Large-scale targeted biomarker analysis of volatile organic compounds in breath by TD-GC-MS

Significance of the Topic

Non-invasive breath analysis for volatile organic compounds (VOCs) is transforming early disease detection and personalized healthcare. While untargeted workflows reveal broad metabolic patterns, they often lack robust compound confirmation and cross-study consistency. A large-scale targeted approach bridges this gap by combining high specificity with comprehensive coverage, enabling reliable biomarker quantitation and consistent comparisons across cohorts.

Objectives and Study Overview

This study aimed to develop and validate a fully integrated, targeted thermal desorption gas chromatography–high-resolution mass spectrometry (TD-GC-HRMS) workflow for breath VOC biomarker research. Key goals included:

• Establishing a reproducible protocol for 200 breath VOC targets aligned with the Breath Biopsy VOC Atlas®.
• Implementing cloud-based data management and automated sequence control within Chromeleon CDS.
• Demonstrating high-confidence compound identification, quantitation precision, and system suitability safeguards.

Methodology and Used Instrumentation

Sample Preparation and Collection:

• Owlstone Medical ReCIVA® Breath Sampler with CASPER portable air supply to minimize ambient contamination.
• Approx. 1.25 L breath per sorbent tube; 36 deuterated internal standards spiked in tubes.

Analytical Platform:

• Thermal desorption unit (Markes TD100xr) coupled to Thermo Scientific Orbitrap Q Exactive.
• Chromeleon CDS v7.3.2 MUb for instrument control, data acquisition, audit trail, and reporting.

Quality Control and Automation:

• System Suitability Testing (SST) with Intelligent Run Control (IRC) halts acquisitions upon out-of-spec conditions.
• Automated performance checks, sample integrity monitoring, and cloud-based remote access to nine networked instruments.

Main Results and Discussion

Compound Identification:

• Composite scoring criteria (≤5 ppm mass error, ≤0.01 min coelution, ±20% ion ratio) achieved Tier 1 identifications using authentic standards and in-house HRAM libraries (>1000 entries).

Quantitative Performance:

• Linear dynamic ranges spanned 20–4000 units with internal standard-corrected regression (1/Amount^2 weighting).
• AGC enabled up to three orders of magnitude quantitation without reanalysis, maintaining ≥70% of targets under 20% RSD across sequences and studies.

Data Handling and Visualization:

• Cloud infrastructure processed ~6 GB per sequence (~96 MB/sample) with annual data volumes of 5–6.6 TB.
• Interactive, sequence-wide tables and plots facilitated rapid curation, quality assessment, and longitudinal data integration.

Benefits and Practical Applications

• High-confidence targeted profiling supports robust biomarker validation in clinical research and QA/QC laboratories.
• Automated SST/IRC safeguards critical samples, preventing data loss from instrument failures.
• Enterprise-wide cloud deployment ensures secure storage, scalable compute, and remote accessibility.

Future Trends and Applications

Advances in breathomics will focus on expanding target panels, integrating AI-driven pattern recognition, and harmonizing multi-omics data. Cloud-native platforms are expected to enable real-time cross-site studies, federated machine learning on VOC signatures, and rapid translation of biomarkers into point-of-care diagnostics.

Conclusion

This work presents a compliant, traceable TD-GC-HRMS workflow for large-scale targeted breath VOC analysis. By unifying advanced instrumentation, automated quality controls, and cloud-based data management, the method achieves high analytical confidence, reproducibility, and scalability for biomarker research and clinical applications.

Reference

• Arulvasan W. et al. (2024) High-quality identification of volatile organic compounds (VOCs) originating from breath. Metabolomics, 20(5):102.
• Arulvasan W. et al. (2025) Optimized breath analysis: customized analytical methods and enhanced workflow for broader detection of VOCs. Metabolomics, 21(1):17.
• Schrimpe-Rutledge AC. et al. (2016) Untargeted metabolomics strategies–challenges and emerging directions. J. Am. Soc. Mass Spectrom., 27(12):1897–1905.
• Xie Z. et al. (2024) Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath. Sci. Rep., 14(1):14568.