Team:ETH Zurich/Description

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A.R.O.M.A.

Project

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Wet Lab

• Tar Receptor Evolution
• Approach A
• Approach B
• Experiments
• InterLab

Hardware

• Hardware Overview
• Microscope
• Mechanics
• Syringe Pump
• Microfluidic Chip
• Bubbling

Software

• Software Overview
• Motility Readout
• Luminescence Algorithm
• Robot Controls
• Path Planning

Model

• Model Overview
• Integrated Models
• Motility Model
• Split Luciferase Model
• Diffusion Model
• Bubbling Model
• Screening Assay Model
• Holographic Imaging Model

Human Practices

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• Education & Engagement

Achievements

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• Parts Overview
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Project
Description.

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Cell-based biosensors allow to simply and selectively sense diverse chemical signals; yet their applications are limited by the minutes-to-hours timescale of gene transcription and translation. To generate a real-time output, we exploit the much faster changes in protein interaction and bacterial movement. Based on the E. coli Tar chemotaxis receptor, we developed two sensing systems: detecting DNA binding of a transcription factor via split luciferase complementation, and imaging the movement of bacteria at the single-cell level. The sensory domain of Tar can be modified to recognize different molecules, extending the applicability of the sensor. To show the advances brought by our system we built AROMA, an autonomous robot that is directly driven by the onboard biosensor. The robot detects the concentration of volatile compounds in air by imaging the bacterial response with a microscope built in-house. This enables our device to locate the source of pollutants or chemical hazards.

Our Solution

Overview Workflow

Uptake

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Mass Transfer of volatile Molecules

In perfect analogy with the human sense of smell, the first step in AROMA’s pipeline is inhaling air through a sampling conduct (i.e. “nose”). Air is bubble into a small reservoir of liquid medium, where the airborne organic molecules are transferred into the liquid for a short period of time. This liquid sample containing the volatile compound of interest is circulated through the robot by means of an array of syringe pumps, reaching the microfluidic chip where the biosensor is waiting. In AROMA, the sensing is carried out by engineered bacteria that are capable of generating a signal in response to the molecule we want to “smell”. To prove the viability of the sampling system, we modeled the volatile molecule diffusion through air as well as the mass transfer from air to water, and tested the bubbling system in our lab.

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Sensing

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Fast Biosensing

The Tar receptor is utilized in two separate approaches for a fast biosensor. To guarantee the desired time scale of seconds to only a few minutes, the underlying pathways solely rely on post-translational processes like phosphorylation or protein-protein interaction. Furthermore, the output was designed to be fast and easily processable.

Approach A: Chemotaxis

The first approach focuses on the natural pathway of the Tar receptor; the bacterial chemotaxis. The Tar receptor transduces an input signal through an intracellular phosphorylation pathway, which directly influences the flagellum – the bacterial swimming device. The flagellum can be in two different rotational states: counter-clockwise (CCW) and clockwise (CW) spinning. A CCW rotation of the flagellum lets the bacterium swim forward, while a CW rotation makes it tumble, i.e. changing its direction. A signal from the Tar receptor about ligand concentration changes affects the rotational state of the flagellum. We apply this mechanism as a biosensor by tethering the bacteria specifically at their flagellum to a glass surface. For this, we coat the surface with an antibody specific for the flagellar protein. This leads to rotation of the bacterium around its own body, which directly corresponds to the flagellar rotation. This allows us to read out the response of the bacterium to different ligand concentrations by light microscopy followed by single-cell image analysis. All of this is done inside a microfluidic chip to guarantee spatial confinement and simple operability in terms of exchanging the medium.

Approach B: Split-protein complementation

This alternative strategy takes advantage of a previously engineered chimeric version of the Tar receptor, in which the cytoplasmatic domain of the membrane protein has been replaced with the signaling domain of EnvZ, an osmolarity sensing protein. This means that in the presence of aspartate the Tar-EnvZ chemeric receptor activates transcription of osmoregulatory genes, mediated by the binding of the OmpR transcription factor to a specific DNA sequence. We can sense the translocation of OmpR to its target DNA sequence by using split protein complementation, a technique in which a reporter protein is divided in two non-functional parts that when brought close together reconstitute the original activity of the reporter, generating a detectable signal. The subparts are typically fused to two biomolecules of interest in order to study their co-localization. In order to take advantage of split protein complementation, we fused OmpR to the N-terminal part of firefly luciferase, and TetR to the C-terminal part. TetR is another transcriptional regulator that binds its target DNA sequence very strongly. By putting the receptor sequences for OmpR and TetR next to each other, we can force the re-assembly of luciferase and detect its light emission using a microscope, which we correlate to ligand concentration.

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approach A

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approach B

Imaging

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Detection of chemotactic or fluorescence signal

Both sensing approaches rely on an imaging system to read out the generated signal. We developed our own microscope, on which we integrate a microfluidic system for specific control over the sensing bacteria. The microscope was built to be simple yet still robust and modular for an implementation on AROMA.

Approach A: Chemotaxis

To reliably detect and resolve spinning bacteria, which is the output of this approach for the biosensor, we built a brightfield microscope powerful enough to resolve single cells with enough contrast. Additionally, the microscope is able to acquire images of the bacteria with a high frame rate to ensure the resolution needed to capture their rotation.
The microfluidic chip allows the exchange of the medium - a step which happens every time the robot undertakes another measurement - in parallel to the image acquisition.

Approach B: Split-protein complementation

While the other approach relies on microscopy images, the split luciferase readout is solely based on intensity measurement. Therefore, we focused on the development of a completely lightproof imaging chamber. With this, we guarantee the direct correlation between the sensed input concentration with the intensity output of the bacteria with as little background signal as possible.
This approach also integrates a microfluidic chip for simple handling of the medium exchange for every measurement.

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Processing

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Motility and Luminescence Readout

We use a pattern recognition algorithm to detect the location of single cells. As the last step of the measurement pipeline, we developed a set of different algorithms to deal with the raw data captured by the image acquisition system. While the OmpR/split luciferase approach simply needs a processing of the intensity signal, the chemotaxis approach requires in-depth single cell image analysis. There, the algorithm detects every cell and determines their rotational change caused by the input signal. The signal processing yields an output, which directly controls the robot.

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