Statistics for real-time deformability cytometry: Clustering, dimensionality reduction, and significance testing

Real-time deformability (RT-DC) is a method for high-throughput mechanical and morphological phenotyping of cells in suspension. While analysis rates exceeding 1000 cells per second allow for a label-free characterization of complex biological samples, e.g., whole blood, data evaluation has so far been limited to a few geometrical and material parameters such as cell size, deformation, and elastic Young's modulus. But as a microscopy-based technology, RT-DC actually generates and yields multidimensional datasets that require automated and unbiased tools to obtain morphological and rheological cell information. Here, we present a statistical framework to shed light on this complex parameter space and to extract quantitative results under various experimental conditions. As model systems, we apply cell lines as well as primary cells and highlight more than 11 parameters that can be obtained from RT-DC data. These parameters are used to identify sub-populations in heterogeneous samples using Gaussian mixture models, to perform a dimensionality reduction using principal component analysis, and to quantify the statistical significance applying linear mixed models to datasets of multiple replicates.

Numerical Simulation of Real-Time Deformability Cytometry To Extract Cell Mechanical Properties

The measurement of cell stiffness is an important part of biological research with diverse applications in biology, biotechnology and medicine. Real-time deformability cytometry (RT-DC) is a new method to probe cell stiffness at high throughput by flushing cells through a microfluidic channel where cell deformation provides an indicator for cell stiffness (Otto et al. Real-time deformability cytometry: on-the-fly cell 725 mechanical phenotyping. Nat. Methods 2015, 12, 199-202). Here, we propose a full numerical model for single cells in a flow channel to quantitatively relate cell deformation to mechanical parameters. Thereby the cell is modeled as a viscoelastic material surrounded by a thin shell cortex, subject to bending stiffness and cortical surface tension. For small deformations our results show good agreement with a previously developed analytical model that neglects the influence of cell deformation on the fluid flow (Mietke et al. Extracting Cell Stiffness from Real-Time Deformability Cytometry: 728 Theory and Experiment. Biophys. J. 2015, 109, 2023-2036). Including linear elasticity as well as neo-Hookean hyperelasticity, our model is valid in a wide range of cell deformations and allows to extract cell stiffness for largely deformed cells. We introduce a new measure for cell deformation that is capable to distinguish between deformation effects stemming from cell cortex and cell bulk elasticity. Finally, we demonstrate the potential of the method to simultaneously quantify multiple mechanical cell parameters by RT-DC.

Real-time deformability cytometry as a label-free indicator of cell function

The mechanical properties of cells are known to be a label-free, inherent marker of biological function in health and disease. Wide-spread utilization has so far been impeded by the lack of a convenient measurement technique with sufficient throughput. To address this unmet need, we have recently introduced real-time deformability cytometry (RT-DC) for continuous mechanical single-cell classification of heterogeneous cell populations at rates of several hundred cells per second. Cells are driven through the constriction zone of a microfluidic chip leading to cell deformations due to hydrodynamic stresses only. Our custom-built image processing software performs image acquisition, image analysis and data storage on the fly. The ensuing deformations can be quantified and an analytical model enables the derivation of cell material properties. Performing RT-DC we highlight its potential to identify rare objects in heterogeneous suspensions and to track drug-induced changes in cells. In summary, RT-DC enables marker-free, quantitative phenotyping of heterogeneous cell populations with a throughput comparable to standard flow cytometry.