Smelling, Seeing, Tasting—Old Senses for New Sensing - ACS Nano

Smelling, Seeing, TastingOld Senses for New Sensing Luca Guerrini,† Eduardo Garcia-Rico,§,∥ Nicolas Pazos-Perez,† and Ramon A. Alvarez-Puebla*,†,‡ †

Department of Physical Chemistry and EMaS, Universitat Rovira i Virgili, Carrer de Marcel·lí Domingo s/n, 43007 Tarragona, Spain Fundacion de Investigacion HM Hospitales, San Bernardo 101, 28015 Madrid, Spain ∥ School of Medicine, San Pablo CEU, Calle Julián Romea 18, 28003 Madrid, Spain ‡ ICREA, Passeig Lluís Companys 23, 08010 Barcelona, Spain §

ABSTRACT: The senses are the physiological mechanisms of perception that enable an organism to interact with the surrounding media. For centuries, humans have utilized these senses in science; vision and olfaction have been used the most extensively in laboratories followed by gustation and somatosensation, whereas audition has only rarely been employed. Most of these applications of senses were developed spontaneously based on the natural behavior of the chemistry of the reactants producing changes in scent, taste, or color. In recent years, by exploiting the outstanding properties of nanoparticles, many groups have demonstrated alternative sensing scenarios where the detection limits are remarkably improved, enabling the recognition of hazardous substances by mere sight, smell, or taste. Such alternative sensing approaches can be divided into two main groups: (i) methods that identify a single analyte by engineering a reaction that promotes a change in color or the generation of a characteristic scent, and (ii) methods that emulate or even improve mammalian senses, especially those related to taste and smell. In this Perspective, we discuss the context of each technology, present prominent examples, and evaluate the complexities, potential pitfalls, and opportunities presented by different re-engineering strategies.

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have relatively poor detection limits, and/or demand special requirements that make them unsuitable for implementation in common biomedical laboratories. In recent years, as an alternative to these classic detection methods, a new family of sensors has been developed, building on the exploitation or even emulation of the primordial sensing elements of a living organism.

rompt, sensitive, and accurate responses of analytical techniques for resolving detection issues, in particular those related to health or the environment, have always been key aspects of (applied) science. The detection/ identification of molecules, cells, or tissues with biological or environmental significance has traditionally employed sensing technologies involving two major complementary approaches: direct and indirect (lock-and-key) sensing. The conventional direct method is based on interactions between the analyte and the sensing element to generate a change that can be transduced into a specific and recognizable signal. To date, many analytical tools based on different physical, chemical, and biological phenomena have been developed for the direct structural characterization of biomolecules, biosensing, biodiagnosis, and biomedical imaging (including mass spectrometry, nuclear magnetic resonance, ultraviolet−visible (UV−vis), Raman or fluorescence spectroscopies, electrochemistry, or chromatography, among others). Conversely, lock-and-key sensors rely on specific recognition events such as antibody− antigen, with widespread techniques like enzyme-linked immunosorbent assay (ELISA) and fluorescence- (FIA) or radio-immunoassays (RIA), which can discern the target analyte among a myriad of other molecular entities within a complex sample. However, none of these direct or lock-and-key methods have thus far been able to fulfill all the needs of modern biomedicine because they are time-consuming, often © 2017 American Chemical Society

THE FIVE SENSES The senses are the physiological mechanisms of perception, which enable an organism to interact with the surrounding media. Traditionally, five senses are recognized for mammals: sight (vision), hearing (audition), taste (gustation), smell (olfaction), and touch (somatosensation), which vary in efficiency between species. Vision is the ability of eyes to detect electromagnetic waves within the visible light range and for the brain to interpret the image. Gustation and olfaction are the two chemical senses of the body. Both rely on the interaction of the flavor or scent with receptors to identify their chemical nature. These senses are discussed extensively below. Somatosensation refers to the mechanical and thermal sensations in the skin, and audition involves the perception of medium vibrations that oscillate between 20 and 20,000 Hz. Published: June 15, 2017 5217

DOI: 10.1021/acsnano.7b03176 ACS Nano 2017, 11, 5217−5222

Perspective

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For centuries, humans have utilized these senses in science. Vision and olfaction have been used the most extensively in laboratories, followed by gustation and somatosensation, whereas audition has only rarely been exploited. Vision has been used for years in the qualitative analytical identification of chemical species through color changes resulting from specific chemical reactions (e.g., pH indicators, coordination of metals with ligands, or formation of colored precipitates, etc.).1 Similarly, olfaction has been employed in the identification of organic or inorganic compounds, especially those associated with characteristic good and bad scents (e.g., hydrogen sulfide, methane, esters, etc.).2 Somatosensation can identify thermal changes in endo- and exothermic reactions, and gustation, which is not regularly used in chemistry, is exercised in gastronomy. Most of these applications of senses were developed spontaneously based on the natural behavior of the chemistry of the reactants producing the change in scent, taste, or color. Also, in most cases, the amount of analyte necessary to promote a change detectable by the human senses is quite high compared with the amounts that can be detected using instrumental techniques. Notwithstanding, in recent years, many groups have demonstrated alternative sensing scenarios where the detection limits are remarkably improved, enabling the recognition of hazardous substances by the mere acts of looking, smelling, or tasting. In particular, many of these methods exploited the outstanding properties of nanoparticles. Such alternative sensing approaches can be divided into two main groups: (i) methods that identify a single analyte by engineering a reaction that promotes a change in color or promotes the generation of a characteristic scent; and (ii) methods that emulate or even improve the mammalian senses, especially those related to taste and smell.

Figure 1. Colorimetric detection of inorganic ions via nanoparticle aggregation. Detection of Hg2+ with DNA-decorated gold nanoparticles at 45 °C showing high specificity for the target analyte. Reproduced with permission from ref 6. Copyright 2007 WileyVCH.

nanofabrication reactions, such as the epitaxial growth of the nanoparticles, can be further exploited to change the color of the colloidal solution in one step by coupling several reactions in which the analyte must be involved. These alternatives work especially well with anisotropic nanoparticles such nanostars11 or nanorods.12 A good example of this methodology is the epitaxial growth of silver on gold nanorods in the presence of a target bacteria, with the subsequent blue shift of the plasmon resonances and the corresponding color change (Figure 3).13 The use of olfaction for scientific sensing is not as popular as the use of visual changes because the intimate contact between the sample and the olfactory receptors, necessary to sense the analyte, may be toxic in many circumstances. Taste-based sensing has also been avoided for similar reasons. The only secure way of using smelling as a sensor is by planning reactions that, in the presence of the analyte, produce a distinctive fragrance. Although not many cases are reported in the literature, this sensing mechanism may offer many advantages over sight since it has the capability of detecting extremely low concentrations of volatile organic compounds present in complex environments. In this issue of ACS Nano, Duncan et al. demonstrate the possibility of detecting bacteria at low concentration in solution simply by using smell.14 This method is based on the addition of the enzyme lipase together with cationic gold nanoparticles and a pro-fragrance. If bacteria are not present, the positively charged nanoparticles interact with lipase, inactivating it. When bacteria are present, the microorganisms compete with lipase for nanoparticle binding, thus enabling the enzymatic generation of an aromatic rose scent. With this system, small quantities of bacteria (100 CFU/mL) can be identified in 15 min.

SENSING BY SEEING OR SMELLING Advances in control of the synthesis of plasmonic nanomaterials have broadened the area of general sensing, enabling nanoparticles to be used as efficient signal transductors with tremendous impact in the specific areas of vision and olfaction sensing. Plasmonic nanoparticles combine specific electromagnetic properties (color and reactivity) with high surface area (reactivity), providing sensitive platforms that can promote and enhance visual or olfactory changes such that human senses can easily identify minute amounts of the target analyte.3,4 For visual sensors, two levels of complexity are often exploited. The first, and most simple, relies on the fact that plasmonic nanoparticles undergo dramatic color changes as a function of their degree of aggregation in solution.5 Thus, by the appropriate functionalization of their surfaces with specific molecules that can selectively recognize the analyte (including chelating agents, antibodies, aptamers, etc.), aggregation can be promoted by the same target species acting as nanoparticle cross-linkers (Figure 1). This aggregation approach has been widely used for the detection of inorganic ions,6 small organic molecules,7 proteins, and nucleic acids,8 and even living organisms.9 In the second level of complexity, the color change associated with the presence of the analyte is triggered by morphological or chemical changes in the plasmonic nanoparticles. For example, nanoparticles can be engineered to react with the analyte, stimulating changes in the nanomaterial surface chemistry that lead to different colloidal morphologies in a posterior growth reaction step (Figure 2).10 Conversely,

In this issue of ACS Nano, Duncan et al. demonstrate the possibility of detecting bacteria at low concentration in solution simply by using smell. ARTIFICIAL SMELLING AND TASTING Both direct visual and olfactory sensors have great applicability in many fields, ranging from the quality assessment of food and water to their use as point-of-care diagnostic devices, especially for field measurements and laboratories with small budgets. However, they suffer from key limitations. First, both vision and olfaction are limited in humans and subjective, and they differ from individual to individual. Second, senses can only be used 5218

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Figure 2. Schematic illustration of the nanoparticle growth mechanism for the colorimetric detection of small molecules. Aptamer−gold nanoparticle probes are formed by adsorption of aptamers onto nanoparticle surfaces via Au−nucleoside affinity. (A) In the presence of target small molecules, aptamer−target interaction results in desorption of aptamer strands from the gold surface. The extent of such desorption correlates with the analyte concentration in the sample. In the absence of target molecules, aptamer strands remain surface bound. (B) Gold nanoparticle growth mediated by hydroxylamine (NH2OH) and hydrogen tetrachloroaurate(III) (HAuCl4) results in varied morphologies and, consequently, different colloidal colors. Overgrown nanoparticles with low amounts of adsorbed aptamer are spherical and red in color, whereas those with high amounts of retained aptamer have a branched morphology and display blue color. Reproduced from ref 10. Copyright 2015 American Chemical Society.

Figure 3. Schematic illustration of a colorimetric assay based on enzyme-induced metallization, for the detection of E. coli cells. In the absence of beta-galactosidase (β-gal), the unhydrolyzed p-aminophenyl β-D-galactopyranoside (PAPG) cannot reduce the silver ions to metallic silver. In this case, the color of the solution remains that yielded by the initial Au nanorods (light pink color). When β-gal is present, due to its release of E. coli upon infection with a bacteriophage, it cleaves PAPG into galactoside and the reducing agent p-aminophenol (PAP), which in turn reduces Ag+ to Ag°. In the presence of Au nanorods, the reduced metallic silver coats the gold surface, resulting in a multicolor shift of the sample solution. The color of the solution varies from light green to orange-red corresponding to the β-gal concentration. Reproduced with permission from ref 13. Copyright 2016 Wiley-VCH.

to qualitatively determine the presence of a target analyte at a relatively high concentration (i.e., the sample cannot be excessively diluted) while a quantitative analysis is virtually impossible. Third, especially in the case of olfaction, training is required for accurate scientific sensing. Fourth, in complex mixtures, the presence of other colors or scents in the sample can severely reduce discrimination. Although most of these limitations can be solved by applying spectroscopic or chromatographic techniques to the described approaches, the artificial emulation of the chemical senses (taste and olfaction) may offer another degree of complementary knowledge to the bioanalytical sciences. Contrary to common lab sensors, which analyze and determine components in a given sample, olfaction and taste

generate a picture of the whole sample. The olfactory system is composed of an array of independent, non-selective, and reversible sensorial elements (cells) that are stimulated by external vapor substances.15 Here, the signal is continuously analyzed by the brain generating a chemical pattern (image) of the “whole” that can be recalled and compared once it is found again (Figure 4A). This powerful system enables the identification of simple or complex mixtures (wines, perfumes, etc.) as a whole, but it is difficult for humans to identify the individual components among those that constitute the mixture. Further, human sensing is also strongly dependent on the specific matrix composition. Taste buds of the gustatory system are composed of just five specific sensing elements for alkaloids (bitter), cations (salty), mono- and disaccharides and 5219

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Figure 4. (A) Olfactory system. (a) Diagram showing the relationship between the olfactory receptor cell sheet in the nose and the glomeruli of the olfactory bulb. (b) Functional magnetic resonance imaging images of the different but overlapping activity patterns seen in the glomerular layer of the olfactory bulb of a mouse exposed to members of the straight-chain aldehyde series, varying from four to six carbon atoms. The lower part of the image in the left panel corresponds to the image on the medial side of the olfactory glomerular layer as shown in (a), see asterisk. Reproduced with permission from ref 15. Copyright 2006 Nature Publishing Group. (B) Gustatory system. (a) Taste buds are composed of 50−150 taste-receptor cells (depending on the species), distributed across different papillae. (b) Circumvallate papillae are found at the very back of the tongue and contain hundreds (mice) to thousands (human) of taste buds. Foliate papillae are present at the posterior lateral edge of the tongue and contain a dozen to hundreds of taste buds. Fungiform papillae contain one or a few taste buds and are found in the anterior two-thirds of the tongue. Taste-receptor cells project microvillae to the apical surface of the taste bud, where they form the “taste pore”. This is the site of interaction with tastings. (c) Recent molecular and functional data have revealed that, contrary to popular belief, there is no tongue “map”: responsiveness to the five basic modalitiesbitter, sour, sweet, salty, and umamiis present in all areas of the tongue. Reproduced with permission from ref 16. Copyright 2006 Nature Publishing Group.

Figure 5. Differential sensing using a hypothetical array-based sensor. A 16-element array (A), is exposed to analyte 1 (wine 1, D) and analyte 2 (wine 2, G). Each element array responds differently to the given analyte generating the response patterns (B) and (E). These can be used directly or to obtain the differential patterns (C) and (F) after subtraction of the pattern (A).

Unfortunately, in artificial tongues and noses, this integration is not possible as most of them use a given array of sensors that is monitored with a specific instrumental technique. Thus, classification of noses and tongues is more related to the physical form of the sample rather than to the basic design of the sensing system. In fact, these devices are usually composed of an array of independent sensors that interact with the sample. This interaction induces a series of physical and/or chemical changes in each of the sensing elements. The collective changes in the entire sensor array can be then

polyols (sweet), diketones (umami), and protons (sour); the existence of a sixth sensing element for aliphatic chains (fatty) remains under debate (Figure 4B).16 Thus, with just five (or six) sensing elements, the gustatory system is not only capable of generating a chemometric pattern of a complex sample but also of identifying the single components of that sample. Notably, in a living organism, information processed by taste and olfaction (as well as by the other senses) is not independent, and it is integrated into single patterns that respond to each of the peculiarities of the sample. 5220

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Figure 6. (A) Heat map of 59 stable sensing features, extracted from 20 different nanomaterial-based sensors on the artificially intelligent nanoarray. Each raw datum in the heat map represents the mean responses for each of the 17 diseases tested in this way. Some sensing features were more sensitive than others to alterations in breath volatile organic compounds. No individual sensing feature was sufficiently informative to discriminate among all diseases, but the overall response patterns did show discriminative potential (columns in the heat map). (B) Clustering analysis of the responses of the sensors. Each cluster represents a similar response profile, suggesting considerable resemblance between samples (subjects) in a specific cluster. The clustering does not appear to be based on any of the potential confounding factors, but there are strong resemblances between subgroups with common pathophysiologies. Reproduced from ref 23. Copyright 2017 American Chemical Society.

exhalates by using metalloporphyrin and gas chromatography,22 to name a few. However, the most astonishing application in the recent times reports the classification of 17 different diseases via pattern analysis of exhalates of over 1000 patients in five different countries.23 This study was carried out by analyzing the variation of electrical resistance of each of the 59 independent sensing elements composed of small thiolated molecules immobilized either on gold nanoparticles or carbon nanotubes and deposited in semicircular microelectronic transducers. Each of the vapor organic compounds analyzed (13 in total), which are naturally contained in each of the sample exhalates, react differently with each of the different sensing elements, yielding a final characteristic disease pattern (Figure 6A) which can be correlated with the rest of diseases by applying hierarchical clustering analysis (Figure 6B).

extracted by means of a variety of techniques, processed by multivariate analysis methods, and presented in complex plots showing unique patterns for a given species or mixture of species. Among the sensor elements used for the assembly of artificial tongues and noses, it is common to find polymers with different functionalities and physical properties, organometallic compounds, dyes, metalloporphyrins, nanoparticles, composite materials, proteins, enzymes, nucleic acids, phages, or lymphocytes.17 Methods for the registration of changes upon exposure to a given analyte include conductivity measurements, weight changes, acoustic waves, surface plasmon resonances, colorimetric methods, radioluminiscence, vapoluminiscence, or fluorescence.4,17 A simple example of an artificial tongue is described in Figure 5. Here, an array of nonspecific, different sensors reacts with two different wines. Such reaction gives rise to different levels of interaction, which are specific for each wine and sensing element, as revealed after interrogation with a spectroscopic, electric, or electrochemical technique (among others). By subtracting the initial sensor pattern from the final one, a characteristic image of each wine is generated and can be used to recognize the sample in the future. Note that in the case of analyzing gases or vapors, a similar scheme would be called “nose”. During the past two decades, considerable efforts have been devoted to exploring the possibilities offered by artificial noses and tongues to monitor and to predict changes related with aroma and taste in several fields, ranging from food science and technology to the environment and health.4,18 Artificial noses and tongues have been applied to the detection of small organic molecules with bacteriophages19 and bacteria with graphene oxide using UV−vis spectroscopy as a transductor,20 analysis of proteins with arrays of polymers and nanoparticles and fluorescence,21 or even in the diagnosis of lung cancer through

CONCLUSIONS AND OUTLOOK As discussed above, sensing using or emulating natural senses is non-invasive, cost-effective, quick, and easy to perform and thus has potential large-scale applications in several fields including medicine and environmental or food sciences. However, a full translation of such potential into practical applications is far from being achieved. In fact, most of the studies reported in the literature are simple first steps toward the generalized use of these sensors in real life. A number of methodological limitations remain the subject of debate. First, issues associated with methodology such as sampling set-ups and sample collection (especially with gases and vapors) need to be addressed by defining optimized and standardized protocols. Second, many sensing schemes have been designed using different principles, which may lead to incompatibility among raw sensor data. Therefore, scientists should collaborate to 5221

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obtain compatible signals that can be used by different systems. This effort should include statistical post-analysis of the raw data in order to generate the characteristic chemometric maps for each sample. From these analyses, we might obtain “universal databases,” which could be stored on cloud servers and potentially downloadable by every user throughout the world. Once these key challenges are addressed, sensing with senses has the potential to emerge as an essential analytical tool in many fields, not only scientific, providing economical and straightforward measurements of the quality of the environment (and related risks) as well as rapid screening of samples or individuals in diagnosis.

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. ORCID

Luca Guerrini: 0000-0002-2925-1562 Ramon A. Alvarez-Puebla: 0000-0003-4770-5756 Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS We acknowledge the support by the Spanish MINECO (CTQ2014-59808R), the Generalitat of Catalonia (AGAUR 2014 480), and the HM Hospitales Group. REFERENCES (1) Vogel, A. I. Vogel’s Qualitative Inorganic Analysis; Longman Scientific & Technical Wiley: London, 1987. (2) Shriner, R. L. The Systematic Identification of Organic Compounds, 8th ed.; Wiley: Hoboken, NJ, 2004. (3) Saha, K.; Agasti, S. S.; Kim, C.; Li, X.; Rotello, V. M. Gold Nanoparticles in Chemical and Biological Sensing. Chem. Rev. 2012, 112, 2739−2779. (4) Smyth, H.; Cozzolino, D. Instrumental Methods (Spectroscopy, Electronic Nose, and Tongue) as Tools to Predict Taste and Aroma in Beverages: Advantages and Limitations. Chem. Rev. 2013, 113, 1429− 1440. (5) Halas, N. J.; Lal, S.; Chang, W.-S.; Link, S.; Nordlander, P. Plasmons in Strongly Coupled Metallic Nanostructures. Chem. Rev. 2011, 111, 3913−3961. (6) Lee, J.-S.; Han, M. S.; Mirkin, C. A. Colorimetric Detection of Mercuric Ion (Hg2+) in Aqueous Media Using DNA-Functionalized Gold Nanoparticles. Angew. Chem., Int. Ed. 2007, 46, 4093−4096. (7) Jiang, Y.; Zhao, H.; Zhu, N.; Lin, Y.; Yu, P.; Mao, L. A Simple Assay for Direct Colorimetric Visualization of Trinitrotoluene at Picomolar Levels Using Gold Nanoparticles. Angew. Chem., Int. Ed. 2008, 47, 8601−8604. (8) Xia, F.; Zuo, X.; Yang, R.; Xiao, Y.; Kang, D.; Vallée-Bélisle, A.; Gong, X.; Yuen, J. D.; Hsu, B. B. Y.; Heeger, A. J.; Plaxco, K. W. Colorimetric Detection of DNA, Small Molecules, Proteins, and Ions Using Unmodified Gold Nanoparticles and Conjugated Polyelectrolytes. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 10837−10841. (9) Pazos-Perez, N.; Pazos, E.; Catala, C.; Mir-Simon, B.; Gómez-De Pedro, S.; Sagales, J.; Villanueva, C.; Vila, J.; Soriano, A.; García De Abajo, F. J.; Alvarez-Puebla, R. A. Ultrasensitive Multiplex Optical Quantification of Bacteria in Large Samples of Biofluids. Sci. Rep. 2016, 6, 29014. (10) Soh, J. H.; Lin, Y.; Rana, S.; Ying, J. Y.; Stevens, M. M. Colorimetric Detection of Small Molecules in Complex Matrixes via Target-Mediated Growth of Aptamer-Functionalized Gold Nanoparticles. Anal. Chem. 2015, 87, 7644−7652. (11) Rodríguez-Lorenzo, L.; De La Rica, R.; Alvarez-Puebla, R. A.; Liz-Marzán, L. M.; Stevens, M. M. Plasmonic Nanosensors with 5222

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