High-Throughput Mechanical Characterization of Giant Unilamellar Vesicles by Real-Time Deformability Cytometry

TL;DR

This paper presents a high-throughput, non-contact framework for mechanically characterizing Giant Unilamellar Vesicles (GUVs) using Real-Time Deformability Cytometry (RT-DC) grounded in a phase-field model that identifies the area expansion modulus $K$ as the dominant membrane parameter. It introduces three fitting strategies—direct fitting, noise-robust collective fitting, and a combined approach—to extract $K$ across diverse flow rates, channel geometries, and buffer viscosities, including heterogeneous vesicle populations. Validation with DOPC and SM–Chol vesicles shows $K$ values in line with literature ranges, while RT-DC offers orders-of-magnitude higher throughput than traditional methods and enables potential sorting by membrane mechanics. Overall, the work establishes RT-DC with robust data-fitting pipelines as a powerful tool for rapid vesicle population screening and membrane mechanics research, with implications for synthetic biology and soft matter science.

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 K 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 K 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.

Figures (5)

• Figure 1: A: Microfluidic setup of RT-DC and illustration of the physical model. A GUV with an area expansion modulus $K$ is immersed in a surrounding liquid of viscosity $\eta$ and traverses a channel of side length $L$ at a flow rate $Q$. B: Snapshots for RT-DC measurements of GUVs showing a spherical initial state and a bullet-shaped stationary state at the end portion of the channel. C: Simulation of a single GUV. Exemplary evolution for initially spherical vesicle of radius $r=6.5\, µ m$ and area expansion modulus $K=0.4 \, N \per m$, colored by amount of surface stretching (see main text). D: Stationary deformation and approximate capillary numbers for different $r$ and $K$ colored by amount of surface stretching. Simulation parameters: $Q' = 0.04 \, µ L \per \s, L' = 20 \, µ m, \eta'= 0.015 \, \Pa \s$.
• Figure 2: Simulation data fits. A: Simulation data (dots) fitted by linear functions (lines) illustrating the linear relation between area and deformation. Exemplary experimental data showing an almost linear relationship in GUV measurements as well. B: Simulation data (dots) with exponential fits (lines) illustrating the relation between area expansion modulus and deformation. C: Function fit for the relation between $K$, area and deformation based on \ref{['eq:K func final']} (direct fitting). D: Fitted function between $K$ and the slope of the linear simulation curves used for the collective fitting method. Parameters: $Q' = 0.04 \, µ L \per \s, L' = 20 \, µ m, \eta'= 0.015 \, \Pa \s$.
• Figure 3: Extraction of $K$ using the combined fitting approach. A: Comparison of the three different fitting methods for a given data set with snapshots of measured GUVs (here pure DOPC vesicles). Left: Extraction of $K$ using collective fitting (\ref{['eq: k slope relation']}). Middle: Extraction of $K$ using direct fitting (\ref{['eq:K func final']}). Computation and visualization of Gaussian mixture model (GMM) with two components. Right: Classification of input data based on the Gaussian mixture model (filtering for outliers). Refitting and noise elimination for the usable data with collective fitting approach (\ref{['eq: k slope relation']}). B: Extraction of $K$ for different sets of pure DOPC vesicles measured at different flow rates ($Q = 0.04 \, µL \per s$, $Q = 0.08 \, µL \per s$). C: Extraction of $K$ for different sets of binary composed SM-Chol vesicles ($1:1$) measured at different flow rates ($Q = 0.04 \, µL \per s$, $Q = 0.08 \, µL \per s$).
• Figure 4: Extraction of area expansion modulus $K$ for a mixture of $1000$ DOPC- and $1000$ SM-Chol-vesicles. Top (left): Given (merged) data set. Top (right) - Step 1: Extraction of $K$ using direct fitting (\ref{['eq:K func final']}). Computation and visualization of Gaussian mixture model with three components. Bottom (left) - Step 2: Classification of input data based on the Gaussian mixture model. Refitting and noise elimination for the two classes with collective fitting approach (\ref{['eq: k slope relation']}). Bottom (right) - Step 3: Comparison of data classifications and $K$-extractions with given ground truth (DOPC-vesicles are colored in red, SM-Chol-vesicles are colored in blue), containing possible unreliably results.
• Figure 5: Volume (normalized) over channel position for RT-DC measurements of GUVs (flowing from right to left) showing almost constant GUV volume across the channel.