Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging

Label-Free Digital Holotomography Reveals Ibuprofen-Induced Morphological Changes to Red Blood Cells

Understanding the dose-dependent effect of over-the-counter drugs on red blood cells (RBCs) is crucial for hematology and digital pathology. Yet, it is challenging to continuously record the real-time, drug-induced shape changes of RBCs in a label-free manner. Here, we demonstrate digital holotomography (DHTM)-enabled real-time, label-free concentration-dependent and time-dependent monitoring of ibuprofen on RBCs from a healthy donor. The RBCs are segmented based on three-dimensional (3D) and four-dimensional (4D) refractive index tomograms, and their morphological and chemical parameters are retrieved with their shapes classified using machine learning. We directly observed the formation and motion of spicules on the RBC membrane when aqueous solutions of ibuprofen were drop-cast on wet blood, creating rough-membraned echinocyte forms. At low concentrations of 0.25-0.50 mM, the ibuprofen-induced morphological change was transient, but at high concentrations (1-3 mM) the spiculated RBC remained over a period of up to 1.5 h. Molecular simulations confirmed that aggregates of ibuprofen molecules at high concentrations significantly disrupted the RBC membrane structural integrity and lipid order but produced negligible effect at low ibuprofen concentrations. Control experiments on the effect of urea, hydrogen peroxide, and aqueous solutions on RBCs showed zero spicule formation. Our work clarifies the dose-dependent chemical effects on RBCs using label-free microscopes that can be deployed for the rapid detection of overdosage of over-the-counter and prescribed drugs.

Automated Dual-Mode Cell Monitoring To Simultaneously Explore Calcium Dynamics and Contraction-Relaxation Kinetics within Drug-Treated Stem Cell-Derived Cardiomyocytes

This manuscript proposes a new dual-mode cell imaging system for studying the relationships between calcium dynamics and the contractility process of cardiomyocytes derived from human-induced pluripotent stem cells. Practically, this dual-mode cell imaging system provides simultaneously both live cell calcium imaging and quantitative phase imaging based on digital holographic microscopy. Specifically, thanks to the development of a robust automated image analysis, simultaneous measurements of both intracellular calcium, a key player of excitation-contraction coupling, and the quantitative phase image-derived dry mass redistribution, reflecting the effective contractility, namely, the contraction and relaxation processes, were achieved. Practically, the relationships between calcium dynamics and the contraction-relaxation kinetics were investigated in particular through the application of two drugs─namely, isoprenaline and E-4031─known to act precisely on calcium dynamics. Specifically, this new dual-mode cell imaging system enabled us to establish that calcium regulation can be divided into two phases, an early phase influencing the occurrence of the relaxation process followed by a late phase, which although not having a significant influence on the relaxation process affects significantly the beat frequency. In combination with cutting-edge technologies allowing the generation of human stem cell-derived cardiomyocytes, this dual-mode cell monitoring approach therefore represents a very promising technique, particularly in the fields of drug discovery and personalized medicine, to identify compounds likely to act more selectively on specific steps that compose the cardiomyocyte contractility.

COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes

We present an automated method for COVID-19 screening using the intra-patient population distributions of bio-optical attributes extracted from digital holographic microscopy reconstructed red blood cells. Whereas previous approaches have aimed to identify infection by classifying individual cells, here, we propose an approach to incorporate the attribute distribution information from the population of a given human subjects' cells into our classification scheme and directly classify subjects at the patient level. To capture the intra-patient distribution information in a generalized way, we propose an approach based on the Bag-of-Features (BoF) methodology to transform histograms of bio-optical attribute distributions into feature vectors for classification via a linear support vector machine. We compare our approach with simpler classifiers directly using summary statistics such as mean, standard deviation, skewness, and kurtosis of the distributions. We also compare to a k-nearest neighbor classifier using the Kolmogorov-Smirnov distance as a distance metric between the attribute distributions of each subject. We lastly compare our approach to previously published methods for classification of individual red blood cells. In each case, the methodology proposed in this paper provides the highest patient classification performance, correctly classifying 22 out of 24 individuals and achieving 91.67% classification accuracy with 90.00% sensitivity and 92.86% specificity. The incorporation of distribution information for classification additionally led to the identification of a singular temporal-based bio-optical attribute capable of highly accurate patient classification. To the best of our knowledge, this is the first report of a machine learning approach using the intra-patient probability distribution information of bio-optical attributes obtained from digital holographic microscopy for disease screening.

Biomechanical properties of native and cultured red blood cells–Interplay of shape, structure and biomechanics

Modern medicine increases the demand for safe blood products. Ex vivo cultured red blood cells (cRBC) are eagerly awaited as a standardized, safe source of RBC. Established culture models still lack the terminal cytoskeletal remodeling from reticulocyte to erythrocyte with changes in the biomechanical properties and interacts with membrane stiffness, viscosity of the cytoplasm and the cytoskeletal network. Comprehensive data on the biomechanical properties of cRBC are needed to take the last step towards translation into clinical use in transfusion medicine. Aim of the study was the comparative analysis of topographical and biomechanical properties of cRBC, generated from human CD34⁺ adult hematopoietic stem/progenitor cells, with native reticulocytes (nRET) and erythrocytes (nRBC) using cell biological and biomechanical technologies. To gain the desired all-encompassing information, a single method was unsatisfactory and only the combination of different methods could lead to the goal. Topographical information was matched with biomechanical data from optical tweezers (OT), atomic force microscopy (AFM) and digital holographic microscopy (DHM). Underlying structures were investigated in detail. Imaging, deformability and recovery time showed a high similarity between cRBC and nRBC. Young’s modulus and plasticity index also confirmed this similarity. No significant differences in membrane and cytoskeletal proteins were found, while lipid deficiency resulted in spherical, vesiculated cells with impaired biomechanical functionality. The combination of techniques has proven successful and experiments underscore a close relationship between lipid content, shape and biomechanical functionality of RBC.

Method to analyze effects of low-level laser therapy on biological cells with a digital holographic microscope

Low-level laser therapy (LLLT) is a therapeutic tool that uses the photobiochemical interaction between light and tissue. Its effectiveness is controversial due to a strong dependence on dosimetric parameters. In this work, we demonstrate that digital holographic microscopy is an effective label-free imaging technique to analyze the effects of LLLT on biological cells, and we propose the full methodology to create correct synthetic aperture phase maps for further extensive, highly accurate statistical analysis. The proposed methodology has been designed to provide a basis for many other biological experiments using quantitative phase imaging. We use SHSY-5Y and HaCaT cells irradiated with different doses of red light for the experiment. The analysis shows quantitative changes in cell dry mass density and the projected cell surface in response to different radiation doses.

COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy

We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data -was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells' classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data.

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are segmented for feature extraction, then a bi-directional long short-term memory network is used to classify between healthy and COVID positive red blood cells based on their spatiotemporal behavior. Individuals are then classified based on the simple majority of their cells' classifications. The proposed system may be beneficial for under-resourced healthcare systems. To the best of our knowledge, this is the first report of digital holographic microscopy for rapid screening of COVID-19.

Biomechanics of Ex Vivo-Generated Red Blood Cells Investigated by Optical Tweezers and Digital Holographic Microscopy

Ex vivo-generated red blood cells are a promising resource for future safe blood products, manufactured independently of voluntary blood donations. The physiological process of terminal maturation from spheroid reticulocytes to biconcave erythrocytes has not been accomplished yet. A better biomechanical characterization of cultured red blood cells (cRBCs) will be of utmost interest for manufacturer approval and therapeutic application. Here, we introduce a novel optical tweezer (OT) approach to measure the deformation and elasticity of single cells trapped away from the coverslip. To investigate membrane properties dependent on membrane lipid content, two culture conditions of cRBCs were investigated, cRBCPlasma with plasma and cRBCHPL supplemented with human platelet lysate. Biomechanical characterization of cells under optical forces proves the similar features of native RBCs and cRBCHPL, and different characteristics for cRBCPlasma. To confirm these results, we also applied a second technique, digital holographic microscopy (DHM), for cells laid on the surface. OT and DHM provided related results in terms of cell deformation and membrane fluctuations, allowing a reliable discrimination between cultured and native red blood cells. The two techniques are compared and discussed in terms of application and complementarity.

Quantitative absorption imaging of red blood cells to determine physical and mechanical properties

Red blood cells or erythrocytes, constituting 40 to 45 percent of the total volume of human blood are vesicles filled with hemoglobin with a fluid-like lipid bilayer membrane connected to a 2D spectrin network. The shape, volume, hemoglobin mass, and membrane stiffness of RBCs are important characteristics that influence their ability to circulate through the body and transport oxygen to tissues. In this study, we show that a simple two-LED set up in conjunction with standard microscope imaging can accurately determine the physical and mechanical properties of single RBCs. The Beer–Lambert law and undulatory motion dynamics of the membrane have been used to measure the total volume, hemoglobin mass, membrane tension coefficient, and bending modulus of RBCs. We also show that this method is sensitive enough to distinguish between the mechanical properties of RBCs during morphological changes from a typical discocyte to echinocytes and spherocytes. Measured values of the tension coefficient and bending modulus are 1.27 × 10⁻⁶ J m⁻² and 7.09 × 10⁻²⁰ J for discocytes, 4.80 × 10⁻⁶ J m⁻² and 7.70 × 10⁻²⁰ J for echinocytes, and 9.85 × 10⁻⁶ J m⁻² and 9.69 × 10⁻²⁰ J for spherocytes, respectively. This quantitative light absorption imaging reduces the complexity related to the quantitative imaging of the biophysical and mechanical properties of a single RBC that may lead to enhanced yet simplified point of care devices for analyzing blood cells.

The constant thickness in the microfluidic channel is used for controlled absorption of red and blue light to measure red blood cell hemoglobin and height mapping. High speed recording of the height mapping provides us the membrane fluctuation.

Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy

We demonstrate a successful deep learning strategy for cell identification and disease diagnosis using spatio-temporal cell information recorded by a digital holographic microscopy system. Shearing digital holographic microscopy is employed using a low-cost, compact, field-portable and 3D-printed microscopy system to record video-rate data of live biological cells with nanometer sensitivity in terms of axial membrane fluctuations, then features are extracted from the reconstructed phase profiles of segmented cells at each time instance for classification. The time-varying data of each extracted feature is input into a recurrent bi-directional long short-term memory (Bi-LSTM) network which learns to classify cells based on their time-varying behavior. Our approach is presented for cell identification between the morphologically similar cases of cow and horse red blood cells. Furthermore, the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset of human red blood cells from healthy individuals and those with sickle cell disease. The results are presented at both the cell and patient levels. To the best of our knowledge, this is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system.

Automated single cardiomyocyte characterization by nucleus extraction from dynamic holographic images using a fully convolutional neural network

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) beating can be efficiently characterized by time-lapse quantitative phase imaging (QPIs) obtained by digital holographic microscopy. Particularly, the CM's nucleus section can precisely reflect the associated rhythmic beating pattern of the CM suitable for subsequent beating pattern characterization. In this paper, we describe an automated method to characterize single CMs by nucleus extraction from QPIs and subsequent beating pattern reconstruction and quantification. However, accurate CM's nucleus extraction from the QPIs is a challenging task due to the variations in shape, size, orientation, and lack of special geometry. To this end, we propose a novel fully convolutional neural network (FCN)-based network architecture for accurate CM's nucleus extraction using pixel classification technique and subsequent beating pattern characterization. Our experimental results show that the beating profile of multiple extracted single CMs is less noisy and more informative compared to the whole image slide. Applying this method allows CM characterization at the single-cell level. Consequently, several single CMs are extracted from the whole slide QPIs and multiple parameters regarding their beating profile of each isolated CM are efficiently measured.

Quantitative phase imaging by wide-field interferometry with variable shearing distance uncoupled from the off-axis angle

We introduce a new shearing interferometry module for digital holographic microscopy, in which the off-axis angle, which defines the interference fringe frequency, is not coupled to the shearing distance, as is the case in most shearing interferometers. Thus, it enables the selection of shearing distance based on the spatial density of the sample, without losing spatial frequency content due to overlapping of the complex wave fronts in the spatial frequency domain. Our module is based on a 4f imaging unit and a diffraction grating, in which the hologram is generated from two mutually coherent, partially overlapping sample beams, with adjustable shearing distance, as defined by the position of the grating, but with a constant off-axis angle, as defined by the grating period. The module is simple, easy to align, and presents a nearly common-path geometry. By placing this module as an add-on unit at the exit port of an inverted microscope, quantitative phase imaging can easily be performed. The system is characterized by a 2.5 nm temporal stability and a 3.4 nm spatial stability, without using anti-vibration techniques. We provide quantitative phase imaging experiments of silica beads with different shearing distances, red blood cell fluctuations, and cancer cells flowing in a micro-channel, which demonstrate the capability and versatility of our approach in different imaging scenarios.

Quantitative analysis of three-dimensional morphology and membrane dynamics of red blood cells during temperature elevation

The optimal functionality of red blood cells is closely associated with the surrounding environment. This study was undertaken to analyze the changes in membrane profile, mean corpuscular hemoglobin (MCH), and cell membrane fluctuations (CMF) of healthy red blood cells (RBC) at varying temperatures. The temperature was elevated from 17 °C to 41 °C within a duration of less than one hour, and the holograms were recorded by an off-axis configuration. After hologram reconstruction, we extracted single RBCs and evaluated their morphologically related features (projected surface area and sphericity coefficient), MCH, and CMF. We observed that elevating the temperature results in changes in the three-dimensional (3D) profile. Since CMF amplitude is highly correlated to the bending curvature of RBC membrane, temperature-induced shape changes can alter CMF’s map and amplitude; mainly larger fluctuations appear on dimple area at a higher temperature. Regardless of the shape changes, no alterations in MCH were seen with temperature variation.

No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.

Automated quantification study of human cardiomyocyte synchronization using holographic imaging

This paper investigates the rhythm strip and parameters of synchronization of human induced pluripotent stem cell (iPS) derived cardiomyocytes. The synchronization is evaluated from quantitative phase images of beating cardiomyocytes which are obtained using the time-lapse digital holographic imaging method. By quantitatively monitoring the dry mass redistribution, digital holography provides the physical contraction-relaxation signal caused by autonomous cardiac action potential. In order to analyze the synchronicity at the cell-to-cell level, we extracted single cardiac muscle cells, which contain the nuclei, from the phase images of cardiomyocytes containing multiple cells resulting from the fusion of k-means clustering and watershed segmentation algorithms. We demonstrate that mature cardiomyocyte cell synchronization can be automatically evaluated by time-lapse microscopic holographic imaging. Our proposed method can be applied for studies on cardiomyocyte disorders and drug safety testing systems.