Correction
Added a missing funding source.
Corrected in version 2. View the version before this correction
Revisiting: Shape transitions of red blood cell under oscillatory flows in microchannels
Mateus Ferreira1* (unauthenticated), Tunde Tembo2
(unauthenticated), Jonas Sandberg3, Erik Gunnarsson4
1Department of Statistics, Nile Delta University, Egypt
2School of Environmental Sciences, University of Cape Highlands, South Africa
3Department of History, University of the Lowlands, Netherlands
4Department of Statistics, Tohoku Coastal University, Japan
* Corresponding author.
Ann. Quantum Condens. Matter Phys. 2024, 4(1), 241-251; https://doi.org/10.58303/sm4kf242
Abstract
This paper aims to examine the ability to control a model of red blood cell (RBC) dynamics and the associated extracellular flow patterns in microfluidic channels via oscillatory flows. Our computational approach employs a hybrid continuum–particle coupling, in which the cell membrane and cytosol fluid are modeled using the dissipative particle dynamics method. The blood plasma is modeled as an incompressible fluid via the immersed boundary method. This coupling is novel because it provides an accurate description of RBC dynamics while the extracellular flow patterns around the RBCs are also captured in detail. Our coupling methodology is validated with available experimental and computational data in the literature and shows excellent agreement. We explore the controlling regimes by varying the shape of the oscillatory flow waveform at the channel inlet. Our simulation results show that a host of RBC morphological dynamics emerges depending on the channel geometry, the incoming flow waveform, and the RBC initial location. Complex dynamics of RBC are induced by the flow waveform. Our results show that the RBC shape is strongly dependent on its initial location. Our results suggest that the controlling of oscillatory flows can be used to induce specific morphological shapes of RBCs and the surrounding fluid patterns in bio-engineering applications.
I. INTRODUCTION
Extensive research has been conducted in the past few decades on the morphological change of red blood cells (RBCs) in fluid flows due to their importance in blood pathology.1–3 It has been shown that the response of the RBC membrane to blood plasma dynamics can affect the overall patterns of microvascular blood flows.4–8 Despite a substantial body of literature, the dynamics of RBCs remain a significant challenge to be studied due to the complexity of various response modes, which result from the interaction of the suspended cellular membrane with shear flow.9 Several factors can affect the dynamics of RBCs, such as stiffness of the membrane,10 shear rate ,11,12 and viscosity contrast (λ) between the blood plasma and the cytosol,12 among other factors. As a result, the RBC deformation process in shear flow is not well-understood, especially under time-dependent shear rates.13,14
In free shear flows with a constant shear rate , the shear strength is the controlling parameter of the RBC dynamics, in which the shape of RBCs becomes increasingly complex (more lobes) as the shear rate increases.15 In particular, for the range of shear rate from 10 to 2000 s−1, the dynamics of RBCs can be classified into three main regions:11 (i) tumbling at weak shear rate ; (ii) circular/elliptical stomatocytes ; and (iii) multilobes . In the tumbling region, the RBC deformation is minimal and reversible, which allows the RBCs to maintain their biconcave discoid shape. As the shear rate increases to 400 s−1, the percentage of discocytes decreases and stomatocytes start to dominate. The rolling and tumbling stomatocytes appear at and 250 s−1, respectively. This pattern persists up until when the stomatocytes assume a shape with an elliptical rim. In the range , RBCs with large lobes on their surface, which are referred to as trilobes or hexalobes, emerge.
Studies of RBC dynamics in microchannels have shown that the RBC can transition from its biconcave discoid shape to different morphologies4,16,17 under specific combinations (state diagram) of viscosity contrast, shear rate (the capillary number—Ca),18 and channel confinement (χ).6,12,17 The state diagram has revealed two main categories of RBC morphological shapes: (i) symmetrical and (ii) asymmetrical types.19–21 The simple type contains three modes:22 (a) bullet, (b) croissant (in rectangular channels), and (c) parachute (in circular channels) shapes, while the complex type includes6,23 (a) slipper; (b) multilobes; (c) trilobes; and (d) hexalobes shapes. The shape transition in the simple type has been shown to reach a terminal shape (bullet, croissant, or parachute). However, it is still not fully clear whether or not the complex shapes are stable or they are just transient states.16,24 In the complex shape mode,23 the shape transition mostly depends on the flow lag, which is the difference between the translation velocity of the RBC and the velocity of blood plasma. In brief, it is unclear how the complex shapes emerge from the biconcave discoid shape.
The following two shapes are the most frequently observed:4 (i) the croissant shape (symmetrical) and (ii) the slipper shape (asymmetrical). In particular, the slipper shape is characterized by the tank-treading motion of the cell membrane, which is essentially a self-rotation of the membrane around its center of mass during the RBC propagation.4,25 Experimental and computational studies have shown that these morphological shapes might result in distinct flow structures of blood plasma in the vicinity of the RBC.4 For instance, there exists a closed vortex downstream of the RBC when the slipper shape emerges.26 Such a vortex is absent during the croissant shape. To the best of our knowledge, there has been no systematic effort to understand the emergence of the extracellular flow patterns as the morphological shape of the RBC changes.
Recently, oscillatory flow (time-dependent shear rate—) has been shown to be a promising technique for cell separation. Because cell deformation is irreversible under time-dependent shear rates,13,27 oscillatory flows have been suggested to sort RBCs based on their size and deformability.14 Oscillatory flows can reduce the required travel distance of cells because they induce the lateral migration of cells in a short axial distance. This feature simplifies the design of microfluidic channels and thus improves the cell separation process.28 However, it is unclear on the process of morphological transition as the RBC responds to the time-dependent shear rate during this lateral migration. Therefore, it is necessary to investigate this process in detail.
In this work, we extended our hybrid continuum–particle simulation methodology29 to study the response of the RBC to time-dependent shear rates. The main development is the inclusion of cytosol fluid dynamics within the intracellular space, which provides a complete description of RBC dynamics. Our paper is organized as follows: first, a brief description of the numerical methods for simulating the blood plasma and the RBC is presented; second, the obtained RBC dynamics are validated with experimental data under (i) stretching force, (ii) constant shear rates (croissant and slipper shapes), and (iii) oscillatory shear rates; third, we perform a parametric study where the shear rate waveform, the peak flow rate, and the initial position of the RBC are varied to induce a host of RBC morphological changes; and finally, the relationship between the RBC’s shape and the extracellular flow patterns is reported as a basis for cell manipulation in future applications.
II. METHODOLOGY
A. Problem formulation
The red blood cell (RBC) is considered as a deformable body, which is immersed in the fluid plasma Ωf, as shown in Fig. 1(a). The RBC consists of two components: (i) the cellular membrane (ΓRBC) and (ii) the cytosol fluid (Ωc). The outer surface of the RBC’s membrane (ΓRBC) is the interface between the RBC and the fluid plasma, which is considered as a Newtonian incompressible fluid. Inside the RBC, the cytosol fluid occupies the inner part of the domain Ωc. The viscosity of the cytosol fluid is different from the one of the plasma fluid (viscosity contrast- ). The RBC deforms under the loading from the fluid domain Ωf onto the interface ΓRBC. In our hybrid continuum–particle approach, different numerical methods are used to model the dynamics of ΓRBC, Ωc, and Ωf, as explained in the following sections.
FIG. 1. Conceptual framework of the numerical methods. (a) The definitions of the computational domain including the mechanical components of the red blood cells (RBC): the blood plasma (Ωf); the cell membrane (ΓRBC); and the cytosol fluid (Ωc). (b) The triangulated surface that represents ΓRBC including the number of vertices (Nv) and the number of elements (Ne). The schematic of the escaped dissipative particle dynamics (DPD) particle (jth-red) from the cytosol fluid outside of the triangulated membrane. Vector rij is the position vector between the membrane vertex i = c and the escaped cytosol particle jth. Here, ne is the normal vector of the element e. The normal vector (ni) at the vertex i = c is computed as the averaged value of normal vectors in the adjacent elements ne in the region of support. The region of support is defined as the area (dashed line—As = ∑eAe/3) connecting the centroid of the elements e = 1, 2, 3, 4, and 5.
B. The idealized shape of the RBC—ΓRBC
C. RBC membrane model—ΓRBC
In this work, the model for membrane mechanics follows closely standard dissipative particle dynamics (DPD) methodologies.30–33 The details of the respective potentials are listed in Appendix A 1.
1. Membrane elastic properties
As the membrane mechanical response is modeled by Eq. (2), it is necessary to derive its elastic properties since they do not explicitly appear in the governing equations.34 Linear analysis30,33 on the non-linear spring network leads to a relationship between the macroscopic elastic properties (shear, area-compression, and Young’s moduli) and the model parameters. The derived linear shear modulus (μ0), the linear area-compression modulus (K), and Young’s modulus (Y) are shown in Appendix A 1 b. In addition, the model bending coefficient kb and the macroscopic membrane bending rigidity kc30,33,35 are related as .
2. Cellular membrane/cytoskeleton self-interaction
FIG. 2. Regularization scheme for the cytoskeletal self-interaction. The intracellular pressure of the cytosol fluid is assumed to increase locally as the two adjacent elements (Element 1 and 2) approach each other. The intracellular pressure is assumed to reach a maximum value of pbs = 4.12 Pa (the regularization condition). The regularized force FE [Eq. (4)] results from the increase in the intracellular pressure.
D. Modeling the interaction between RBC’s membrane (ΓRBC) and the cytosol fluid (Ωc)
The interaction between the membrane and the cytosol fluid leads to the emergence of the overall RBC dynamics.38 Differentiating between membrane and cytosol particles is essential for accurately capturing RBC mechanics, as it enables realistic modeling of membrane elasticity, internal viscosity contrast, and deformation behaviors under flow.39 This interaction is modeled using the dissipative particles dynamics (DPD method). DPD is a microscopic simulation technique widely used to model the flow of complex fluids. Here, the flow is described as a group of clustered interacting particles according to the Lagrangian approach.30 In this work, the cytosol within the RBC is modeled using a set of randomly distributed DPD particles (Nf) that fill the internal volume of the cell,30,31 as shown in Fig. 1(a). Therefore, both the membrane and the cytosol fluid are considered collections of DPD particles.
Due to the different nature of the interactions, the components of the force of each particle depend on the nature of the particle i (either membrane or cytosol particle). In general, each DPD particle i interact with surrounding particles j (either a membrane or cytosol fluid particle) within a cutoff radius rc through three pairwise additive forces: (a) the conservative force ; (b) the dissipative force ; (c) the random force , and the penalty force . The relative position vector between the particles i and j and related terms are given by rij = ri − rj, the distance rij = |rij|, and the unit vector . In addition, vi,j = vi − vj is the relative velocity between the particles i and j with velocities vi and vj.
The mathematical formulation of the conservative force , the dissipative force , the random force , and the penalty force for the membrane and the cytosol fluid particles are explained in Appendix A 2.
Here, the computed force is the sum of the external and internal forces as . The external force is either known (applied directly on the cell membrane) or computed from the extracellular fluid flows (fluid-structure interaction), as explained in the following.
E. Continuum approach for the blood plasma
The fluid solver is based on the sharp-interface curvilinear-immersed boundary (CURVIB) method (https://www.osti.gov/biblio/1312901) in a background structured-grid that contains the RBC model.29 The computational grid is formulated using the hybrid staggered/non-staggered methodology.42 The Navier–Stokes equations are discretized using second-order accurate differencing for all spatial derivatives. The discrete equations are integrated in time via a second-order accurate fractional step method. The momentum equation is solved with an implicit Runge–Kutta solver. The total mass flux of the entire domain is enforced strictly to machine zero to satisfy the incompressibility constraint of the blood plasma. The flexible generalized Minimal residual (FGMRES) method with multigrid pre-conditioner is used to solve the Poisson equation, which results from the discrete continuity equation.43
The complex surface of ΓRBC is handled by the sharp-interface immersed boundary method, which requires only the velocity reconstruction at the immersed surface along the local normal to the body.43 The presence of ΓRBC is represented by a layer of computational grid points (immersed surface—IB nodes), which is distributed around the surface ΓRBC. The boundary conditions reconstructed at the IB nodes are the three velocity components, which are reconstructed by interpolation.44 In this work, a linear velocity profile along the normal direction of the immersed surface is assumed near RBC’s boundary.45 Therefore, the presence of the IB nodes in our algorithm can be considered as an interface where the fluid and the cellular structure exchanges. The CURVIB method used here has been applied and validated in various FSI problems across different biological engineering areas.46–49
To enable the continuum–particle coupling of the blood plasma and the cellular structures in our FSI algorithm, the flow imparted loads must be calculated on ΓRBC as required by Eq. (9).
F. Fluid–structure interaction simulation of RBC in flows
G. Computational setups
Fluid–structure interaction simulations are performed to determine the dynamics of RBC in a confined micro-channel.4 The physical properties of the RBC are described in Table I. The corresponding DPD parameters are shown for the corresponding number of vertices Nv in Table II. The computation domain is defined as a rectangular channel containing a single RBC, as illustrated in Fig. 3(a). The dimensions of the domain along the x, y, and z are Lx (the length), Ly (the width), and Lz (the height), respectively. The computational domain is discretized as a structured grid of size Ni × Nj × Nk with the spatial resolution in three directions (i, j, k) are Δx ×Δy ×Δz, respectively. The no-slip condition is applied to all channel walls. The details of the channels used in the simulations are listed in Table III.
TABLE I. Physical parameters describing the RBC characteristics.
| RBC physical parameters | |
|---|---|
| RBC diameter (D0) | 7.82 µm |
| RBC area | 135.0 × 10−12 m2 |
| RBC volume | 94.0 × 10−18 m3 |
| Elastic shear modulus (μ0) | 6.3 µN/m |
| Young’s modulus (Y) | 18.9 µN/m |
| Bending rigidity (kc) | 3.0 × 10−19 J |
| Membrane viscosity (ηm) | 22.0 × 10−3 Pa s |
| Boltzmann’s constant (kB) | 1.380 649 × 10−23 m2 kg s−2 K−1 |
| Temperature (T) | 298 K |
TABLE II. Coarse-grained parameters for the RBC membrane model for different numbers of vertices Nv. The definitions of the parameters , l0, lmax, p, kp, and θ0 are explained in Appendixes A 3 and A 4. The corresponding values of Young’s modulus, global area, local area, and volume constraints in DPD units are YM = 392.5, ka = 4900, kd = 100, kv = 5000, respectively. Other parameters are α = 1 and .
| N v | l0 (m) | lmax (m) | p (m) | kp (Nm2) | θ0 (deg) | |
|---|---|---|---|---|---|---|
| 500 | 8.07 | 5.5614 × 10−7 | 1.2235 × 10−6 | 1.9933 × 10−9 | 6.6288 × 10−25 | 6.86 |
| 1 000 | 8.07 | 3.7992 × 10−7 | 8.3582 × 10−7 | 2.9179 × 10−9 | 2.1132 × 10−25 | 4.69 |
| 3 000 | 8.07 | 2.2818 × 10−7 | 5.0199 × 10−7 | 4.8584 × 10−9 | 4.5783 × 10−26 | 2.82 |
| 9 000 | 8.07 | 1.3035 × 10−7 | 2.8678 × 10−7 | 8.5044 × 10−9 | 8.5357 × 10−27 | 1.61 |
| 24 472 | 8.26 | 7.5331 × 10−8 | 1.6573 × 10−7 | 1.4716 × 10−8 | 1.6474 × 10−27 | 0.93 |
FIG. 3. (a) Computational setup for the FSI simulation of a single RBC in a rectangular channel of size Lx × Ly × Lz. The inlet plane is shown in blue, which shows uniform grid lines to illustrate the computational mesh. The RBC is placed at an axial distance x0 from the inlet plane. (b) The sketch of the cross section of the computational domain to illustrate the definition of the radial shift (r). The dashed line represents the RBC placed at the channel’s center line. The solid line depicts how the cell is transversely shifted from the cross-sectional center along the bisector of the first quadrant by a radial shift (r) in the y–z plane (Table VI).
TABLE III. Different channel geometries and their associated computational grids to simulate the dynamics of RBC in fluid flows. The channels have rectangular cross sections of size Lx, Ly, and Lz along the axial, spanwise, and vertical directions, respectively. Ni, Nj, and Nk are the number of grid points in x, y, and z directions, respectively. χ is the channel confinement, which is defined in Sec. II G.
| Channel | Lx × Ly × Lz (μm) | Ni × Nj × Nk | Δx ×Δy ×Δz (μm) | χ |
|---|---|---|---|---|
| 1 | 90 × 12 × 10 | 151 × 101 × 101 | 0.6 × 0.12 × 0.1 | 0.65 |
| 2 | 80 × 16 × 16 | 151 × 101 × 101 | 0.54 × 0.16 × 0.16 | 0.4 |
| 3 | 80 × 21 × 21 | 151 × 151 × 151 | 0.54 × 0.14 × 0.14 | 0.3 |
| 4 | 90 × 12 × 10 | 151 × 231 × 231 | 0.6 × 0.05 × 0.005 | 0.65 |
1. Constant shear rate condition (I0)
Following the experimental study of Guckenberger et al.4 (Channel-1, Table III), FSI simulations of the RBC in channel flow with a constant flow rate are carried out with x0 = 22.5 µm. To highlight the constant flow rate, the notation I0 is introduced to emphasize this condition. As shown in Table IV, a constant inflow velocity U(t) = ψ0 is required at the inlet of the computational domain. Two values of ψ0 are considered: (i) ψ0 = U3 = 2 mm s−1 and (ii) ψ0 = U4 = 6 mm s−1. In these cases, two values of the radial shift are also investigated: r1 = 0 and r3 = 0.7 µm. To simplify the discussions, the numerical values for the bulk velocity ψ0 will not be explicitly referred to. Instead, only the acronyms (U3 and U4) will be used for reasons that will be evident in the subsequent texts.
TABLE IV. Summary of the validation cases under constant shear rates (I0U3r1χ1 and I0U4r3χ1) and stepwise oscillatory flows (Isψ1r1χ2, Isψ2r1χ2, Isψ3r1χ2 and Isψ4r1χ3, Isψ5r1χ3, and Isψ6r1χ3). The stepwise oscillatory flows with the forward (ψf) and backward (ψb) velocities and the forward Capillary number are defined in Sec. II G 2. The maximum shear rate and the maximum Reynolds number (Ref) are defined in Sec. II G 1. The definition of RBC’s radial shift r is shown in Fig. 3(b).
| Case | Inflow | ψf (mm s−1) | ψb (mm s−1) | r (μm) | Re f | Ca f | |
|---|---|---|---|---|---|---|---|
| I 0 U 3 r 1 χ 1 | I 0 | 2 | ⋯ | 200 | 0 | 1.34 × 10−2 | 0.49 |
| I 0 U 4 r 3 χ 1 | I 0 | 6 | ⋯ | 600 | 0.7 | 4 × 10−2 | 1.47 |
| I s ψ 1 r 1 χ 2 | I s | 1.05 | −0.27 | 66 | 0 | 7 × 10−3 | 0.1 |
| I s ψ 2 r 1 χ 2 | I s | 1.58 | −0.39 | 98 | 0 | 1.05 × 10−2 | 0.15 |
| I s ψ 3 r 1 χ 2 | I s | 2.1 | −0.53 | 132 | 0 | 1.4 × 10−2 | 0.2 |
| I s ψ 4 r 1 χ 3 | I s | 1.9 | −0.48 | 119 | 0 | 1.27 × 10−2 | 0.1 |
| I s ψ 5 r 1 χ 3 | I s | 2.8 | −0.7 | 175 | 0 | 1.87 × 10−2 | 0.15 |
| I s ψ 6 r 1 χ 3 | I s | 3.7 | −0.93 | 232 | 0 | 2.47 × 10−2 | 0.2 |
Using these notations, the FSI simulation cases are named using the convention for each type of inflow waveform (I), the bulk velocity (U), the radial shift (r), and the channel type, respectively. The first case (I0U3r1χ1) is configured with and r = r1 = 0 µm. The second case (I0U4r3χ1) is carried out with ψ0 = U4 = 6 mm s−1 and r = r3 = 0.7 µm. Here, the Reynolds number is defined as . The kinematic fluid viscosity of blood plasma is chosen as . The summary of the parameters for each simulation case is shown in Table IV.
First, trelax = 10 and 7.0 ms are set for I0U3r1χ1 (croissant) and I0U4r3χ1 (slipper) simulations, respectively. After the relaxation period, a linear ramping period is set for each simulation case tramp = 30 and 20 ms for I0U3r1χ1 and I0U4r3χ1, respectively. During this ramping period, the bulk velocity U(t) is linearly increased. The value of U(t) reaches ψ0 at the end of the ramping period.
2. Stepwise oscillatory flows (Is)
FIG. 4. Propulsion step Δxc [Eq. (18)] as a function of the forward capillary number Caf [Eq. (17)]. The propulsion step Δxc is shown in terms of the length scale (Ls = 8 µm). (a) The bulk flow waveform of the inflow (U(t)) has a stepwise shape [see Eq. (16)]. Two-time instances ( and ) are shown to exemplify the changes in RBC shapes over time. (b) Three values of Caf = 0.1, 0.15, and 0.2 (red squares and blue circles) are simulated. The computed values of Δxc are compared to the study in Ref. 13 (solid lines).
There are six values of ψf examined as ψ1 = 1.05, ψ2 = 1.58, ψ3 = 2.1, ψ4 = 1.9, ψ5 = 2.8, and ψ6 = 3.7 mm s−1, respectively. Following the naming convention of the simulations, six cases are formed with the respective parameters: Isψ1r1χ2; Isψ2r1χ2; Isψ3r1χ2; and Isψ4r1χ3, Isψ5r1χ3, and Isψ6r1χ3, as shown in Table IV. As the waveform applied is of a stepwise nature, there is no relaxation time taken into consideration for these cases (trelax = 0).
3. Sinusoidal flow simulations
FIG. 5. (a) Sinusoidal inflow velocity U(t) profile with the forward (Tf), resting (Tr), and backward (Tb) time intervals. (b) Four inflow types with different rations are considered (see also Table V). The time instances , , and shown in panel (a) represent instances at which the RBC shapes are recorded. The exact values of the time instances are shown in Fig. 9 and Tables VIII–XI.
TABLE V. Controlling parameters of the pulsatile waveforms (I1, I2, I3, and I4). The waveforms are characterized by the intervals of the forward (Tf), rest (Tr), and backward (Tb) periods. The shapes of the waveforms are shown in Fig. 5.
| Time | |||
|---|---|---|---|
| Waveforms | Tf (ms) | Tr (ms) | Tb (ms) |
| I 1 | 20 | 10 | 20 |
| I 2 | 25 | 12.5 | 12.5 |
| I 3 | 27.3 | 9 | 13.7 |
| I 4 | 28.6 | 7.1 | 14.3 |
TABLE VI. Summary of the combinatoric configurations for steady and pulsatile flow simulations. The combination of the waveform type, the forward flow velocity, the radial shift, and the channel confinement results in the simulation configurations of ImUnrpχs. Here, m = 1, 2, 3, and 4; n = 1, 2, 3, and 4; p = 1, 2, and 3; and s = 1, 2, and 3. The profile of the inflow waveforms (I1, I2, I3, and I4) are shown in Fig. 5. The peak forward flow velocity U [Fig. 5(a)] varies from 1 to 6 mm/s. The radial shift of the RBC centroid along the bisector of the y–z plane at the initial time is defined in Fig. 3(b).
| Subscript | Inflow waveform | ψf (mm s−1) | Radial shift r (μm) |
|---|---|---|---|
| 1 | I 1 | U1 = 1 | r1 = 0 |
| 2 | I 2 | U2 = 1.5 | r2 = 0.4 |
| 3 | I 3 | U3 = 2 | r3 = 0.7 |
| 4 | I 4 | U4 = 6 | ⋯ |
TABLE VII. Summary of the 36 sinusoidal flow cases in Sec. II G 3 with a confinement of χ1 = 0.65. Tables (a), (b), (c), or (d) each consist of nine possible combinations between the peak forward flow U and the radial shift r for each type of waveform I1, I2, I3, and I4, respectively. The exact numeric value of U1, U2, U3 and r1, r2, r3 are shown in Table VI.
| (a) | r 1 | r 2 | r 3 | |
|---|---|---|---|---|
| I 1 | U 1 | I 1 U 1 r 1 χ 1 | I 1 U 1 r 2 χ 1 | I 1 U 1 r 3 χ 1 |
| U 2 | I 1 U 2 r 1 χ 1 | I 1 U 2 r 2 χ 1 | I 1 U 2 r 3 χ 1 | |
| U 3 | I 1 U 3 r 1 χ 1 | I 1 U 3 r 2 χ 1 | I 1 U 3 r 3 χ 1 | |
| (b) | r 1 | r 2 | r 3 | |
|---|---|---|---|---|
| I 2 | U 1 | I 2 U 1 r 1 χ 1 | I 2 U 1 r 2 χ 1 | I 2 U 1 r 3 χ 1 |
| U 2 | I 2 U 2 r 1 χ 1 | I 2 U 2 r 2 χ 1 | I 2 U 2 r 3 χ 1 | |
| U 3 | I 2 U 3 r 1 χ 1 | I 2 U 3 r 2 χ 1 | I 2 U 3 r 3 χ 1 | |
| (c) | r 1 | r 2 | r 3 | |
|---|---|---|---|---|
| I 3 | U 1 | I 3 U 1 r 1 χ 1 | I 3 U 1 r 2 χ 1 | I 3 U 1 r 3 χ 1 |
| U 2 | I 3 U 2 r 1 χ 1 | I 3 U 2 r 2 χ 1 | I 3 U 2 r 3 χ 1 | |
| U 3 | I 3 U 3 r 1 χ 1 | I 3 U 3 r 2 χ 1 | I 3 U 3 r 3 χ 1 | |
| (d) | r 1 | r 2 | r 3 | |
|---|---|---|---|---|
| I 4 | U 1 | I 4 U 1 r 1 χ 1 | I 4 U 1 r 2 χ 1 | I 4 U 1 r 3 χ 1 |
| U 2 | I 4 U 2 r 1 χ 1 | I 4 U 2 r 2 χ 1 | I 4 U 2 r 3 χ 1 | |
| U 3 | I 4 U 3 r 1 χ 1 | I 4 U 3 r 2 χ 1 | I 4 U 3 r 3 χ 1 | |
III. RESULTS
A. Coarse-graining validation
A stretching test is carried out and aimed to replicate the experimental test of Ref. 51. In our simulations, the parameters used to describe the physical characteristics of the RBC are listed in Table I. Following the coarse-graining procedure, the model parameters for the cell membrane such as the equilibrium length, the persistence length, the spring stiffness, and the spontaneous angle are computed for each value of Nv, as in Table II. The cytosol fluid is modeled by a set of particles Nf = 100, placed within the interior volume of the cell membrane, as shown in Fig. 6(b).
FIG. 6. (a) Axial (Da) and transverse (Dt) diameters (dashed lines) of the RBC under incremental stretching at different coarse-graining levels (Nv) (also see Table II). The experimental data of Ref. 51 are represented by the black diamonds. (b) The deformed shapes of the RBC membrane and the cytoplasm (green particles) under different stretching forces (Fstretch).
In this experiment, two external forces Fstretch and −Fstretch with opposite directions are applied on both sides of the RBC. The magnitude of the force Fstretch is increased in a stepwise manner from 0 to 200 pN (a total of 16 steps). The axial diameter (Da) and transverse diameter (Dt) are measured for every step. The definitions of Da and Dt are shown in Fig. 6(a). Da is calculated as |xmax − xmin| representing the axial distance between the maximum x position, denoted as xmax, and the minimum x position, denoted as xmin of the membrane vertices. Dt is calculated using the formula , where cy and cz represent the y and z coordinates of the RBC’s center of mass, respectively. The simulations are performed systematically with different RBC surface mesh resolutions by changing the number of vertices (Nv) (also see Table II).
The current RBC model accurately replicates the elastic response of the RBC under stretching forces, as revealed by the results of Da and Dt in Fig. 6. During stretching, the dynamic response of cytosol particles is visible, indicating the coupling between the membrane and the cytosol fluid. The shapes of the RBC under loading conditions agree with ones from experimental data of Ref. 51. The computed values of the axial (Da) and transverse (Dt) diameters agree well with the experimental values, as seen in Fig. 6(a). In particular, the values of Da and Dt are consistent across the different values of Nv, which indicates a robust performance of the coarse-graining procedure. There is a disagreement between the simulated results and the experimental value of Dt. Examining the shapes of the RBC in the simulations [Fig. 6(b)], it is revealed that the RBC tends to rotate around the stretching direction. This rotation leads to the difference between the experimental and numerical results of Dt. In brief, the mechanics of RBC is well-replicated by the computational model across different levels of coarse-graining. Thus, the value of Nv = 1000 is chosen to report the dynamics of the RBC.
B. Model validation
1. Deformation of the RBC under a constant shear rates
Under constant shear rate conditions (I0), as described in Sec. II G 1, two districts of the RBC shape are observed: (i) the croissant shape (I0U3r1χ1‐) and (ii) the slipper shape (I0U4r3χ1—), as shown in Fig. 7.
FIG. 7. Transitions from the idealized shape to realistic shapes under the impact of a constant shear rate (I0) in (a) croissant shape (I0U3r1χ1) and (b) slipper shape (I0U4r3χ1). The RBC membrane only exhibits the tank-treading effect in the slipper shape (I0U4r3χ1), which is characterized by the motions of two Lagrangian markers V1 and V2. The slipper shape is maintained by the counterclockwise rotation (the green arrow) of the cellular membrane around the RBC’s centroid. The multilobe shape appears (c) under the oscillatory flow (Isψ6r1χ3) during the backward phase (0.7T).
Under low shear rate (I0U3r1χ1), the RBC is initially placed along the centerline of the microchannel (discocyte shape). As the RBC interacts with the incoming flow, it deforms and eventually transitions to a croissant shape, as shown in Fig. 7(a). The terminal shape (croissant) is attained as the RBC continues to propagate along the channel’s symmetry axis. Note that the croissant shape, in this case, is not fully axis-symmetric. In addition, the croissant shape observed in this study is comparable with the parachute shape from our previous work,29 although not identical due to differences in channel geometry and flow conditions.
Under high shear rate (I0U4r3χ1), the RBC transitions to the slipper shape, as shown in Fig. 7(b), which exhibits a bistability mode with tank-treading behavior. Note that the RBC is placed at a radial shift r3 = 0.7 µm. Thus, the initial location of the RBC is not at the channel’s symmetry axis. The tank-treading effect is a complex dynamic in which the RBC membrane propagates axially along the channel while it rotates around its own center of mass, as seen in Fig. 7(b). A counterclockwise rotation is observed as indicated by the locations of two membrane particles (Lagrangian points—V1 and V2) at different time instances (t1 = 22 ms and t2 = 25 ms).
In both the croissant or slipper shapes, the transition from the initial shape (discocyte) to the terminal shape (either croissant or slipper) occurs within around 30 ms, as seen in Fig. 7. Our simulation results agree well with the corresponding experimental data of Ref. 4 as well as those described in recent experiments on RBC transient dynamics.52,53 Furthermore, these transitions are in good agreement with the shape diagram54 for different capillary numbers and confinements. In conclusion, our simulations are able to replicate the dynamics of the croissant and slipper shapes excellently.
The extracellular patterns of the croissant and slipper shapes also agree well with the experimental data.4,26 The extracellular flow pattern can be visualized by reconstructing the relative flow velocity field26 (co-moving frame). The relative velocity is defined as the difference between the flow velocity and the RBC’s centroid velocity, as shown in Fig. 8. In the croissant shape (I0U3r1χ1), the velocity streamlines closely resemble an axi-symmetrical flow pattern [Fig. 8(a)]. The downstream side of the RBC membrane deforms significantly, whereas the upstream side barely changes, as depicted in Fig. 8(b). In the slipper shape (I0U4r3χ1), there exists an asymmetrical vortical structure in the vicinity of the RBC membrane. As the slipper shape emerges, a fully closed vortex ring is created by a separated flow region, which is close to the channel wall. In short, the emergence of the RBC shape dictates the extracellular flow pattern.
FIG. 8. Extracellular flow patterns for (a) the croissant shape (I0U3r1χ1) and (b) the slipper shape (I0U4r3χ1). The flow streamlines are reconstructed using the co-moving frame method, as discussed in Sec. III B 1. The tank-treading effect induces a closed vortex to form on the upstream side of the RBC.
2. Propulsion of RBC under stepwise oscillatory flows (Is)
Under stepwise flow waveform (Is), our simulation results agree well with the propulsion step map (Δxc, Caf), which was developed13 for both channels χ2 = 0.5 and χ3 = 0.38. In both cases, the propulsion step (Δxc) is observed to monotonically increase with the values of Caf. However, Δxc is higher in the lower confinement channel (χ3), which indicates the importance of channel confinement.
In all simulation cases (Isψ1r1χ3, Isψ2r1χ3, Isψ3r1χ3, Isψ4r1χ3, Isψ5r1χ3, and Isψ6r1χ3), the RBC transitions from the idealized discocyte to the biconcave shape during the forward phase with all values of the peak forward flow (ψf = 1.05 mm s−1 to ψf = 4.34 mm s−1), as shown in Fig. 7(c). Strikingly, the complex multilobe shape emerges during the backward phase Tb. The elastic response of the RBC membrane to the oscillatory flow during the cycle T is depicted for the case Isψ6r1χ3 in Fig. 7(c). The reversal of the flow direction during Tb results in membrane buckling and stretching, which give rise to the multilobe shape. Note that these cases are initially placed at the channel center (r = r1 = 0). Therefore, it is possible to induce complex dynamics of RBCs in a confined channel by changing the inflow waveform only.
C. The impact of sinusoidal flows on RBC dynamics
1. The emergence of RBC shapes
The sinusoidal flow waveform (I1, I2, I3, and I4) further adds complexity to the membrane dynamics as the shape of RBC is highly sensitive to the extracellular flow condition. As a result of the pulsatile flow condition, the RBC shape continuously responds to the applied flow in the channel. Our simulations show that the RBC alternates its shapes in one of the following types: (1) croissant; (2) slipper; (3) trilobes; (4) simple/complex/elongated multilobes; (5) rolling stomatocytes; (6) hexalobes; and (7) rolling discocyte, as shown in Fig. 9 and Tables VIII–XI. The emergence of each type will be discussed in the following.
FIG. 9. Emergence of complex shapes induced by different inlet sinusoidal waveforms at different time instances in the flow cycle t1 (end of forward phase), t2 (during the resting period), and t3 (backward flow phase). (a) I1U1r1χ1, (b) I3U2r1χ1, (c) I4U2r1χ1, and (d) I4U3r3χ1.
TABLE IX. Summary of the RBC morphology transition sequences recorded at different time instances under I2 waveform, and the different flow velocities U1, U2, U3 and initial placements r1, r2, r3. Here, the numerical value in the table represents the first time instance that the RBC deformed shape appears. The acronyms C, S, CM, EM, and RD represent the croissant, slipper, complex multilobes, elongated multilobes, and rolling discocyte, respectively. The exact numeric value of U1, U2, U3 and r1, r2, r3 are shown in Table VI.
| Waveform (I2) | C | S | CM | EM | RD | ||||
|---|---|---|---|---|---|---|---|---|---|
| r 1 | r 2 | r 3 | r 1 | r 2 | r 3 | r 1 | r 2 | r 3 | |
| U 1 | 0.27 | 0.29 | 0.29 | 0.9 | ⋯ | ⋯ | 1.55 | 1.33 | 1.33 |
| U 2 | 0.2 | 0.22 | 0.22 | 1.06 | ⋯ | ⋯ | 1.44 | 1.3 | 1.3 |
| U 3 | 0.2 | 0.22 | 0.22 | 1.06 | 1.16 | 1.16 | 1.8 | 1.3 | 1.3 |
TABLE XI. Summary of the RBC morphology transition sequences recorded at different time instances under I4 waveform, and the different flow velocities U1, U2, U3 and initial placements r1, r2, r3. Here, the numerical value in the table represents the first time instance that the RBC deformed shape appears. The acronyms C, S, CM, EM, RD, and HX represent the croissant, slipper, complex multilobes, elongated multilobes, rolling discocyte, and hexalobes, respectively. The exact numeric value of U1, U2, U3 and r1, r2, r3 are shown in Table VI.
| Waveform (I4) | C | S | CM | EM | RD | HX | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| r 1 | r 2 | r 3 | r 1 | r 2 | r 3 | r 1 | r 2 | r 3 | r 1 | |
| U 1 | 0.32 | 0.31 | 0.31 | 0.6 | ⋯ | ⋯ | 1.38 | 1.27 | 1.27 | ⋯ |
| U 2 | 0.27 | 0.24 | 0.24 | 0.56 | ⋯ | ⋯ | 1.5 | 1.24 | 1.24 | 1.15 |
| U 3 | 0.27 | 0.24 | 0.24 | 0.56 | 1.15 | 1.15 | 1.1 | 1.24 | 1.24 | ⋯ |
In all cases, the RBC evolves to the croissant (C) or the slipper (S) shape during the forward phase (0 < t < Tf) of the flow cycle , as shown in Tables VIII–XI. This process is demonstrated for the cases I1U1r1χ1, I3U2r1χ1, I4U2r1χ1, and I4U3r3χ1, as seen in Fig. 9 (first column). Note that the transition to C or S mode from the biconcave shape is dependent on the value of the radial shift (r). As shown in Tables VIII–XI, the S mode appears only when the RBC is initially placed not exactly at the cross-sectional center (r > 0) (for example, I4U3r3χ1). The RBC remains in C mode during the forward phase if it is initially placed at the cross section center (r = 0) regardless of the bulk flow waveform I1, I2, I3, or I4 as seen in cases I1U1r1χ1, I3U2r1χ1, and I4U2r1χ1. In brief, the croissant and the slipper shapes exist during the forward phase and their emergence depends on the radial shift (r).
The RBC transitions from the simple shapes (croissant and slipper) toward more complex shapes (trilobes, simple/complex/elongated multilobes, rolling stomatocytes, hexalobes, and rolling discocyte) later in the flow cycle during the resting/reverse periods , as shown in Tables VIII–XI and Fig. 9 (middle column). The shape transformation is initiated by the buckling of the RBC membrane, which takes place in the resting interval (Tr) (see Fig. 5) of the flow. As a result of the change in flow direction in Tb, the RBC experiences considerable stretching and compression, leading to significant alterations in its membrane shape, as seen in Fig. 9. Note that the RBC does not return to either C or S mode at the beginning of the second cycle T < t < 2T, as shown in Tables VIII–XI. Thus, the shape transition process is irreversible even if the inflow waveform is symmetric (I1), as shown in Table VIII.
TABLE VIII. Summary of the RBC morphology transition sequences recorded at different time instances under I1 waveform, and different flow velocities U1, U2, U3 and radial shift r1, r2, r3. Here, the numerical value in the table represents the first time instance that the RBC deformed shape appears. The acronyms C, S, CM, M, T, RS, EM, and RD represent the croissant, slipper, complex multilobes, multilobes, trilobes, rolling stomatocytes, elongated multilobes, and rolling discocyte, respectively. The exact numeric value of U1, U2, U3 and r1, r2, r3 are shown in Table VI.
| C | S | CM | M | T | RS | EM | RD | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Waveform (I1) | r 1 | r 2 | r 3 | r 1 | r 1 | r 1 | r 1 | r 1 | r 1 | r 2 | r 3 |
| U 1 | 0.32 | 0.3 | 0.3 | ⋯ | 0.75 | ⋯ | 0.9 | ⋯ | 1.8 | 1.25 | 1.25 |
| U 2 | 0.21 | 0.2 | 0.2 | 0.5 | ⋯ | 0.8 | ⋯ | 1.2 | ⋯ | 1.21 | 1.21 |
| U 3 | 0.21 | 0.2 | 0.2 | 0.5 | ⋯ | 0.8 | ⋯ | 1.2 | ⋯ | 1.21 | 1.21 |
2. The impacts of the initial position (the radial shift—r)
Our results in Fig. 9 show that the initial position (r) plays a critical role in the emergence of RBC shapes. The RBC is placed initially at the channel axis r = r1 = 0 under both the symmetric (I1U1r1χ1) and asymmetric (I3U2r1χ1 and I4U2r1χ1) waveforms shown in Figs. 9(a)–9(c), respectively. In these cases (I1U1r1χ1, I3U2r1χ1, and I4U2r1χ1), the RBCs all transition sequentially from the croissant shape toward the multilobes/complex multilobes and finally one of the poly-lobes (trilobes, rolling stomatocytes, or hexalobes). When r = r3 > 0 in the case I4U3r3χ1, the RBC mostly remains in the slipper shape during the forward phase (t < Tf), as seen in Fig. 9(d). It transitions toward the elongated multilobe during the backflow phase (Tf + Tr < t < T). Finally, the RBC becomes a rolling discocyte in the second cycle (t ≈ 1.2T). Note that r3 is a small value (0.7 µm) and thus the shape transition process is strongly sensitive to the initial placement of the RBC. The dependence of RBC dynamics on the value of r is explained in the following.
When the RBC is positioned at the centerline (r = 0) of the channel, its migration is highly dependent on the type of inflow waveform. When the RBC is subjected to a symmetric waveform (I1) as shown in Fig. 10(a), it oscillates around its initial position with a minimal migration along the channel (axial) direction. However, as the inflow profile transitions to an asymmetric waveform (I2, I3, and I4) with an increasing Tf, the RBC gains more momentum during the forward phase and propels far away from its initial position, as shown in Fig. 10(a) (left). The value of the axial displacement (Δxc) (t) increases sequentially from I1, I2, I3 to I4. Δxc reaches a maximum value of ∼4 × Ls at the end of the second cycle for the case I4. In the lateral direction, Δyc reaches a value of ∼0.16 × Ls at the end of the first cycle for the symmetric waveform (I1), as shown in Fig. 10(b) (left). For the cases I2, I3, and I4, the values of Δyc remain comparably low during the cycles. Furthermore, the vertical displacement Δzc seen in Fig. 10(c) follows a monotonically upward trend throughout the second cycle. This results in a maximum vertical displacement Δzc of ∼0.25 × Ls for I1. For other waveforms (I2, I3, and I4), a smaller upward trend is observed with a vertical displacement Δzc ≈ 0.08 × Ls at the end of the first cycle. During the second cycle, the cell is observed to migrate downward. In summary, the symmetric waveform (I1) leads to a maximum displacement in the transverse directions while the asymmetric waveforms (I2, I3, and I4) result in the maximum axial displacement for r = 0.
FIG. 10. Impacts of the radial shift r on the time evolution of RBC’s centroid displacement (Δxc, Δyc, Δzc), which are measured in units of the length scale Ls. The evolution of the centroid position is examined under two conditions: (i) centered initial position r = r1 = 0 [left column—(a)–(c)] for the cases I1U1r1χ1, I2U1r1χ1, I3U1r1χ1, and I4U1r1χ1 and (ii) off-centered initial position r = r2 = 0.4 µm [right column—(d)–(f)] for the cases I1U2r2χ1, I2U2r2χ1, I3U2r2χ1, and I4U2r2χ1, as described in Table VI.
As the RBC is placed at the off-centered location (r = r2 = 0.4 µm), as seen in Fig. 10(d), its axial migration is similar to ones with r = 0 [Fig. 10(a)]. The axial displacement (Δxc) is dependent on the applied inflow waveform and also reaches 4 × Ls for the I4 waveform. These values indicate that the radial shift (r) does not significantly affect the axial migration of the RBC. However, the lateral migration of RBC in Figs. 10(e) and 10(f) does show a dependence of the applied waveform. The values of Δyc and Δzc reach 0.14 × Ls for I1 and I2 at the end of the second cycle, respectively. Surprisingly, the impact of the applied waveform seems to diminish as the value of Tf increases. Comparing the case I3U2r2χ1 and I4U2r2χ1 (I3 and I4), the values of Δyc and Δzc shown in Figs. 10(e) and 10(f) are nearly identical, resulting in a vertical displacement of ∼0.04 × Ls. To summarize, the impact of the applied waveform is significant on the axial displacement of the RBC but it is milder on the lateral displacements when r > 0. The impact of the applied waveform diminishes as the forward time Tf increases.
3. The impacts of the waveform (I)
It is striking to observe the irreversible dynamics of RBC in Fig. 11. When the RBC is subjected to the symmetric waveform (I1) with different radial shifts r = r1, r2, and r3, the RBC oscillates around its initial position with a minimal displacement along the axial direction as shown in the cases I1U1r1χ1, I1U1r2χ1, and I1U1r3χ1. Despite the inflow waveform being completely symmetrical (a sine function—I1), the axial position of the RBC in Fig. 11(a) (left column) shows a net positive value of the displacement Δxc at the end of the first (t = T) and second cycle (t = 2T) even when there is no radial shift (r = 0). Although small, this positive value of Δxc indicates that the RBC does not go back exactly to its initial location, which is Δxc(t = 0) = 0. At all values of the radial shift of r = 0, 0.4, and 0.7 µm, this irreversible dynamics is even more evident, as shown in the lateral displacements in Figs. 11(b) and 11(c). The magnitudes of Δyc and Δzc are comparable for all values of r during the cycles. For the case I1U1r1χ1 (r = 0), the value of Δyc reaches a value of ∼0.16 × Ls at the end of the first cycle. For the cases I1U1r2χ1 and I1U1r3χ1, the values of Δyc and Δzc reach ∼0.25 × Ls at the end of the second cycle. In the vertical direction (zc) shown in Fig. 11(c), the well-centered RBC (r = 0) is influenced by the change of flow direction, which is depicted by the upward and downward trends in the first cycle. However, the cell follows a dominant upward trend during the entire second cycle, resulting in a lateral migration of around 0.25 × Ls. Therefore, there exists a significant lateral migration of the RBC during its propagation regardless of its initial position under the symmetrical waveform condition (I1). In conclusion, a symmetrical flow waveform (I1) results in minimal propulsion along the axial direction but a significant lateral migration.
FIG. 11. Impacts of the waveform (I) on the time evolution of RBC’s centroid displacement (Δxc, Δyc, Δzc), which are measured in units of the length scale Ls under two conditions: (i) the symmetric I1 [left column—(a)–(c)] and (ii) the asymmetric I4 [right column—(d)–(f)] waveforms at different values of the radial shift r1, r2, and r3. The symmetric flow cases (left column) include I1U1r1χ1, I1U1r2χ1, and I1U1r3χ1. The asymmetric flow cases (right column) include I4U1r1χ1, I4U1r2χ1, and I4U1r3χ1 cases. The exact values of r1, r2, and r3 are described in Table VI.
Under asymmetric waveform I4, the RBC propels along the channel direction with an axial displacement of ∼2 × Ls in each cycle, as shown in Fig. 11(d). As the waveform becomes asymmetric with a longer forward phase, the RBC does not go back significantly during the reverse phase. It rather remains at a displacement value of Δxc ≈ 1.9 × Ls at the end of the first cycle. It continues to propel in the second cycle up to Δxc ≈ 4.0 × Ls. Surprisingly, the lateral displacements of the RBC (Δyc, Δzc) are smaller in comparison with ones in the symmetric case (I1). The values of (Δyc, Δzc) are within 0.15 × Ls for all cases I4U1r1χ1, I4U1r2χ1, and I4U1r3χ1, as shown in Figs. 11(e) and 11(f). In brief, the RBC propels significantly under the impact of the asymmetrical flow waveform I4 along the axial direction but it does not migrate significantly in the lateral directions.
4. Extracellular flow dynamics at the vicinity of the RBC under oscillatory flows
The emergence of the RBC shape has a close relationship with the flow pattern of the surrounding fluid (extracellular flow). Under the impact of the channel confinement, the deformation of RBC is well regulated by the flow waveform, which results in distinct extracellular flow patterns surrounding the RBC, as shown in Figs. 9 and 12. To highlight the impact of the RBC motion, the flow pattern is visualized in the co-moving frame with the RBC’s centroid (see Sec. III B 1). Thus, the flow streamlines are represented from the perspective of the RBC.
FIG. 12. Flow patterns surrounding the RBC under different membrane shapes (also see Fig. 9). The flow patterns are visualized using streamlines of the velocity field in the co-moving frame with the RBC (also see Sec. III B 1). The representative flow patterns are shown for (a) the multilobes (I1U1r1χ1); (b) rolling stomatocytes (I1U1r1χ1); (c) slipper (I1U3r3χ1); and (d) croissant (I1U3r1χ1); (e) complex multilobes (I1U3r1χ1); (f) trilobes (I1U3r1χ1); (g) elongated multilobes (I1U3r1χ1); (h) hexalobes (I4U2r1χ1); and (i) rolling discocyte (I1U1r2χ1).
The case I1U1r1χ1 is selected to illustrate the evolution of flow pattern as the RBC deforms from a relatively simple shape to a more complicated shape, as depicted in Figs. 12(a) and 12(b). This case is chosen because the temporal variation of the waveform is completely symmetrical (I1). Moreover, the RBC is placed initially at the channel axis (r = r1 = 0) with the lowest forward velocity ψf = U1 = 1 mm s−1. In the case I1U1r1χ1, Fig. 12(a) revealed that the RBC has a multilobes shape at the end of the forward phase. The presence of the large lobes results in a more convoluted streamlines pattern during the resting phase. As the RBC undergoes a morphological transition to rolling stomatocytes at the end of the first cycle (t = 0.9T), the streamlines exhibit changes [Fig. 12(b)]. During the second cycle, the RBC gradually transforms into a rolling discocyte by the end of the second cycle (t = 1.8T).
The impact of the radial shift (r) on the RBC shape and the resulting flow pattern is significant. To highlight the impact of the initial location, the case I1U3r3χ1 was selected to visualize the flow patterns. As shown in Fig. 12(c), due to the off-centered initial location (r > 0), the slipper shape emerges during the forward phase. A closed vortex ring is also observed downstream of the RBC as the flow velocity reaches its maximum magnitude in the forward phase. This phenomenon is similar to the one observed in the constant shear rate case (I0U4r3χ1 with U4 = 6 mm s−1) in Fig. 8(b). This is remarkable since the peak flow ψf is rather three times lower in this case ψf = U3 = 2 mm/s.
The case I1U3r1χ1 [Figs. 12(d)–12(g)] is selected to illustrate further the impact of the peak forward flow ψf. In this case, the peak velocity is ψf = U3 = 2 mm s−1. In comparison with the case I1U1r1χ1 [Figs. 12(a) and 12(b)], only the value of the peak velocity ψf is increased. However, the RBC shape evolves in a completely different sequence, as opposed to the case I1U1r1χ1. During the forward phase, the RBC transitions quickly to the croissant shape in Fig. 12(d) at (t = 0.28T), just after the peak forward flow. The flow patterns are similar to those observed under case I0U3r1χ1 in Fig. 8(a). During the resting period (Tf < T < Tf + Tr), the flow velocity surrounding the cell decreased notably and the complex multilobes shape emerges, as seen in Fig. 12(e). The flow pattern is perturbed minimally surrounding the RBC as its shape turns to the trilobes shape, as depicted in Fig. 12(f). During the forward flow phase of the second cycle, the elongated multilobes appear (t = 1.2T).
The overall dynamics of the RBC over the cycles depend significantly on the radial shift. As demonstrated in Tables VIII–XI, the multilobes shape appears at the beginning of each cycle if the RBC is located initially at the channel center r = 0. Under the specific condition of the case I4U2r1χ1, the multilobes shape can further transform into the hexalobes shape during the forward flow phase of the second cycle (t = 1.15T), as shown in Table XI. Its corresponding flow patterns are shown in Fig. 12(h), in which the extracellular flow was observed to exhibit a minimal disturbance around the hexalobes shape as the RBC. For the majority of the off-centered cases (r > 0), the rolling discocyte case is found the most commonly, as shown in Tables VIII–XI. The flow pattern around a discocyte is exemplified in Fig. 12(i) for the case I1U1r2χ1 at t = 1.25T. Here, the flow streamlines show a distinct separation of upstream and downstream regions.
IV. DISCUSSION
Due to the membrane flexibility, RBC deforms swiftly under the shear flow.11 This morphological feature can be exploited to understand the mechanical properties of the RBC membrane53 and thus it has the potential to identify the pathological changes52 of RBC’s membrane. However, the exact mechanism of this response is not yet fully understood. In addition, experiments14 have shown a strong proof-of-principle in which unsteady shear flows characterized by forward–backward profiles were exploited to produce distinct deformation-induced actuation of cells with different elastic properties. This is significant and is potentially useful in cell sorting and separation applications. In this work, we explore the impacts of the unsteady shear rate to control cell deformation and migration in microchannels.
Our numerical method is based on the continuum–particle coupling,29 which allows the simulations of RBC dynamics under physiological conditions. Our numerical results show excellent agreements with available in vitro and computational data both in cellular mechanics and extracellular flow pattern of the blood plasma.4,26,51 While most previous studies30,31 have only focused on the impact of constant shear rate on the dynamics of the RBCs, our results show that the unsteady shear rate can induce complex RBC’s morphology in confined channels, as discussed in the following.
A. The emergence of the croissant shape and the slipper shape under a constant shear rate )
Recent studies4,26 in rectangular microchannels, which are identical to our channels as shown in Fig. 3 and Table III, suggest that the emergence of the RBC shape is dependent on the radial shift (r—see Fig. 3 for its definition). In previous studies,4,26 the croissant shape emerged at if the RBC is placed exactly at the channel’s center (r = 0). On the other hand, the slipper shape emerged whenever the RBC was not placed exactly at the centerline (r > 0). The RBC was found to exhibit a (tank-treading) slipper shape at sufficiently high shear rate and off-centered placement (r > 0).4,26 In cylindrical microchannels,17 similar observations were confirmed, albeit at lower shear rates . These studies point to the importance of the radial shift in regulating RBC dynamics. Our results shown in Fig. 7 agree with these observations (also see Tables III and IV). Our results show the appearance of croissant-to-slipper transition as the capillary number (and thus ) increases from 0.1 to 0.37 for a confinement of χ = 0.65. The croissant shape emerges when the initial position of the RBC is placed exactly at the channel centerline at a sufficiently low shear rate (Ca = 0.1). When the shear rate is increased to Ca = 0.37, the slipper shape emerges. Furthermore, our model is able to capture the intricate dynamics of the tank-treading motion, which is characterized by the rotation of the membrane at the shear rate of 600 s−1, as illustrated in Fig. 7. Therefore, our results further confirm the importance of the radial shift in the croissant-to-slipper transition.
B. The impact of time-varying shear rate on RBC shape
When the inflow varies in a stepwise manner, as seen in Fig. 4, the shear rate changes as a function of time with distinct forward (Tf) and backward (Tb) time phases. In all cases (Isψ1r1χ2, Isψ2r1χ2, Isψ3r1χ2, Isψ4r1χ3, Isψ5r1χ3, and Isψ6r1χ3), the RBC is placed exactly at the channel axis (r = r1 = 0). The shape transitions are accomplished through consistent transient stretching and compression of the membrane. This occurs as the RBC experiences forward and backward flow phases during the flow cycles. The RBC transitions from a discocyte shape toward a croissant shape during its forward propulsion, as shown in Fig. 7. Although the backward phase induces the buckling of the cellular membrane, the RBC shape remains relatively symmetrical with respect to the channel axis (multilobes) as shown in Fig. 7 at the end of Tb. This is remarkable given that the maximum shear rate during the backward phase can be sufficiently large but this symmetry is still maintained. Comparing the case I0U3r1χ1 and I0U4r3χ1 in Table IV, our results suggest that the complete break of symmetry52 (slipper shape) is observed only when the radial shift exists (r > 0).
When applying different sinusoidal waveforms (I1, I2, I3, and I4) shown in Fig. 5, our results show the ubiquitous presence of croissant and slipper shapes across all shear rates (, and 200 s−1). The slipper shape appears at (t ≈ 0.3T) whenever the RBC is placed off the channel’s axis (r > 0), as shown in Tables VIII–XI. Note that these waveforms are different in terms of the forward (Tf) and backward (Tb) phases, with the backward phase being the shortest in I4. As shown in Table VIII, the slipper shape is observed even when the waveform is completely symmetric (I1) given that r > 0, as in (a) I1U1r2χ1, (b) I1U1r3χ1, (c) I1U2r2χ1, (d) I1U2r3χ1, (e) I1U3r2χ1, and (f) I1U3r3χ1. Hence, our results indicate that the flow waveform does not affect the emergence of the RBC shape during the forward phase. Instead, the radial shift plays an essential role in this process.
It has been demonstrated11 that the presence of the discocyte shape is correlated with weak shear rates . Under , the RBC typically maintains its discocyte shape with an 80% probability.11 However, as the shear rate gradually rises , the likelihood of a discocyte shape decreases to 30%. This observation has been validated even when different viscosity ratios (λ)12 are considered. In our work, the discocyte shape is ubiquitously observed during the second flow cycle across all applied waveforms (I1, I2, I3, and I4) and shear rates (, 150, and 200 s−1) with (r > 0) as shown in Tables VIII–XI. Therefore, our results further confirm that discocyte is the most common shape with the range shear rate less than 200 s−1.
In our study, complex shapes evolve from simpler shapes under the influence of time-dependent waveforms. In the experimental work11 under constant shear rates, stomatocytes shape was observed to dominate the RBC population (65%) when the shear rate was between . In our study, the elliptical-rim-shaped stomatocytes only emerge under symmetric waveform (I1) in the case I1U1r1χ1 under the peak shear rate of . At high constant shear rates , experimental data11 showed that polylobes shape could emerge. This polylobes shape is characterized by a large number of lobes on the RBC surface, known as multilobes, trilobes, and hexalobes. In the current study, polylobes are also observed across all applied waveforms, given that the cell is placed initially at the channel axis (r = r1 = 0) even at weak shear rates as in Fig. 9 and Tables VIII–XI. For example, the multilobes are observed with all waveforms. The trilobes are both observed in the symmetric waveform (I1U2r1χ1 and I1U3r1χ1—Table VIII) or the asymmetric waveform (I3U2r1χ1—Table X). The hexalobes shape only appears under the most asymmetric waveform with , as shown in Table XI. In the previous work,55 the RBC shape has been reported to deform further into elongated shapes as the shear rate increases. Our results support this observation shown in Fig. 12(g) as the elongated multilobes appear during the backward flow phase. Examining Tables VIII–XI, our results suggest that this elongated shape is generally present regardless of the applied waveform but it only manifests under higher shear rates of at least . In particular, the elongated multilobes are observed under I1U2r1χ1, I1U3r1χ1, I2U3r2χ1, I2U3r3χ1, I3U3r2χ1, and I3U3r3χ1. In essence, our results strongly suggest that the complex shape can appear in a micro-channel even at weak shear rates given that the inflow is time-dependent.
TABLE X. Summary of the RBC morphology transition sequences recorded at different time instances under I3 waveform, and the different flow velocities U1, U2, U3 and initial placements r1, r2, r3. Here, the numerical value in the table represents the first time instance that the RBC deformed shape appears. The acronyms C, S, CM, T, EM, and RD represent the croissant, slipper, complex multilobes, trilobes, elongated multilobes, and rolling discocyte, respectively. The exact numeric value of U1, U2, U3 and r1, r2, r3 are shown in Table VI.
| Waveform (I3) | C | S | CM | T | EM | RD | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| r 1 | r 2 | r 3 | r 1 | r 1 | r 2 | r 3 | r 1 | r 2 | r 3 | |
| U 1 | 0.34 | 0.27 | 0.27 | 0.58 | ⋯ | ⋯ | ⋯ | 1.35 | 1.35 | 1.35 |
| U 2 | 0.28 | 0.22 | 0.22 | 0.62 | 0.94 | ⋯ | ⋯ | 1.8 | 1.32 | 1.32 |
| U 3 | 0.28 | 0.22 | 0.22 | 0.62 | ⋯ | 1.18 | 1.18 | 1.4 | 1.32 | 1.32 |
While the radial shift is important for the emergence of the RBC shape, it is less important in controlling the cell displacement (the center of mass), as shown in Fig. 11. The background flow velocities vary largely over the cycle in the forward and reverse phases, depending on the flow waveform, as shown in Fig. 5. This variation dominates the plasma flow surrounding the RBC. Therefore, small radial shifts (0–0.7 µm) play a minimal role in regulating the overall displacement of the RBC’s center of mass.
C. Controlling lateral migration of cells with oscillatory flows
Microfluidic devices are typically used to isolate and separate cells.56 While these devices are promising for many cell-sorting applications,57,58 the main challenge is the difficulty in obtaining high-throughputs due to the required length of the microfluidic channels. Changing the geometrical design of channels59 has been proposed as one efficient way. Recent studies have shown that varying the shear rates in time13,14 can reduce the required length based on the concept of velocity lift,60 which is the factor that drives the RBC’s migration toward the center of the channel.
As the inertial effect is negligible at a very low Reynolds number (Re ≈ 0.01), the flow is reversible for a rigid body. Thus, a rigid body will return to its initial position if the inflow conditions in the backward phase are reversed in the exact opposite way of its own during the forward phase. However, the RBC is not a rigid body and its membrane is highly flexible. Experiments14 have shown that the responses of RBCs are different from the ones of stiff beads in oscillatory flows. The RBC migration is observed to have a net actuation in oscillating flows whereas a stiff bead does not.
Our results in Figs. 10 and 11 for the symmetric waveform (I1) show that the RBC does not go back to its initial position at the end of the cycle. There is an axial shift of the RBC from its original position (Δxc ≠ 0) at the end of the cycle. Moreover, the RBC migrates significantly in the lateral cross section (Δyc ≫ 0 and (Δzc ≫ 0), as shown in Fig. 10. Our results in Figs. 11(b) and 11(e) indicate that the RBC undergoes lateral migration. This migration is particularly evident in the cases I1U1r3χ1 and I4U1r3χ1, where the cell migrates in the lateral direction for a distance of ∼0.25Ls and 0.13Ls, respectively. Comparing the shapes of the waveform I1, I2, I3, and I4 in Fig. 5, our results suggest that significant lateral migration can be induced by adjusting the forward time interval (Tf). With our sinusoidal waveforms shown in Fig. 5, Tf should be less than three times the backward flow phase Tb () to be able to induce significant lateral migration. This lateral migration in oscillatory flows might be used to separate cells selectively based on their mechanical properties.
Our findings in Figs. 8 and 12 show that the extracellular flow patterns are directly influenced by the dynamics of the RBC. Under a stationary condition in Fig. 8, the extracellular flow dynamics in the croissant shape are distinctively different from the ones of the slipper shape. In particular, the flow around the steady croissant shape was similar to that of a rigid sphere,61 in which the flow streamlines move nearly symmetrically inward and outward from the cell in the upstream and downstream sides, respectively. In contrast, a fully closed vortex ring (“bolus”) was observed downstream the cell for the slipper shape.4 Our results in Fig. 12 suggest that the extracellular flow patterns are far more complicated and highly dependent on the type of inflow waveform in time-dependent shear rates. It is important to note that the extracellular flow has been found to play an important role in drug delivery strategies26 due to its potential use of particle trapping. Therefore, our results suggest that controlling the inflow waveform either by adjusting the peak flow ψf or the shape of the waveform (specifically Tf) might lead to the desired effects in delivering small particles (e.g., therapeutic nano-particles) to the RBCs.
D. Limitations
While the current model has successfully simulated various RBC morphologies under different flow conditions, it is crucial to acknowledge its limitations. First, the current model assumes that the impact of the collision force of the cytosol particles [Eq. (8)] is negligible in determining the shape of the cellular membrane, as shown in many validations of Sec. III A with high shear rates. However, it is important to recognize that this collision force might have impacts on the dynamics of the membrane and cytosol under very low shear rates. Therefore, future work will need to develop new algorithms for the collision scheme.30,40 Second, analyzing the distribution of cytosol fluid particles and their conformational temperature could offer valuable insights into cytosol dynamics. In this work, the number of cytosol particles (Nf) is set to be 100, which is much less than the number of membrane particles (Nv). The coarse resolution of the cytosol fluid may lead to significant under-representation of hydrodynamic interactions and spatial gradients in the cytoplasm. Therefore, the intracellular flow cannot be reliably reproduced. In future studies, a larger number of Nf should be implemented to capture the intracellular flow patterns accurately. Third, previous work62 has shown that Eq. (A11) does not satisfy angular momentum conservation. This violation might induce inaccurate behaviors in simulating the dynamics of fluids such as the cytosol fluid. This issue needs to be investigated in future work. Fourth, the intracellular pressure coefficient pbs = 4.12 Pa is chosen empirically, which is based on the range of RBC intracellular pressure.63 Further investigations might be needed to identify the optimal value of pbs. In addition, clustering of red blood cells is an important phenomenon, which determines the overall rheology of blood flow. Clusters of RBCs are formed by the adherence of individual cells, which have been shown to be dependent not only on the characteristics of the RBC membrane but also on the hydrodynamic condition of the plasma solution.64 While this clustering process is beyond the scope of this work, our methodology can be extended to study this phenomenon by incorporating the interaction forces between adjacent cell membranes. This issue will be addressed in our future work. Finally, our coupling methodology utilizes the sharp-interface immersed boundary method,45 which requires only the velocities on the cellular membrane as boundary conditions. In this work, the physical time step is chosen in the order of microseconds. Therefore, the thermal fluctuations of the velocity fields on the cellular membrane are fully resolved via the implicit momentum solver of the fractional step method (Sec. II E). Future studies need to be carried out to accelerate the convergence of the fractional step method for the Navier–Stokes equations and allow larger physical time steps.
V. CONCLUSION
• The RBC can transform into a variety of shapes, such as multilobes, trilobes, and hexalobes, by varying the sinusoidal waveform even when it is subjected to a relatively weak shear rate .
• Simple shapes such as croissant, slipper, and rolling discocyte appear when the RBC is subjected to all waveforms. However, complex shapes such as rolling stomatocytes, trilobes, and hexalobes appeared only under specific conditions. In our study, the RBC transitions into eight shapes under the symmetric waveform (I1) and into five shapes under the asymmetric waveform (I2). Therefore, it is possible to attain a certain shape using an appropriate waveform.
• Under the symmetric flow waveform, the axial displacement of the RBC is rather minimal. However, the lateral displacements are significantly large. Under the asymmetric flow waveform, the RBC experiences a large axial displacement but small lateral displacements.
• The maximum lateral displacement of the RBC during its propagation depends on the initial radial shift (r). This maximum value is also dependent on the asymmetry of the flow waveform (I).
• The extracellular flow surrounding the RBC depends on its morphological shape. The flow pattern is thus distinct and unique for each shape.
ACKNOWLEDGMENTS
This work was supported by the NSF under Grant No. 1946202 ND-ACES and a start-up package of Trung Le from North Dakota State University. The authors acknowledge the use of computational resources at the Center for Computationally Assisted Science and Technology CCAST-NDSU, which is supported by the NSF under Grant No. MRI 2019077. The authors also received Allocation No. CTS200012 from the Extreme Science and Engineering Discovery Environment (XSEDE). We acknowledge the financial support from Grant No. NIH-2P20GM103442-19A1 to train undergraduate students in Biomedical Engineering.
AUTHOR DECLARATIONS
Conflict of Interest
The authors have no conflicts to disclose.
Author Contributions
Lahcen Akerkouch: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Validation (equal); Writing – original draft (equal); Writing – review & editing (equal). Trung Bao Le: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Methodology (equal); Resources (equal); Software (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal).
APPENDIX A: MATHEMATICAL FORMULATION OF DISSIPATIVE PARTICLE DYNAMICS
1. RBC membrane model—ΓRBC
In this work, the dissipative particle dynamics (DPD) methodologies for the membrane mechanics follow closely the previous studies in the literature.30–33
a. Action potential models
The viscoelasticity property of the membrane is modeled by a network of non-linear springs. The wormlike chain (WLC) spring accounts for the attractive force while the repulsive force is defined by the power (POW) spring. The combination of these springs is denoted as the WLC-POW model, which results in a system of non-linear springs with a non-zero equilibrium length,30 as discussed in the following.
b. Membrane elastic properties
2. Modeling the interaction between RBC’s membrane (ΓRBC) and the cytosol fluid (Ωc)
The mathematical formulation of the conservative force , the dissipative force , the random force , and the penalty force for the membrane and the cytosol fluid particles are explained in the following.
a. The conservative force
TABLE XII. Force strength coefficients for DPD interactions.
| Interaction | a ij | γ | r c |
|---|---|---|---|
| Fluid-fluid | 20 | 116.4 | 1 |
| Membrane-fluid | 20 | 116.4 | 1 |
b. The dissipative force
c. The random force
d. Plasma and cytosol viscosity contrast
TABLE XIII. Relationship between DPD parameters and the physical units for viscosity ratio λ = 5. rc, δt, μcytosol, and γ correspond to the cutoff radius, time step, dynamic viscosity of the cytosol, and coefficient defining the strength of the dissipative force, respectively. The definitions of the parameters rM, τ, η, and ηM are explained in Table I and sections of Appendixes A 2 d and A 3.
| Parameters | DPD value | Scaling value | Physical value |
|---|---|---|---|
| r c | 1 | r M | 9.7 × 10−7 m |
| δt | 10−3 | τ | 2.5 × 10−4 s |
| μcytosol(γ = 10) | 0.5 | 6 × 10−3 Pa s |
3. Scaling of model and physical units
One challenge in DPD modeling is establishing a relationship between the modeled quantities and the physical values. Since this relationship is not explicit, it is necessary to use a scaling argument to recover this relationship.30,36 For each parameter, the superscript M and P correspond to the model and physical units, respectively. The details of the physical parameters for the RBC are shown in Table I.
4. Coarse-graining procedure
A full-scale model of the RBC typically consists of millions of particles, which are required to accurately simulate protein dynamics.76 However, it is not feasible to use such a full-scale model in a fluid–structure interaction (FSI) simulation due to the high computational cost. We followed the coarse-graining procedure of Ref. 31 to represent the RBC membrane by a smaller number of particles (coarse-grained model). This procedure does not allow a detailed simulation of separate proteins, but it is versatile enough to capture the overall dynamics of the RBC membrane. The parameters of the coarse-grained model (c) are computed from the ones of the fine-scaled model (f) by using a scaling procedure. Examples of such parameters are explained in the following.
The details of the parameters for the coarse-graining process are shown in Table II.
APPENDIX B: COMPUTATIONAL COST
• Test 1—Strong scaling test: 8 × 106 grid points for the blood plasma domain (Channel-4 in Table III) is considered while the number of vertices for the triangulated mesh of the RBC is kept as Nv = 500 vertices. The purpose of this test is to investigate the scalability of the FSI coupling as the number of CPUs increases.
• Test 2—DPD scaling test: multiple resolutions of the RBC triangulated mesh are tested Nv = 500, 1000, 2000, 3000, 5000 and 9000 with a fixed computational domain of 1 × 106 grid points (Channel-1 in Table III) for the blood plasma on a single CPU. The purpose of this test is to investigate the computational cost of the DPD algorithm as the number of particles increases.
FIG. 13. Scalability test. (a) Total wall-clock time (tsolver) per time step for the solver to resolve the plasma flow and membrane dynamics using different computational resources (number of CPUs). Here, the computational configuration includes a RBC with Nv = 500, which is immersed in a plasma domain with 8 × 106 grid points (Channel-4, Table III). The dashed line shows the linear scaling. (b) The performance of the DPD solver with respect to (Nv = 500, 1000, 2000, 3000, 5000 and 9000). The computational configuration is Channel-1 (Table III). The wall-clock time of the DPD solver (tDPD) for one simulation time step is plotted against Nv in the log–log scale. The dashed line represents the scaling.
We carry out simulations for only two cycles (2T) in all cases as it is sufficient to identify the differences in the shape transformation of RBCs under different flow conditions. For future bio-engineering applications, it is necessary to perform simulations in many cycles. As shown in Fig. 14, the case I1U1r2χ1 is simulated in seven cycles. The RBC tends to repeat its morphological shape in each cycle (after the third cycle). However, its shape does not return exactly to the one in the previous cycle. Comparing the instances (for example, t5 = 3.25T and t7 = 5.25T), it is clear that the shape varies significantly from one cycle to another. Such variability needs to be investigated in future studies.
FIG. 14. Morphology changes of the RBC under asymmetric sinusoidal waveform I1U1r2χ1. The shape transitions were recorded over an extended period of seven cycles (7T).
To demonstrate the distribution of loading condition on the surface of the RBC membrane, the magnitude of the external loading is plotted in Fig. 15 for the case I0U4r3χ1. Although the incoming flow is stationary, the loading condition changes over time as the RBC deforms. The non-uniform distribution of the applied load offers important insights into the mechanisms behind the RBC shape transitions. Therefore, further investigation of this phenomenon is needed in future work.
FIG. 15. Loading condition on the case I0U4r3χ1 (slipper shape) over three time instances t0 = 0 s, t1 = 20 ms, and t2 = 25 ms.
REFERENCES
- 1 D. K. Kaul, M. E. Fabry, P. Windisch, S. Baez, and R. L. Nagel, J. Clin. Invest. 72, 22 (1983).10.1172/jci1109606874947 PMC1129157
- 2 G. A. Barabino, M. O. Platt, and D. K. Kaul, Annu. Rev. Biomed. Eng. 12, 345 (2010).10.1146/annurev-bioeng-070909-10533920455701
- 3 T. W. Secomb, Annu. Rev. Fluid Mech. 49, 443 (2017).10.1146/annurev-fluid-010816-060302
- 4 A. Guckenberger, A. Kihm, T. John, C. Wagner, and S. Gekle, Soft Matter 14, 2032 (2018).10.1039/c7sm02272g29473072
- 5 P. Balogh and P. Bagchi, Phys. Fluids 30, 051902 (2018).10.1063/1.5024783
- 6 F. Reichel, J. Mauer, A. Nawaz, G. Gompper, J. Guck, and D. Fedosov, Biophys. J. 107, 14–24 (2019).10.1016/j.bpj.2019.05.022PMC662683431235179
- 7 Y. Rashidi, G. Simionato, Q. Zhou, T. John, A. Kihm, M. Bendaoud, T. Krüger, M. O. Bernabeu, L. Kaestner, M. W. Laschke et al., Biophys. J. 122, 1526 (2023).10.1016/j.bpj.2023.03.02036932676 PMC10147840
- 8 A. Gürbüz, O. S. Pak, M. Taylor, M. V. Sivaselvan, and F. Sachs, Biophys. J. 122, 2230 (2023).10.1016/j.bpj.2023.01.01036639868 PMC10257124
- 9 P. Vlahovska, Y. Young, G. Danker, and C. Misbah, J. Fluid Mech. 678, 221 (2011).10.1017/jfm.2011.108
- 10 B. Czaja, M. Gutierrez, G. Zavodszky, D. de Kanter, A. Hoekstra, and O. Eniola-Adefeso, PLoS Comput. Biol. 16, e1007716 (2020).10.1371/journal.pcbi.100771632163405 PMC7093031
- 11 L. Lanotte, J. Mauer, S. Mendez, D. Fedosov, J. Fromental, V. Claveria, F. Nicoud, G. Gompper, and M. Abkarian, Proc. Natl. Acad. Sci. U.S.A. 113, 13289–13294 (2016).10.1073/pnas.160807411327834220 PMC5127344
- 12 J. Mauer, S. Mendez, L. Lanotte, F. Nicoud, M. Abkarian, G. Gompper, and D. A. Fedosov, Phys. Rev. Lett. 121, 118103 (2018).10.1103/physrevlett.121.11810330265089
- 13 W. Schmidt, A. Fortsch, M. Laumann, and W. Zimmermann, Phys. Rev. Fluids 7, L032201 (2022).10.1103/PhysRevFluids.7.L032201
- 14 S. W. Krauss, P.-Y. Gires, and M. Weiss, Phys. Rev. Fluids 7, L082201 (2022).10.1103/physrevfluids.7.l082201
- 15 J. Dupire, M. Socol, and A. Viallat, Proc. Natl. Acad. Sci. U. S. A. 109, 20808 (2012).10.1073/pnas.121023610923213229 PMC3529085
- 16 H. Noguchi and G. Gompper, Proc. Natl. Acad. Sci. U.S.A. 102, 14159–14164 (2005).10.1073/pnas.050424310216186506 PMC1242298
- 17 D. Fedosov, M. Peltomaki, and G. Gompper, Soft Matter 10, 4258 (2014).10.1039/C4SM00248B24752231
- 18 B. Kaoui, J. Harting, and C. Misbah, Phys. Rev. E 83, 066319 (2011).10.1103/physreve.83.06631921797489
- 19 G. Tomaiulo, M. Simeone, V. Martinelli, B. Rotoli, and S. Guido, Soft Matter 5, 3736 (2009).10.1039/B904584H
- 20 S. Quint, A. F. Christ, A. Guckenberger, S. Himbert, L. Kaestner, S. Gekle, and C. Wagner, Appl. Phys. Lett. 111, 103701 (2017).10.1063/1.4986392
- 21 A. Kihm, L. Kaestner, C. Wagner, and S. Quint, PLoS Comput. Biol. 14, e1006278 (2018).10.1371/journal.pcbi.100627829906283 PMC6021115
- 22 G. Coupier, A. Farutin, C. Minetti, T. Podgorski, and C. Misbah, Phys. Rev. Lett. 108, 178106 (2012).10.1103/physrevlett.108.17810622680911
- 23 B. Kaoui, G. Biros, and C. Misbah, Phys. Rev. Lett. 103, 188101 (2009).10.1103/physrevlett.103.18810119905834
- 24 B. Kaoui, N. Tahiri, T. Biben, H. Ez-Zahraouy, A. Benyoussef, G. Biros, and C. Misbah, Phys. Rev. E 84, 041906 (2011).10.1103/physreve.84.04190622181174
- 25 J. L. McWhirter, H. Noguchi, and G. Gompper, Proc. Natl. Acad. Sci. U. S. A. 106, 6039 (2009).10.1073/pnas.081148410619369212 PMC2669370
- 26 F. Yaya, J. Römer, A. Guckenberger, T. John, S. Gekle, T. Podgorski, and C. Wagner, Microcirculation 28, e12693 (2021).10.1111/micc.1269333666310
- 27 B. R. Mutlu, J. F. Edd, and M. Toner, Proc. Natl. Acad. Sci. U. S. A. 115, 7682 (2018).10.1073/pnas.172142011529991599 PMC6065022
- 28 A. Lafzi, A. H. Raffiee, and S. Dabiri, Phys. Rev. E 102, 063110 (2020).10.1103/physreve.102.06311033466115
- 29 L. Akerkouch and T. B. Le, Fluids 6, 139 (2021).10.3390/fluids6040139
- 30 D. A. Fedosov, B. Caswell, and G. E. Karniadakis, Biophys. J. 98, 2215–2225 (2010).10.1016/j.bpj.2010.02.00220483330 PMC2872218
- 31 I. V. Pivkin and G. E. Karniadakis, Phys. Rev. Lett. 101, 118105 (2008).10.1103/physrevlett.101.11810518851338
- 32 D. A. Fedosov, W. Pan, B. Caswell, G. Gompper, and G. E. Karniadakis, Proc. Natl. Acad. Sci. U. S. A. 108, 11772 (2011).10.1073/pnas.110121010821730178 PMC3141939
- 33 L. Zhou, S. Feng, H. Liu, and J. Chang, Eng. Anal. Boundary Elem. 107, 47 (2019).10.1016/j.enganabound.2019.07.004
- 34 M. Dao, L. J, and S. Suresh, Mater. Sci. Eng., B 26, 1232–1244 (2006).10.1016/j.msec.2005.08.020
- 35 W. Helfrich, Z. Naturforsch., C 28, 693–703 (1973).10.1515/znc-1973-11-12094273690
- 36 Z. Peng, X. Li, I. V. Pivkin, M. Dao, G. E. Karniadakis, and S. Suresh, Proc. Natl. Acad. Sci. U. S. A. 110, 13356–13361 (2013).10.1073/pnas.131182711023898181 PMC3746879
- 37 X. Li, H. Lu, and Z. Peng, Handbook of Materials Modeling Applications: Current and Emerging Materials (Springer Cham, 2018).
- 38 S. Himbert, A. D’Alessandro, S. Qadri, M. Majcher, T. Hoare, W. Sheffield, M. Nagao, J. Nagle, and M. Rheinstädter, PLoS One 17, e0269619 (2022).10.1371/journal.pone.026961935913930 PMC9342732
- 39 A. Bohiniková, I. Jančigová, and I. Cimrák, Micromachines 12, 974 (2021).10.3390/mi1208097434442596 PMC8398941
- 40 S. Mokhtarian, A. Pishevar, and M. Saidi, Hindawi 6, 812408 (2014).10.1155/2014/812408
- 41 R. D. Groot and P. B. Warren, J. Chem. Phys. 107, 4423 (1997).10.1063/1.474784
- 42 A. Gilmanov and F. Sotiropoulos, J. Comput. Phys. 207, 457 (2005).10.1016/j.jcp.2005.01.020
- 43 L. Ge and F. Sotiropoulos, J. Comput. Phys. 225, 1782 (2007).10.1016/j.jcp.2007.02.01719194533 PMC2635106
- 44 A. Gilmanov, T. B. Le, and F. Sotiropoulos, J. Comput. Phys. 300, 814 (2015).10.1016/j.jcp.2015.08.008
- 45 A. Gilmanov, F. Sotiropoulos, and E. Balaras, J. Comput. Phys. 191, 660 (2003).10.1016/s0021-9991(03)00321-8
- 46 T. B. Le, I. Borazjani, and F. Sotiropoulos, J. Biomech. Eng. 132, 111009 (2010).10.1115/1.400270221034150
- 47 T. B. Le and F. Sotiropoulos, Eur. J. Mech. B 35, 20 (2012).10.1016/j.euromechflu.2012.01.013PMC338855422773898
- 48 T. Le, M. Elbaz, R. V. D. Geest, and F. Sotiropoulos, Flow Turbul. Combust. 102, 3 (2019).10.1007/s10494-018-0003-7
- 49 T. B. Le, J. Biomech. 116, 110238 (2021).10.1016/j.jbiomech.2021.11023833485144
- 50 I. Borazjani, L. Ge, and F. Sotiropoulos, J. Comput. Phys. 227, 7587 (2008).10.1016/j.jcp.2008.04.02820981246 PMC2963478
- 51 J. Mills, L. Qie, M. Dao, C. Lim, and S. Suresh, Mech. Chem. Biosyst. 1, 169–180 (2004).16783930
- 52 S. M. Recktenwald, K. Graessel, F. M. Maurer, T. John, S. Gekle, and C. Wagner, Biophys. J. 121, 23 (2022).10.1016/j.bpj.2021.12.00934896369 PMC8758421
- 53 G. Prado, A. Farutin, C. Misbah, and L. Bureau, Biophys. J. 108, 2126 (2015).10.1016/j.bpj.2015.03.04625954871 PMC4423063
- 54 D. Agarwal and G. Biros, Phys. Rev. Fluids 7, 093602 (2022).10.1103/physrevfluids.7.093602
- 55 X. Li, Z. Peng, H. Lei, M. Dao, and G. E. Karniadakis, Philos. Trans. R. Soc., A 372, 20130389 (2014).10.1098/rsta.2013.0389PMC408452924982252
- 56 D. R. Gossett, W. M. Weaver, A. J. Mach, S. C. Hur, H. T. K. Tse, W. Lee, H. Amini, and D. Di Carlo, Anal. Bioanal. Chem. 397, 3249 (2010).10.1007/s00216-010-3721-920419490 PMC2911537
- 57 S. Suresh, Acta Biomater. 3, 413 (2007).10.1016/j.actbio.2007.04.00217540628 PMC2917191
- 58 M. M. Brandao, A. Fontes, M. L. Barjas-Castro, L. C. Barbosa, F. F. Costa, C. L. Cesar, and S. T. O. Saad, Eur. J. Haematol. 70, 207 (2003).10.1034/j.1600-0609.2003.00027.x12656742
- 59 A. Amirouche, J. Esteves, A. Lavoignat, S. Picot, R. Ferrigno, and M. Faivre, Biomicrofluidics 14, 024116 (2020).10.1063/5.000519832549922 PMC7190370
- 60 Q. M. Qi and E. S. G. Shaqfeh, Phys. Rev. Fluids 2, 093102 (2017).10.1103/physrevfluids.2.093102
- 61 W. Lee, H. Aminia, H. Stonec, and T. Carloa, Proc. Natl. Acad. Sci. U.S.A. 107, 22413–22418 (2010).10.1073/pnas.101029710721149674 PMC3012521
- 62 K. Müller, D. A. Fedosov, and G. Gompper, J. Comput. Phys. 281, 301 (2015).10.1016/j.jcp.2014.10.017
- 63 R. P. Rand and A. C. Burton, Biophys. J. 4, 115 (1964).10.1016/s0006-3495(64)86773-414130437 PMC1367460
- 64 S. M. Recktenwald, Y. Rashidi, I. Graham, P. E. Arratia, F. Del Giudice, and C. Wagner, Soft Matter 20, 4950 (2024).10.1039/d4sm00446a38873747
- 65 X. Cheng, C. Caruso, W. W. A. Lam, and M. Graham, Soft Matter 21, 5793 (2025).10.1039/d4sm01519c40568855
- 66 P. Balogh and P. Bagchi, Biophys. J. 113, 2815 (2017).10.1016/j.bpj.2017.10.02029262374 PMC5770972
- 67 G. Li, Y. Qiang, H. Li, X. Li, M. Dao, and G. E. Karniadakis, Biophys. J. 122, 2590 (2023).10.1016/j.bpj.2023.05.02237231647 PMC10323029
- 68 C. Wu, S. Wang, X. Qi, W. Yan, and X. Li, Phys. Fluids 33, 111906 (2021).10.1063/5.0071441
- 69 R. Wells and H. Schmid-Schönbein, J. Appl. Physiol. 27, 213 (1969).10.1152/jappl.1969.27.2.2135796309
- 70 J. L. McWhirter, H. Noguchi, and G. Gompper, Soft Matter 7, 10967–10977 (2011).10.1039/c1sm05794d
- 71 G. Ghigliotti, H. Selmi, L. E. Asmi, and C. Misbah, Phys. Fluids 24, 101901 (2012).10.1063/1.4757394
- 72 G. Tomaiuolo, L. Lanotte, G. Ghigliotti, C. Misbah, and S. Guido, Phys. Fluids 24, 051903 (2012).10.1063/1.4721811
- 73 N. Mai-Duy, N. Phan-Thien, T. Y. N. Nguyen, and T. Tran-Cong, Phys. Fluids 32, 053313 (2020).10.1063/5.0010275
- 74 X. Fan, N. Phan-Thien, S. Chen, X. Wu, and T. Yong Ng, Phys. Fluids 18, 063102 (2006).10.1063/1.2206595
- 75 A. Ghoufi, J. Emile, and P. Malfreyt, Eur. Phys. J. E 36, 10 (2013).10.1140/epje/i2013-13010-723361618
- 76 Y.-H. Tang, L. Lu, H. Li, C. Evangelinos, L. Grinberg, V. Sachdeva, and G. E. Karniadakis, Biophys. J. 112, 2030 (2017).10.1016/j.bpj.2017.04.02028538143 PMC5444005
- 77 T. Ye, D. Pan, C. Huang, and M. Liu, Phys. Fluids 31, 011301 (2019).10.1063/1.5068697
Declarations
Funding
This work was supported by an institutional research grant. (A funding source omitted from the original version has been added.)
Competing Interests
None.
Data Availability
The datasets generated and analyzed during the study are available from the corresponding author on reasonable request.
Demonstration content: the full text of this article is adapted from “Shape transitions of red blood cell under oscillatory flows in microchannels” by Akerkouch and colleagues (AIP Advances, 2025; https://doi.org/10.1063/5.0278720), which is available under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).
Copyright and License
© 2024 Meridian Academic Press