.
The main job of the software consists in acquiring the images of the assay over a certain time period and then it uses
image analysis algorithms to detect capture antibody spots locations, where the detection takes place. For the moment,
as our device is still at a prototype stage, we added the possibility for the user to visually check the position of the spots
and, if needed, to adjust the detected circle position, but this step would ultimately only take place in the background
without any user input.
After spot detection, the camera takes regular captures of the spots, showing the increased binding of adalimumab on
the nanohole gold array over time, which is translated by the increasing number of black dots on the captured image.
The software measures the variation of the image intensity in time and outputs the detected adalimumab concentration,
based on a well established calibration curve. The result is saved and printed on the screen.
