Detection technologies of volatile organic compounds in the breath for cancer diagnoses

A matrix-centered view of mass spectrometry platform innovation for volatilome research

Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.

Recent progress on media for biological sample preparation

The analysis of biological samples is highly valuable for disease diagnosis and treatment, forensic examination, and public safety. However, the serious matrix interference effect generated by biological samples severely affects the analysis of trace analytes. Sample preparation methods are introduced to address the limitation by extracting, separating, enriching, purifying trace target analytes from biological samples. With the raising demand of biological sample analysis, a review focuses on media for biological sample preparation and analysis over the last 5 years is presented. High-performance media in biological sample preparation are first reviewed, including porous organic frameworks, imprinted polymers, hydrogels, ionic liquids, and bioactive media. Then, application of media for different biological sample preparation and analysis is briefly introduced, including liquid samples of body fluids, solid samples (hair, feces, and tissues), and gas samples of exhale breath gas. Finally, conclusions and outlooks on media promoting biological sample preparation are presented.

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Discovering the volatilome fingerprint of selected traditional Cuban wines elaborated with native grapes, tropical fruits, and rice using DHS-TD-GC-MS

Cuban wine is a traditional alcoholic beverage elaborated with a wide variety of raw materials, such as native grapes, tropical fruits, and rice, and different winemaking processes. Research on Cuban wines is almost nonexistent, and therefore, a study of these wines is necessary to improve their quality. Dynamic headspace (DHS)-TD-gas chromatography-mass spectrometry (GC-MS) analysis was carried out to establish the different aroma fingerprints of different Cuban wines. A total of 42 volatile aroma metabolites (VAMs) were identified, including esters, alcohols, aldehydes, acids, volatile phenols, terpenes, and lactones. The odorant activity values (OAV) of each VAM were obtained, and the esters were the most relevant group due to their highest OAV. Ethyl octanoate, hexanoate, and butanoate stand out and are considered key odorants in the aromatic fingerprint. The VAMs were grouped into seven aromatic series. Fruity series showed the highest OAVs due to the contribution of ethyl esters and acetates. Principal component analysis was used to identify the specific parameters most accurately reflecting the differences between the wines. Showing that fruity, spicy, and chemical aromatic series allow distinguishing the wines into three aroma types. These results may provide useful information for the selection of raw materials and optimization of the traditional winemaking processes of Cuban wines. PRACTICAL APPLICATION: This research contributes to knowledge of the aroma and the oenological parameters of traditional and selected Cuban wines (rice wine, tropical fruit wine, and native grape varieties). The establishing of the aroma fingerprint of these wines provides useful information for the industrial development of a quality product that may then be promoted in other areas of the world.

Distinguish Esophageal Cancer Cells through VOCs Induced by Methionine Regulation

Detection of exhaled volatile organic compounds (VOCs) is promising for noninvasive screening of esophageal cancer (EC). Cellular VOC analysis can be used to investigate potential biomarkers. Considering the crucial role of methionine (Met) during cancer development, exploring associated abnormal metabolic phenotypes becomes imperative. In this work, we employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to investigate the volatile metabolic profiles of EC cells (KYSE150) and normal esophageal epithelial cells (HEECs) under a Met regulation strategy. Using untargeted approaches, we analyzed the metabolic VOCs of the two cell types and explored the differential VOCs between them. Subsequently, we utilized targeted approaches to analyze the differential VOCs in both cell types under gradient Met culture conditions. The results revealed that there were five/six differential VOCs between cells under Met-containing/Met-free culture conditions. And the difference in levels of two characteristic VOCs (1-butanol and ethyl 2-methylbutyrate) between the two cell types intensified with the increase of the Met concentration. Notably, this is the first report on VOC analysis of EC cells and the first to consider the effect of Met on volatile metabolic profiles. The present work indicates that EC cells can be distinguished through VOCs induced by Met regulation, which holds promise for providing novel insights into diagnostic strategies.

Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry

Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequence-to-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC-MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC-MS data of complex samples.

Evaluation of Fourier deconvolution ion mobility spectrometer as high-performance gas chromatography detector for the analysis of plant extract flavors

The Fourier deconvolution ion mobility spectrometer (FDIMS) offers multiplexing and improves the resolving power and signal-to-noise ratio. To evaluate the FDIMS as a detector for gas chromatography for the analysis of complex samples, we connected a drift tube ion mobility spectrometer to a commercial gas chromatograph and compared the performance including resolving power, sensitivity, and linear range using 2,6-di‑tert-butylpyridine. Mixed standards were also injected into the tandem system to evaluate the performance under optimized conditions. A complex plant extract sample used as natural flavoring was investigated using the resulting system. The results show that the instrument implemented with the Fourier deconvolution multiplexing method demonstrated higher performance over the traditional signal averaging method including higher resolving power, better limit of detection, and wider linear range for a variety of compounds and natural plant extract flavorings.

Three-dimensional rose-like zinc oxide fiber coating for simultaneous extraction of polychlorinated biphenyls and polycyclic aromatic hydrocarbons by headspace solid phase microextraction

The three-dimensional (3D) rose-like zinc oxide (ZnO) material was prepared by a simple one-step CTAB-assisted hydrothermal strategy and used as a headspace solid-phase microextraction (HS-SPME) coating. Polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) were analyzed by gas chromatography with flame ionization detector (GC-FID), and conclusively applied to ultrasensitive detection in lake and river water. Compared with one-dimensional (1D) pencil-like ZnO, the layer-by-layer petal-like structure could fully expose mass adsorption sites on the surface, which could significantly improve the adsorption. The enrichment factors with 7535-8595 for PCBs and 3855-7320 for PAHs were achieved. The established method provided a satisfactory linear range (0.005-30 ng·mL-1), coefficient (R2 > 0.9978), ultra-low limit detection (1-3 pg·mL-1), and long service life (≥ 150 times). The recoveries of 83.42-120.86 % were obtained in the real detection application of lake and river water. This work demonstrated that 3D rose-like ZnO with low cost, simple synthesis, fast extraction ability and high enrichment performance was an ideal coating material, which was hoped to enrich other compounds with similar structures with PCBs and PAHs.

Overview of the Metallization Approaches for Carbyne-Based Devices

Metallization for contacts in organic electronic nanodevices is of great importance for their performance. A lot of effects can appear at the contact/organic interface and modify the contact parameters, such as contact resistance, adhesive strength, and bonding ability. For novel materials, it is important to study the interactions with metal atoms to develop a suitable technology for contacts, fulfilling to the greatest extent the above-mentioned parameters. A novel material is carbyne, which is still under intensive research because of its great potential in electronics, especially for sensing applications. However, the most appropriate metallization strategy for carbyne-based devices is still unknown, so the interactions between carbyne and metal films should be studied to more precisely direct the development of the metallization technology, and to form contacts that are not limiting factors for device performance.

Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations

Lung cancer is a leading cause of death worldwide, mostly due to diagnostics in the advanced stage. Therefore, the development of a quick, simple, and non-invasive diagnostic tool to identify cancer is essential. However, the creation of a reliable diagnostic tool is possible only in case of selectivity to other diseases, particularly, cancer of other localizations. This paper is devoted to the study of the variability of exhaled breath samples among patients with lung cancer and cancer of other localizations, such as esophageal, breast, colorectal, kidney, stomach, prostate, cervix, and skin. For this, gas chromatography-mass spectrometry (GC-MS) was used. Two classification models were built. The first model separated patients with lung cancer and cancer of other localizations. The second model classified patients with lung, esophageal, breast, colorectal, and kidney cancer. Mann-Whitney U tests and Kruskal-Wallis H tests were applied to identify differences in investigated groups. Discriminant analysis (DA), gradient-boosted decision trees (GBDT), and artificial neural networks (ANN) were applied to create the models. In the case of classifying lung cancer and cancer of other localizations, average sensitivity and specificity were 68% and 69%, respectively. However, the accuracy of classifying groups of patients with lung, esophageal, breast, colorectal, and kidney cancer was poor.