OcclusionChip: A Functional Microcapillary Occlusion Assay Complementary to Ektacytometry for Detection of Small-Fraction Red Blood Cells with Abnormal Deformability

Red blood cell (RBC) deformability is a valuable hemorheological biomarker that can be used to assess the clinical status and response to therapy of individuals with sickle cell disease (SCD). RBC deformability has been measured by ektacytometry for decades, which uses shear or osmolar stress. However, ektacytometry is a population based measurement that does not detect small-fractions of abnormal RBCs. A single cell-based, functional RBC deformability assay would complement ektacytometry and provide additional information. Here, we tested the relative merits of the OcclusionChip, which measures RBC deformability by microcapillary occlusion, and ektacytometry. We tested samples containing glutaraldehyde-stiffened RBCs for up to 1% volume fraction; ektacytometry detected no significant change in Elongation Index (EI), while the OcclusionChip showed significant differences in Occlusion Index (OI). OcclusionChip detected a significant increase in OI in RBCs from an individual with sickle cell trait (SCT) and from a subject with SCD who received allogeneic hematopoietic stem cell transplant (HSCT), as the sample was taken from normoxic (pO2:159 mmHg) to physiologic hypoxic (pO2:45 mmHg) conditions. Oxygen gradient ektacytometry detected no difference in EI for SCT or HSCT. These results suggest that the single cell-based OcclusionChip enables detection of sickle hemoglobin (HbS)-related RBC abnormalities in SCT and SCD, particularly when the HbS level is low. We conclude that the OcclusionChip is complementary to the population based ektacytometry assays, and providing additional sensitivity and capacity to detect modest abnormalities in red cell function or small populations of abnormal red cells

Catch Bonds in Sickle Cell Disease: Shear-Enhanced Adhesion of Red Blood Cells to Laminin

Could the phenomenon of catch bonding—force-strengthened cellular adhesion—play a role in sickle cell disease, where abnormal red blood cell (RBC) adhesion obstructs blood flow? Here we investigate the dynamics of sickle RBCs adhering to a surface functionalized with the protein laminin (a component of the extracellular matrix around blood vessels) under physiologically relevant micro-scale flow. First, using total internal reflectance microscopy we characterize the spatial fluctuations of the RBC membrane above the laminin surface before detachment. The complex dynamics we observe suggest the possibility of catch bonding, where the mean detachment time of the cell from the surface initially increases to a maximum and then decreases as a function of shear force. We next conduct a series of shear-induced detachment experiments on blood samples from 25 sickle cell disease patients, quantifying the number and duration of adhered cells under both sudden force jumps and linear force ramps. The experiments reveal that a subset of patients does indeed exhibit catch bonding. By fitting the data to a theoretical model of the bond dynamics, we can extract the mean bond lifetime versus force for each patient. The results show a striking heterogeneity among patients, both in terms of the qualitative behavior (whether or not there is catch bonding) and in the magnitudes of the lifetimes. Patients with large bond lifetimes at physiological forces are more likely to have certain adverse clinical features, like a diagnosis of pulmonary arterial hypertension and intracardiac shunts. By introducing an in vitro platform for fully characterizing RBC-laminin adhesion dynamics, our approach could contribute to the development of patient-specific anti-adhesive therapies for sickle cell disease. The experimental setup is also easily generalizable to studying adhesion dynamics in other cell types, for example leukocytes or cancer cells, and can incorporate disease-relevant environmental conditions like oxygen deprivation

Sickle Red Blood Cell-Derived Extracellular Vesicles Activate Endothelial Cells and Enhance Sickle Red Cell Adhesion Mediated by von Willebrand Factor

Endothelial activation and sickle red blood cell (RBC) adhesion are central to the pathogenesis of sickle cell disease (SCD). Quantitatively, RBC-derived extracellular vesicles (REVs) are more abundant from SS RBCs compared with healthy RBCs (AA RBCs). Sickle RBC-derived REVs (SS REVs) are known to promote endothelial cell (EC) activation through cell signalling and transcriptional regulation at longer terms. However, the SS REV-mediated short-term non-transcriptional response of EC is unclear. Here, we examined the impact of SS REVs on acute microvascular EC activation and RBC adhesion at 2 h. Compared with AA REVs, SS REVs promoted human pulmonary microvascular ECs (HPMEC) activation indicated by increased von Willebrand factor (VWF) expression. Under microfluidic conditions, we found abnormal SS RBC adhesion to HPMECs exposed to SS REVs. This enhanced SS RBC adhesion was reduced by haeme binding protein haemopexin or VWF cleaving protease ADAMTS13 to a level similar to HPMECs treated with AA REVs. Consistent with these observations, haemin- or SS REV-induced microvascular stasis in SS mice with implanted dorsal skin-fold chambers that was inhibited by ADAMTS13. The adhesion induced by SS REVs was variable and was higher with SS RBCs from patients with increased markers of haemolysis (lactate dehydrogenase and reticulocyte count) or a concomitant clinical diagnosis of deep vein thrombosis. Our results emphasise the critical contribution made by REVs to the pathophysiology of SCD by triggering acute microvascular EC activation and abnormal RBC adhesion. These findings may help to better understand acute pathophysiological mechanism of SCD and thereby the development of new treatment strategies using VWF as a potential target

Motion Blur Microscopy

Repository of machine learning training data for the paper "Motion blur microscopy

Design and electromagnetic optimization of a respiration harvester

This work reports the design and electromagnetic optimization of a MEMS-scale turbo generator for harvesting fluidic energy in human exhalation. The device is composed of a turbine with dual -layer integrated permanent magnets, ball bearings, and stators with micro coils. For an efficient energy conversion, number of magnetic poles should be optimized to obtain maximum flux density in a given device geometry, where magnetic reluctance and leakage act as two competing effects. This optimization has been performed for different commercially available magnet thicknesses with fixed inner and outer radii of 2 mm and 8 mm, respectively. It has been shown that the optimum number of poles for 0.5 mm -thick magnets is 28, while pole numbers as high as 32 lead to higher flux densities for thinner magnets. The results presented here shed light on the efficient design of magnetic micromachines in similar scales

Integrating Deep Learning with Microfluidics for Biophysical Classification of Sickle Red Blood Cells Adhered to Laminin

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive 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. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring