RSC Adv.2016 6(86):82616-82624
Cross-reactive, self-encoded polymer film arrays for sensor applications.
Jessica E. Fitzgerald,a
The Mammalian Olfactory System as a Model Biomimetic Sensor
Over the past three decades, many research efforts have looked to the mammalian olfactory system as a model for the development of a medical diagnostic device. Overall, there are about 1000 genes that encode olfactory receptors (ORs), and each OR has multiple sites for odorant binding, enabling the detection of more than one odorant for each OR, a characteristic called cross-reactivity. Each combination of activated receptors creates a unique signaling code for a specific odorant, making it possible to distinguish between thousands of odorants. Researchers have been working to develop biomimetic devices called ‘electronic/artificial noses’ to detect certain olfactory elements present in both vapors and liquids that can indicate a certain disease or illness. These devices have proved to be successful for a wide variety of applications, providing cost-effective, minimally invasive, and highly accurate vapor component analysis.1
Barcoded Resins (BCRs) as E-nose Sensors
BCRs have unique Raman and infrared (FTIR) spectra arising from their polymer composition, which are converted into a barcode for rapid identification (Figure 1). Upon interaction with an analyte, the vibrational signatures of the BCRs change, resulting in slight but detectable spectral variations for each BCR. The collective response (or odor-specific patterns) can then be quantified using multivariate data analysis. The spectral changes reflected in each BCR – around 700 unique BCRs are currently available2– can then be used as a data point for pattern recognition.
Herein, we aimed to differentiate synthetic vapors and validate the BCRs’ potential as successful e-nose sensors. We tested 5 analyte vapors (methanol, hexane, ethyl acetate, acetonitrile, and methylamine) representing a variety of different chemical structures and classes. 64 unique BCR sensors were arranged in an 8×8 array, and nitrogen gas containing the analyte of interest was passed over the array (Figure 2). For both FTIR and Raman analysis, we took a spectrum of each sensor before and after analyte exposure. Through multivariate data analysis—correlating the composition of each BCR to its corresponding spectra—we calculated a theta value that was representative of the degree of change in the spectrum for each sensor upon exposure to the analyte of interest. The theta values for each BCR sensor were then used to construct a vapor fingerprint, unique to each analyte (Figure 3). The results obtained after data analyses demonstrated that the sensor arrays have unique and reproducible response patterns for each analyte (especially for the FTIR data). Thus, we propose that our sensor arrays can be used as an e-nose to detect a broad range of volatile analytes after an analyte recognition training process.3 This approach can be extended for the high-throughput screening and identification of a wide range of analytes. The simple and low-cost fabrication of the present sensor arrays coupled with excellent reproducibility ensures a facile application in biodiagnostics. Work to expand the repertoire of analytes and BCR sensors to gain a better handle on the scope and versatility of this system, to test the ability of the sensor arrays in quantitative measurements of mixture components, and to miniaturize the sensor device, are currently underway in our laboratories.
Figure 1. Subset of Raman (Left) and Infrared (Right) Spectroscopic Barcodes of Barcoded Resins (BCRs). Image reproduced, with permission, from4.
Figure 2. Schematic diagram of analyte delivery setup and analysis system for recording sensor array response to a volatile analyte. Exposure to an analyte vapor induces changes in the FTIR and Raman spectra that are recorded and compared to the unexposed sensor array. Image reproduced, with permission, from 3.
Disease Diagnostics and Future Outlook
Many disease pathologies have been identified from the unique combination of metabolites, or metabolic byproducts, produced. Some of these metabolites are volatile organic compounds (VOCs), which are small molecules that enter exhaled breath through gas exchange at the alveolar–capillary membrane of the respiratory tract. While the VOCs produced in each disease are thought to be primarily from oxidative stress, the subsequent effect of each disease on the body is unique and leads to the production of disease-specific VOC profiles. E-noses can differentiate diseases via ‘breathprint’ outputs; that is, each patient’s exhaled VOC profile produces a unique response pattern from the sensor array, enabling disease differentiation by comparing new patient breathprints with two controls: breathprints of healthy patients and breathprints of patients for whom a disease diagnosis has been confirmed.
Throughout the lifetime of e-nose device development, studies have been conducted to obtain breathprints for a wide variety of diseases, including many types of cancer. Lung diseases such as chronic obstructive pulmonary disorder (COPD), lung cancer, and asthma have all been uniquely distinguished from both healthy controls and each other. One recent study showed that different histologies of lung cancer can be successfully differentiated using a colorimetric, or color-changing, e-nose to record breath biosignatures. Various other cancers also have unique VOC profiles that can be characterized for diagnosis, including breast cancer, colorectal cancer, and prostate cancer. In addition to diagnosis through exhaled breath analysis, colorectal cancer and prostate cancer were diagnosed by e-nose analysis of urine or fecal headspace (the volume of air directly above the sample in an enclosed container). Overall, e-nose devices offer promise as a novel area to develop as they offer a potentially highly impactful solution for early detection of a range of medical diagnoses as well as the potential for more closely matching treatment to patient-specific disease pathology and monitoring of treatment response. Nonetheless, some challenges remain for future device development and implementation, including improving device accuracy without sacrificing cost and ease of use.1
Figure 3. Histogram of the individual FTIR (left) and Raman (right) responses (angle value) of the 64 polymer sensors to (A) hexane; (B) methylamine; (C) acetonitrile; (D) methanol; (E) ethylacetate. Image reproduced, with permission, from 3.
- Fitzgerald, J. E., Bui, E. T. H., Simon, N. M. & Fenniri, H. Artificial Nose Technology: Status and Prospects in Diagnostics. Trends Biotechnol.xx, 1–10 (2016).
- Fenniri, H., Chun, S., Terreau, O. & Bravo-Vasquez, J. P. Preparation and infrared/raman classification of 630 spectroscopically encoded styrene copolymers. J. Comb. Chem.10, 31–36 (2008).
- Fitzgerald, J. E., Zhu, J., Bravo-Vasquez, J. P. & Fenniri, H. Cross-reactive, self-encoded polymer film arrays for sensor applications. RSC Adv.6, 82616–82624 (2016).
- Fenniri, H., Chun, S., Ding, L., Zyrianov, Y. & Hallenga, K. Preparation, physical properties, on-bead binding assay and spectroscopic reliability of 25 barcoded polystyrene – poly(ethylene glycol) graft copolymers. J. Am. Chem. Soc.125, 10546–10560 (2003).