Biomechanics and biorheology of red blood cells in sickle cell anemia

Li, Xuejin; Dao, Ming; Lykotrafitis, George; Karniadakis, George Em

doi:10.1016/j.jbiomech.2016.11.022

Cited by 96 publications

(63 citation statements)

References 86 publications

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“…Hence, it does not model the interaction between cell membrane and polymer fibers, and the potential influences on morphological distortion of RBCs and the attendant alteration in their mechanical properties. This deficiency could be addressed in future work by recourse to a hybrid model that encompasses the molecular and cellular scales by combing the MS-RBC model with particle-based HbS polymer models developed recently [51–53]. In addition, it would require further computational validation and extensive testing of these patient-specific models against clinical and experimental studies to make future predictions more reliable.…”

Section: Resultsmentioning

confidence: 99%

Patient-specific modeling of individual sickle cell behavior under transient hypoxia

Dao

et al. 2017

PLoS Comput Biol

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Sickle cell disease (SCD) is a highly complex genetic blood disorder in which red blood cells (RBC) exhibit heterogeneous morphology changes and decreased deformability. We employ a kinetic model for cell morphological sickling that invokes parameters derived from patient-specific data. This model is used to investigate the dynamics of individual sickle cells in a capillary-like microenvironment in order to address various mechanisms associated with SCD. We show that all RBCs, both hypoxia-unaffected and hypoxia-affected ones, regularly pass through microgates under oxygenated state. However, the hypoxia-affected cells undergo sickling which significantly alters cell dynamics. In particular, the dense and rigid sickle RBCs are obstructed thereby clogging blood flow while the less dense and deformable ones are capable of circumnavigating dead (trapped) cells ahead of them by choosing a serpentine path. Informed by recent experiments involving microfluidics that provide in vitro quantitative information on cell dynamics under transient hypoxia conditions, we have performed detailed computational simulations of alterations to cell behavior in response to morphological changes and membrane stiffening. Our model reveals that SCD exhibits substantial heterogeneity even within a particular density-fractionated subpopulation. These findings provide unique insights into how individual sickle cells move through capillaries under transient hypoxic conditions, and offer novel possibilities for designing effective therapeutic interventions for SCD.

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Section: Resultsmentioning

confidence: 99%

Patient-specific modeling of individual sickle cell behavior under transient hypoxia

Dao

et al. 2017

PLoS Comput Biol

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“…The CG membrane and RBC models developed in the last decade have been successfully implemented to uncover various membrane functions and characteristics. A complete picture of applications of CG particle models for membrane and RBC in physiological and pathological conditions and relevant references can be found in recent reviews [126][127][128] .…”

Section: Discussionmentioning

confidence: 99%

Modeling biomembranes and red blood cells by coarse-grained particle methods

Chang

Yang

et al. 2017

Appl. Math. Mech.-Engl. Ed.

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In this work, we review previously developed coarse-grained (CG) particle models for biological membrane and red blood cells (RBCs) and discuss the advantages of the CG particle method over the continuum and atomic simulations on modeling biological phenomena. CG particle models can largely increase the length scale and time scale of atomic simulations by eliminating fast degrees of freedom while preserving the mesoscopic structures and properties of the simulated system. On the other hand, CG particle models can be used to capture microstructural alternations in diseased RBCs and simulate topological changes of biological membrane and RBCs, which are major challenges to typical continuum representations of membrane and RBCs. The power and versatility of the CG particle methods are demonstrated through simulating the dynamical processes involving significant topological changes, such as lipid self-assembly, vesicle fusion and membrane budding.

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“…Consequently sRBCs are more adhesive and less deformable than healthy RBCs. This 24 increased membrane rigidity, along with altered adhesion characteristics that heighten interactions 25 with the endothelium and plasma, directly give rise to SCD's key manifestation: recurring, painful 26 vaso-occlusive crisis events triggered by sRBC aggregation and blood vessel clogging [4,9,10]. The 27 problem thus lends itself very naturally towards exploration in a microfluidic or adhesion assay setup.…”

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Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells

Praljak

Iram

Goreke

et al. 2020

Preprint

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AbstractSickle cell disease (SCD), a group of inherited blood disorders with significant morbidity and early mortality, affects a sizeable global demographic largely of African and Indian descent. It is manifested in a mutated form of hemoglobin that distorts the red blood cells into a characteristic sickle shape with altered biophysical properties. Sickle red blood cells (sRBCs) show heightened adhesive interactions with inflamed endothelium, triggering obstruction of blood vessels and painful vaso-occlusive crisis events. Numerous studies have reported microfluidic-assay-based disease monitoring tools which rely on quantifying adhesion characteristics of adhered sRBCs from high resolution channel images. The current workflow for analyzing images from these assays relies on manual cell counting and detailed morphological characterization by a specially trained worker, which is time and labor intensive. Moreover manual counts by different individuals are prone to artifacts due to user bias. We present here a standardized and reproducible image analysis workflow designed to tackle these issues, using a two part deep neural network architecture that works in tandem for automatic, fast and reliable segmentation and classification into subtypes of adhered cell images. Our training utilized an exhaustive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. The automated image analysis performs robustly in comparison to human classification: accuracies were similar to or better than those of the trained personnel, while the overall analysis time was improved by two orders of magnitude.

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Biomechanics and biorheology of red blood cells in sickle cell anemia

Cited by 96 publications

References 86 publications

Patient-specific modeling of individual sickle cell behavior under transient hypoxia

Patient-specific modeling of individual sickle cell behavior under transient hypoxia

Modeling biomembranes and red blood cells by coarse-grained particle methods

Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells

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