Effect of osmolality on erythrocyte rheology and perfusion of an artificial microvascular network

Abstract

Plasma sodium concentration is normally held within a narrow range. It may, however, vary greatly under pathophysiological conditions. Changes in osmolality lead to either swelling or shrinkage of red blood cells (RBCs). Here we investigated the influence of suspension osmolality on biophysical properties of RBCs and their ability to perfuse an artificial microvascular network (AMVN). Blood was drawn from healthy volunteers. RBC deformability was measured by osmotic gradient ektacytometry over a continuous range of osmolalities. Packed RBCs were suspended in NaCl solutions (0.45, 0.6, 0.9, 1.2, and 1.5 g/dL), resulting in supernatant osmolalities of 179±4, 213±1, 283±2, 354±3, and 423±5 mOsm/kg H2O. MCV (mean corpuscular volume) and MCHC (mean corpuscular hemoglobin concentration), were determined using centrifuged microhematocrit. RBC suspensions at constant cell numbers were used to measure viscosity at shear rates ranging from 0.11 to 69.5 s−1 and the perfusion rate of the AMVN. MCV was inversely and MCHC directly proportional to osmolality. RBC deformability was maximized at isosmotic conditions (290 mOsm/kg H2O) and markedly decreased by either hypo- or hyperosmolality. The optimum osmolality for RBC suspension viscosity was shifted towards hyperosmolality, while lower osmolalities increased suspension viscosity exponentially. However, the AMVN perfusion rate was maximized at 290 mOsm/kg H2O, and changed by less than 10% over a wide range of osmolalities. These findings contribute to the basic understanding of blood flow in health and disease, and may have significant implications for the management of osmotic homeostasis in clinical practice.

Shape matters: the effect of red blood cell shape on perfusion of an artificial microvascular network

“…For all of our samples, MCHC ranged from 32.0 to 34.0 g/dL, and the maximum decrease in AMVN perfusion rate was 0.084 nL/sec (or 33.6% of the perfusion rate for discocytes). We have previously shown that changes in MCHC of up to 7.8 g/dL produce a decrease in the AMVN perfusion rate of less than 0.016 nL/sec (or 6.6% of discocyte) . The decreases in AMVN perfusion rate experienced by induced echinocyte II (9.0% of discocyte), echinocyte III (24.9% of discocyte), spheroechinocytes (30.6% of discocyte), and spherocytes (33.6% of discocyte) were all significantly greater than would be expected based on change in MCHC alone.…”

Influence of red blood cell aggregation on perfusion of an artificial microvascular network

“…The scientific background, design, and fabrication of the AMVN devices, and the methods used for measuring the AMVN perfusion rate have been described previously. [32][33][34][35][36][37] Each AMVN device contained three identical networks of "capillary" microchannels (widths 5-70 μm) arranged in a pattern inspired by rat mesentery microvasculature, and each having an independent inlet port connected to the "capillary" network via a 70-μm-wide channel ("arteriole") and converging to a common outlet port by a 70-μm-wide channel ("venule"). 32 All microchannels comprising the AMVN were 5-μm deep.…”

“…Images were captured with a high-speed camera (100 fps, Flea3, Point Grey Research, Inc., Richmond, Canada) and analyzed offline with a custom image analysis algorithm implemented in MATLAB 2014b (The Math Works Inc., Natick, MA). 32,35 The image analysis algorithm compared subsequent images to determine the change in position of RBCs between images, thereby enabling determination of RBC solution velocity. The AMVN perfusion rate was then calculated by multiplying the solution velocity by the cross-sectional area of the microchannel (5 μm×70 μm=350 μm 2 for "venules").…”

“…Our data, therefore, should only be used to draw conclusions about the hemodynamic effects of RBC aggregation induced They should not be directly and uncritically extrapolated to the microcirculation in vivo as dextran solutions, although studied intensively, do not represent physiological suspensions. Nevertheless, the AMVN has been rigorously validated in several studies32,[34][35][36][37]59,60 and it is arguably the best available model system for measuring the influence of various properties of blood on microvascular perfusion in vivo.…”

A Quasi-Mechanistic Mathematical Representation for Blood Viscosity

“…RBC deformability may also be altered by previous cell trauma due to shear exposure [32]. RBC aggregation is affected by concentration of large proteins or macromolecules that can form links between the cells as well as by shear [22,23], cell size, shape, and deformability, pH [33], and plasma osmolality [34].…”