Technology Development

The technology development track of Zurich Exhalomics aims at further developing technologies and software solutions suitable for efficient real-time analysis of the exhalome. Experts from ETH Zurich, the University of Zurich and Empa work in close collaboration with physicians of the University Hospital Zurich and the University Children's Hospital Zurich, in order to enable smooth integration of the technologies into the clinical application and taking into account best sensitivity and user friendliness.

On-line, real-time ambient ionization mass spectrometry

Project Mass Spectroscopy
Project Mass Spectroscopy

Renato Zenobi (ETH), Pablo Martinez-Lozano Sinues (UniBasel/UKBB), Malcolm Kohler (UZH/USZ)

The aim of this project is to facilitate the diagnosis of respiratory diseases and monitoring of treatment effects via the chemical analysis of exhaled breath, using on-line, real-time ambient ionization mass spectrometry. Over the last 3 years, we have been involved in the discovery of disease-specific biomarkers in the breath of patients with respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), obstructive sleep apnoea (OSA), idiopathic pulmonary fibrosis, cystic fibrosis, and in the determination of the pharmacokinetics of drugs and their metabolites by real-time monitoring the exhaled breath of a model animals (mice) and humans. In the future, we would like to standardize and simplify the instrumentation, without compromising the sensitivity and selectivity, to allow it to be deployed in clinics and doctor’s offices. Previously determined biomarkers for the above mentioned respiratory diseases will be validated in a series of large-scale cohort studies.

We are also performing research in related areas, such as detection of dopants, absolute quantification of biomarkers in breath via reference standards, and development of new sampling devices for breath that are adapted to special circumstances.

Chemoresistive Sensors for Breath Analysis

Project Chemoresistive Sensors
Project Chemoresistive Sensors

Sotiris Pratsinis (ETH), Andreas Güntner (ETH), Malcolm Kohler (UZH/USZ)

This project aims to develop and assess portable, simple-in-use and inexpensive breathensors for the non-invasive detection of diseases (e.g. diabetes and kidney dysfunction) and to guide interventions (such as dieting or exercise). Tailored, molecule-selective sensing materials, arrays and filters (e.g. for acetone, NH3, isoprene and formaldehyde) are explored at the Particle Technology Laboratory at ETH. Such sensors have been used already to accurately monitor fat burn rates in real-time and they can be readily incorporated into portable analyzers for application in widespread populations.

MIR laser spectroscopy for multicomponent breath analysis

Project MIR laser spectroscopy

Jérôme Faist (ETH) and Lukas Emmenegger (Empa)

Many inorganic (e.g. NO, NH3) and volatile organic molecules (VOCs, e.g. acetone, ethanol or isoprene) provide valuable information on the general health status and the relationships between nutrition, energy balance and metabolic diseases. During the last 3 years, we have focused on multicomponent gas detection and the analysis of acetone and ethanol in human breath by MIR laser spectroscopy. This development shall be continued using the latest advances in MIR laser sources, miniaturized electronics and medical diagnostics. We will target multicomponent sensors that simultaneously detect a suite of relevant molecules, leveraging on recent multi-wavelength and broadly tunable quantum cascade lasers in a simple benchtop design for the application in intervention studies and clinical diagnostics. Special focus will be given to the development of Mid-IR frequency comb spectroscopy for the analysis of trace gases and their isotopes with potential applications in breath analysis.

Automated data analysis

Project Data Analysis

Joachim Buhmann (ETH), Renato Zenobi (ETH), Malcolm Kohler (UZH/USZ) and Pablo Martinez-Lozano Sinues (UniBasel/UKBB)

Real time mass spectrometry has been proposed as a potential fast and non-invasive diagnostic tool of diseases, however large amount of data are generated. To facilitate large-scale research and the integration as a diagnostic tool into the daily work of doctors, we alleviate the bottleneck of manually processing the data by an automated pipeline of statistical analysis. Focus is given to an automated and validated pre-processing of the scored spectra to detect biomarkers, identify subgroups, and to classify diseases via exhaled breath analysis.