Volatile Organic Compounds Signatures Research Articles

A compact breath breathalyzer for identifying the non-alcoholic fatty liver disease biomarker

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disease worldwide. Currently, its diagnosis relies primarily on imaging and histological examinations, which are invasive and prone to misdiagnosis in the early stage. To address these limitations, detection and analysis of volatile organic compounds (VOCs) in human breath can be a rapid and non-invasive screening method for NAFLD. In this study, a compact breath breathalyzer was developed, utilizing a miniaturized gas chromatography chip with the STM32 microcontroller as the main control chip to manage airflow, temperature, and receive terminal signals from the photoionization detector. In the experiment, a gas mixture comprising five VOCs (pentane, acetone, toluene, octane, and decane) was selected as the simulated typical disease biomarkers in human breath to investigate the breathalyzer's performance and optimize testing conditions for multi-polar and wide-boiling-range breath samples. Results show that the breathalyzer can detect low-boiling components (< 100°C) such as the isoprene and acetone, with a detection limit less than 50 ppb which are two commonly biomarkers of NAFLD. Furthermore, breath samples were collected from 35 non-diseased individuals, and NAFLD early-stage patient samples were simulated by increasing the isoprene concentration by 10 ppb. Convolutional neural network (CNN) were used to identify the VOC signatures in gas chromatograms with predictive accuracy of 85% for the classification model. Therefore, the compact breath breathalyzer has potential application in the rapid and early screening of NAFLD.

Discovery and Validation of a Volatile Signature of Eosinophilic Airway Inflammation in Asthma.

Volatile organic compounds (VOCs) in asthmatic breath may be associated with sputum eosinophilia. We developed a volatile biomarker-signature to predict sputum eosinophilia in asthma. VOCs emitted into the space above sputum samples (headspace) from severe asthmatics (n=36) were collected onto sorbent tubes and analysed using thermal desorption gas chromatography-mass spectrometry (TD-GC-MS). Elastic net regression identified stable VOCs associated with sputum eosinophilia ≥3% and generated a volatile biomarker signature. This VOC signature was validated in breath samples from: (I) acute asthmatics according to blood eosinophilia ≥0.3x109cells/L or sputum eosinophilia of ≥ 3% in the UK EMBER consortium (n=65) and U-BIOPRED-IMI consortium (n=42). Breath samples were collected onto sorbent tubes (EMBER) or Tedlar bags (U-BIOPRED) and analysed by gas-chromatography-mass spectrometry (GC×GC-MS -EMBER or GC-MS -U-BIOPRED). The in vitro headspace identified 19 VOCs associated with sputum eosinophilia and the derived VOC signature yielded good diagnostic accuracy for sputum eosinophilia ≥ 3% in headspace (AUROC (95% CI) 0.90(0.80-0.99), p<0.0001), correlated inversely with sputum eosinophil % (rs= -0.71, p<0.0001) and outperformed FeNO (AUROC (95% CI) 0.61(0.35-0.86). Analysis of exhaled breath in replication cohorts yielded a VOC signature AUROC (95% CI) for acute asthma exacerbations of 0.89(0.76-1.0) (EMBER cohort) with sputum eosinophilia and 0.90(0.75-1.0) in U-BIOPRED - again outperforming FeNO in U-BIOPRED 0.62 (0.33-0.90). We have discovered and provided early-stage clinical validation of a volatile biomarker signature associated with eosinophilic airway inflammation. Further work is needed to translate our discovery using point of care clinical sensors.

Abstract 4433: Secondary electrospray ionization mass spectrometry for volatile analysis of lung cancer

Abstract Lung cancer remains a leading cause of cancer-related mortality globally, necessitating innovative approaches for early detection and improved understanding of its metabolic landscape and consequences. Secondary Electrospray Ionization Mass Spectrometry (SESI-MS), an innovative analytical technique, has emerged as a powerful tool for the analysis of volatile organic compounds (VOCs) associated with various diseases, from metabolic disorders to bacterial infections. In this proof-of-concept study, we first utilized SESI-MS analyses of four representative lung cancer cells of both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) to demonstrate its capability to profile VOCs released by lung cancer cells. The technique allows for real-time, non-invasive analysis of the headspace above cultured cells, enabling the detection of unique VOC signatures associated with different lung cancer subtypes. Meanwhile, SESI-MS also facilitated the discrimination specific cells based on distinct VOC patterns. Furthermore, we also performed cisplatin treatment experiments and observed the metabolic signature shift post drug treatment. These results collectively offered potential applications in early diagnosis and personalized treatment strategies. Next, we continued with in vivo investigations to further extend the utility of SESI-MS in lung cancer research. Animal models, mimicking human lung cancer pathophysiology, have been subjected to SESI-MS analysis to identify volatile biomarkers indicative of tumor development and progression. Several representative cancer drug treatments were performed to the lung cancer mice model, and the drug-induced responses were also monitored via SESI-MS breath analyses. The in vivo SESI-MS approach detected many interesting breath VOC features that could provide valuable insights into the dynamic changes in VOC profiles during various stages of lung cancer, and therefore has the potential to offer a deeper understanding of the disease's metabolic alterations and potential avenues for targeted therapeutic interventions. In conclusion, SESI-MS-based volatile analysis presents a robust and versatile approach for investigating lung cancer, and our ongoing work has the potential to bridge the gap between in vitro and in vivo models for better translational research outcomes. The detection of unique VOC signatures associated with lung cancer cells and tumors holds significant promise for early detection, subtype classification, and monitoring of treatment responses. As technology continues to advance, SESI-MS stands poised to play a pivotal role in transforming the landscape of lung cancer diagnostics and research. Citation Format: Jiangjiang (Chris) Zhu, Fouad Choueiry. Secondary electrospray ionization mass spectrometry for volatile analysis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4433.

Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity.

Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media. Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation. The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature. This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

Identification of Volatile Markers of Colorectal Cancer from Tumor Tissues Using Volatilomic Approach.

The human body releases numerous volatile organic compounds (VOCs) through tissues and various body fluids, including breath. These compounds form a specific chemical profile that may be used to detect the colorectal cancer CRC-related changes in human metabolism and thereby diagnose this type of cancer. The main goal of this study was to investigate the volatile signatures formed by VOCs released from the CRC tissue. For this purpose, headspace solid-phase microextraction gas chromatography-mass spectrometry was applied. In total, 163 compounds were detected. Both cancerous and non-cancerous tissues emitted 138 common VOCs. Ten volatiles (2-butanone; dodecane; benzaldehyde; pyridine; octane; 2-pentanone; toluene; p-xylene; n-pentane; 2-methyl-2-propanol) occurred in at least 90% of both types of samples; 1-propanol in cancer tissue (86% in normal one), acetone in normal tissue (82% in cancer one). Four compounds (1-propanol, pyridine, isoprene, methyl thiolacetate) were found to have increased emissions from cancer tissue, whereas eleven showed reduced release from this type of tissue (2-butanone; 2-pentanone; 2-methyl-2-propanol; ethyl acetate; 3-methyl-1-butanol; d-limonene; tetradecane; dodecanal; tridecane; 2-ethyl-1-hexanol; cyclohexanone). The outcomes of this study provide evidence that the VOCs signature of the CRC tissue is altered by the CRC. The volatile constituents of this distinct signature can be emitted through exhalation and serve as potential biomarkers for identifying the presence of CRC. Reliable identification of the VOCs associated with CRC is essential to guide and tune the development of advanced sensor technologies that can effectively and sensitively detect and quantify these markers.

Changes in exhaled volatile organic compounds following indirect bronchial challenge in suspected asthma

BackgroundInhaled mannitol provokes bronchoconstriction via mediators released during osmotic degranulation of inflammatory cells, and, hence represents a useful diagnostic test for asthma and model for acute attacks. We hypothesised that...

Investigating the Use of SARS-CoV-2 (COVID-19) Odor Expression as a Non-Invasive Diagnostic Tool-Pilot Study.

Since the beginning of the COVID-19 pandemic, there has been enormous interest in the development of measures that would allow for the swift detection of the disease. The rapid screening and preliminary diagnosis of SARS-CoV-2 infection allow for the instant identification of possibly infected individuals and the subsequent mitigation of the disease spread. Herein, the detection of SARS-CoV-2-infected individuals was explored using noninvasive sampling and low-preparatory-work analytical instrumentation. Hand odor samples were obtained from SARS-CoV-2-positive and -negative individuals. The volatile organic compounds (VOCs) were extracted from the collected hand odor samples using solid phase microextraction (SPME) and analyzed using gas chromatography coupled with mass spectrometry (GC-MS). Sparse partial least squares discriminant analysis (sPLS-DA) was used to develop predictive models using the suspected variant sample subsets. The developed sPLS-DA models performed moderately (75.8% (±0.4) accuracy, 81.8% sensitivity, 69.7% specificity) at distinguishing between SARS-CoV-2-positive and negative -individuals based on the VOC signatures alone. Potential markers for distinguishing between infection statuses were preliminarily acquired using this multivariate data analysis. This work highlights the potential of using odor signatures as a diagnostic tool and sets the groundwork for the optimization of other rapid screening sensors such as e-noses or detection canines.

Urinary volatile Organic compounds in non-alcoholic fatty liver disease (NAFLD), type two diabetes mellitus (T2DM) and NAFLD-T2DM coexistence.

Accumulating evidence have shown a significant correlation between urinary volatile organic compounds (VOCs) profile and the manifestation of several physiological and pathological states, including liver diseases. Previous studies have investigated the urinary metabolic signature as a non-invasive tool for the early discrimination between non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), which nowadays represents one of the most important challenges in this context, feasible only by carrying out liver biopsy. The aim of the study was to investigate the differences in the urinary VOCs profiles of non-alcoholic fatty liver disease (NAFLD) patients, diabetes mellitus (T2DM) subjects and NAFLD/T2DM patients. Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) was applied to profile the urinary VOCs. Urine samples were analysed both under acid and alkaline conditions, to obtain a range of urinary volatiles with different physicochemical properties. Urinary VOCs profiles of 13 NAFLD patients, 13 T2DM subjects and 13 NAFLD/T2DM patients were investigated by multivariate and univariate data analysis techniques which allowed to identify 21 volatiles under alkaline conditions able to describe the NAFLD/T2DM group concerning the other two groups. Our results suggest that VOCs signatures can improve the knowledge of the pathological condition where NAFLD coexists with T2DM and discovering new features that are not simply the sum of the two diseases. These preliminary findings may be considered as hypothesis-generating, to be clearly confirmed by larger prospective investigations.

Identification of Exhaled Metabolites in Children with Cystic Fibrosis.

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge (m/z) features, using on-line secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), which distinguish children with cystic fibrosis from healthy controls. The aim of this study was to annotate as many m/z features as possible with putative chemical structures. Compound identification was performed by applying a rigorous workflow, which included the analysis of on-line MS2 spectra and a literature comparison. A total of 49 discriminatory exhaled compounds were putatively identified. A group of compounds including glycolic acid, glyceric acid and xanthine were elevated in the cystic fibrosis group. A large group of acylcarnitines and aldehydes were found to be decreased in cystic fibrosis. The proposed compound identification workflow was used to identify signatures of volatile organic compounds that discriminate children with cystic fibrosis from healthy controls, which is the first step for future non-invasive and personalized applications.

Metabolic phenotyping of acquired ampicillin resistance using microbial volatiles from Escherichia coli cultures.

This study sought to assess the volatile organic compound (VOC) profiles of ampicillin-resistant and -susceptible Escherichia coli to evaluate whether VOC analysis may be utilized to identify resistant phenotypes. An E. coli BL21 (DE3) strain and its pET16b plasmid transformed ampicillin-resistant counterpart were cultured for 6h in drug-free, low- and high-concentrations of ampicillin. Headspace analysis was undertaken using thermal desorption-gas chromatography-mass spectrometry. Results revealed distinct VOC profiles with ampicillin-resistant bacteria distinguishable from their susceptible counterparts using as few as six compounds. A minimum of 30 compounds (fold change >2, p ≤ 0.05) were differentially expressed between the strains across all set-ups. Furthermore, three compounds (indole, acetoin and 3-methyl-1-butanol) were observed to be significantly more abundant (fold change >2, p ≤ 0.05) in the resistant strain compared to the susceptible strain both in the presence and in the absence of drug stress. Results indicate that E. coli with acquired ampicillin resistance exhibit an altered VOC profile compared to their susceptible counterpart both in the presence and in the absence of antibiotic stress. This suggests that there are fundamental differences between the metabolisms of ampicillin-resistant and -susceptible E. coli which may be detected by means of VOC analysis. Our findings suggest that VOC profiles may be utilized to differentiate between resistant and susceptible bacteria using just six compounds. Consequently, the development of machine-learning models using VOC signatures shows considerable diagnostic applicability for the rapid and accurate detection of antimicrobial resistance.

Comparative Analysis of Volatiles Emitted from Tomato and Pepper Plants in Response to Infection by Two Whitefly-Transmitted Persistent Viruses.

Simple SummaryViruses transmitted by the whitefly Bemisia tabaci, a cosmopolitan pest that inflicts damage on many agricultural crops, are among the most important plant viruses and cause devastating damage to agriculture worldwide. Begomoviruses and a new polerovirus transmitted by the whitefly cause serious damage to tomato and pepper crops in Israel, respectively. Here, both viruses were studied with regard to their ability to induce the production of volatile organic compounds (VOCs) upon plant infection, which are thought to mediate communication with the environment. The results showed that infection with each virus alone, or infestation with whiteflies, without viruses, induced shared and unique VOC accumulation. The VOCs identified in these unique responses suggest that plants can respond specifically to virus infection or insect infestation, and those VOCs can be used individually or as blends for monitoring and disrupting pest populations.The whitefly Bemisia tabaci is one of the most important agricultural pests due to its extreme invasiveness, insecticide resistance, and ability to transmit hundreds of plant viruses. Among these, Begomoviruses and recombinant whitefly-borne Poleroviruses are transmitted persistently. Several studies have shown that upon infection, plant viruses manipulate plant-emitted volatile organic compounds (VOCs), which have important roles in communication with insects. In this study, we profiled and compared the VOCs emitted by tomato and pepper plant leaves after infection with the Tomato yellow leaf curl virus (TYLCV) (Bogomoviruses) and the newly discovered Pepper whitefly-borne vein yellows virus (PeWBVYV) (Poleroviruses), respectively. The results identified shared emitted VOCs but also uncovered unique VOC signatures for each virus and for whitefly infestation (i.e., without virus infection) independently. The results suggest that plants have general defense responses; however, they are also able to respond individually to infection with specific viruses or infestation with an insect pest. The results are important to enhance our understanding of virus- and insect vector-induced alteration in the emission of plant VOCs. These volatiles can eventually be used for the management of virus diseases/insect vectors by either monitoring or disrupting insect–plant interactions.

The Human Skin Volatolome: A Systematic Review of Untargeted Mass Spectrometry Analysis.

The analysis of volatile organic compounds (VOCs) can provide important clinical information (entirely non-invasively); however, the exact extent to which VOCs from human skin can be signatures of health and disease is unknown. This systematic review summarises the published literature concerning the methodology, application, and volatile profiles of skin VOC studies. An online literature search was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis, to identify human skin VOC studies using untargeted mass spectrometry (MS) methods. The principal outcome was chemically verified VOCs detected from the skin. Each VOC was cross-referenced using the CAS number against the Human Metabolome and KEGG databases to evaluate biological origins. A total of 29 studies identified 822 skin VOCs from 935 participants. Skin VOCs were commonly sampled from the hand (n = 9) or forearm (n = 7) using an absorbent patch (n = 15) with analysis by gas chromatography MS (n = 23). Twenty-two studies profiled the skin VOCs of healthy subjects, demonstrating a volatolome consisting of aldehydes (18%), carboxylic acids (12%), alkanes (12%), fatty alcohols (9%), ketones (7%), benzenes and derivatives (6%), alkenes (2%), and menthane monoterpenoids (2%). Of the VOCs identified, 13% had putative endogenous origins, 46% had tentative exogenous origins, and 40% were metabolites from mixed metabolic pathways. This review has comprehensively profiled the human skin volatolome, demonstrating the presence of a distinct VOC signature of healthy skin, which can be used as a reference for future researchers seeking to unlock the clinical potential of skin volatolomics. As significant proportions of identified VOCs have putative exogenous origins, strategies to minimise their presence through methodological refinements and identifying confounding compounds are discussed.

Study on the Discrimination of Possible Error Sources That Might Affect the Quality of Volatile Organic Compounds Signature in Dairy Cattle Using an Electronic Nose.

Simple SummaryIn recent decades, remarkable progress in the development of electronic nose (EN) technologies, particularly for disease detection, has been accomplished through the disclosure of novel methods and associated devices, mainly for the detection of volatile organic compounds (VOCs). Herein, we assessed the ability of a novel EN technology (MENT-EGAS prototype) to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. Principal Component Analyses (PCA) evidenced the presence in the analyzed samples of sufficient information to consent the discrimination of different environmental backgrounds, feed headspaces and exhalated breath between two groups of cows fed with two different types of feed. Moreover, discrimination was also observed within the same group between exhalated breaths sampled before and after feed intake. Based on these findings, we provided evidence that the MENT-EGAS prototype can identify error sources with accuracy. Livestock precision farming technologies are powerful tools for monitoring animal health and welfare parameters in a continuous and automated way.Electronic nose devices (EN) have been developed for detecting volatile organic compounds (VOCs). This study aimed to assess the ability of the MENT-EGAS prototype-based EN to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. This study was performed on a dairy farm using 11 (n = 11) multiparous Holstein-Friesian cows. The cows were divided into two groups housed in two different barns: group I included six lactating cows fed with a lactating diet (LD), and group II included 5 non-lactating late pregnant cows fed with a far-off diet (FD). Each group was offered 250 g of their respective diet; 10 min later, exhalated breath was collected for VOC determination. After this sampling, 4 cows from each group were offered 250 g of pellet concentrates. Ten minutes later, the exhalated breath was collected once more. VOCs were also measured directly from the feed’s headspace, as well as from the environmental backgrounds of each. Principal component analyses (PCA) were performed and revealed clear discrimination between the two different environmental backgrounds, the two different feed headspaces, the exhalated breath of groups I and II cows, and the exhalated breath within the same group of cows before and after the feed intake. Based on these findings, we concluded that the MENT-EGAS prototype can recognize several error sources with accuracy, providing a novel EN technology that could be used in the future in precision livestock farming.

Post-COVID syndrome screening through breath analysis using electronic nose technology

There is an urgent need to have reliable technologies to diagnose post-coronavirus disease syndrome (PCS), as the number of people affected by COVID-19 and related complications is increasing worldwide. Considering the amount of risks associated with the two chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), there is an immediate requirement for a screening method for PCS, which also produce symptoms similar to these conditions, especially since very often, many COVID-19 cases remain undetected because a good share of such patients is asymptomatic. Breath analysis techniques are getting attention since they are highly non-invasive methods for disease diagnosis, can be implemented easily for point-of-care applications even in primary health care centres. Electronic (E-) nose technology is coming up with better reliability, ease of operation, and affordability to all, and it can generate signatures of volatile organic compounds (VOCs) in exhaled breath as markers of diseases. The present report is an outcome of a pilot study using an E-nose device on breath samples of cohorts of PCS, asthma, and normal (control) subjects. Match/no-match and k-NN analysis tests have been carried out to confirm the diagnosis of PCS. The prediction model has given 100% sensitivity and specificity. Receiver operating characteristics (ROC) has been plotted for the prediction model, and the area under the curve (AUC) is obtained as 1. The E-nose technique is found to be working well for PCS diagnosis. Our study suggests that the breath analysis using E-nose can be used as a point-of-care diagnosis of PCS.Trial registrationBreath samples were collected from the Kasturba Hospital, Manipal. Ethical clearance was obtained from the Institutional Ethics Committee, Kasturba Medical College, Manipal (IEC 60/2021, 13/01/2021) and Indian Council of Medical Research (ICMR) (CTRI/2021/02/031357, 06/02/2021) Government of India; trials were prospectively registered.Graphical abstract

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS.

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.

Medical Diagnosis Using Volatile Organic Compounds Sensors

Volatile organic compounds (VOCs) are important biomarkers in exhaled breath or skin secretion of patients under various medical or pre-medical conditions. As such, VOCs have been explored as alternative biomarkers in the detection of diseases, including asthma, tuberculosis, and lung cancer. In this regard, a rapid, cost-effective, facile, and sample-free strategy is advantageous and critically needed for premedical screening, medical diagnosis, and health monitoring. In this review, we present an overview of the latest progress of using nanomaterial-based chemo-resistive VOC sensors for fast, real-time, and non-invasive diagnosis of diseases via detecting VOCs from exhaled breath and other sources from human body. The origin and emission of VOCs are summarized from human body, and the VOC signatures are discussed as related to specific disease. Targeting specific VOCs, chemoresistive sensors using different nanomaterials are reviewed in terms of their sensing performance metrics including sensitivity, selectivity, response/recovery time, and stability. Various strategies for improving VOC sensor performance are discussed, specifically on the material and signal processing-based approaches.

Minimal Gluten Exposure Alters Urinary Volatile Organic Compounds in Stable Coeliac Disease.

Coeliac disease (CD) patients are distinguishable from healthy individuals via urinary volatile organic compounds (VOCs) analysis. We exposed 20 stable CD patients on gluten-free diet (GFDs) to a 14-day, 3 g/day gluten challenge (GCh), and assessed urinary VOC changes. A control cohort of 20 patients continued on GFD. Urine samples from Days 0, 7, 14, 28 and 56 were analysed using Lonestar FAIMS and Markes Gas Chromatography–Time of Flight–Mass Spectrometer (GC-TOF-MS). VOC signatures on D (day) 7–56 were compared with D0. Statistical analysis was performed using R. In GCh patients, FAIMS revealed significant VOC differences for all time points compared to D0. GC-TOF-MS revealed significant changes at D7 and D14 only. In control samples, FAIMS revealed significant differences at D7 only. GC-TOF-MS detected no significant differences. Chemical analysis via GC-MS-TOF revealed 12 chemicals with significantly altered intensities at D7 vs. D0 for GCh patients. The alterations persisted for six chemicals at D14 and one (N-methyltaurine) remained altered after D14. This low-dose, short-duration challenge was well tolerated. FAIMS and GC-TOF-MS detected VOC signature changes in CD patients when undergoing a minimal GCh. These findings suggest urinary VOCs could have a role in monitoring dietary compliance in CD patients.

Single-Walled Carbon Nanotube-Based Field-Effect Transistors Functionalized with Odorant Receptors for Biosensing Applications

Bioelectronic detection with functionalized carbon nanotube transistor devices, known as CNT-FET, offers signal levels comparable with voltage clamp, multiplexing potential similar to microarray and amenability for miniaturization. The feasibility of CNT-FET biosensors was demonstrated in the detection of nucleic acids and various protein biomarkers and they are considered the next generation bioelectronic-based biosensors. Detection of volatiles and specifically volatile organic compounds (VOC) with CNT-FETs is less common, let alone devices that were biofunctionalized for specificity. The detection of VOCs has been demonstrated in a wide range of applications, with early reports focusing on environmental exogenous VOCs due to their adverse effects on human health; bacterial VOC signatures used as disease biomarkers, etc.. Methods that rely on separation techniques coupled with mass spectrometry are considered the ’gold standard’ in this field. These methods require expensive equipment, labor-intensive preparation steps and trained personnel, and are not suitable for field use. A method enabling high-throughput screening of VOCs such as insect behavior-modifying drugs, disease biomarkers, etc. is highly desirable.Bioelectronic sensors are ideally suited for the detection of pathogenic volatile markers (VOC). In order to confer ultrahigh specificity on an electronic sensor, a bio-recognition element needs to be incorporated with the transducer. In the case of VOC sensing, one such promising element is the natural insect odorant receptor (OR). Insect OR exhibit remarkable sensitivities and the ability to selectively detect a vast number of VOCs. These heteromeric transmembrane proteins transduce signals by acting as ligand-gated ion channels. We have recently developed OR-functionalized carbon nanotube field-effect transistor (FET) devices. These FET devices integrate an isolated single-walled carbon nanotube (SWCNT) as the conducting channel material.Indole and skatoles are microbial volatile organic compounds (VOCs). Depending on their concentrations, these odorants indicate environmental contaminants, metabolic disorders, food spoilage, human presence and malodorants.The indolergic receptor (IndolOR) named OR9 is the most sensitive odorant receptor discovered so far. We have developed a bioelectronic assay platform in the form of OR9-functionalized CNT-FET arrays. Our unique functionalization method is based on directing the attachment of a native OR9-containing nanovesicles (which we have exogenously expressed and functionally characterized) to the CNT-FET point defect. Target binding-induced ionic current and conformational changes of OR9 affect an electric field that modulates the device conductance. Figure 1

Virus-induced Volatile Organic Compounds Are Detectable in Exhaled Breath during Pulmonary Infection.

Rationale: Chronic obstructive pulmonary disease (COPD) is a condition punctuated by acute exacerbations commonly triggered by viral and/or bacterial infection. Early identification of exacerbation triggers is important to guide appropriate therapy, but currently available tests are slow and imprecise. Volatile organic compounds (VOCs) can be detected in exhaled breath and have the potential to be rapid tissue-specific biomarkers of infection etiology. Objectives: To determine whether volatile organic compound measurement could distinguish viral from bacterial infection in COPD. Methods: We used serial sampling within invitro and invivo studies to elucidate the dynamic changes that occur in VOC production during acute respiratory viral infection. Highly sensitive gas chromatography-mass spectrometry techniques were used to measure VOC production from infected airway epithelial-cell cultures and in exhaled breath samples from healthy subjects experimentally challenged with rhinovirus (RV)-A16 and from subjects with COPD with naturally occurring exacerbations. Measurements and Main Results: We identified a novel VOC signature comprising decane and other long-chain alkane compounds that is induced during RV infection of cultured airway epithelial cells and is also increased in the exhaled breath from healthy subjects experimentally challenged with RV and from patients with COPD during naturally occurring viral exacerbations. These compounds correlated with the magnitude of antiviral immune responses, viral burden, and exacerbation severity but were not induced by bacterial infection, suggesting that they represent a specific virus-inducible signature. Conclusions: Our study highlights the potential for measurement of exhaled breath VOCs as rapid, noninvasive biomarkers of viral infection. Further studies are needed to determine whether measurement of these signatures could be used to guide more targeted therapy with antibiotic/antiviral agents for COPD exacerbations.

Breath Biopsy and Discovery of Exclusive Volatile Organic Compounds for Diagnosis of Infectious Diseases.

Exhaled breath contains thousand metabolites and volatile organic compounds (VOCs) that originated from both respiratory tract and internal organ systems and their microbiomes. Commensal and pathogenic bacteria and virus of microbiomes are capable of producing VOCs of different chemical classes, and some of them may serve as biomarkers for installation and progression of various common human diseases. Here we describe qualitative and quantitative methods for measuring VOC fingerprints generated by cellular and microbial metabolic and pathologic pathways. We describe different chemical classes of VOCs and their role in the host cell-microbial interactions and their impact on infection disease pathology. We also update on recent progress on VOC signatures emitted by isolated bacterial species and microbiomes, and VOCs identified in exhaled breath of patients with respiratory tract and gastrointestinal diseases, and inflammatory syndromes, including the acute respiratory distress syndrome and sepsis. The VOC curated databases and instrumentations have been developed through statistically robust breathomic research in large patient populations. Scientists have now the opportunity to find potential biomarkers for both triage and diagnosis of particular human disease.