Engineering a metabolomics workflow to capture single-cell diversity in 3D models

Doctoral Thesis

Cellular heterogeneity is a fundamental feature of biological systems and plays a critical role in disease progression, therapeutic response, and the emergence of drug-resistant subpopulations. However, the functional biochemical mechanisms underlying this heterogeneity remain challenging to resolve, as conventional approaches either obscure cellular diversity through population averaging or fail to preserve the biological context in which cellular phenotypes arise. This thesis addresses these challenges through the development of an integrated single-cell metabolomics workflow designed to capture metabolic heterogeneity within physiologically relevant 3D environments.

The workflow combines computational, analytical, and microfluidic innovations to improve the quality, depth, and contextual relevance of single-cell measurements. A dedicated computational framework, MeDUSA, enables robust processing and biological interpretation of complex single-cell mass spectrometry data...Show moreCellular heterogeneity is a fundamental feature of biological systems and plays a critical role in disease progression, therapeutic response, and the emergence of drug-resistant subpopulations. However, the functional biochemical mechanisms underlying this heterogeneity remain challenging to resolve, as conventional approaches either obscure cellular diversity through population averaging or fail to preserve the biological context in which cellular phenotypes arise. This thesis addresses these challenges through the development of an integrated single-cell metabolomics workflow designed to capture metabolic heterogeneity within physiologically relevant 3D environments.

The workflow combines computational, analytical, and microfluidic innovations to improve the quality, depth, and contextual relevance of single-cell measurements. A dedicated computational framework, MeDUSA, enables robust processing and biological interpretation of complex single-cell mass spectrometry data, while integration of FAIMS enhances analytical selectivity and metabolite detection. These advances are brought together with live single-cell sampling in vascularized 3D organ-on-chip models, enabling the investigation of heterogeneous cellular responses to therapeutic compounds within their native microenvironment. Complementary evaluation of microfluidic technologies for organoids and spheroids further establishes design principles for integrating complex 3D models with metabolomics.

Together, this work establishes a foundation for context-aware single-cell metabolomics and demonstrates its potential to reveal functional metabolic diversity that remains hidden in conventional experimental systems.
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Single-cell

Mass spectrometry

Metabolomics

3D cell models

All authors: Hetzel, L.A.

Supervisor: Hankemeier, T.

Co-supervisor: Ali, A.

Committee: Eck, M. van; Lange, E.C.M. de; Bailey M.; Lanekoff, I.; Hooft, J. van der; Mahfouz, A.

Qualification: Doctor (dr.)

Awarding Institution: Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University

Date: 2026-09-09

ISBN (print): 9789090430515

Leiden University Scholarly Publications

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Engineering a metabolomics workflow to capture single-cell diversity in 3D models