Viscoelasticity as a biomarker for high-throughput flow cytometry

Abstract

The mechanical properties of living cells are a label-free biophysical marker of cell viability and health; however, their use has been greatly limited by low measurement throughput. Although examining individual cells at high rates is now commonplace with fluorescence activated cell sorters, development of comparable techniques that nondestructively probe cell mechanics remains challenging. A fundamental hurdle is the signal response time. Where light scattering and fluorescence signatures are virtually instantaneous, the cell stress relaxation, typically occurring on the order of seconds, limits the potential speed of elastic property measurement. To overcome this intrinsic barrier to rapid analysis, we show here that cell viscoelastic properties measured at frequencies far higher than those associated with cell relaxation can be used as a means of identifying significant differences in cell phenotype. In these studies, we explore changes in erythrocyte mechanical properties caused by infection with Plasmodium falciparum and find that the elastic response alone fails to detect malaria at high frequencies. At timescales associated with rapid assays, however, we observe that the inelastic response shows significant changes and can be used as a reliable indicator of infection, establishing the dynamic viscoelasticity as a basis for nondestructive mechanical analogs of current high-throughput cell classification methods.

Figure 1

(a) Illustration of linear optical tweezers: the astigmatic beam of a laser diode bar emitting infrared light at 810 nm from a 1 μm × 100 μm area is imaged using a 20× microscope objective and refocused using a 40× objective into a microfluidic channel containing a cell solution. The sample is illuminated using a 12 W LED and imaged to a high-speed CMOS camera system recording at 1000 fps. A frequency generator is employed to oscillate the laser intensity, while the original modulation signal and the cell response are recorded on a computer. (b) The anisotropy of optical forces exerted on a captured RBC by the linear optical trap generates directed cell deformation along the trap axis. To see this figure in color, go online.

Figure 2

We observe the characteristic delayed viscoelastic behavior of erythrocytes in response to external loading. Employing applied optical forces over a broad range of modulation frequencies, we measure the phase-shifted cell response depending on the timescales of applied loading. To see this figure in color, go online.

Figure 3

(a) Mean period of oscillation from averaging over all data points with identical phase. A sine function is fitted to the mean deformation to determine amplitude and phase shift of the cell response with respect to the external signal. (b) Raw data and fit function obtained from the mean cell deformation. To see this figure in color, go online.

Figure 4

Measured distributions of cell deformation for ∼100 cells for each modulation frequencies and cell type. At near-equilibrium frequencies, a distinctly reduced deformability of infected erythrocytes is confirmed, whereas at higher frequencies samples become undistinguishable based on degree of stretching alone. To see this figure in color, go online.

Figure 5

Frequency sweep of (a) relative cell deformation and (b) phase shift between stimulus and cell response, where error bars visualize one standard deviation of the observed experimental spread and lines are used to guide the eye. Although cell elasticity fails as a criterion for classification at high frequencies, cell viscosity manifesting in observed phase shift, allows identification of infected erythrocytes. To see this figure in color, go online.

Figure 6

(a) Scatter plot of measured deformations and (b) phase differences at nonequilibrium frequencies ≥5 Hz (1200 cells) show the applicability of cell viscosity to identify a malaria infection at high frequencies where elasticity fails. Ellipses are used to guide the eye. To see this figure in color, go online.

Figure 7

(a) Storage and (b) loss moduli of infected and uninfected erythrocytes calculated from measured deformation parameters and ray-tracing simulations. The storage module of both cell types cross at moderate and overlap at high frequencies, whereas the loss modulus allows a clear identification of each type. Error bars indicate the spread of measured cell populations with lines provided to guide the eye. To see this figure in color, go online.

Figure 8

Distributions of the recorded deformation (a) and phase shift (b) measured at frequencies of 100 Hz at rates of >20 cells/s of infected and uninfected populations of ∼3000 cells. To see this figure in color, go online.