Ion mobility spectrometry in breath research

BreathXplorer: Processing Online Breathomics Data Generated from Direct Analysis Using High-Resolution Mass Spectrometry

Nontargeted breath analysis in real time using high-resolution mass spectrometry (HRMS) is a promising approach for high coverage profiling of metabolites in human exhaled breath. However, the information-rich and unique non-Gaussian metabolic signal shapes of real-time HRMS-based data pose a significant challenge for efficient data processing. This work takes a typical real-time HRMS technique as an example, i.e. secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), and presents BreathXplorer, an open-source Python package designed for the processing of real-time exhaled breath data comprising multiple exhalations. BreathXplorer is composed of four main modules. The first module applies either a topological algorithm or a Gaussian mixture model (GMM) to determine the start and end points of each exhalation. Next, density-based spatial clustering of applications with noise (DBSCAN) is employed to cluster m/z values belonging to the same metabolic feature, followed by applying an intensity relative standard deviation (RSD) filter to extract real breath metabolic features. BreathXplorer also offers functions of (1) feature alignment across the samples and (2) associating MS/MS spectra with their corresponding metabolic features for downstream compound annotation. Manual inspection of the metabolic features extracted from SESI-HRMS breath data suggests that BreathXplorer can achieve 100% accuracy in identifying the start and end points of each exhalation and acquire accurate quantitative measurements of each breath feature. In a proof-of-concept study on exercise breathomics using SESI-HRMS, BreathXplorer successfully reveals the significantly changed metabolites that are pertinent to exercise. BreathXplorer is publicly available on GitHub (https://github.com/HuanLab/breathXplorer). It provides a powerful and convenient-to-use tool for the researchers to process breathomics data obtained by directly analysis using HRMS.

A solid state electrolyte based enzymatic acetone sensor

This paper introduces a novel solid-state electrolyte-based enzymatic sensor designed for the detection of acetone, along with an examination of its performance under various surface modifications aimed at optimizing its sensing capabilities. To measure acetone concentrations in both liquid and vapor states, cyclic voltammetry and amperometry techniques were employed, utilizing disposable screen-printed electrodes consisting of a platinum working electrode, a platinum counter electrode, and a silver reference electrode. Four different surface modifications, involving different combinations of Nafion (N) and enzyme (E) layers (N + E; N + E + N; N + N + E; N + N + E + N), were tested to identify the most effective configuration for a sensor that can be used for breath acetone detection. The sensor's essential characteristics, including linearity, sensitivity, reproducibility, and limit of detection, were thoroughly evaluated through a range of experiments spanning concentrations from 1 µM to 25 mM. Changes in acetone concentration were monitored by comparing currents readings at different acetone concentrations. The sensor exhibited high sensitivity, and a linear response to acetone concentration in both liquid and gas phases within the specified concentration range, with correlation coefficients ranging from 0.92 to 0.98. Furthermore, the sensor achieved a rapid response time of 30-50 s and an impressive detection limit as low as 0.03 µM. The results indicated that the sensor exhibited the best linearity, sensitivity, and limit of detection when four layers were employed (N + N + E + N).

AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

Exhaled Aldehydes as Biomarkers for Lung Diseases: A Narrative Review

Breath analysis provides great potential as a fast and non-invasive diagnostic tool for several diseases. Straight-chain aliphatic aldehydes were repeatedly detected in the breath of patients suffering from lung diseases using a variety of methods, such as mass spectrometry, ion mobility spectrometry, or electro-chemical sensors. Several studies found increased concentrations of exhaled aldehydes in patients suffering from lung cancer, inflammatory and infectious lung diseases, and mechanical lung injury. This article reviews the origin of exhaled straight-chain aliphatic aldehydes, available detection methods, and studies that found increased aldehyde exhalation in lung diseases.

Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer

Nowadays in clinical practice there is a pressing need for potential biomarkers that can identify lung cancer at early stage before becoming symptomatic or detectable by conventional means. Several researchers have independently pointed out that the volatile organic compounds (VOCs) profile can be considered as a lung cancer fingerprint useful for diagnosis. In particular, 16% of volatiles contributing to the human volatilome are found in urine, which is therefore an ideal sample medium. Its analysis through non-invasive, relatively low-cost and straightforward techniques could offer great potential for the early diagnosis of lung cancer. In this study, urinary VOCs were analysed with a gas chromatography-ion mobility spectrometer (GC-IMS) and an electronic nose (e-nose) made by a matrix of twelve quartz microbalances (QMBs) complemented by a photoionization detector (PID). This clinical prospective study involved 127 individuals, divided into two groups: 46 with lung cancer stage I-II-III confirmed by computerized tomography (CT) or positron emission tomography-(PET) imaging techniques and histology (biopsy), and 81 healthy controls. Both instruments provided a multivariate signal which, after being analysed by a machine learning algorithm, identified eight VOCs that could distinguish lung cancer patients from healthy ones. The eight VOCs are 2-pentanone, 2-hexenal, 2-hexen-1-ol, hept-4-en-2-ol, 2-heptanone, 3-octen-2-one, 4-methylpentanol, 4-methyl-octane. Results show that GC-IMS identifies lung cancer with respect to the control group with a diagnostic accuracy of 88%. Sensitivity resulted as being 85%, and specificity was 90% - Area Under the Receiver Operating Characteristics (AUROC): 0.91. The contribution made by the e-nose was also important, even though the results were slightly less sensitive with an accuracy of 71.6%. Moreover, of the eight VOCs identified as potential biomarkers, five VOCs had a high sensitivity (p≤ 0.06) for early stage (stage I) lung cancer.

Deep Learning for Type 1 Diabetes Mellitus Diagnosis Using Infrared Quantum Cascade Laser Spectroscopy

An estimated 10.5% of the world’s population aged 20–79 years are currently living with diabetes in 2021. An urgent task is to develop a non-invasive express-diagnostics of diabetes with high accuracy. Type 1 diabetes mellitus (T1DM) diagnostic method based on infrared laser spectroscopy of human exhaled breath is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We propose a method for collecting and drying an exhaled human air sample and have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers with an accuracy of 99.7%, recall 99.6% and AUC score 99.9% was used. The demonstrated results require clarification on a larger dataset and series of clinical studies and, further, the method can be implemented in routine medical practice.

Taking leads out of nature, can nano deliver us from COVID-like pandemics?

The COVID-19 crisis has alerted the research community to re-purpose scientific tools that can effectively manage emergency pandemic situations. Researchers were never so desperate to discover a 'magic bullet' that has significant clinical benefits with minimal or no side effects. At the beginning of the pandemic, due to restricted access to traditional laboratory techniques, many research groups delved into computational screening of thousands of lead molecules that could inhibit SARS-CoV-2 at one or more stages of its infectious cycle. Several in silico studies on natural derivatives point out their potency against SARS-CoV-2 proteins. However, theoretical predictions and existing knowledge on related molecules reflect their poor oral bioavailability due to biotransformation in the gut and liver. Nanotechnology has evolved into a key field for precise and controlled delivery of various drugs that lack aqueous solubility, have low oral bioavailability and possess pronounced toxicity in their native form. In this review, we discuss various nanoformulations of natural products with favorable ADME properties, and also briefly explore nano-drug delivery to lungs, the primary site of SARS-CoV-2 infection. Natural products are also envisioned to augment nanotechnology-based 1) personnel protective equipment for ex vivo viral inactivation and 2) wearable sensors that perform rapid and non-invasive analysis of volatile organic compounds in exhaled breath of the infected person after therapeutic food consumption.

Identification of volatile compounds from bacteria by spectrometric methods in medicine diagnostic and other areas: current state and perspectives

Diagnosis of bacterial infections until today mostly relies on conventional microbiological methods. The resulting long turnaround times can lead to delayed initiation of adequate antibiotic therapy and prolonged periods of empiric antibiotic therapy (e.g., in intensive care medicine). Therewith, they contribute to the mortality of bacterial infections and the induction of multidrug resistances. The detection of species specific volatile organic compounds (VOCs) emitted by bacteria has been proposed as a possible diagnostic approach with the potential to serve as an innovative point-of-care diagnostic tool with very short turnaround times. A range of spectrometric methods are available which allow the detection and quantification of bacterial VOCs down to a range of part per trillion. This narrative review introduces the application of spectrometric analytical methods for the purpose of detecting VOCs of bacterial origin and their clinical use for diagnosing different infectious conditions over the last decade. KEY POINTS: • Detection of VOCs enables bacterial differentiation in various medical conditions. • Spectrometric methods may function as point-of-care diagnostics in near future.

Changes in volatile organic compounds provoked by lipopolysaccharide- or alpha toxin-induced inflammation in ventilated rats

Inflammation may alter volatile organic compounds (VOCs) in exhaled breath. We therefore used ion mobility spectrometry (IMS) to evaluate exhaled breath components in two non-infectious inflammatory models. Fifty male Sprague Dawley rats were anesthetized and ventilated for 24 h. Five treatments were randomly assigned: (1) lipopolysaccharide low dose [5 mg/kg]; (2) lipopolysaccharide high dose [10 mg/kg]; (3) alpha toxin low dose [40 µg/kg]; (4) alpha toxin high dose [80 µg/kg]; and, (5) NaCl 0.9% as control group. Gas was sampled from the expiratory line of the ventilator every 20 min and analyzed with IMS combined with a multi-capillary column. VOCs were identified by comparison with an established database. Survival analysis was performed by log-rank test, other analyses by one-way or paired ANOVA-tests and post-hoc analysis according to Holm-Sidak. Rats given NaCl and low-dose alpha toxin survived 24 h. The median survival time in alpha toxin high-dose group was 23 (95%-confidence interval (CI): 21, 24) h. In contrast, the median survival time in rats given high-dose lipopolysaccharide was 12 (95% CI: 9, 14) and only 13 (95% CI: 10, 16) h in those given high-dose lipopolysaccharide. 73 different VOCs were detected, of which 35 were observed only in the rats, 38 could be found both in the blank measurements of ventilator air and in the exhaled air of the rats. Forty-nine of the VOCs were identifiable from a registry of compounds. Exhaled volatile compounds were comparable in each group before injection of lipopolysaccharide and alpha toxin. In the LPS groups, 1-pentanol increased and 2-propanol decreased. After alpha toxin treatment, 1-butanol and 1-pentanol increased whereas butanal and isopropylamine decreased. Induction of a non-infectious systemic inflammation (niSI) by lipopolysaccharide and alpha toxin changes VOCs in exhaled breath. Exhalome analysis may help identify niSI.

Volatile organic compounds in head and neck squamous cell carcinoma-An in vitro pilot study

Owing to the lack of specific symptoms, diagnosis of head and neck squamous cell carcinoma (HNSCC) may be delayed. We evaluated volatile organic compounds in tumor samples from patients suffering from HNSCC and tested the hypothesis that there is a characteristic altered composition in the headspace of HNSCC compared with control samples from the same patient with normal squamous epithelium. These results provide the basis for future noninvasive breath analysis in HNSCC. Headspace air of suspected tumor and contralateral control samples in 20 patients were analyzed using ion-mobility spectrometry. Squamous cell carcinoma was diagnosed in 16 patients. In total, we observed 93 different signals in headspace measurements. Squamous cell carcinomas revealed significantly higher levels of volatile cyclohexanol (0.54 ppb_v , 25th to 75th percentiles 0.35-0.86) compared with healthy squamous epithelium (0.24 ppb_v , 25th to 75th percentiles 0.12-0.3; p < 0.001). In conclusion, head and neck squamous cell carcinoma emitted significantly higher levels of volatile cyclohexanol in headspace compared with normal squamous epithelium. These findings form the basis for future breath analysis for diagnosis, therapy control and the follow-up of HNSSC to improve therapy and aftercare.

Volatile Organic Compounds in Patients With Acute Kidney Injury and Changes During Dialysis

OBJECTIVES

To characterize volatile organic compounds in breath exhaled by ventilated care patients with acute kidney injury and changes over time during dialysis.

DESIGN

Prospective observational feasibility study.

SETTING

Critically ill patients on an ICU in a University Hospital, Germany.

PATIENTS

Twenty sedated, intubated, and mechanically ventilated patients with acute kidney injury and indication for dialysis.

INTERVENTIONS

Patients exhalome was evaluated from at least 30 minutes before to 7 hours after beginning of continuous venovenous hemodialysis.

MEASUREMENTS AND MAIN RESULTS

Expired air samples were aspirated from the breathing circuit at 20-minute intervals and analyzed using multicapillary column ion-mobility spectrometry. Volatile organic compound intensities were compared with a ventilated control group with normal renal function. A total of 60 different signals were detected by multicapillary column ion-mobility spectrometry, of which 44 could be identified. Thirty-four volatiles decreased during hemodialysis, whereas 26 remained unaffected. Forty-five signals showed significant higher intensities in patients with acute kidney injury compared with control patients with normal renal function. Among these, 30 decreased significantly during hemodialysis. Volatile cyclohexanol (23 mV; 2575th, 19-38), 3-hydroxy-2-butanone (16 mV, 9-26), 3-methylbutanal (20 mV; 14-26), and dimer of isoprene (26 mV; 18-32) showed significant higher intensities in acute kidney impairment compared with control group (12 mV; 10-16 and 8 mV; 7-14 and not detectable and 4 mV; 0-6; p < 0.05) and a significant decline after 7 hours of continuous venovenous hemodialysis (16 mV; 13-21 and 7 mV; 6-13 and 9 mV; 8-13 and 14 mV; 10-19).

CONCLUSIONS

Exhaled concentrations of 45 volatile organic compounds were greater in critically ill patients with acute kidney injury than in patients with normal renal function. Concentrations of two-thirds progressively decreased during dialysis. Exhalome analysis may help quantify the severity of acute kidney injury and to gauge the efficacy of dialysis.

Hybrid Analytical Platform Based on Field-Asymmetric Ion Mobility Spectrometry, Infrared Sensing, and Luminescence-Based Oxygen Sensing for Exhaled Breath Analysis

The reliable online analysis of volatile compounds in exhaled breath remains a challenge, as a plethora of molecules occur in different concentration ranges (i.e., ppt to %) and need to be detected against an extremely complex background matrix. Although this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three different detector types based on truly orthogonal measurement principles as an alternative solution: Field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). By carefully aligning the experimental needs and measurement protocols of all three methods, they were successfully integrated into a single compact analytical platform suitable for online measurements. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N₂), oxygen (O₂), carbon dioxide (CO₂), and acetone as a model volatile organic compound (VOC) commonly present in breath. All three target analytes could be detected within their respectively breath-relevant concentration range, i.e., CO₂ and O₂ at 3-5 % and at ~19.6 %, respectively, while acetone could be detected with LOQs as low as 165-405 ppt. Orthogonality of the three methods operating in concert was clearly proven, which is essential to cover a possibly wide range of detectable analytes. Finally, the remaining challenges toward the implementation of the developed hybrid FAIMS-FTIR-LS system for exhaled breath analysis for metabolic studies in small animal intensive care units are discussed.

Statistical analysis of MCC-IMS data for two group comparisons-an exemplary study on two devices

The Multi-capillary-column-Ion-mobility-spectrometry (MCC-IMS) technology for measuring breath gas can be used for distinguishing between healthy and diseased subjects or between different types of diseases. The statistical methods for classifying the corresponding breath samples typically neglects potential confounding clinical and technical variables, reducing both accuracy and generalizability of the results. Especially measuring samples on different technical devices can heavily influence the results. We conducted a controlled breath gas study including 49 healthy volunteers to evaluate the effect of the variables sex, smoking habits and technical device. Every person was measured twice, once before and once after consuming a glass of orange juice. The two measurements were obtained on two different devices. The evaluation of the MCC-IMS data regarding metabolite detection was performed once using the software VisualNow, which requires manual interaction, and once using the fully automated algorithm SGLTR-DBSCAN. We present statistical solutions, peak alignment and scaling, to adjust for the different devices. For the other potential confounders sex and smoking, in our study no significant influence was identified.

Characterization of the aroma release and perception of white bread during oral processing by gas chromatography-ion mobility spectrometry and temporal dominance of sensations analysis

The purpose of this study was to investigate the aroma release and perception from white bread during oral processing by gas chromatography-ion mobility spectrometry (GC-IMS) and dynamic sensory evaluation of temporal dominance of sensations (TDS). TDS curves indicated that two maximum aroma perception signals, fermentation-like and flour-like attributes, were perceived at the beginning and swallowing, respectively. The fermentation-like, flour-like, and sour attributes were the 3 dominant aromas during oral processing. A total of 35 volatile compounds were detected in the mouth cavity during chewing white bread, 19 of them were confirmed and quantified by using the respective external standard. Based on PLSR analysis, 8 aroma compounds were predicted as potent odorants contributing to the aroma perception from chewing white bread. By application of odor activity values analysis and addition experiments, ethyl butanoate, butyl acetate, hexanal, 3-(methylthio)-propanal, 3-methylbutanal, and 2,3-butanedione were confirmed as the key odorants contributing to the aroma perception during chewing of white bread.

Ultra-high-resolution ion mobility spectrometry-current instrumentation, limitations, and future developments

With recent advances in ionization sources and instrumentation, ion mobility spectrometers (IMS) have transformed from a detector for chemical warfare agents and explosives to a widely used tool in analytical and bioanalytical applications. This increasing measurement task complexity requires higher and higher analytical performance and especially ultra-high resolution. In this review, we will discuss the currently used ion mobility spectrometers able to reach such ultra-high resolution, defined here as a resolving power greater than 200. These instruments are drift tube IMS, traveling wave IMS, trapped IMS, and field asymmetric or differential IMS. The basic operating principles and the resulting effects of experimental parameters on resolving power are explained and compared between the different instruments. This allows understanding the current limitations of resolving power and how ion mobility spectrometers may progress in the future. Graphical abstract.

Bedside Measurement of Volatile Organic Compounds in the Atmosphere of Neonatal Incubators Using Ion Mobility Spectrometry

Background: Early and non-invasive diagnosis of common diseases is of great importance in the care of preterm infants. We hypothesized that volatile organic compounds (VOC) can be successfully measured in the neonatal incubator atmosphere. Methods: This is a feasibility study to investigate whether the discrimination of occupied and unoccupied neonatal incubators is possible by bedside measurement of volatile organic compounds (VOCs) on the neonatal intensive care unit. VOC profiles were measured in the incubator air using ion mobility spectrometry coupled to multi-capillary columns (BreathDiscovery B&S Analytik GmbH, Dortmund, Germany). Results: Seventeen incubators occupied by preterm infants (50 measurements) and nine unoccupied neonatal incubators were sampled, using 37 room air measurements as controls. Three VOC signals that allow the discrimination between occupied and unoccupied incubators were identified. The best discrimination was reached by peak P20 exhibiting a sensitivity, specificity, positive predictive value and negative predictive value of 94.0, 88.9, 97.3, and 72.3%, respectively. Use of a decision tree improved these values to 100.0, 88.9, 98.0, and 100.0%, respectively. Discussion: A bedside method that allows the characterization of VOC profiles in the neonatal incubator atmosphere using ion mobility spectrometry was established. Occupied and unoccupied incubators could be discriminated by characterizing VOC profiles. This technique has the potential to yield results within minutes. Thus, future studies are recommended to test the hypothesis that VOCs within neonatal incubators are useful biomarkers for non-invasive diagnostics in preterm neonates.

Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols

Human breath, along with urine and blood, has long been one of the three major biological media for assessing human health and environmental exposure. In fact, the detection of odor on human breath, as described by Hippocrates in 400 BC, is considered the first analytical health assessment tool. Although less common in comparison to contemporary bio-fluids analyses, breath has become an attractive diagnostic medium as sampling is non-invasive, unlimited in timing and volume, and does not require clinical personnel. Exhaled breath, exhaled breath condensate (EBC), and exhaled breath aerosol (EBA) are different types of breath matrices used to assess human health and disease state. Over the past 20 years, breath research has made many advances in assessing health state, overcoming many of its initial challenges related to sampling and analysis. The wide variety of sampling techniques and collection devices that have been developed for these media are discussed herein. The different types of sensors and mass spectrometry instruments currently available for breath analysis are evaluated as well as emerging breath research topics, such as cytokines, security and airport surveillance, cellular respiration, and canine olfaction.

Multi-capillary Column Ion Mobility Spectrometry of Volatile Metabolites for Phenotyping of Microorganisms

Rational strain engineering requires solid testing of phenotypes including productivity and ideally contributes thereby directly to our understanding of the genotype-phenotype relationship. Actually, the test step of the strain engineering cycle becomes the limiting step, as ever advancing tools for generating genetic diversity exist. Here, we briefly define the challenge one faces in quantifying phenotypes and summarize existing analytical techniques that partially overcome this challenge. We argue that the evolution of volatile metabolites can be used as proxy for cellular metabolism. In the simplest case, the product of interest is a volatile (e.g., from bulk alcohols to special fragrances) that is directly quantified over time. But also nonvolatile products (e.g., from bulk long-chain fatty acids to natural products) require major flux rerouting that result potentially in altered volatile production. While alternative techniques for volatile determination exist, rather few can be envisaged for medium to high-throughput analysis required for phenotype testing. Here, we contribute a detailed protocol for an ion mobility spectrometry (IMS) analysis that allows volatile metabolite quantification down to the ppb range. The sensitivity can be exploited for small-scale fermentation monitoring. The insights shared might contribute to a more frequent use of IMS in biotechnology, while the experimental aspects are of general use for researchers interested in volatile monitoring.

Review: Endogenously Produced Volatiles for In Vitro Toxicity Testing Using Cell Lines

Due to the ∼86,000 chemicals registered under the Toxic Substances Control Act and increasing ethical concerns regarding animal testing, it is not economically or technically feasible to screen every registered chemical for toxicity using animal-based toxicity assays. To address this challenge, regulatory agencies are investigating high-throughput screening in vitro methods to increase speed of toxicity testing, while reducing the overall cost. One approach for rapid toxicity testing currently being investigated is monitoring of volatile emissions produced by cell lines in culture. Such a metabolomics approach would measure gaseous emissions from a cell line and determine if such gaseous metabolites are altered upon exposure to a xenobiotic. Herein, we describe the history and rationale of monitoring endogenously produced volatiles for identification of pathologic conditions, as well as emerging applications in toxicity testing for such an approach.

Ion Distribution Profiling in an Ion Mobility Spectrometer by Laser-Induced Fluorescence

Measuring the ion distribution pattern in a drift tube under atmospheric pressure is very useful for studies of ion motion and design of ion mobility spectrometers (IMS); however, no mature method is available for conducting such measurements at present. We propose a simple and low-cost technique for profiling the two-dimensional ion distribution in any cross section of a drift tube. Similar to particle-image velocimetry, we first send sample ions with fluorescence properties into the drift tube and use a receiving plate to collect and accumulate them. Then, the receiving plate is illuminated by exciting light, and the ion distribution appears as a fluorescence image. In this study, Rhodamine 6G was selected as a typical fluorescence-tracer particle. Electrospray ionization (ESI) was chosen as an ionization source to keep the fluorophore undamaged. A plasma-cleaned coverslip was placed at the detection position as a receiving plate. When a layer of ions was collected, the slide was placed under the exciting light with a wavelength of 473 nm. A camera with a 490 nm high-pass light filter was used to capture the fluorescence image representing the ion distribution. The measured-ion detection efficiency of the method was 156 ion/dN, which is equivalent to the level of IonCCD. In addition, we studied the ion-passing characteristics of a Bradbury-Nielsen (BN) ion shutter and the ion-focusing effect in the drift tube using this method. The two-dimensional ion-distribution images behind the ion shutter and the images of the focused ion spot were first observed experimentally. Further theoretical analysis yielded the same conclusions as the experimental results, proving the feasibility of this method and producing a deeper understanding of ion motion in the IMS. This method has promising prospective application to the design, debugging, and optimization of IMS instruments and hyphenated systems.

Cancer breath testing: a patent review

INTRODUCTION

Human breath can contain thousands of volatile organic compounds (VOCs) and semi-volatile compounds that are related to metabolism and other biochemical processes. The presence of cancer cells can affect the identity and abundances of chemicals in breath when compared to those in healthy control subjects, which can be used to indicate the likelihood of a patient having cancer. Recently, the chemical analysis of exhaled breath from patients has been shown to be promising for diagnosing many different types of cancers, including lung, breast, colon, head, neck, and prostate, along with pre-cancerous conditions (dysplasia).

AREAS COVERED

Here, we reviewed the sampling, analytical and data analysis methods reported in the recent patent literature related to cancer breath testing (2014-2017). In addition, the different types of cancer biomarkers that were disclosed are discussed.

EXPERT OPINION

The major advantages of breath testing compared to conventional X-ray and imaging based methods includes simplicity of use, non-invasiveness, and the potential to detect cancer at a relatively early stage. Such methods are also suitable to perform population screening because of their non-invasiveness. However, the establishment of standard sampling, detection and quantification methods for breath testing is required before the methods can be employed for clinical diagnosis.

Fast Orthogonal Separation by Superposition of Time of Flight and Field Asymmetric Ion Mobility Spectrometry

Ion mobility spectrometry is a powerful and low-cost technique for the identification of chemical warfare agents, toxic chemicals, or explosives in air. Drift tube ion mobility spectrometers (DT-IMS) separate ions by the absolute value of their low field ion mobility, while field asymmetric ion mobility spectrometers (FAIMS) separate them by the change of their ion mobility at high fields. However, using one of these devices alone, some common and harmless substances show the same response as the hazardous target substances. In order to increase the selectivity, orthogonal data are required. Thus, in this work, we present for the first time an ambient pressure ion mobility spectrometer which is able to separate ions both by their differential and low field mobility, providing additional information for selectivity enhancement. This novel field asymmetric time of flight ion mobility spectrometer (FAT-IMS) allows high repetition rates and reaches limits of detection in the low ppb range common for DT-IMS. The device consists of a compact 44 mm drift tube with a tritium ionization source and a resolving power of 70. An increased separation of four substances with similar low field ion mobility is shown: phosgene (K₀ = 2.33 cm²/(V s)), 1,1,2-trichlorethane (K₀ = 2.31 cm²/(V s)), chlorine (K₀ = 2.24 cm²/(V s)), and nitrogen dioxide (K₀ = 2.25 cm²/(V s)). Furthermore, the behavior and limits of detection for acetonitrile, dimethyl methylphosphonate, diisopropyl methyl phosphonate in positive polarity and carbon dioxide, sulfur dioxide, hydrochloric acid, cyanogen chloride, and hydrogen cyanide in negative polarity are investigated.

Portable FAIMS: Applications and Future Perspectives

Miniaturized mass spectrometry (MMS) is optimal for a wide variety of applications that benefit from field-portable instrumentation. Like MMS, field asymmetric ion mobility spectrometry (FAIMS) has proven capable of providing in situ analysis, allowing researchers to bring the lab to the sample. FAIMS compliments MMS very well, but has the added benefit of operating at atmospheric pressure, unlike MS. This distinct advantage makes FAIMS uniquely suited for portability. Since its inception, FAIMS has been envisioned as a field-portable device, as it affords less expense and greater simplicity than many similar methods. Ideally, these are simple, robust devices that may be operated by non-professional personnel, yet still provide adequate data when in the field. While reducing the size and complexity tends to bring with it a loss of performance and accuracy, this is made up for by the incredibly high throughput and overall convenience of the instrument. Moreover, the FAIMS device used in the field can be brought back to the lab, and coupled to a conventional mass spectrometer to provide any necessary method development and compound validation. This work discusses the various considerations, uses, and applications for portable FAIMS instrumentation, and how the future of each applicable field may benefit from the development and acceptance of such a device.

Exhaled Volatile Organic Compounds of Infection: A Systematic Review

With heightened global concern of microbial drug resistance, advanced methods for early and accurate diagnosis of infection are urgently needed. Analysis of exhaled breath volatile organic compounds (VOCs) toward detecting microbial infection potentially allows a highly informative and noninvasive alternative to current genomics and culture-based methods. We performed a systematic review of research literature reporting human and animal exhaled breath VOCs related to microbial infections. In this Review, we find that a wide range of breath sampling and analysis methods are used by researchers, which significantly affects interstudy method comparability. Studies either perform targeted analysis of known VOCs relating to an infection, or non-targeted analysis to obtain a global profile of volatile metabolites. In general, the field of breath analysis is still relatively immature, and there is much to be understood about the metabolic production of breath VOCs, particularly in a host where both commensal microflora as well as pathogenic microorganisms may be manifested in the airways. We anticipate that measures to standardize high throughput sampling and analysis, together with an increase in large scale collaborative international trials, will bring routine breath VOC analysis to improve diagnosis of infection closer to reality.

A detailed comparison of analysis processes for MCC-IMS data in disease classification-Automated methods can replace manual peak annotations

Motivation

Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process.

Method

We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios.

Results

The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology.

Volatile organic compounds in ventilated critical care patients: a systematic evaluation of cofactors

Background

Expired gas (exhalome) analysis of ventilated critical ill patients can be used for drug monitoring and biomarker diagnostics. However, it remains unclear to what extent volatile organic compounds are present in gases from intensive care ventilators, gas cylinders, central hospital gas supplies, and ambient air. We therefore systematically evaluated background volatiles in inspired gas and their influence on the exhalome.

Methods

We used multi-capillary column ion-mobility spectrometry (MCC-IMS) breath analysis in five mechanically ventilated critical care patients, each over a period of 12 h. We also evaluated volatile organic compounds in inspired gas provided by intensive care ventilators, in compressed air and oxygen from the central gas supply and cylinders, and in the ambient air of an intensive care unit. Volatiles detectable in both inspired and exhaled gas with patient-to-inspired gas ratios < 5 were defined as contaminating compounds.

Results

A total of 76 unique MCC-IMS signals were detected, with 39 being identified volatile compounds: 73 signals were from the exhalome, 12 were identified in inspired gas from critical care ventilators, and 34 were from ambient air. Five volatile compounds were identified from the central gas supply, four from compressed air, and 17 from compressed oxygen. We observed seven contaminating volatiles with patient-to-inspired gas ratios < 5, thus representing exogenous signals of sufficient magnitude that might potentially be mistaken for exhaled biomarkers.

Conclusions

Volatile organic compounds can be present in gas from central hospital supplies, compressed gas tanks, and ventilators. Accurate assessment of the exhalome in critical care patients thus requires frequent profiling of inspired gases and appropriate normalisation of the expired signals.

Online Measurement of Exhaled NO Concentration and Its Production Sites by Fast Non-equilibrium Dilution Ion Mobility Spectrometry

Exhaled nitric oxide (NO) is one of the most promising breath markers for respiratory diseases. Its profile for exhalation and the respiratory NO production sites can provide useful information for medical disease diagnosis and therapeutic procedures. However, the high-level moisture in exhaled gas always leads to the poor selectivity and sensitivity for ion spectrometric techniques. Herein, a method based on fast non-equilibrium dilution ion mobility spectrometry (NED-IMS) was firstly proposed to directly monitor the exhaled NO profile on line. The moisture interference was eliminated by turbulently diluting the original moisture to 21% of the original with the drift gas and dilution gas. Weak enhancement was observed for humid NO response and its limit of detection at 100% relative humidity was down to 0.58 ppb. The NO concentrations at multiple exhalation flow rates were measured, while its respiratory production sites were determined by using two-compartment model (2CM) and Högman and Meriläinen algorithm (HMA). Last but not the least, the NO production sites were analyzed hourly to tentatively investigate the daily physiological process of NO. The results demonstrated the capacity of NED-IMS in the real-time analysis of exhaled NO and its production sites for clinical diagnosis and assessment.

Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles

Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1].

Online breath analysis of propofol during anesthesia: clinical application of membrane inlet-ion mobility spectrometry

BACKGROUND

Breath analysis of propofol is a potential noninvasive method for approximating the plasma propofol concentration. There have been various reported techniques for measuring the exhaled propofol concentration at steady state; however, the propofol concentration undergoes marked changes during clinical anesthesia. Therefore, this study investigated the use of membrane inlet-ion mobility spectrometry (MI-IMS) to monitor exhaled propofol discontinuously and continuously during propofol anesthesia.

METHODS

The study included 19 patients of American Society of Anesthesiologists physical status I or II. In experiment I (discontinuous study), breath and blood samples were collected discontinuously, with stable target propofol concentrations of 2.8 μg/ml, 3.2 μg/ml, 3.5 μg/ml, and 3.8 μg/ml. In experiment II (continuous study), propofol concentration was maintained at 3.5 μg/ml after induction, and exhaled breath was collected continuously every 3 min during propofol infusion. Relationships of the exhaled propofol concentration with the plasma propofol concentration, measured by high-performance liquid chromatography and the continuously measured bispectral (BIS) index were investigated.

RESULTS

Comparison of the exhaled and plasma propofol concentrations revealed a bias ± precision of 2.1% ± 14.6% (95% limits of agreement: - 26.5-30.7%) in experiment I and - 10.4% ± 13.2 (- 36.3-15.4%) in experiment II. In both experiments, exhaled propofol concentrations measured by MI-IMS were consistent with, the propofol effect represented by the BIS index.

CONCLUSIONS

MI-IMS may be a suitable method to predict plasma propofol concentration online during propofol anesthesia. Monitoring exhaled propofol may improve the safety of propofol anesthesia.

Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview

Currently, a small number of diseases, particularly cardiovascular (CVDs), oncologic (ODs), neurodegenerative (NDDs), chronic respiratory diseases, as well as diabetes, form a severe burden to most of the countries worldwide. Hence, there is an urgent need for development of efficient diagnostic tools, particularly those enabling reliable detection of diseases, at their early stages, preferably using non-invasive approaches. Breath analysis is a non-invasive approach relying only on the characterisation of volatile composition of the exhaled breath (EB) that in turn reflects the volatile composition of the bloodstream and airways and therefore the status and condition of the whole organism metabolism. Advanced sampling procedures (solid-phase and needle traps microextraction) coupled with modern analytical technologies (proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, e-noses, etc.) allow the characterisation of EB composition to an unprecedented level. However, a key challenge in EB analysis is the proper statistical analysis and interpretation of the large and heterogeneous datasets obtained from EB research. There is no standard statistical framework/protocol yet available in literature that can be used for EB data analysis towards discovery of biomarkers for use in a typical clinical setup. Nevertheless, EB analysis has immense potential towards development of biomarkers for the early disease diagnosis of diseases.

Exhaled breath analysis for lung cancer detection using ion mobility spectrometry

BACKGROUND

Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique.

METHODS

The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation.

RESULTS

A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%.

CONCLUSION

In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.