High-throughput mechanical characterization of giant unilamellar vesicles by real-time deformability cytometry

Real-time deformability cytometry (RT-DC) enables high-throughput, contact-free mechanical characterization of soft microscopic objects. Here we apply this technique to giant unilamellar vesicles (GUVs). To interpret vesicle deformation in RT-DC, we present a simulation-based model taking into account the area expansion modulus as the dominant mechanical parameter. Using phase-field simulations over a wide parameter space, we find GUV deformation to depend linearly on GUV area. Based on these results, we derive two complementary fitting strategies for extracting the area expansion modulus <i>K</i> from RT-DC data: a direct model-based fit for single-vesicle characterization and a noise-resistant collective approach that enables robust population-level estimates. Furthermore, we introduce a combined fitting method that integrates both approaches to filter outliers and improve accuracy in heterogeneous or noisy datasets. All methods scale across varying flow rates, channel geometries and buffer viscosities, and produce predictions of <i>K</i> consistent with literature values for different lipid compositions. Compared to traditional techniques such as micropipette aspiration, our approach offers orders of magnitude higher throughput without mechanical contact, making it particularly suitable for GUV population studies. Beyond mechanical phenotyping, this framework opens new avenues for sorting vesicle populations based on membrane mechanics, a capability of growing interest in synthetic biology and soft matter research.

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Publication Details

Journal
Soft Matter
Published
2025-12-12
DOI
https://doi.org/10.1039/d5sm01140j
Primary Topic
Lipid Membrane Structure and Behavior
Type
article
Field-Weighted Citation Impact
0.00

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article

High-throughput mechanical characterization of giant unilamellar vesicles by real-time deformability cytometry

Tobias Abele, Sebastian Aland, Stefan J. Maurer, Kerstin Göpfrich et al.
Soft Matter
Lipid Membrane Structure and Behavior
article

High-throughput mechanical characterization of giant unilamellar vesicles by real-time deformability cytometry

Tobias Abele, Sebastian Aland, Stefan J. Maurer, Kerstin Göpfrich, Maximilian Kloppe
article en

Abstract

Real-time deformability cytometry (RT-DC) enables high-throughput, contact-free mechanical characterization of soft microscopic objects. Here we apply this technique to giant unilamellar vesicles (GUVs). To interpret vesicle deformation in RT-DC, we present a simulation-based model taking into account the area expansion modulus as the dominant mechanical parameter. Using phase-field simulations over a wide parameter space, we find GUV deformation to depend linearly on GUV area. Based on these results, we derive two complementary fitting strategies for extracting the area expansion modulus <i>K</i> from RT-DC data: a direct model-based fit for single-vesicle characterization and a noise-resistant collective approach that enables robust population-level estimates. Furthermore, we introduce a combined fitting method that integrates both approaches to filter outliers and improve accuracy in heterogeneous or noisy datasets. All methods scale across varying flow rates, channel geometries and buffer viscosities, and produce predictions of <i>K</i> consistent with literature values for different lipid compositions. Compared to traditional techniques such as micropipette aspiration, our approach offers orders of magnitude higher throughput without mechanical contact, making it particularly suitable for GUV population studies. Beyond mechanical phenotyping, this framework opens new avenues for sorting vesicle populations based on membrane mechanics, a capability of growing interest in synthetic biology and soft matter research.

Soft MatterVol. 22(3)
Heidelberg University (DE), Heidelberg University of Education (DE), Center for Systems Biology Dresden (DE), Hochschule für Technik und Wirtschaft Dresden – University of Applied Sciences (DE), TU Bergakademie Freiberg (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Technische Universität Dresden, Technische Universität Bergakademie Freiberg
Openalex Percentile: Top 64%
Lipid Membrane Structure and Behavior
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