Compatibility of different growth media with the proposed 2D biosensor.
Progress in the field of pathogen detection relies on at least one of the following three qualities: selectivity, speed, and cost-effectiveness. Here, we demonstrate a proof of concept for an optical biosensing system for the detection of the opportunistic human pathogen Pseudomonas aeruginosa while addressing the abovementioned traits through a modular design. The biosensor detects pathogen-specific quorum sensing molecules and generates a fluorescence signal via an intracellular amplifier. Using a tailored measurement device built from low-cost components, the image analysis software detected the presence of P. aeruginosa in 42 min of incubation. Due to its modular design, individual components can be optimized or modified to specifically detect a variety of different pathogens. This biosensor system represents a successful integration of synthetic biology with software and hardware engineering.
- quorum sensing
- signal amplification
- whole-cell biosensor
- customized hardware
- online image analysis
- point of contact
- synthetic biology
- Pseudomonas aeruginosa
A prerequisite for countermeasures against opportunistic pathogens is their rapid detection [1, 2]. In contrast, conventional diagnostic methods often utilize time-consuming techniques such as microscopy and cultivation in different media  and bear the risk of false-positive or false-negative results . Traditionally, microbiological tests have hence been performed by trained personnel in stationary laboratories, because the complex instrumentation hinders transportation .
Established methods for detection and identification of pathogenic bacteria most commonly rely on PCR, culture, and counting or immunological techniques such as ELISA. PCR-based methods are extremely sensitive but require purified samples and hours of processing as well as staff trained in molecular biology. Immunological methods are similarly sensitive but often require costly analytes (e.g., labeled antibodies). For detailed information, such as sensitivity, please refer to the “Discussion and outlook” section. Another commercially available technique for pathogen detection is flow cytometry, which offers rapid, quantitative measurements of multiple parameters of individual cells. However, it is expensive and requires stable growth conditions for the organisms to allow reproducible results . Considering these limitations, the need for rapid, specific, and inexpensive point-of-contact tests becomes apparent. Furthermore, these tests should be intuitive to conduct while providing the same or a higher sensitivity than traditional detection methods [1, 7].
Biosensors represent a promising approach for pathogen detection and have the potential to fulfill the aforementioned demands . For example, biosensors offer advantages such as high specificity and sensitivity . Increasing effort has been spent on the development of biosensors that allow for portable microbiological tests since the 1990s [6, 8].
A biosensor can be defined as an analytical device in which a biologically active component (e.g., an enzyme, antibody, whole cell) is immobilized onto the surface of a transducing element (electronic, optic, or optoelectronic), allowing the detection of target analytes in complex mixtures . A typical biosensor comprises three main parts: the bio-recognition component, the interface, and the transducing element . The biological component specifically recognizes the analyte, and the biochemical interaction is then converted into a quantifiable signal via the transducer . The choice of the interface and immobilization technique depends on the selected biological element and transducer . Based on the method utilized for signal transduction, biosensors can be roughly classified into four basic groups, namely, optical, mass, electrochemical, and thermal sensors .
Optical biosensors are particularly interesting for detection of pathogens because of their higher sensitivity than electrochemical biosensors. For example, optical biosensors based on surface plasmon resonance (SPR) are already commercially available in a portable format (Spreeta System, Texas Instruments). Drawbacks of this technique are comparably high costs and complexity requiring trained staff for operation .
2. The five key elements of the proposed biosensor
The present work provides proof of concept for a novel approach toward a cost-efficient, optical biosensor, which enables safe and simple detection of pathogens and does not require highly trained staff for operation. The detection system was designed for investigation of solid surfaces, for example, to assess cleaning success in a hospital environment, which is receiving increasing interest . This project was performed and has successfully competed in the International Genetically Engineered Machine (iGEM) competition 2014 .
The potential of the proposed system lies within the combination of biology and engineering as the development of biosensors is highly interdisciplinary . Five key components, namely, biomolecular detection (I) with intracellular signal amplification (II) embedded into a two-dimensional sensor chip (III), a custom incubation device (IV), and automated image analysis (V), constitute the functional biosensor as displayed in Figure 1. In terms of the biological component, the present project comprised the genetic engineering of sensor cells (introduction of the amplifying reporter circuit in
As a model organism for demonstrating the biosensor’s functionality, the well-studied opportunistic pathogen
2.1. Quorum sensing in
Bacteria have evolved complex systems to sense their environment. Quorum sensing (QS) networks present a way to synchronize behavior, such as bioluminescence, biofilm formation, sporulation, and the secretion of virulence factors, on a population-wide scale .
In QS systems of bacteria, an autoinducer (AI) is produced by one or more synthases and is secreted from the cell. The cell can in turn detect the autoinducers through receptors in the cytosol (single-step response regulation in Gram-negative bacteria) or in the membrane (two-step response regulation in Gram-positive bacteria). Once a minimal threshold concentration is reached at higher cell densities, the activated AI receptors can induce or repress specific gene expression programs. The induction of the QS regulon leads to the expression of more AI synthase, amplifying the QS signaling . However, most often the QS systems of one bacterial species extend beyond the basic circuit described above. Such configurations can include a multitude of circuits in parallel or series as well as competitive setups and on-off switches .
The implementation of the
2.2. Molecular signal amplification
The biological component of the proposed biosensor was embodied by genetically modified
The traditional way to report the binding of 3OC12-HSL to the constitutively expressed LasR would be the expression of a fluorescent protein, such as GFP, under the control of the
2.2.1. Quenching of GFP fluorescence
The quenching of GFP fluorescence in the fusion protein is based on Förster resonance energy transfer (FRET), a process by which the energy of an excited donor fluorophore is transferred to an acceptor molecule whose absorption spectrum overlaps with the emission spectrum of the donor . The energy can then be released, for example, by fluorescence of a longer wavelength or by heat. Yellow fluorescent protein (YFP) represents a suitable FRET acceptor for GFP. Emission resulting from YFP was avoided by using a nonfluorescent mutant of YFP called resonance energy-accepting chromoprotein (REACh ). Two REACh variants were generated by introducing the mutation Y145W (REACh1) and the double mutation Y145W/H148 (REACh2) into an enhanced YFP (eYFP) by QuikChange mutagenesis. Ganesan et al.  reported a reduction in fluorescence of 82 and 98% for REACh1 and REACh2, respectively.
Both REACh variants were genetically fused to GFP (mut3b ) via a linker, which brings both proteins in close proximity, facilitating FRET  from GFP to REACh, thus quenching the fluorescence. The linker harbors a cleavage site for the TEV protease (ENLYFQ\S) allowing the separation from the quencher. In the present study, the TEV protease is expressed under control of the
2.2.2. Validation of the reporter system
For initial validation the developed reporter system was tested via β-D-1-thiogalactopyranoside (IPTG) induction using a well-characterized T7 promoter instead of the
To test the hypothesis that the GFP-REACh fusion proteins in combination with the cleavage amplification results in a faster response than the conventional approach, the kinetics of our reporter strategy were compared to a strain expressing GFP under the control of an IPTG-inducible
2.3. Immobilization of sensor cells
The sensor cells were immobilized in rectangular layers (chips), thus creating an interface between the biological component and the technical component (transducer). Main objectives during the design of the interface were to enable viability and storability of the immobilized sensor cells, reproducibility of the fluorescence response, as well as cost-efficiency. For proof of concept, a simple and robust design was chosen.
A variety of different methods have been used for immobilization of whole cells, which can be divided into six general types: covalent coupling, affinity immobilization, adsorption, confinement in liquid-liquid emulsion, capture behind semipermeable membranes, and entrapment . An established technique for immobilization of living cells is entrapment, which refers to the physical containment of organisms inside a matrix or fibers, thus creating a protective barrier around the cells . Matrices used for entrapment can be synthetic polymers, such as polyester, or natural polymers, such as agar, agarose, or alginate . Entrapment allows to preserve and prolong cell viability, for example, during storage [26, 27], which matched the intentions of this work.
Important prerequisites for the entrapment matrix of the sensor cells were physical rigidity, safety, resistance against biological degradation, transparency, as well as the possibility to conduct matrix synthesis at mild conditions, suitable for living cells. Inorganic polymers such as polyacrylamide were ruled out due to the carcinogenicity of the monomers and rather harsh polymerization conditions . Natural polymers allow for higher diffusion rates than inorganic polymers (tested for small molecules ) and are less expensive and less hazardous in production than synthetic polymers. The organic polymer agarose offers several advantages including easy handling, resistance to microbial degradation, and favorable conditions for entrapped cells . Thus, agarose was the polymer of choice for immobilization of cells and formation of chips.
2.3.1. Optimization of chip casting mold and medium
First, a casting mold for rapid and reproducible manufacturing of the 2D sensor chip was developed. A plain surface was a prerequisite for automated image evaluation. Low agarose concentrations (<3.0%) were chosen to reduce consumable costs and to ensure rapid diffusion of the analyte (HSL) to the immobilized sensor cells.
Manufacturing of the agarose gel was conducted based on existing protocols for entrapping living cells in melted polymers. In brief, the temperature of the polymer solution was adjusted to 45°C and was quickly poured into the respective mold after mixing with the sensor cells. Sensor cells were spun down from a liquid culture (50 mL LB, 5 g∙L−1 NaCl, 10 g∙L−1 tryptone, 5 g∙L−1 yeast extract) and resuspended in 1 mL LB medium (21°C) before mixing with the temperature-adjusted agarose solution, resulting in a cell concentration of approximately 5.6×109 cells/mL. Before usage, solidified and cutout sensor chips were incubated for 1 h at 37°C.
An open casting mold, which exploited the surface tension of the polymer solution to achieve a plain chip surface, was most successful for the production of sensor chips. After discarding a small gel area in direct contact with the edges of the mold (Figure 5, left), bubble-free sensor chips with a plain surface were readily obtained from this approach. The open mold allowed for simple, reproducible, and rapid manufacturing of sensor chips and was hence the method of choice for this work. An agarose concentration of 1.5% was found to be sufficient to cast robust sensor chips. For an accelerated manufacturing process, multiple sensor chips were casted simultaneously using an extended mold (Figure 5, left).
Further, to meet the nutritional needs of the sensor cells while minimizing background fluorescence, different complex media (Luria-Bertani or LB medium, Terrific-Broth or TB medium, nutrient agar or NA medium) as well as minimal media (Hartmans minimal or HM medium, M9 minimal medium) were tested with respect to sensor cell growth and the presence of background fluorescence. Background fluorescence was investigated in a commercial gel imaging system (GelDoc™ XR, Biorad, Germany) as well as in the custom-made optical detection device constructed in this work as described in the following section. The results are summarized in Table 1, and a comparison of the background fluorescence of sensor chips comprising the respective media is displayed in Figure 5 (right). Only LB medium allowed for sufficient growth of the sensor cells. Its background fluorescence in the custom-made optical detection device was acceptable, most likely due to the narrow excitation profile compared to the commercial device.
|Luria-Bertani medium||Terrific-Broth medium||Nutrient agar medium||M9 minimal medium||Hartmans minimal medium|
|Growth of sensor cells||+||+||—||—||—|
|BF, gel imaging system||—||—||+||+||+|
|BF, custom-made device||+||—||+||+||+|
Background fluorescence appeared to be more intense in complex media than in minimal media. To identify a possible cause for this observation, minimal M9 medium was supplemented with 2% Casamino acids (Figure 5, right, bottom row). Background fluorescence was stronger in supplemented minimal medium matching reports in literature , possibly due to an increased concentration of aromatic amino acids possessing inherent fluorescence.
Activity of the sensor cells after immobilization was investigated in a subsequent experiment by inducing a fluorescent signal with 0.2 μL of a 500 μg∙mL−1 HSL (3-oxo-C12) solution (Figure 6A). One and a half hours post induction, a fluorescence signal was visible even to the naked eye, indicating that the sensor cells were in fact still viable after immobilization. No apparent change in fluorescence was observable for the negative control (Figure 6B).
For easier handling and experimentation, storability of the sensor chips of several days was desired. Activity of the immobilized sensor cells after storage under different conditions was investigated by induction with HSL. Generation of a fluorescence signal was used as an indicator for cell viability. After storage at −20°C, no fluorescence was observed after thawing and inducing the sensor chips. The addition of glycerol in different concentrations (5–10% v/v) did not improve cell survival at −20°C. The shelf life at 4°C was 5 days, allowing a batch-wise production and storage for later use. Exceeding this storage duration led to an insufficient fluorescence response upon induction.
Additional experiments were carried out to investigate the biosafety of the proposed sensor chips, because a release of the genetically modified sensor cells from the sensor chips represented a possible risk in handling. A simple approach for investigating the biosafety of the sensor chips was replica plating on agar plates containing the respective antibiotic. An average of five colony-forming units (CfU) was found (n = 3), indicating that some cells were in fact able to escape the agarose entrapment. Therefore, measures to achieve a complete entrapment, for example by increasing the agarose concentration, should be evaluated to render the system as safe for the use in non–GMO-certified areas.
2.4. Integrated cultivation and detection device
The two-dimensional approach of sensing pathogens on agarose chips requires a specialized device for detecting and interpreting the fluorescent signals generated by the immobilized sensor strain. Since the results from commercially available plate readers and gel imaging systems did not yield a sufficient spatial resolution, a custom-made device was designed and constructed as pictured in Figure 7 (left).
The device consists of two enclosed compartments, separated by laser-cut plates of acrylic glass. The inner compartment serves for cultivation and illumination of the sensor chip. The outer compartment contains a Raspberry Pi microcomputer, an Arduino microcontroller, and a camera for imaging. Figure 7 (right) schematically shows the individual components of the device and their interaction.
Once the chip is prepared and a sample taken, a petri dish containing the chip is inserted into the inner compartment, which serves as an
During the experiment, the parameters are controlled by an Arduino Uno and a Raspberry Pi. The Arduino has two main functions: first, it is responsible for controlling the incubation temperature in the inner compartment. Based on measurements from the temperature sensor, it sets the power input for the Peltier elements, thus heating or cooling the interior of the device. Second, the Arduino controls the LEDs illuminating the chip. When a control command from the Raspberry Pi is received, the two channels of the connected relay are turned on or off, switching the state of the LEDs, respectively. Thus, the chip is exposed to the specific wavelength emitted by the LEDs, in this case 480 nm for the excitation of the unquenched GFP.
Upon user input, the Raspberry Pi triggers the camera module to take an image of the chip. A filter slide is placed in front of the lens to block the excitation wavelength from the LEDs and to specifically transmit the emission wavelength of the fluorophore. In this configuration, a highly resolved fluorescent signal is obtained. The image is further processed by the Raspberry Pi and displays the analysis results via the graphical user interface (GUI) on a built-in 7-inch display located in the outer casing. The GUI (Figure 8, left) runs on either the Raspberry Pi or an externally connected computer; it enables the user to adjust the camera settings, take a single image or start time-lapse imaging, and to monitor the imaging process. Moreover, it allows execution of the analysis software for saved images as described in detail below. The communication between the GUI and the hardware is ensured by the backend software. It receives the respective commands (e.g., for capturing an image) from the GUI and subsequently forwards them to the according hardware. Therefore, the backend is responsible for image acquisition. An exemplary chain of commands for taking an image is depicted in Figure 8 (right). The backend runs on the Raspberry Pi.
For the detection of
2.5. Analysis of spatial fluorescence
Automated, fast, and reliable analysis of raw sensor data is critical for a diagnostic device. Since, in the case of the 2D biosensor, the raw sensor measurement is a series of pictures taken by the onboard camera, an image analysis pipeline is required. Here, a novel pipeline is presented involving segmentation through statistical region merging (SRM ), thresholding in hue-saturation-value (HSV)-color space, and a final classification step. This leads to segmentation of the fluorescent regions in the biosensor chip, thus identifying chips or chip regions containing pathogens.
2.5.1. Image segmentation
Onboard image analysis on embedded computing hardware is subject to rigorous performance constraints due to the poor availability of existing analysis packages and the limited computing power. This complicates the use of sophisticated analysis pipelines. At the same time, the need for quantification of fluorescent regions on the biosensor mandates the image to be segmented into foreground (fluorescent) and background regions, also called super-pixels. This is necessary because only after a segmentation mask is computed for an input image, the number of independent fluorescent regions in the image, their intensity, and their area can be quantified. Statistical region merging is an image segmentation algorithm which is both light-weight and does not require expensive tuning of algorithm-specific hyperparameters . In contrast to other clustering algorithms, it also produces deterministic results, which increases the reproducibility of the analysis pipeline. The SRM algorithm has one important hyperparameter
The input image (Figure 10A) is segmented into super-pixels, and the list of regions is filtered to obtain only candidate regions of fluorescence (Figure 10B). Since the color of the fluorescence signal is known, the regions can be thresholded based on their HSV color representation. For selection of GFP-fluorescent regions, super-pixels that have hue (color shade) in the interval [0.462, 0.520], saturation of 0.99, and value (brightness) in the interval [0.25, 0.32] were considered. This thresholding step removes background regions and is performed at low computational cost (Figure 10C).
Since false positives can remain after filtering, they are removed from the list of candidate regions by classifying each region into noise or signal. First, the classification applies a smoothing procedure to the region mask. This is achieved by convolving the region mask with a disk filter (Figure 10D). Then, for each pixel
A subsequent thresholding step selects pixels that fulfill where
2.5.2. Quantification of the fluorescence signal
The image analysis pipeline outlined above was implemented in both MATLAB and C++. It allowed the detection of fluorescence with little tuning of hyperparameters (
3. Discussion and outlook
In this work, a modular biosensor for the detection of the opportunistic human pathogen P. aeruginosa was developed. Five key components, (2.1) a selective molecular detection mechanism, (2.2) an integrated amplification step, (2.3) a gentle immobilization technique, (2.4) a low-cost cultivation and optical detection device, and (2.5) a graphical analysis software, were integrated. The resulting modular biosensor demonstrates the power of combining synthetic biology with software and hardware engineering by detecting P. aeruginosa in less than 1 h of analysis time. Table 2 provides a comparison of the sensor system developed in this study to existing detection methods for P. aeruginosa.
|Principle of detection||Details||Advantages||Disadvantages|
|PCR||Targeting ||High selectivity and reliability, conclusive and unambiguous results, fast compared to culturing methods||No discrimination between viable and nonviable cells, purification step required|
|Culture and colony counting||Simple and traditional plating method, sensitivity: 20 CFU/mL ||Moderate selectivity, simple, inexpensive, low detection limit||Time-consuming cultivation of several days, detects only viable/culturable organisms, unspecific|
|Immunology||ELISA applying antibodies to detect cell surface antigens , typical sensitivity: 106 CFU/mL ||High selectivity, faster than PCR-based techniques||Complex and expensive, less sensitive than PCR, regularly requires cultural enrichment|
|Modular biosensor presented in this study||Transcription factors recognize pathogen-specific quorum sensing molecules; signal is transduced through activation of quenched fluorophores, tested number of cells: 6×105 CFU||Inexpensive (no expensive reagents or equipment required), rapid (short cultivation without pretreatment), simple (no highly trained personnel required)||Selectivity and sensitivity dependent on detection system, viable cells required|
|Nucleic acid biosensor||Reception through (‑)ssDNA probe coupled to piezoelectric transduction, sensitivity: 0.1 μg/mL ||Detection in under 3 h, high selectivity||Low sensitivity, complex immobilization on hybrid membrane|
|Molecular imprinting polymer-based biosensor||Recognition of bacterial structure in combination with dielectrophoresis, sensitivity 103 CFU/mL ||Detection time of 3 min, high sensitivity, no pretreatment necessary||Cross-reactivity with bacteria of similar shape|
|Droplet-based microfluidic biosensor||Detection of virulence factors via surface-enhanced Raman spectroscopy, sensitivity: 0.5 μM pyocyanin ||Low sample volume, low detection limit for pathogen-specific virulence factor pyocyanin||Expensive, trained personnel required, increased technological effort, fluid samples only, extensive interpretation of data needed|
In addition to the detection methods compared in Table 2, there are several whole-cell approaches. Most of the previously developed whole-cell biosensors deliver an optical output . In a previous work, Struss et al. developed a whole-cell biosensor detecting AHLs of gram-negative bacteria, particularly
Enhancement and optimization of the proposed biosensor system beyond the proof of principle demonstrated in this work can be realized by modifying each of the five key elements as well as their interactions. The individual key elements can be optimized as follows.
The utilization of the pathogen’s inherent QS system guarantees a high specificity as the receptor for the AI is unique. However, this poses a challenge if multiple pathogens are desired to be detected simultaneously. First, only QS molecules can be recognized by a molecular sensing system of the presented type. In theory, other secreted compounds can be used for detection, though potentially reducing the specificity. Second, the sensing system should be introduced into a separate sensing organism to completely avoid interaction, especially if a closely related QS system and a signal amplification as presented here are utilized. This may lead to insufficient spatial resolution as many different sensing cells are required to be incorporated in the same sensor chips. An equal distribution of each type of sensing cell needs to be ensured and reciprocal interference avoided. The feasibility hereof has already been proven in previous work .
By introducing the REACh quenching system, the fluorescence response was amplified and accelerated compared to conventional GFP expression. Quenched fluorophores are constitutively expressed, and a constant pool of reporter molecules is built up. Upon the presence of inducers and a subsequent expression of the protease, they are unquenched resulting in a fast and strong fluorescent signal. Since the two expression cassettes for the GFP-REACh fusion protein and the TEV protease are currently on two separate plasmids, using a single plasmid would increase the robustness of the detection system, as two plasmid expression systems are considered less stable. As a proof of principle, the system was tested using IPTG-induced expression of the TEV protease. As a next step, the system would be adjusted by exchanging the T7 promoter with the HSL-bound LasR-inducible
Engineering of the agarose chips for entrapment of the sensor cells represents a simple yet efficient way for a two-dimensional detection method. The immobilized sensor cells survived and still performed as expected, even after short-term storage at 4°C. A fluorescence signal was generated upon induction, thus proving a sufficient diffusion of the inducer through the chip. As discussed above, adjustment of the agarose concentration used for production of the sensor chips represents a simple way to further optimize the sensor chips. Increasing the agarose concentration could focus the fluorescent response on a smaller area by restricting diffusion of the analyte, however, under the prerequisite that the diffusion is fast enough to reach the sensor cells within a short time. Additionally, adjustment of the agarose concentration affects the biosafety as the ability of the chip to contain the sensor cells is altered. To ensure a sufficient quantity and spread of the cells, an array-based technique for pattering the sensor cells onto a chip surface could be used to enable high-throughput analysis . Several techniques for printing bacteria on surfaces have already been used successfully [42, 43].
The optical detection device represents a simple and cost-effective solution for the rapid visualization and analysis of the 2D fluorescent signal. In situ cultivation with automatic, real-time monitoring of the fluorescence resulted in the detection of
The analysis software pipeline recognized and distinguished fluorescent signals of certain shapes and marked them for an easy interpretation by the user. However, the lack of sufficient amounts of real input data may imply a subjectivity of the analysis. Further testing needs to be done to prove universal applicability. In this regard, the precision vs. recall trade-off of the software is required to be further investigated to determine ratios between false positives and false negatives. Additionally, time-lapse data should be featured not only in the GUI but in the analysis as well. Since the project was conducted, the computational capabilities of embedded hardware have dramatically improved. Future adoptions of this work should therefore utilize state-of-the-art embedded hardware and software packages.
In general, the presented biosensor represents a proof of concept of a modular whole-cell, point-of-contact biosensing system. It enables rapid and inexpensive detection of
The biosensor system presented in this chapter was developed by the 15 members of the “iGEM Team Aachen 2014” . The team was supported by the Institute of Applied Microbiology (iAMB), the Institute of Biotechnology, and the Institute for Molecular Biotechnology, all three at RWTH Aachen University as well as the Institute of Bio- and Geosciences Biotechnology (IBG-1) at Forschungszentrum Jülich GmbH. Financial support originated from numerous organizations, including the aforementioned institutes, the Helmholtz Initiative for Synthetic Biology as well as other institutional and private donors listed on the project website .
Experiments were performed by the “iGEM Team Aachen 2014” members, namely, Vera Alexandrova, Nina Bailly, Philipp Demling, Florian Gohr, René Hanke, Markus Joppich, Ansgar Niemöller, Patrick Opdensteinen, Michael Osthege, Björn Peeters, Julia Plum, Stefan Reinhold, Anna Schechtel, Eshani Sood, and Arne Zimmermann. The team was advised by Suresh Sudarsan and Ljubica Vojcic and instructed by Lars Blank, Wolfgang Wiechert, and Ulrich Schwaneberg. The chapter was written by (alphabetically) Nina Bailly, Philipp Demling, Florian Gohr, René Hanke, Patrick Opdensteinen, and Michael Osthege. Markus Joppich, Suresh Sudarsan, Ulrich Schwaneberg, Lars Blank, and Wolfgang Wiechert reviewed this chapter.