Techniques and issues in breath and clinical sample headspace analysis for disease diagnosis

Non-destructive egg breed separation using advanced VOC analytical techniques HSSE-GC-MS, PTR-TOF-MS, and SIFT-MS: Assessment of performance and systems' complementarity

Over the past decade, advanced analytical techniques have been utilized to examine volatile organic compounds (VOCs) in eggs. These VOCs offer valuable insights into factors such as freshness, fertility, the presence of cracks, embryo sex, and breed. In our study, we assessed three mass spectrometry-based systems (headspace sorptive extraction gas chromatography-mass spectrometry; HSSE-GC-MS, proton transfer reaction time-of-flight-mass spectrometry; PTR-TOF-MS; and selected ion flow tube mass spectrometry; SIFT-MS) to analyze and identify VOCs present in intact hatching eggs from three distinct breeds (Dekalb white layer, Shaver brown layer, and Ross 308 broiler). The eggs were sampled on incubation days 2 and 8, to identify VOCs that distinguish breeds irrespective of incubation day. VOC measurements were conducted on 15 eggs per breed by placing them together with PDMS-coated stir bars inside inert Teflon® air sampling bags. After an accumulation period of 2 h, the headspace was analyzed using PTR-TOF-MS and SIFT-MS, while the VOCs adsorbed onto the stir bars were analyzed using GC-MS for additional compound identification. Partial least squares discriminant analysis (PLS-DA) models were constructed for breed differentiation, and variable selection was performed. As a result, 111 VOCs were identified using HSSE-GC-MS, with alcohols and esters being the most abundant. The PLS-DA models demonstrated the efficacy of breed discrimination, with the HSSE-GC-MS and the PTR-TOF-MS exhibiting the highest balanced accuracy of 95.5 % using a reduced set of 11 VOCs and 5 product ions, respectively. The SIFT-MS model had a balanced accuracy of 92.8 % with a reduced set of 11 product ions. Furthermore, complementarity was observed between HSSE-GC-MS, which primarily selected higher molecular weight VOCs, and PTR-TOF-MS and SIFT-MS. A higher correlation was found for compound abundances between the HSSE-GC-MS and the PTR-TOF-MS relative to the SIFT-MS, indicating that the PTR-TOF-MS was better suited to quantify specific compounds identified by the HSSE-GC-MS. Finally, the findings support the presence of VOCs originating from both synthetic and natural sources, highlighting the ability of the VOC analysis systems to non-destructively perform quality control and reveal differences in management practices or biological information encoded in eggs.

Changes in lung epithelial cell volatile metabolite profile induced by pro-fibrotic stimulation with TGF-β1

Volatile organic compounds (VOCs) have shown promise as potential biomarkers in idiopathic pulmonary fibrosis. Measuring VOCs in the headspace ofin vitromodels of lung fibrosis may offer a method of determining the origin of those detected in exhaled breath. The aim of this study was to determine the VOCs associated with two lung cell lines (A549 and MRC-5 cells) and changes associated with stimulation of cells with the pro-fibrotic cytokine, transforming growth factor (TGF)-β1. A dynamic headspace sampling method was used to sample the headspace of A549 cells and MRC-5 cells. These were compared to media control samples and to each other to identify VOCs which discriminated between cell lines. Cells were then stimulated with the TGF-β1 and samples were compared between stimulated and unstimulated cells. Samples were analysed using thermal desorption-gas chromatography-mass spectrometry and supervised analysis was performed using sparse partial least squares-discriminant analysis (sPLS-DA). Supervised analysis revealed differential VOC profiles unique to each of the cell lines and from the media control samples. Significant changes in VOC profiles were induced by stimulation of cell lines with TGF-β1. In particular, several terpenoids (isopinocarveol, sativene and 3-carene) were increased in stimulated cells compared to unstimulated cells. VOC profiles differ between lung cell lines and alter in response to pro-fibrotic stimulation. Increased abundance of terpenoids in the headspace of stimulated cells may reflect TGF-β1 cell signalling activity and metabolic reprogramming. This may offer a potential biomarker target in exhaled breath in IPF.

Early Detection of Prostate Cancer: The Role of Scent

Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time

Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.

Volatile organic compounds as a potential screening tool for neoplasm of the digestive system: a meta-analysis

This meta-analysis was aimed to estimate the diagnostic performance of volatile organic compounds (VOCs) as a potential novel tool to screen for the neoplasm of the digestive system. An integrated literature search was performed by two independent investigators to identify all relevant studies investigating VOCs in diagnosing neoplasm of the digestive system from inception to 7th December 2020. STATA and Revman software were used for data analysis. The methodological quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate mixed model was used and meta-regression and subgroup analysis were performed to identify possible sources of heterogeneity. A total of 36 studies comprised of 1712 cases of neoplasm and 3215 controls were included in our meta-analysis. Bivariate analysis showed a pooled sensitivity of 0.87 (95% confidence interval (CI) 0.83–0.90), specificity of 0.86 (95% CI 0.82–0.89), a positive likelihood ratio of 6.18 (95% CI 4.68–8.17), and a negative likelihood ratio of 0.15 (95% CI 0.12–0.20). The diagnostic odds ratio and the area under the summary ROC curve for diagnosing neoplasm of the digestive system were 40.61 (95% CI 24.77–66.57) and 0.93 (95% CI 0.90–0.95), respectively. Our analyses revealed that VOCs analysis could be considered as a potential novel tool to screen for malignant diseases of the digestive system.

Exhaled Volatile Organic Compounds for Identifying Patients With Chronic Pulmonary Aspergillosis

Background: Diagnosing chronic pulmonary aspergillosis is a major challenge in clinical practice. The development and validation of a novel, sensitive and specific assay for diagnosing chronic pulmonary aspergillosis is urgently needed.

Methods: From April 2018 to June 2019, 53 patients with chronic pulmonary aspergillosis (CPA), 32 patients with community-acquired pneumonia (CAP) and 48 healthy controls were recruited from the First Affiliated Hospital of Guangzhou Medical University. Clinical characteristics and samples were collected at enrollment. All exhaled breath samples were analyzed offline using thermal desorption single-photon ionization time-of-flight mass spectrometry; to analyze the metabolic pathways of the characteristic volatile organic compounds, serum samples were subjected to ultrahigh-performance liquid chromatography.

Results: We identified characteristic volatile organic compounds in patients with chronic pulmonary aspergillosis, which mainly consisted of phenol, neopentyl alcohol, toluene, limonene and ethylbenzene. These compounds were assessed using a logistic regression model. The sensitivity and specificity were 95.8 and 96.9% for discriminating patients in the CPA group from those in the CAP group and 95.8 and 97.9% for discriminating patients in the CPA group from healthy controls, respectively. The concentration of limonene (m/z 136) correlated significantly positively with anti-Aspergillus fumigatus IgG antibody titers (r = 0.420, P < 0.01). After antifungal treatment, serum IgG and the concentration of limonene (m/z 136) decreased in the subgroup of patients with chronic pulmonary aspergillosis.

Conclusions: We identified VOCs that can be used as biomarkers for differential diagnosis and therapeutic response prediction in patients with chronic pulmonary aspergillosis.

Volatile organic compounds in bile can distinguish pancreatic cancer from chronic pancreatitis: a prospective observational study

BACKGROUND

Early and accurate diagnosis of pancreatic cancer is important. Our aim was to identify potential volatile organic compounds (VOCs) in the bile that can help distinguish pancreatic cancer from chronic pancreatitis.

METHODS

In this prospective observational study, bile was aspirated from patients undergoing endoscopic retrograde cholangiopancreatography for chronic pancreatitis and pancreatic cancer, and the gaseous headspace was analyzed using mass spectrometry.

RESULTS

The study included a discovery cohort of 57 patients (46 pancreatic cancer, 11 chronic pancreatitis) and a validation cohort of 31 patients (19 and 12, respectively). Using logistic regression analysis, the model [0.158 × age + 9.747 × log (ammonia) - 3.994 × log (acetonitrile) + 5.044 × log (trimethylamine) - 30.23] successfully identified patients with pancreatic cancer with a sensitivity of 93.5 % and specificity of 100 % (likelihood ratio 40.9, area under the curve 0.98, 95 % confidence interval 0.95 - 1.00). The diagnostic accuracy of this model was confirmed in the second independent validation cohort.

CONCLUSION

The measurement of VOCs in bile helped to accurately distinguish pancreatic cancer from chronic pancreatitis.

Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.

Breath selection methods for compact mass spectrometry breath analysis

Compact mass spectrometry (CMS) is a versatile and transportable analytical instrument that has the potential to be used in clinical settings to quickly and non-invasively detect a wide range of relevant conditions from breath samples. The purpose of this study is to optimise data preprocessing protocols by three proposed methods of breath sampling, using the CMS. It also lays out a general framework for which data processing methods can be evaluated.

METHODS

This paper considers data from three previous studies, each using a different breath sampling method. These include a peppermint washout study using continuous breath sampling with a purified air source, an exercise study using continuous breath sampling with an ambient air source, and a single breath sampling study with an ambient air source. For each dataset, different breath selection (data preprocessing) methods were compared and benchmarked according to predictive performance on a validation set and quantitative reliability of m/z bin intensity measurements.

RESULTS

For both continuous methods, the best breath selection method improved the predictive model compared to no preselection, as measured by the 95% CI range for Youden's index, from 0.68-0.86 to 0.86-0.97 for the exercise study and 0.69-0.82 to 1.00-1.00 for the peppermint study. The reliability of intensity measurements for both datasets (as measured by median relative standard deviation (RSD)), was improved slightly by the best selection method compared to no preselection, from 18% to 14% for the exercise study and 7%-5% for the peppermint study. For the single breath samples, all the models resulted in perfect prediction, with a 95% CI range for Youden's index of 1.00-1.00. The reliability of the proposed method was 38%.

CONCLUSION

The method of selecting exhaled breath from CMS data can affect the reliability of the measurement and the ability to distinguish between breath samples taken under different conditions. The application of appropriate data processing methods can improve the quality of the data and results obtained from CMS. The methods presented will enable untargeted analysis of breath VOCs using CMS to be performed.

Comparing patterns of volatile organic compounds exhaled in breath after consumption of two infant formulae with a different lipid structure: a randomized trial

Infant formulae have been used since decades as an alternative to or a complement to human milk. Human milk, the "gold standard" of infant nutrition, has been studied for its properties in order to create infant formulae that bring similar benefits to the infant. One of the characteristics of milk is the size of the lipid droplets which is known to affect the digestion, gastric emptying and triglyceride metabolism. In the current study a concept infant milk formula with large, phospholipid coating of lipid droplets (mode diameter 3-5 μm; NUTURIS, further described as "active"), was compared to a commercially available formula milk characterised by smaller lipid droplets, further described as "control" (both products derived from Nutricia). We investigated whether we could find an effect of lipid droplet size on volatile compounds in exhaled air upon ingestion of either product. For that purpose, exhaled breath was collected from a group of 29 healthy, non-smoking adult males before ingestion of a study product (baseline measurements, T0) and at the following time points after the test meal: 30, 60, 120, 180 and 240 min. Volatile organic compounds (VOCs) in breath were detected by gas chromatography-time-of-flight-mass spectrometry. Any differences in the time course of VOCs patterns upon intake of active and control products were investigated by regularised multivariate analysis of variance (rMANOVA). The rMANOVA analysis revealed statistically significant differences in the exhaled breath composition 240 min after ingestion of the active formula compared to control product (p-value < 0.0001), but did not show significant changes between active and control product at any earlier time points. A set of eight VOCs in exhaled breath had the highest contribution to the difference found at 240 minutes between the two formulas. A set of ten VOCs was different between baseline and the two formulae at T240 with p-value < 0.0001. To our knowledge this is the first study that shows the ability of VOCs in exhaled breath to monitor metabolic effects after ingestion of infant formulae with different lipid structure. The statistically significant differences in compound abundance found between active and control formula milk may be related to: (i) specific differences in the digestion, (ii) absorption of lipids and proteins and (iii) assimilation of the products in the gut.

Breath based volatile organic compounds in the detection of breast, lung, and colorectal cancers: A systematic review

BACKGROUND

Detecting volatile organic compounds (VOCs) could provide a rapid, noninvasive, and inexpensive screening tool for detecting cancer.

OBJECTIVE

In this systematic review, we identified specific exhaled breath VOCs correlated with lung, colorectal, and breast cancer.

METHODS

We identified relevant studies published in 2015 and 2016 by searching Pubmed and Web of Science. The protocol for this systematic review was registered in PROSPERO and the PRISMA guidelines were used in reporting. VOCs and performance data were extracted.

RESULTS

Three hundred and thirty three records were identified and 43 papers were included in the review, of which 20 were review articles themselves. We identified 17 studies that listed the VOCs with at least a subset of statistics on detection cutoff levels, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and gradient.

CONCLUSIONS

Breath analysis for cancer screening and early detection shows promise, because samples can be collected easily, safely, and frequently. While gas chromatography-mass spectrometry is considered the gold standard for identifying specific VOCs, breath analysis has moved into analyzing patterns of VOCs using a variety of different multiple sensor techniques, such as eNoses and nanomaterials. Further development of VOCs for early cancer detection requires clinical trials with standardized breath sampling methods.

Non-separative mass spectrometry methods for non-invasive medical diagnostics based on volatile organic compounds: A review

In this review, an assessment of non-separative methods based on mass spectrometry used to analyse volatile organic compounds in the field of bioanalysis is performed. The use of non-separative methods based on mass spectrometry has been established as an attractive option for analysing compounds. These instrumental configurations are suitable for biomedical applications because of their versatility, rapid output of results, and the wide range of volatile organic compounds that can be determined. Here, techniques such as headspace sampling coupled to mass spectrometry, membrane introduction mass spectrometry, selected ion flow tube mass spectrometry, proton transfer reaction mass spectrometry, secondary electrospray ionization mass spectrometry and ion mobility mass spectrometry, are evaluated. Samples involving non-invasive methods of collection, such as urine, saliva, breath and sweat, are mainly considered. To the best of our knowledge, a comprehensive review of all the non-separative instrumental configurations applied to the analysis of gaseous samples from all matrices non-invasively collected has not yet been carried out. The assessment of non-separative techniques for the analysis of these type of samples can be considered a key issue for future clinical applications, as they allow real-time sample analysis, without patient suffering. Any contribution to the early diagnosis of disease can be considered a priority for the scientific community. Therefore, the identification and determination of volatile organic compounds related to particular diseases has become an important field or research.

A detection dog for obstructive sleep apnea

PURPOSE

We sought to assess whether a dog can be trained to distinguish obstructive sleep apnea patients from healthy controls based on the olfactory detection of urine.

METHODS

Urine samples were collected from 23 adult male obstructive sleep apnea patients and from 20 voluntary adult male volunteers. Three dogs were trained through reinforced operant conditioning.

RESULTS

Two of the three dogs correctly detected two thirds of obstructive sleep apnea patients (p < 0.000194 and p < 0.000003, respectively).

CONCLUSIONS

We found that dogs can be trained to distinguish obstructive sleep apnea patients from healthy controls based on the smell of urine. Potentially, dogs could be utilized to identify novel biomarkers or possibly screen for obstructive sleep apnea.

Quantitative Detection of Ethanol/Acetone in Complex Solutions Using Raman Spectroscopy Based on Headspace Gas Analysis

This paper demonstrated the quantitative detection of ethanol and acetone mixtures in complex solutions with Raman spectroscopy based on headspace gas analysis. By analyzing the volatile components in the headspace, their concentrations in liquid solutions were determined. We constructed our own Raman spectroscopy system to detect the headspace gas quantitatively over a solution in a sealed vial. The Raman spectra of the headspace gases over standard solutions were standardized for finding the concentrations of ethanol, acetone, and ethanol-acetone in mixture solutions. The results showed that the concentration of a gaseous component in the headspace gas was proportional to its ratio in the liquid solution. We obtained a linear relationship between the spectral intensity of volatile components in headspace and the concentration of the liquid solutions. Then, we analyzed the alcohol concentration in a white wine and a Chinese liquor called Fen Chiew by measuring the Raman spectra of the headspace gas over their liquids. For the river water sample, we also implemented our headspace gas detection with Raman spectra to obtain the concentration of acetone in the river sample. This work demonstrated the facilitation of headspace gas analysis by the qualitative and quantitative determination of volatile substances from liquid samples using Raman spectroscopy.

On the importance of accurate quantification of individual volatile metabolites in exhaled breath

It is argued that shortcomings of certain approaches to breath analysis research based on superficial interpretation of non-quantitative data are inadvertently inhibiting the progression of non-invasive breath analysis into clinical practice. The objective of this perspective is to suggest more clinically profitable approaches to breath research. Thus, following a discourse on the challenges and expectations in breath research, a brief indication is given of the analytical techniques currently used for the analysis of very humid exhaled breath. The seminal work that has been carried out using GC-MS revealed that exhaled breath comprises large numbers of trace volatile organic compounds, VOCs. Unfortunately, analysis of these valuable GC-MS data is mostly performed using chemometrics to distinguish the VOC content of breath samples collected from patients and healthy controls, and reliable quantification of the VOCs is rarely deemed necessary. This limited approach ignores the requirements of clinically acceptable biomarkers and misses the opportunity to identify relationships between the concentrations of individual VOCs and certain related physiological or metabolic parameters. Therefore, a plea is made for more effort to be directed towards the positive identification and accurate quantification of individual VOCs in exhaled breath, which are more physiologically meaningful as best exemplified by the quantification of breath nitric oxide, NO. Support for the value of individual VOC quantification is illustrated by the SIFT-MS studies of breath hydrogen cyanide, HCN, a biomarker of Pseudomonas aeruginosa infection, breath acetic acid as an indicator of airways acidification in cystic fibrosis patients, and n-pentane as a breath biomarker of inflammation in idiopathic bowel disease patients. These single VOCs could be used as non-invasive monitors of the efficacy of therapeutic intervention. The increase of breath methanol following the ingestion of a known amount of the sweetener aspartame impressively shows that accurate breath analysis is a reliable indicator of blood concentrations. However, using individual VOCs for specific disease diagnosis does have its problems and it is, perhaps, more appropriate to see their concentrations as proxy markers of general underlying physiological change. We dedicate this perspective to Lars Gustafsson for his seminal work on breath research and especially for his pioneering work on nitric oxide measurements in exhaled breath in asthma, which best shows the utility and value of the quantification of individual breath biomarkers on which this perspective focuses.

Quantum cascade lasers (QCLs) in biomedical spectroscopy

This review focuses on the recent applications of QCLs in mid-IR spectroscopy of clinically relevant samples.