Real-Time Quantification of Amino Acids in the Exhalome by Secondary Electrospray Ionization-Mass Spectrometry: A Proof-of-Principle Study.

BACKGROUND: Amino acids are frequently determined in clinical chemistry. However, current analysis methods are time-consuming, invasive, and suffer from artifacts during sampling, sample handling, and sample preparation. We hypothesized in this proof-of-principle study that plasma concentrations of amino acids can be estimated by measuring their concentrations in exhaled breath. A novel breath analysis technique described here allows such measurements to be carried out in real-time and noninvasively, which should facilitate efficient diagnostics and give insights into human physiology. METHODS: The amino acid profiles in 37 individuals were determined by ion-exchange HPLC in blood plasma and simultaneously in breath by secondary electrospray ionization coupled to high-resolution mass spectrometry. Participants were split into training and test sets to validate the analytical accuracy. Longitudinal profiles in 3 individuals were additionally obtained over a 12-h period. RESULTS: Concentrations of 8 slightly volatile amino acids (A, V, I, G, P, K, F, Orn) could be determined in exhaled breath with a CV of <10%. Exhalome validation studies yielded high accuracies for each of these amino acids, on average only 3% less compared to plasma concentrations (95% CI ±13%). Higher variations were found only for amino acids with a low plasma concentration. CONCLUSIONS: This study demonstrates for the first time that amino acids can be quantified in the human breath and that their concentrations correlate with plasma concentrations. Although this noninvasive technique needs further investigation, exhalome analysis may provide significant benefits over traditional, offline analytical methods.

Noninvasive strategies for breast cancer early detection.

Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.

Fingerprinting breast cancer vs. normal mammary cells by mass spectrometric analysis of volatiles.

There is increasing interest in the development of noninvasive diagnostic methods for early cancer detection, to improve the survival rate and quality of life of cancer patients. Identification of volatile metabolic compounds may provide an approach for noninvasive early diagnosis of malignant diseases. Here we analyzed the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. Volatile compounds in the headspace of conditioned culture medium were directly fingerprinted by secondary electrospray ionization-mass spectrometry. The mass spectra were subsequently treated statistically to identify discriminating features between normal vs. cancerous cell types. We were able to classify different samples by using feature selection followed by principal component analysis (PCA). Additionally, high-resolution mass spectrometry allowed us to propose their chemical structures for some of the most discriminating molecules. We conclude that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.

Mass spectrometry research at the Laboratory for Organic Chemistry, ETH Zurich.

This contribution covers the most important activities of the Zenobi research group at the Organic Chemistry Laboratory, ETH Zurich. We work in a number of interrelated areas that encompass fundamental/mechanistic research, instrument and methods development, and applications. This is illustrated with examples from the mass spectrometric study of noncovalent interactions, using both native ESI and MALDI for ionization, the investigation of the gas-phase conformation of ionized bio-macromolecules, the use of ambient mass spectrometry for rapid, on-line analyses of, for example, exhaled breath, and the use of MALDI and microarray technologies for studying metabolites with extreme sensitivity, sufficient to probe the metabolites from single cells.

Real-time breath analysis with active capillary plasma ionization-ambient mass spectrometry.

On-line analysis of exhaled human breath is a growing area in analytical science, for applications such as fast and non-invasive medical diagnosis and monitoring. In this work, we present a novel approach based on ambient ionization of compounds in breath and subsequent real-time mass spectrometric analysis. We introduce a plasma ionization source for this purpose, which has no need for additional gases, is very small, and is easily interfaced with virtually any commercial atmospheric pressure ionization mass spectrometer (API-MS) without major modifications. If an API-MS instrument exists in a laboratory, the cost to implement this technology is only around [Formula: see text]500, far less than the investment for a specialized mass spectrometric system designed for volatile organic compounds (VOCs) analysis. In this proof-of-principle study we were able to measure mass spectra of exhaled human breath and found these to be comparable to spectra obtained with other electrospray-based methods. We detected over 100 VOCs, including relevant metabolites like fatty acids, with molecular weights extending up to 340 Da. In addition, we were able to monitor the time-dependent evolution of the peaks and show the enhancement of the metabolism after a meal. We conclude that this approach may complement current methods to analyze breath or other types of vapors, offering an affordable option to upgrade any pre-existing API-MS to a real-time breath analyzer.

Monitoring diurnal changes in exhaled human breath.

The development of noninvasive analytical techniques is of interest to the field of chronobiology, in order to reveal the human metabolome that seems to show temporal patterns and to predict internal body time. We report on the real-time mass spectrometric analysis of human breath as a potential method to be used in this field. The breath of 12 subjects was analyzed during 9 days by secondary electrospray ionization-mass spectrometry (SESI-MS). The samples were collected during four time slots: morning (8:00-11:00), before lunch (11:00-13:00), after lunch (13:00-15:00), and late afternoon (15:00-18:00). A total of 203 mass spectra were statistically analyzed. Univariate analysis revealed a number of features with a marked temporal behavior. Principal component analysis/canonical analysis showed a clear temporal evolution of the breath patterns. A blind cross-validation yielded 84% of correct classifications of the time slot at which the breath samples were collected. We conclude that this approach seems to have potential for the investigation of biological clocks, including the description of internal body time, which may have important implications for the timing of pharmacotherapy.

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition.

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.