Microfluidic Microcirculation Mimetic as a Tool for the Study of Rheological Characteristics of Red Blood Cells in Patients with Sickle Cell Anemia

Sickle cell disorder (SCD) is a multisystem disease with heterogeneous phenotypes. Al- though all patients have the mutated hemoglobin (Hb) in the SS phenotype, the severity and frequency of complications are variable. When exposed to low oxygen tension, the Hb molecule becomes dense and forms tactoids, which lead to the peculiar sickled shapes of the affected red blood cells, giving the disorder its name. This sickle cell morphology is responsible for the profound and widespread pathologies associated with this disorder, such as vaso-occlusive crisis (VOC). How much of the clinical manifestation is due to sickled erythrocytes and what is due to the relative contributions of other elements in the blood, especially in the microcapillary circulation, is usually not visualized and quantified for each patient during clinical management. Here, we used a microfluidic microcirculation mimetic (MMM), which has 187 capillary-like constrictions, to impose deformations on erythrocytes of 25 SCD patients, visualizing and characterizing the morpho-rheological properties of the cells in normoxic, hypoxic (using sodium meta-bisulfite) and treatment conditions (using hydroxyurea). The MMM enabled a patient-specific quantification of shape descriptors (circularity and roundness) and transit time through the capillary constrictions, which are readouts for morpho-rheological proper- ties implicated in VOC. Transit times varied significantly (p < 0.001) between patients. Our results demonstrate the feasibility of microfluidics-based monitoring of individual patients for personalized care in the context of SCD complications such as VOC, even in resource-constrained setting

Cell Mechanics Based Computational Classification of Red Blood Cells Via Unsupervised Machine Intelligence Applied to Morpho-Rheological Markers

Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques

Real-time deformability cytometry: On-the-fly cell mechanical phenotyping

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.We introduce real-time deformability cytometry (RT-DC) for continuous cell mechanical characterization of large populations (>100,000 cells) with analysis rates greater than 100 cells/s. RT-DC is sensitive to cytoskeletal alterations and can distinguish cell-cycle phases, track stem cell differentiation into distinct lineages and identify cell populations in whole blood by their mechanical fingerprints. This technique adds a new marker-free dimension to flow cytometry with diverse applications in biology, biotechnology and medicine.We thank A. Taubenberger, M. Herbig, C. Liebers, L. Menschner, C. Klug, R. Berner, M. Bornhäuser, C. Bryant, E. Chilvers, B. Friedrich, A. Voigt, J. Tegenfeldt, M. Tschöp, F. Amblard, T. Hyman and S. Grill for technical support, advice and engaging discussions. The HL60/S4 cells were a generous gift of D. and A. Olins (University of New England). Financial support from the Alexander-von-Humboldt Stiftung (Humboldt-Professorship to J.G.), Sächsisches Ministerium für Wissenschaft und Kunst (TG70 grant to O.O. and J.G.), DFG-Center for Regenerative Medicine of the Technische Universität Dresden (seed grant to J.G.), Deutsche Forschungsgemeinschaft (DFG Emmy Noether Grant to J.M., MA 5831/1-1; KFO249 Gerok position to N.T.), Deutsche Gesellschaft für Pädiatrische Infektiologie (N.T.) and Studienstiftung des Deutschen Volkes (A.M.), Max Planck Society (E.F.-F.) and Leverhulme and Newton Trust (Early Career Fellowship to S.P.) is gratefully acknowledged

Intelligent image-based deformation-assisted cell sorting with molecular specificity

Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels. Sorting RT-FDC combines real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves to transfer molecular specificity to label-free, image-based cell sorting using an efficient deep neural network