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.

Leukocyte adhesion to P-selectin and the inhibitory role of Crizanlizumab in sickle cell disease: A standardized microfluidic assessment.

Upregulated expression of P-selectin on activated endothelium and platelets significantly contributes to the initiation and progression of vaso-occlusive crises (VOC), a major cause of morbidity in sickle cell disease (SCD). Crizanlizumab (ADAKVEO®), a humanized monoclonal antibody against P-selectin, primarily inhibits the interaction between leukocytes and P-selectin, and has been shown to decrease the frequency of VOCs in clinical trials. However, the lack of reliable in vitro assays that objectively measure leukocyte adhesion to P-selectin remains a critical barrier to evaluating and improving the therapeutic treatment in SCD. Here, we present a standardized microfluidic BioChip whole blood adhesion assay to assess leukocyte adhesion to P-selectin under physiologic flow conditions. Our results demonstrated heterogeneous adhesion by leukocytes to immobilized P-selectin, and dose-dependent inhibition of this adhesion following pre-exposure to Crizanlizumab. Importantly, treatment with Crizanlizumab following adhesion to P-selectin promoted detachment of rolling, but not of firmly adherent leukocytes. Taken together, our results suggest that the microfluidic BioChip system is a promising in vitro assay with which to screen patients, monitor treatment response, and guide current and emerging anti-adhesive therapies in SCD.

Whole blood viscosity and red blood cell adhesion: Potential biomarkers for targeted and curative therapies in sickle cell disease.

Sickle cell disease (SCD) is a recessive genetic blood disorder exhibiting abnormal blood rheology. Polymerization of sickle hemoglobin, due to a point mutation in the ß-globin gene of hemoglobin, results in aberrantly adhesive and stiff red blood cells (RBCs). Hemolysis, abnormal RBC adhesion, and abnormal blood rheology together impair endothelial health in people with SCD, which leads to cumulative systemic complications. Here, we describe a microfluidic assay combined with a micro particle image velocimetry technique for the integrated in vitro assessment of whole blood viscosity (WBV) and RBC adhesion. We examined WBV and RBC adhesion to laminin (LN) in microscale flow in whole blood samples from 53 individuals with no hemoglobinopathies (HbAA, N = 10), hemoglobin SC disease (HbSC, N = 14), or homozygous SCD (HbSS, N = 29) with mean WBV of 4.50 cP, 4.08 cP, and 3.73 cP, respectively. We found that WBV correlated with RBC count and hematocrit in subjects with HbSC or HbSS. There was a significant inverse association between WBV and RBC adhesion under both normoxic and physiologically hypoxic (SpO2 of 83%) tests, in which lower WBV associated with higher RBC adhesion to LN in subjects with HbSS. Low WBV has been found by others to associate with endothelial activation. Altered WBV and abnormal RBC adhesion may synergistically contribute to the endothelial damage and cumulative pathophysiology of SCD. These findings suggest that WBV and RBC adhesion may serve as clinically relevant biomarkers and endpoints in assessing emerging targeted and curative therapies in SCD.

Membrane bending and sphingomyelinase-associated, sulfatide-dependent hypoxic adhesion of sickle mature erythrocytes.

Abnormal erythrocyte adhesion owing to polymerization of sickle hemoglobin is central to the pathophysiology of sickle cell disease (SCD). Mature erythrocytes constitute >80% of all erythrocytes in SCD; however, the relative contributions of erythrocytes to acute and chronic vasculopathy in SCD are not well understood. Here, we showed that bending stress exerted on the erythrocyte plasma membrane by polymerization of sickle hemoglobin under hypoxia, enhances sulfatide-mediated abnormal mature erythrocyte adhesion. We hypothesized that sphingomyelinase (SMase) activity, which is upregulated by accumulated bending energy, leads to elevated membrane sulfatide availability, and thus, hypoxic mature erythrocyte adhesion. We found that mature erythrocyte adhesion to laminin in controlled microfluidic experiments is significantly greater under hypoxia than under normoxia (1856 ± 481 vs 78 ± 23, mean ± SEM), whereas sickle reticulocyte (early erythrocyte) adhesion, high to begin with, does not change (1281 ± 299 vs 1258 ± 328, mean ± SEM). We showed that greater mean accumulated bending energy of adhered mature erythrocytes was associated with higher acid SMase activity and increased mature erythrocyte adhesion (P = .022, for acid SMase activity and P = .002 for the increase in mature erythrocyte adhesion with hypoxia, N = 5). In addition, hypoxia results in sulfatide exposure of the erythrocyte membrane, and an increase in SMase, whereas anti-sulfatide inhibits enhanced adhesion of erythrocytes. These results suggest that the lipid components of the plasma membrane contribute to SCD complications. Therefore, sulfatide and the components of its upregulation pathway, particularly SMase, should be further explored as potential therapeutic targets for inhibiting sickle erythrocyte adhesion.

Point-of-care microchip electrophoresis for integrated anemia and hemoglobin variant testing.

Anemia affects over 25% of the world's population with the heaviest burden borne by women and children. Genetic hemoglobin (Hb) variants, such as sickle cell disease, are among the major causes of anemia. Anemia and Hb variant are pathologically interrelated and have an overlapping geographical distribution. We present the first point-of-care (POC) platform to perform both anemia detection and Hb variant identification, using a single paper-based electrophoresis test. Feasibility of this new integrated diagnostic approach is demonstrated via testing individuals with anemia and/or sickle cell disease. Hemoglobin level determination is performed by an artificial neural network (ANN) based machine learning algorithm, which achieves a mean absolute error of 0.55 g dL-1 and a bias of -0.10 g dL-1 against the gold standard (95% limits of agreement: 1.5 g dL-1) from Bland-Altman analysis on the test set. Resultant anemia detection is achieved with 100% sensitivity and 92.3% specificity. With the same tests, subjects with sickle cell disease were identified with 100% sensitivity and specificity. Overall, the presented platform enabled, for the first time, integrated anemia detection and hemoglobin variant identification using a single point-of-care test.