Focusing and alignment of erythrocytes in a viscoelastic medium

AI based image analysis of red blood cells in oscillating microchannels

The flow dynamics of red blood cells in vivo in blood capillaries and in vitro in microfluidic channels is complex. Cells can obtain different shapes such as discoid, parachute, slipper-like shapes and various intermediate states depending on flow conditions and their viscoelastic properties. We use artificial intelligence based analysis of red blood cells (RBCs) in an oscillating microchannel to distinguish healthy red blood cells from red blood cells treated with formaldehyde to chemically modify their viscoelastic behavior. We used TensorFlow to train and validate a deep learning model and achieved a testing accuracy of over 97%. This method is a first step to a non-invasive, label-free characterization of diseased red blood cells and will be useful for diagnostic purposes in haematology labs. This method provides quantitative data on the number of affected cells based on single cell classification.

Technologies for measuring red blood cell deformability

Human red blood cells (RBCs) are approximately 8 μm in diameter, but must repeatedly deform through capillaries as small as 2 μm in order to deliver oxygen to all parts of the body. The loss of this capability is associated with the pathology of many diseases, and is therefore a potential biomarker for disease status and treatment efficacy. Measuring RBC deformability is a difficult problem because of the minute forces (∼pN) that must be exerted on these cells, as well as the requirements for throughput and multiplexing. The development of technologies for measuring RBC deformability date back to the 1960s with the development of micropipette aspiration, ektacytometry, and the cell transit analyzer. In the past 10 years, significant progress has been made using microfluidics by leveraging the ability to precisely control fluid flow through microstructures at the size scale of individual RBCs. These technologies have now surpassed traditional methods in terms of sensitivity, throughput, consistency, and ease of use. As a result, these efforts are beginning to move beyond feasibility studies and into applications to enable biomedical discoveries. In this review, we provide an overview of both traditional and microfluidic techniques for measuring RBC deformability. We discuss the capabilities of each technique and compare their sensitivity, throughput, and robustness in measuring bulk and single-cell RBC deformability. Finally, we discuss how these tools could be used to measure changes in RBC deformability in the context of various applications including pathologies caused by malaria and hemoglobinopathies, as well as degradation during storage in blood bags prior to blood transfusions.

Deep learning-based hologram generation using a white light source

Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.

Viscoelastic microfluidics: progress and challenges

The manipulation of cells and particles suspended in viscoelastic fluids in microchannels has drawn increasing attention, in part due to the ability for single-stream three-dimensional focusing in simple channel geometries. Improvement in the understanding of non-Newtonian effects on particle dynamics has led to expanding exploration of focusing and sorting particles and cells using viscoelastic microfluidics. Multiple factors, such as the driving forces arising from fluid elasticity and inertia, the effect of fluid rheology, the physical properties of particles and cells, and channel geometry, actively interact and compete together to govern the intricate migration behavior of particles and cells in microchannels. Here, we review the viscoelastic fluid physics and the hydrodynamic forces in such flows and identify three pairs of competing forces/effects that collectively govern viscoelastic migration. We discuss migration dynamics, focusing positions, numerical simulations, and recent progress in viscoelastic microfluidic applications as well as the remaining challenges. Finally, we hope that an improved understanding of viscoelastic flows in microfluidics can lead to increased sophistication of microfluidic platforms in clinical diagnostics and biomedical research.

Manipulation of micro- and nanoparticles in viscoelastic fluid flows within microfluid systems

Manipulation of micro- and nanoparticles in complex biofluids is highly demanded in most biological and biomedical applications. A significant number of microfluidic platforms have been developed for inexpensive, rapid, accurate, and efficient particle manipulation. Due to the enormous potential of viscoelastic fluids (VEFs) for particle manipulation, various emerging microfluidic-based VEFs techniques have been presented over the last decade. This review provides an intuitive understanding of VEF physics for particle separation in different microchannel geometries. Besides, active and passive VEF methods are critically reviewed, highlighting the potential and practical challenges of each technique for particle/cell focusing, sorting, and separation. The outcome of this study could enable recognizing deliverable VEF technology with the promising prospect in the manipulation of submicron biological samples (e.g., exosomes, DNA, and proteins).

Impedance-based viscoelastic flow cytometry

Elastic nature of the viscoelastic fluids induces lateral migration of particles into a single streamline and can be used by microfluidic based flow cytometry devices. In this study, we investigated focusing efficiency of polyethylene oxide based viscoelastic solutions at varying ionic concentration to demonstrate their use in impedimetric particle characterization systems. Rheological properties of the viscoelastic fluid and particle focusing performance are not affected by ionic concentration. We investigated the viscoelastic focusing dynamics using polystyrene (PS) beads and human red blood cells (RBCs) suspended in the viscoelastic fluid. Elasto-inertial focusing of PS beads was achieved with the combination of inertial and viscoelastic effects. RBCs were aligned along the channel centerline in parachute shape which yielded consistent impedimetric signals. We compared our impedance-based microfluidic flow cytometry results for RBCs and PS beads by analyzing particle transit time and peak amplitude at varying viscoelastic focusing conditions obtained at different flow rates. We showed that single orientation, single train focusing of nonspherical RBCs can be achieved with polyethylene oxide based viscoelastic solution that has been shown to be a good candidate as a carrier fluid for impedance cytometry.

Learning-based automatic sensing and size classification of microparticles using smartphone holographic microscopy

A microparticle classifier is established by synergetic integration of smartphone-based digital in-line holographic microscopy and supervised machine learning.

Collective dynamics of red blood cells on an in vitro microfluidic platform

Understanding the dynamics of blood flow in physiologically relevant confinements turns out to be an outstanding proposition in biomedical research. Despite the large number of studies being reported to theoretically elucidate the dynamics of red blood cells (RBCs) in confined geometries, in vitro experimental studies unveiling the implications of the collective dynamics of red blood cells in physiologically relevant bio-mimetic microfluidic channels remain elusive. Here, we investigate the implications of complex dynamvic interactions between the whole blood and a deformable channel wall fabricated using a hydrogel matrix. For a range of flow rates, we map the trajectories of the RBCs for varying levels of softness of the microchannel wall. We compare these scenarios with the reference cases of rigid polydimethylsiloxane (PDMS) channels. Our results reveal that the smallest channels investigated herein exhibit the most intricate interactions between the collective dynamics of the RBC and the wall flexibility, attributable to confinement-induced hydrodynamic interactions in the presence of spatially varying shear rates. These results may open up new paradigms in conceptual understanding of in vivo dynamics of blood flow through simple in vitro experiments on a simple microfluidic platform.

Splitting and separation of colloidal streams in sinusoidal microchannels

The control of the distribution of colloidal particles in microfluidic flows plays an important role in biomedical and industrial applications. A particular challenge is to induce cross-streamline migration in laminar flows, enabling the separation of colloidal particles according to their size, shape or elasticity. Here we show that viscoelastic fluids can mediate cross-streamline migration of deformable spherical and cylindrical colloidal particles in sinusoidal microchannels at low Reynolds numbers. For colloidal streams focused into the center of the channel entrance this leads to a symmetric stream-splitting and separation into four substreams. The degree of stream splitting and separation can be controlled via the flow rates, viscoelasticity of the focusing fluid, and the spatial microchannel modulation with an upper limit when reaching the microchannel walls. We demonstrate that this effect can be used to separate flexible particles of different size and shape. This methodology of cross-stream migration has thus great potential for the passive separation of colloids and cells in microfluidic channels.

Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells

Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

Recent progress of particle migration in viscoelastic fluids

Recently, research on particle migration in non-Newtonian viscoelastic fluids has gained considerable attention. In a viscoelastic fluid, three dimensional (3D) particle focusing can be easily realized in simple channels without the need for any external force fields or complex microchannel structures compared with that in a Newtonian fluid. Due to its promising properties for particle precise focusing and manipulation, this field has been developed rapidly, and research on the field has been shifted from fundamentals to applications. This review will elaborate the recent progress of particle migration in viscoelastic fluids, especially on the aspect of applications. The hydrodynamic forces on the micro/nano particles in viscoelastic fluids are discussed. Next, we elaborate the basic particle migration in viscoelasticity-dominant fluids and elasto-inertial fluids in straight channels. After that, a comprehensive review on the applications of viscoelasticity-induced particle migration (particle separation, cell deformability measurement and alignment, particle solution exchange, rheometry-on-a-chip and others) is presented; finally, we thrash out some perspectives on the future directions of particle migration in viscoelastic fluids.

Continuous Separation of White Blood Cells From Whole Blood Using Viscoelastic Effects

White blood cells (WBCs) are the only cellular constituent containing genetic materials, and, hence, are candidate biomarkers for a host of diseases. However, conventional methods for WBC separation tend to have low sample purity and separation efficiency, which will have adverse implications on downstream polymerase chain reaction (PCR) analyses. In this study, we introduce a two-stage microfluidic device which harnesses the elastic property of a non-Newtonian fluid for size-based separation of WBCs from whole blood. The device displayed high resolution and efficiency in separating polystyrene particles and blood cells of different sizes up to a flow rate of 150 μL/min in polyvinylpyrrolidone solutions. We performed a separate parametric study to evaluate the effects of the fluid elasticity and flow rate on the separation performance. The hematocrit of the blood sample was varied from 0.01% to 20% to investigate the effect of increased intercellular interactions on the separation efficiency. An optimized set of parameters was selected to demonstrate the applicability of the device to the separation of WBCs from diluted whole blood, with excellent efficiency and purity (>90%). This microfluidic device will be especially useful for blood fractionation applications requiring high sample purity and speedy processing. Additionally, the apparent flow-rate insensitivity of the separation allows for its potential use in point-of-care applications.

Electrokinetically driven continuous-flow enrichment of colloidal particles by Joule heating induced temperature gradient focusing in a convergent-divergent microfluidic structure

Enrichment of colloidal particles in continuous flow has not only numerous applications but also poses a great challenge in controlling physical forces that are required for achieving particle enrichment. Here, we for the first time experimentally demonstrate the electrokinetically-driven continuous-flow enrichment of colloidal particles with Joule heating induced temperature gradient focusing (TGF) in a microfluidic convergent-divergent structure. We consider four mechanisms of particle transport, i.e., advection due to electroosmosis, electrophoresis, dielectrophoresis and, and further clarify their roles in the particle enrichment. It is experimentally determined and numerically verified that the particle thermophoresis plays dominant roles in enrichment of all particle sizes considered in this study and the combined effect of electroosmosis-induced advection and electrophoresis is mainly to transport particles to the zone of enrichment. Specifically, the enrichment of particles is achieved with combined DC and AC voltages rather than a sole DC or AC voltage. A numerical model is formulated with consideration of the abovementioned four mechanisms, and the model can rationalize the experimental observations. Particularly, our analysis of numerical and experimental results indicates that thermophoresis which is usually an overlooked mechanism of material transport is crucial for the successful electrokinetic enrichment of particles with Joule heating induced TGF.