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Jianlu Zheng

Publications and source records attributed to Jianlu Zheng.

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Quantifying reticulocyte biomechanics in health and disease

Red blood cell (RBC) populations are mechanically heterogeneous, yet how this shapes transport, clogging, and rheology in confined environments remains unclear. We combine microfluidic microchannel experiments with dissipative particle dynamics (DPD) simulations to study how reticulocyte morphology, deformability, and cell-cell hydrodynamic coupling govern microconfined blood flow, and link these to acute and chronic mountain sickness. Reticulocyte-rich samples show three subtypes (multilobular, cup-shaped, near-discocytic), parameterized (R1-R3) by fitting microchannel transit and shape-under-flow data. Single-cell simulations show that 5-micron microchannels amplify mechanical heterogeneity (R1 transits 30-50% more slowly than softer cells), whereas bending-dominated splenic slits discriminate subtypes by only 10-20%. Pairwise simulations show that a leading cell never lets a follower pass below its own single-cell threshold - so the order-of-magnitude, wake-"unjamming" reduction is absent - but the leader's compliance shapes crowded single-file passage: a soft reticulocyte leader lowers a trailing stiff cell's critical passage pressure by ~12% relative to a stiff (sickle-trait) leader and speeds its transit by ~10%. The controlling variable is the single-cell critical pressure gradient Delta_P_c, which rises monotonically with membrane stiffness from control discocytes through reticulocytes to sickle-cell-trait cells. Our simulations reproduce the shear-thinning viscosity of control blood, against which the reported chronic-mountain-sickness hyperviscosity reflects predominantly hematocrit-driven crowding rather than a change in single-cell rheology. These results place benign acclimatization, chronic-mountain-sickness hyperviscosity, and sickle-cell-trait splenic syndrome on a single mechanical axis defined by Delta_P_c relative to the splenic operating pressure.

cond-mat.soft

Quantifying the biophysical properties of stomatocytes in health and disease

Hereditary stomatocytosis (HS) comprises red blood cell (RBC) disorders characterized by cup-shaped erythrocytes that respond oppositely to splenectomy: curative in overhydrated HS (OHS) but potentially thrombogenic in dehydrated HS (DHS/xerocytosis). This paradox persists because RBC biomechanics is governed by partly independent parameters--shear modulus, bending rigidity, surface-to-volume ratio (S/V), and cytoplasmic viscosity--that existing assays capture only piecemeal. Here we combine dissipative particle dynamics (DPD) simulations with microfluidic imaging to construct a control discocyte and three stomatocyte models (ST-RBC1-3) at fixed membrane area and decreasing volume (109.7, 101.5, 89.8 fL), spanning the OHS-to-DHS range. Tracing this parameter set through five mechanically orthogonal assays, we find that interendothelial-slit (IES) traversal is geometry-dominated: overhydrated ST-RBC1 requires an order of magnitude higher critical pressure than healthy RBCs, whereas dehydrated ST-RBC3 passes freely. ST-RBC3 nonetheless suppresses membrane tank-treading and raises low-shear whole-blood viscosity by ~29% at physiological haematocrit, comparable to Gaucher-disease hyperviscosity. A funnel-obstacle chip amplifies these differences into a label-free centerline-offset signal predicted to separate all four RBC types (~4.5 standard deviations between extreme phenotypes). These results unite single-cell mechanics, splenic filtration, and hemorheology in one framework, resolve the splenectomy paradox, and point toward microfluidic pre-operative risk stratification in HS.

q-bio.CB

An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays

Understanding sickle cell dynamics requires accurate identification of morphological transitions under diverse biophysical conditions, particularly in densely packed and overlapping cell populations. Here, we present an automated deep learning framework that integrates AI-assisted annotation, segmentation, classification, and instance counting to quantify red blood cell (RBC) populations across varying density regimes in time-lapse microscopy data. Experimental images were annotated using the Roboflow platform to generate labeled dataset for training an nnU-Net segmentation model. The trained network enables prediction of the temporal evolution of the sickle cell fraction, while a watershed algorithm resolves overlapping cells to enhance quantification accuracy. Despite requiring only a limited amount of labeled data for training, the framework achieves high segmentation performance, effectively addressing challenges associated with scarce manual annotations and cell overlap. By quantitatively tracking dynamic changes in RBC morphology, this approach can more than double the experimental throughput via densely packed cell suspensions, capture drug-dependent sickling behavior, and reveal distinct mechanobiological signatures of cellular morphological evolution. Overall, this AI-driven framework establishes a scalable and reproducible computational platform for investigating cellular biomechanics and assessing therapeutic efficacy in microphysiological systems.

cs.CV