Quantitative microscopy and nanoscopy of sickle red blood cells performed by wide field digital interferometry

Abstract

We have applied wide-field digital interferometry (WFDI) to examine the morphology and dynamics of live red blood cells (RBCs) from individuals who suffer from sickle cell anemia (SCA), a genetic disorder that affects the structure and mechanical properties of RBCs. WFDI is a noncontact, label-free optical microscopy approach that can yield quantitative thickness profiles of RBCs and measurements of their membrane fluctuations at the nanometer scale reflecting their stiffness. We find that RBCs from individuals with SCA are significantly stiffer than those from a healthy control. Moreover, we show that the technique is sensitive enough to distinguish classes of RBCs in SCA, including sickle RBCs with apparently normal morphology, compared to the stiffer crescent-shaped sickle RBCs. We expect that this approach will be useful for diagnosis of SCA and for determining efficacy of therapeutic agents.

Measurement Techniques for Red Blood Cell Deformability: Recent Advances

“…Quantitative phase microscopy measured decreased membrane fluctuations for sickle RBCs (Shaked, Satterwhite et al,2011). FTLS showed significantly altered elastic and viscous membrane properties in sickle RBCs (Kim, Higgins et al,2012).…”

Anisotropic light scattering of individual sickle red blood cells

“…24 These altered viscoelastic properties of the sickle cell can explain the significantly decreased dynamic fluctuations in the sickle cell membrane. 15 In conclusion, we present anisotropic light scattering of sickle RBCs. The aFTLS technique, a variation of FTLS, precisely and systematically measures anisotropic light scattering of asymmetric small objects.…”

“…14 The measured topographies of the sickle cells are consistent with a recent study using quantitative phase imaging. 15 To retrieve anisotropic light scattering, we introduce aFTLS analysis (Fig. 2).…”

Hybrid random walk-linear discriminant analysis method for unwrapping quantitative phase microscopy images of biological samples

“…LDA was performed pixelwise based on 17 parameters: (1) the raw phase data after Goldstein unwrapping, (2) the estimated image background constructed by masking the location of any apparent cells, then fitting a plane to the remaining background regions to remove any tilt, (3) (9) a 3 × 3 mean filtered image, (10) a 3 × 3 median filtered image, (11) the phase derivative variance (PDV) of the image, defined as the variance of the partial derivative of the phase in a four-connected neighborhood at each pixel and computed using the method described in the phase quality guided path following method 15 and implemented in MATLAB by Spottiswoode,35 (12) the natural logarithm of the PDV image to provide data with comparable dynamic range to the other measures, (13) the phase modulation magnitude, or amplitude of the intensity fluctuations in the phase-shifted fringes, computed by the Bruker optical profilometer, 31 (14) the natural logarithm of the phase modulation image, (15) the Laplacian filtered phase modulation image, (16) the intensity image, and (17) the Laplacian filtered intensity image.…”

“…1,2 These samples typically consist of single cells or cell clusters, which are imaged over time to track changes in dry mass 3 or refractive index, 4,5 for example, to monitor cellular growth [6][7][8] and growth regulation, 9 quantify intercellular interactions 10 or subcellular features, 11 and measure mechanical properties of cells, such as red blood cells. [12][13][14] In any interferometric QPI dataset, the phase ambiguities inherent to QPI have to be removed to achieve continuous phase distributions and precise, reproducible measurements of these samples. 15 Basic phase unwrapping of large jump discontinuities are easily handled by a variety of algorithms, including the Flynn or widely used Goldstein method.…”