| Literature DB >> 31565508 |
Jonas Gienger1, Hermann Gross1, Volker Ost1,2, Markus Bär1, Jörg Neukammer1.
Abstract
Light scattering by single cells is widely applied for flow cytometric differentiation of cells. However, even for human red blood cells (RBCs), which can be modeled as homogeneous dielectric particles, the potential of light scattering is not yet fully exploited. We developed a dedicated flow cytometer to simultaneously observe the forward scattering cross section (FSC) of RBCs for orthogonal laser beams with incident wave vectors k → 1 and k → 2 . At a wavelength λ = 632.8 nm , bimodal distributions are observed in two-dimensional dot plots of FSC( k → 1 ) vs. FSC( k → 2 ), which result from the RBCs' random orientation around the direction of flow, as well as from the distributions of their size and their optical properties. Typically, signals of 7.5 × 10 4 RBCs were analyzed. We actively oriented the cells in the cytometer to prove that orientation is the main cause of bimodality. In addition, we studied the wavelength dependence of FSC( k → 1 ) using λ = 413.1 nm , 457.9 nm , 488 nm and 632.8 nm, covering both weak and strong light absorption by the RBCs. Simulations of the light scattering by single RBCs were performed using the discrete dipole approximation (DDA) for a range of sizes, orientations and optical properties to obtain FSC distributions from RBC ensembles. Using the axisymmetric biconcave equilibrium shape of native RBCs, the experimentally observed distributions cannot be reproduced. If, however, an elongated shape model is employed that accounts for the stretching of the cell by hydrodynamic forces in the cytometer, the features of the strongly bimodal measured frequency distributions are reproduced by the simulation. Elongation ratios significantly greater than 1 in the range of 1.5 to 2.5 yield the best agreement between experiments and simulated data.Entities:
Year: 2019 PMID: 31565508 PMCID: PMC6757475 DOI: 10.1364/BOE.10.004531
Source DB: PubMed Journal: Biomed Opt Express ISSN: 2156-7085 Impact factor: 3.732