Quantitative microscopy and nanoscopy of sickle red blood cells performed by wide field digital interferometry

Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose using smartphone-based autofluorescence microscopy (Smart-AM) for imaging label-free blood smears at subcellular resolution with automatic hematological analysis. Smart-AM enables rapid and label-free visualization of morphological features of normal and abnormal blood cells (including leukocytes, erythrocytes, and thrombocytes). Moreover, assisted with deep-learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images which show clear cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.

Implementation of a portable diffraction phase microscope for digital histopathology

Quantitative phase imaging (QPI) has a significant advantage in histopathology as it helps in differentiating biological tissue structures and cells without the need for staining. To make this capability more accessible, it is crucial to develop compact and portable systems. In this study, we introduce a portable diffraction phase microscopy (DPM) system that allows the acquisition of phase map images from various organs in mice using a low-NA objective lens. Our findings indicate that the cell and tissue structures observed in portable DPM images are similar to those seen in conventional histology microscope images. We confirmed that the developed system's performance is comparable to the benchtop DPM system. Additionally, we investigate the potential utility of digital histopathology by applying deep learning technology to create virtual staining of DPM images.

Computing Sickle Erythrocyte Health Index on quantitative phase imaging and machine learning

Sickle cell disease (SCD) is a genetic disorder characterized by abnormal hemoglobin and deformation of red blood cells (RBCs), leading to complications and reduced life expectancy. This study developed an in vitro assessment, the Sickle Erythrocyte Health Index, using quantitative phase imaging (QPI) and machine learning to model the health of RBCs in people with SCD. The health index combines assessment of cell deformation, sickle-shaped classification, and membrane flexibility to evaluate erythrocyte health. Using QPI and image processing, the percentage of sickle-shaped cells and membrane flexibility were quantified. Statistically significant differences were observed between individuals with and without SCD, indicating the impact of underlying pathophysiology on erythrocyte health. Additionally, sodium metabisulfite led to an increase in sickle-shaped cells and a decrease in flexibility in the sickle cell blood samples. Based on these findings, two approaches were used to calculate the Sickle Erythrocyte Health Index: one using hand-crafted features and one using learned features from deep learning models. Both indices showed significant differences between non-SCD and SCD groups and sensitivity to changes induced by sodium metabisulfite. The Sickle Erythrocyte Health Index has important clinical implications for SCD management and could be used by providers when making treatment decisions. Further research is warranted to evaluate the clinical utility and applicability of the Sickle Erythrocyte Health Index in diverse patient populations.

Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning

Sickle cell disease (SCD) is an inherited hematological disorder associated with high mortality rates, particularly in sub-Saharan Africa. SCD arises due to the polymerization of sickle hemoglobin, which reduces flexibility of red blood cells (RBCs), causing blood vessel occlusion and leading to severe morbidity and early mortality rates if untreated. While sickle solubility tests are available to sub-Saharan African population as a means for detecting sickle hemoglobin (HbS), the test falls short in assessing the severity of the disease and visualizing the degree of cellular deformation. Here, we propose use of holographic cytometry (HC), a high throughput, label-free imaging modality, for comprehensive morphological profiling of RBCs as a means to detect SCD. For this study, more than 2.5 million single-cell holographic images from normal and SCD patient samples were collected using the HC system. We have developed an approach for specially defining training data to improve machine learning classification. Here, we demonstrate the deep learning classifier developed using this approach can produce highly accurate classification, even on unknown patient samples.

Viewing life without labels under optical microscopes

Optical microscopes today have pushed the limits of speed, quality, and observable space in biological specimens revolutionizing how we view life today. Further, specific labeling of samples for imaging has provided insight into how life functions. This enabled label-based microscopy to percolate and integrate into mainstream life science research. However, the use of labelfree microscopy has been mostly limited, resulting in testing for bio-application but not bio-integration. To enable bio-integration, such microscopes need to be evaluated for their timeliness to answer biological questions uniquely and establish a long-term growth prospect. The article presents key label-free optical microscopes and discusses their integrative potential in life science research for the unperturbed analysis of biological samples.

Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning

Anemia affects about the 25% of the global population and can provoke severe diseases, ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth failures. About 10% of the patients are affected by rare anemias of which 80% are hereditary. Early differential diagnosis of anemia enables prescribing patients a proper treatment and diet, which is effective to mitigate the associated symptoms. Nevertheless, the differential diagnosis of these conditions is often difficult due to shared and overlapping phenotypes. Indeed, the complete blood count and unaided peripheral blood smear observation cannot always provide a reliable differential diagnosis, so that biomedical assays and genetic tests are needed. These procedures are not error-free, require skilled personnel, and severely impact the financial resources of national health systems. Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence. Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia classes with minimal morphological dissimilarities. It is worth to notice that only a few tens of cells from each patient are sufficient to obtain a correct diagnosis, with the advantage of significantly limiting the volume of blood drawn. This work paves the way to a wider use of home screening systems for point of care blood testing and telemedicine with lab-on-chip platforms.

Elastic property of sickle cell anemia and sickle cell trait red blood cells

SIGNIFICANCE

We introduce a model for better calibration of the trapping force using an equal but oppositely directed drag force acting on a trapped red blood cell (RBC). We demonstrate this approach by studying RBCs' elastic properties from deidentified sickle cell anemia (SCA) and sickle cell trait (SCT) blood samples.

AIM

A laser trapping (LT) force was formulated and analytically calculated in a cylindrical model. Using this trapping force relative percent difference, the maximum (longitudinal) and minimum (transverse) radius rate and stiffness were used to study the elasticity.

APPROACH

The elastic property of SCA and SCT RBCs was analyzed using LT technique with computer controlled piezo-driven stage, in order to trap and stretch the RBCs.

RESULTS

For all parameters, the results show that the SCT RBC samples have higher elastic property than the SCA RBCs. The higher rigidity in the SCA cell may be due to the lipid composition of the membrane, which was affected by the cholesterol concentration.

CONCLUSIONS

By developing a theoretical model for different trapping forces, we have also studied the elasticity of RBCs in SCT (with hemoglobin type HbAS) and in SCA (with hemoglobin type HbSS). The results for the quantities describing the elasticity of the cells consistently showed that the RBCs in the SCT display lower rigidity and higher deformability than the RBCs with SCA.

Accurate real-time monitoring of high particulate matter concentration based on holographic speckles and deep learning

Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipments. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

Correlation of dynamic membrane fluctuations in red blood cells with diabetes mellitus and cardiovascular risks

The rheological and physiological properties of red blood cells (RBCs) are affected by many factors in the vascular environment. Among them, membrane fluctuations (MFs), particularly dynamic fluctuations in RBC cell membrane thickness (RBC-MFs), are likely to be altered by the level of glycation of haemoglobin in patients with diabetes mellitus (DM). We investigated the associations of RBC-MFs with physiological variables associated with DM and cardiovascular diseases (CVDs). Forty-one healthy control subjects and 59 patients with DM were enrolled. Five-microliter samples of blood were collected and diluted 400 times. To measure the RBC-MFs, holotomography was used, which non-invasively and precisely analyses the optical characteristics of RBCs. Associations between the RBC-MFs and biochemical parameters related to glucose homeostasis and lipid profiles were investigated. Independent associations of the RBC-MFs with the presence of CVDs were also analysed. RBC-MFs were lower in patients with DM than in healthy participants (61.64 ± 7.49 nm vs 70.65 ± 6.65 nm, P = 1.4 × 10⁻⁸). RBC-MFs correlated modestly with glycated haemoglobin level (ρ = − 0.47) and weakly with age (ρ = − 0.36), duration of diabetes (ρ = − 0.36), fasting plasma glucose level (ρ = − 0.37), and the 10-year Framingham risk score (ρ = − 0.38) (all P < 0.05). Low RBC-MFs were independently associated with the presence of CVDs after adjusting for CVD risk factors. The weak but significant associations of RBC-MFs with cardiometabolic risk factors and CVDs suggest that such deformity of circulating RBCs may be a useful marker of vascular complications of DM.

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network

SIGNIFICANCE

Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable.

AIM

An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization.

APPROACH

Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images.

RESULTS

The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed.

CONCLUSIONS

The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.

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.

Three-Dimensional Shapes and Cell Deformability of Rat Red Blood Cells during and after Asphyxial Cardiac Arrest

Changes in microcirculation are believed to perform an important role after cardiac arrest. In particular, rheological changes in red blood cells (RBCs) have been observed during and after ischemic-reperfusion injury. Employing three-dimensional laser interferometric microscopy, we investigated three-dimensional shapes and deformability of RBCs during and after asphyxial cardiac arrest in rats at the individual cell level. Rat cardiac arrest was induced by asphyxia. Five rats were maintained for 7 min of no-flow time, and then, cardiopulmonary resuscitation (CPR) was started. Blood samples were obtained before cardiac arrest, during CPR, and 60 min after return of spontaneous circulation (ROSC). Quantitative phase imaging (QPI) techniques based on laser interferometry were used to measure the three-dimensional refractive index (RI) tomograms of the RBC, from which structural and biochemical properties were retrieved. Dynamic membrane fluctuations in the cell membrane were also quantitatively and sensitively measured in order to investigate cell deformability. Mean corpuscular hemoglobin, mean cell volume, mean corpuscular hemoglobin concentration, and red blood cell distribution width remained unchanged during CPR and after ROSC compared with those before cardiac arrest. QPI results revealed that RBC membrane fluctuations, sphericity, and surface area did not change significantly during CPR or after ROSC compared with initial values. In conclusion, no three-dimensional shapes and cell deformability changes in RBCs were detected.

Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer-based off-axis digital holographic microscopy

We combined Michelson-interferometer-based off-axis digital holographic microscopy (DHM) with a common flow cytometry (FCM) arrangement. Utilizing object recognition procedures and holographic autofocusing during the numerical reconstruction of the acquired off-axis holograms, sharply focused quantitative phase images of suspended cells in flow were retrieved without labeling, from which biophysical cellular features of distinct cells, such as cell radius, refractive index and dry mass, can be subsequently retrieved in an automated manner. The performance of the proposed concept was first characterized by investigations on microspheres that were utilized as test standards. Then, we analyzed two types of pancreatic tumor cells with different morphology to further verify the applicability of the proposed method for quantitative live cell imaging. The retrieved biophysical datasets from cells in flow are found in good agreement with results from comparative investigations with previously developed DHM methods under static conditions, which demonstrates the effectiveness and reliability of our approach. Our results contribute to the establishment of DHM in imaging FCM and prospect to broaden the application spectrum of FCM by providing complementary quantitative imaging as well as additional biophysical cell parameters which are not accessible in current high-throughput FCM measurements.

Studying nucleic envelope and plasma membrane mechanics of eukaryotic cells using confocal reflectance interferometric microscopy

Mechanical stress on eukaryotic nucleus has been implicated in a diverse range of diseases including muscular dystrophy and cancer metastasis. Today, there are very few non-perturbative methods to quantify nuclear mechanical properties. Interferometric microscopy, also known as quantitative phase microscopy (QPM), is a powerful tool for studying red blood cell biomechanics. The existing QPM tools, however, have not been utilized to study biomechanics of complex eukaryotic cells either due to lack of depth sectioning, limited phase measurement sensitivity, or both. Here, we present depth-resolved confocal reflectance interferometric microscopy as the next generation QPM to study nuclear and plasma membrane biomechanics. The proposed system features multiple confocal scanning foci, affording 1.5 micron depth-resolution and millisecond frame rate. Furthermore, a near common-path interferometer enables quantifying nanometer-scale membrane fluctuations with better than 200 picometers sensitivity. Our results present accurate quantification of nucleic envelope and plasma membrane fluctuations in embryonic stem cells.

Biomechanical studies of eukaryotic cells have been limited due to low sensitivity and axial resolution in interferometric imaging. Here, the authors present depth-resolved confocal reflectance interferometric microscopy with high sensitivity and temporal resolution, which enables quantification of nucleic envelope and plasma membrane fluctuations.

Shear Modulus Measurement by Quantitative Phase Imaging and Correlation with Atomic Force Microscopy

Many approaches have been developed to characterize cell elasticity. Among these, atomic force microscopy (AFM) combined with modeling has been widely used to characterize cellular compliance. However, such approaches are often limited by the difficulties associated with using a specific instrument and by the complexity of analyzing the measured data. More recently, quantitative phase imaging (QPI) has been applied to characterize cellular stiffness by using an effective spring constant. This metric was further correlated to mass distribution (disorder strength) within the cell. However, these measurements are difficult to compare to AFM-derived measurements of Young's modulus. Here, we describe, to our knowledge, a new way of analyzing QPI data to directly retrieve the shear modulus. Our approach enables label-free measurement of cellular mechanical properties that can be directly compared to values obtained from other rheological methods. To demonstrate the technique, we measured shear modulus and phase disorder strength using QPI, as well as Young's modulus using AFM, across two breast cancer cell-line populations dosed with three different concentrations of cytochalasin D, an actin-depolymerizing toxin. Comparison of QPI-derived and AFM moduli shows good agreement between the two measures and further agrees with theory. Our results suggest that QPI is a powerful tool for cellular biophysics because it allows for optical quantitative measurements of cell mechanical properties.

Quantitative phase imaging of erythrocytes under microfluidic constriction in a high refractive index medium reveals water content changes

Changes in the deformability of red blood cells can reveal a range of pathologies. For example, cells which have been stored for transfusion are known to exhibit progressively impaired deformability. Thus, this aspect of red blood cells has been characterized previously using a range of techniques. In this paper, we show a novel approach for examining the biophysical response of the cells with quantitative phase imaging. Specifically, optical volume changes are observed as the cells transit restrictive channels of a microfluidic chip in a high refractive index medium. The optical volume changes indicate an increase of cell's internal density, ostensibly due to water displacement. Here, we characterize these changes over time for red blood cells from two subjects. By storage day 29, a significant decrease in the magnitude of optical volume change in response to mechanical stress was witnessed. The exchange of water with the environment due to mechanical stress is seen to modulate with storage time, suggesting a potential means for studying cell storage.

Molecular and biophysical analysis of apoptosis using a combined quantitative phase imaging and fluorescence resonance energy transfer microscope

Apoptotic mechanisms are often dysregulated in cancerous phenotypes. Additionally, many anticancer treatments induce apoptosis and necrosis, and the monitoring of this apoptotic activity can allow researchers to identify therapeutic efficiency. Here, we introduce a microscope which combines quantitative phase imaging (QPI) with the ability to detect molecular events via fluorescence (or Förster) resonance energy transfer (FRET). The system was applied to study cells undergoing apoptosis to correlate the onset of apoptotic enzyme activity as observed using a FRET-based apoptosis sensor with whole cell morphological changes analyzed via QPI. The QPI data showed changes in cell disorder strength during the initiation of apoptotic enzymatic activity.

Invited Article: Digital refocusing in quantitative phase imaging for flowing red blood cells

Quantitative phase imaging (QPI) offers high optical path length sensitivity, probing nanoscale features of live cells, but it is typically limited to imaging just few static cells at a time. To enable utility as a biomedical diagnostic modality, higher throughput is needed. To meet this need, methods for imaging cells in flow using QPI are in development. An important need for this application is to enable accurate quantitative analysis. However, this can be complicated when cells shift focal planes during flow. QPI permits digital refocusing since the complex optical field is measured. Here we analyze QPI images of moving red blood cells with an emphasis on choosing a quantitative criterion for digitally refocusing cell images. Of particular interest is the influence of optical absorption which can skew refocusing algorithms. Examples of refocusing of holographic images of flowing red blood cells using different approaches are presented and analyzed.

Cellular shear stiffness reflects progression of arsenic-induced transformation during G1

Cancer cells consistently exhibit decreased stiffness; however, the onset and progression of this change have not been characterized. To study the development of cell stiffness changes, we evaluated the shear stiffness of populations of cells during transformation to a carcinogenic state. Bronchial epithelial cells were exposed to sodium arsenite to initiate early stages of transformation. Exposed cells were cultured in soft agar to further transformation and select for clonal populations exhibiting anchorage-independent growth. Shear stiffness of various cell populations in G1 was assessed using a novel non-invasive assay that applies shear stress with fluid flow and evaluates nanoscale deformation using quantitative phase imaging (QPI). Arsenic-treated cells exhibited reduced stiffness relative to control cells, while arsenic clonal lines, selected by growth in soft agar, were found to have reduced stiffness relative to control clonal lines, which were cultured in soft agar but did not receive arsenic treatment. The relative standard deviation (RSD) of the stiffness of Arsenic clones was reduced compared with control clones, as well as to the arsenic-exposed cell population. Cell stiffness at the population level exhibits potential to be a novel and sensitive framework for identifying the development of cancerous cells.

Endoscopic diffraction phase microscopy

In this Letter, we present, to our knowledge, the first endoscopic diffraction phase microscopy (eDPM) system. This instrument consists of a gradient-index-lens-based endoscope probe followed by a DPM module, which enables single-shot phase imaging at a single-cell-level resolution. Using the phase information provided by eDPM, we show that the geometric aberrations associated with the endoscope can be reduced by digitally applying a spectral phase filter to the raw data. The filter function is a linear combination of polynomials with weighting optimized to improve resolution. We validate the principle of the proposed method using reflective semiconductor samples and blood cells. This research extends the current scope of quantitative phase imaging applications, and proves its potential for future in vivo studies.

Measurements of three-dimensional refractive index tomography and membrane deformability of live erythrocytes from Pelophylax nigromaculatus

Unlike mammalian erythrocytes, amphibian erythrocytes have distinct morphological features including large cell sizes and the presence of nuclei. The sizes of the cytoplasm and nuclei of erythrocytes vary significantly over different species, their environments, or pathophysiology, which makes hematological studies important for investigating amphibian species. Here, we present a label-free three-dimensional optical quantification of individual amphibian erythrocytes from frogs Pelophylax nigromaculatus (Rana nigromaculata). Using optical diffraction tomography, we measured three-dimensional refractive index (RI) tomograms of the cells, which clearly distinguished the cytoplasm and nuclei of the erythrocytes. From the measured RI tomograms, we extracted the relevant biochemical parameters of the cells, including hemoglobin contents and hemoglobin concentrations. Furthermore, we measured dynamic membrane fluctuations and investigated the mechanical properties of the cell membrane. From the statistical and correlative analysis of these retrieved parameters, we investigated interspecific differences between frogs and previously studied mammals.

Label-Free Optical Marker for Red-Blood-Cell Phenotyping of Inherited Anemias

The gold-standard methods for anemia diagnosis are complete blood counts and peripheral-smear observations. However, these do not allow for a complete differential diagnosis as that requires biochemical assays, which are label-dependent techniques. On the other hand, recent studies focus on label-free quantitative phase imaging (QPI) of blood samples to investigate blood diseases by using video-based morphological methods. However, when sick cells are very similar to healthy ones in terms of morphometric features, identification of a blood disease becomes challenging even with QPI. Here, we introduce a label-free optical marker (LOM) to detect red-blood-cell (RBC) phenotypes, demonstrating that a single set of all-optical parameters can clearly identify a signature directly related to an erythrocyte disease through modeling each RBC as a biological lens. We tested this novel biophotonic analysis by proving that several inherited anemias, such as iron-deficiency anemia, thalassemia, hereditary spherocytosis, and congenital dyserythropoietic anemia, can be identified and sorted, thus opening a novel route for blood diagnosis on a completely different concept based on LOMs.

Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)

Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.

Widefield standing wave microscopy of red blood cell membrane morphology with high temporal resolution

We report the first demonstration of widefield standing wave (SW) microscopy of fluorescently labelled red blood cells at high speeds that allow for the rapid imaging of membrane deformations. Using existing and custom MATLAB functions, we also present a method to generate 2D and 3D reconstructions of the SW data for improved visualization of the cell. We compare our technique with standard widefield epifluorescence imaging and show that the SW technique not only reveals more topographical information about the specimen but does so without increasing toxicity or the rate of photobleaching and could make this a powerful technique for the diagnosis or study of red blood cell morphology and biomechanical characteristics.

Wide field of view common-path lateral-shearing digital holographic interference microscope

Quantitative three-dimensional (3-D) imaging of living cells provides important information about the cell morphology and its time variation. Off-axis, digital holographic interference microscopy is an ideal tool for 3-D imaging, parameter extraction, and classification of living cells. Two-beam digital holographic microscopes, which are usually employed, provide high-quality 3-D images of micro-objects, albeit with lower temporal stability. Common-path digital holographic geometries, in which the reference beam is derived from the object beam, provide higher temporal stability along with high-quality 3-D images. Self-referencing geometry is the simplest of the common-path techniques, in which a portion of the object beam itself acts as the reference, leading to compact setups using fewer optical elements. However, it has reduced field of view, and the reference may contain object information. Here, we describe the development of a common-path digital holographic microscope, employing a shearing plate and converting one of the beams into a separate reference by employing a pin-hole. The setup is as compact as self-referencing geometry, while providing field of view as wide as that of a two-beam microscope. The microscope is tested by imaging and quantifying the morphology and dynamics of human erythrocytes.

Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration

We present a simple and effective method to eliminate system aberrations in quantitative phase imaging. Using spiral phase integration, complete information about system aberration is calculated from three laterally shifted phase images. The present method is especially useful when measuring confluent samples in which acquisition of background area is challenging. To demonstrate validity and applicability, we present measurements of various types of samples including microspheres, HeLa cells, and mouse brain tissue. Working conditions and limitations are systematically analyzed and discussed.

Morphometric analysis of erythrocytes from patients with thalassemia using tomographic diffractive microscopy

Complete blood count is the most common test to detect anemia, but it is unable to obtain the abnormal shape of erythrocytes, which highly correlates with the hematologic function. Tomographic diffractive microscopy (TDM) is an emerging technique capable of quantifying three-dimensional (3-D) refractive index (RI) distributions of erythrocytes without labeling. TDM was used to characterize optical and morphological properties of 172 erythrocytes from healthy volunteers and 419 erythrocytes from thalassemic patients. To efficiently extract and analyze the properties of erythrocytes, we developed an adaptive region-growing method for automatically delineating erythrocytes from 3-D RI maps. The thalassemic erythrocytes not only contained lower hemoglobin content but also showed doughnut shape and significantly lower volume, surface area, effective radius, and average thickness. A multi-indices prediction model achieved perfect accuracy of diagnosing thalassemia using four features, including the optical volume, surface-area-to-volume ratio, sphericity index, and surface area. The results demonstrate the ability of TDM to provide quantitative, hematologic measurements and to assess morphological features of erythrocytes to distinguish healthy and thalassemic erythrocytes.

Evaluation of the metastatic potential of malignant cells by image processing of digital holographic microscopy data

The cell refractive index has been proposed as a putative cancer biomarker of great potential, being correlated with cell content and morphology, cell division rate and membrane permeability. We used digital holographic microscopy to compare the refractive index and dry mass density of two B16 murine melanoma sublines of different metastatic potential. Using statistical methods, the distribution of phase shifts within the reconstructed quantitative phase images was analyzed by the method of bimodality coefficients. The observed correlation of refractive index, dry mass density and bimodality profile with the metastatic potential of the cells was validated by real time impedance-based assay and clonogenic tests. We suggest that the refractive index and bimodality analysis of quantitative phase image histograms could be developed as optical biomarkers useful in label-free detection and quantitative evaluation of cell metastatic potential.

Label-free nanoscale characterization of red blood cell structure and dynamics using single-shot transport of intensity equation

We report the results of characterization of red blood cell (RBC) structure and its dynamics with nanometric sensitivity using transport of intensity equation microscopy (TIEM). Conventional transport of intensity technique requires three intensity images and hence is not suitable for studying real-time dynamics of live biological samples. However, assuming the sample to be homogeneous, phase retrieval using transport of intensity equation has been demonstrated with single defocused measurement with x-rays. We adopt this technique for quantitative phase light microscopy of homogenous cells like RBCs. The main merits of this technique are its simplicity, cost-effectiveness, and ease of implementation on a conventional microscope. The phase information can be easily merged with regular bright-field and fluorescence images to provide multidimensional (three-dimensional spatial and temporal) information without any extra complexity in the setup. The phase measurement from the TIEM has been characterized using polymeric microbeads and the noise stability of the system has been analyzed. We explore the structure and real-time dynamics of RBCs and the subdomain membrane fluctuations using this technique.

Optophysiology of cardiomyocytes: characterizing cellular motion with quantitative phase imaging

Quantitative phase imaging enables precise characterization of cellular shape and motion. Variation of cell volume in populations of cardiomyocytes can help distinguish their types, while changes in optical thickness during beating cycle identify contraction and relaxation periods and elucidate cell dynamics. Parameters such as characteristic cycle shape, beating frequency, duration and regularity can be used to classify stem-cell derived cardiomyocytes according to their health and, potentially, cell type. Unlike classical patch-clamp based electrophysiological characterization of cardiomyocytes, this interferometric approach enables rapid and non-destructive analysis of large populations of cells, with longitudinal follow-up, and applications to tissue regeneration, personalized medicine, and drug testing.

Applying Pattern Recognition to High-Resolution Images to Determine Cellular Signaling Status

Two frequently used tools to acquire high- resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and living cells. In current practice, these images are viewed by eye to determine cellular status, e.g., activated versus resting. Automatic and quantitative data analysis is lacking. This paper develops an algorithm of pattern recognition that works very effectively for AFM and SEM images. Using rat basophilic leukemia cells, our approach creates a support vector machine to automatically classify resting and activated cells. Ten-fold cross-validation with cells that are known to be activated or resting gives a good estimate of the generalized classification results. The pattern recognition of AFM images achieves 100% accuracy, while SEM reaches 95.4% for our images as well as images published in prior literature. This outcome suggests that our methodology could become an important and frequently used tool for researchers utilizing AFM and SEM for structural characterization as well as determining cellular signaling status and function.

Optical Phase Measurements of Disorder Strength Link Microstructure to Cell Stiffness

There have been sustained efforts on the part of cell biologists to understand the mechanisms by which cells respond to mechanical stimuli. To this end, many rheological tools have been developed to characterize cellular stiffness. However, measurement of cellular viscoelastic properties has been limited in scope by the nature of most microrheological methods, which require direct mechanical contact, applied at the single-cell level. In this article, we describe, to our knowledge, a new analysis approach for quantitative phase imaging that relates refractive index variance to disorder strength, a parameter that is linked to cell stiffness. Significantly, both disorder strength and cell stiffness are measured with the same phase imaging system, presenting a unique alternative for label-free, noncontact, single-shot imaging of cellular rheologic properties. To demonstrate the potential applicability of the technique, we measure phase disorder strength and shear stiffness across five cellular populations with varying mechanical properties and demonstrate an inverse relationship between these two parameters. The existence of this relationship suggests that predictions of cell mechanical properties can be obtained from examining the disorder strength of cell structure using this, to our knowledge, novel, noncontact technique.

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease

Parkinson's disease (PD) is a common neurodegenerative disease. However, therapeutic methods of PD are still limited due to complex pathophysiology in PD. Here, optical measurements of individual neurons from in vitro PD model using optical diffraction tomography (ODT) are presented. By measuring 3D refractive index distribution of neurons, morphological and biophysical alterations in in-vitro PD model are quantitatively investigated. It was found that neurons show apoptotic features in early PD progression. The present approach will open up new opportunities for quantitative investigation of the pathophysiology of various neurodegenerative diseases. © 2017 International Society for Advancement of Cytometry.

Refractive index tomograms and dynamic membrane fluctuations of red blood cells from patients with diabetes mellitus

In this paper, we present the optical characterisations of diabetic red blood cells (RBCs) in a non-invasive manner employing three-dimensional (3-D) quantitative phase imaging. By measuring 3-D refractive index tomograms and 2-D time-series phase images, the morphological (volume, surface area and sphericity), biochemical (haemoglobin concentration and content) and mechanical (membrane fluctuation) parameters were quantitatively retrieved at the individual cell level. With simultaneous measurements of individual cell properties, systematic correlative analyses on retrieved RBC parameters were also performed. Our measurements show there exist no statistically significant alterations in morphological and biochemical parameters of diabetic RBCs, compared to those of healthy (non-diabetic) RBCs. In contrast, membrane deformability of diabetic RBCs is significantly lower than that of healthy, non-diabetic RBCs. Interestingly, non-diabetic RBCs exhibit strong correlations between the elevated glycated haemoglobin in RBC cytoplasm and decreased cell deformability, whereas diabetic RBCs do not show correlations. Our observations strongly support the idea that slow and irreversible glycation of haemoglobin and membrane proteins of RBCs by hyperglycaemia significantly compromises RBC deformability in diabetic patients.

Quantifying collagen fiber orientation in breast cancer using quantitative phase imaging

Tumor progression in breast cancer is significantly influenced by its interaction with the surrounding stromal tissue. Specifically, the composition, orientation, and alignment of collagen fibers in tumor-adjacent stroma affect tumor growth and metastasis. Most of the work done on measuring this prognostic marker has involved imaging of collagen fibers using second-harmonic generation microscopy (SHGM), which provides label-free specificity. Here, we show that spatial light interference microscopy (SLIM), a label-free quantitative phase imaging technique, is able to provide information on collagen-fiber orientation that is comparable to that provided by SHGM. Due to its wide-field geometry, the throughput of the SLIM system is much higher than that of SHGM and, because of the linear imaging, the equipment is simpler and significantly less expensive. Our results indicate that SLIM images can be used to extract important prognostic information from collagen fibers in breast tissue, potentially providing a convenient high throughput clinical tool for assessing patient prognosis.

Quantitative phase imaging for cell culture quality control

The potential of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) for quantification of cell culture quality was explored. Label-free QPI of detached single cells in suspension was performed by Michelson interferometer-based self-interference DHM. Two pancreatic tumor cell lines were chosen as cellular model and analyzed for refractive index, volume, and dry mass under varying culture conditions. Firstly, adequate cell numbers for reliable statistics were identified. Then, to characterize the performance and reproducibility of the method, we compared results from independently repeated measurements and quantified the cellular response to osmolality changes of the cell culture medium. Finally, it was demonstrated that the evaluation of QPI images allows the extraction of absolute cell parameters which are related to cell layer confluence states. In summary, the results show that QPI enables label-free imaging cytometry, which provides novel complementary integral biophysical data sets for sophisticated quantification of cell culture quality with minimized sample preparation. © 2017 International Society for Advancement of Cytometry.

Measuring sickle cell morphology during blood flow

During a sickle cell crisis in sickle cell anemia patients, deoxygenated red blood cells may change their mechanical properties and block small blood vessels, causing pain, local tissue damage, and possibly organ failure. Measuring the structural and morphological changes in sickle cells is important for understanding the factors contributing to vessel blockage and for developing an effective treatment. In this work, we image blood cells from sickle cell anemia patients using spectrally encoded flow cytometry, and analyze the interference patterns between reflections from the cell membranes. Using a numerical simulation for calculating the interference pattern obtained from a model of a red blood cell, we propose an analytical expression for the three-dimensional shape of characteristic sickle cells and compare our results to a previously suggested model. Our imaging approach offers new means for analyzing the morphology of sickle cells, and could be useful for studying their unique physiological and biomechanical properties.

Quantitative phase imaging for medical diagnosis

Optical microscopy is an indispensable diagnostic tool in modern healthcare. As a prime example, pathologists rely exclusively on light microscopy to investigate tissue morphology in order to make a diagnosis. While advances in light microscopy and contrast markers allow pathologists to visualize cells and tissues in unprecedented detail, the interpretation of these images remains largely subjective, leading to inter- and intra-observer discrepancy. Furthermore, conventional microscopy images capture qualitative information which makes it difficult to automate the process, reducing the throughput achievable in the diagnostic workflow. Quantitative Phase Imaging (QPI) techniques have been advanced in recent years to address these two challenges. By quantifying physical parameters of cells and tissues, these systems remove subjectivity from the disease diagnosis process and allow for easier automation to increase throughput. In addition to providing quantitative information, QPI systems are also label-free and can be easily assimilated into the current diagnostic workflow in the clinic. In this paper we review the advances made in disease diagnosis by QPI techniques. We focus on the areas of hematological diagnosis and cancer pathology, which are the areas where most significant advances have been made to date. [Image adapted from Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, Proc. Natl. Acad. Sci. 105, 13730-13735 (2008).].

Increased Red Blood Cell Stiffness Increases Pulmonary Vascular Resistance and Pulmonary Arterial Pressure

Patients with sickle cell anemia (SCD) and pulmonary hypertension (PH) have a significantly increased risk of sudden death compared to patients with SCD alone. Sickled red blood cells (RBCs) are stiffer, more dense, more frequently undergo hemolysis, and have a sixfold shorter lifespan compared to normal RBCs. Here, we sought to investigate the impact of increased RBC stiffness, independent of other SCD-related biological and mechanical RBC abnormalities, on the hemodynamic changes that ultimately cause PH and increase mortality in SCD. To do so, pulmonary vascular impedance (PVZ) measures were recorded in control C57BL6 mice before and after ∼50 μl of blood (Hct = 45%) was extracted and replaced with an equal volume of blood containing either untreated RBCs or RBCs chemically stiffened with glutaraldehyde (Hct = 45%). Chemically stiffened RBCs increased mean pulmonary artery pressure (mPAP) (13.5 ± 0.6 mmHg at baseline to 23.2 ± 0.7 mmHg after the third injection), pulmonary vascular resistance (PVR) (1.23 ± 0.11 mmHg*min/ml at baseline to 2.24 ± 0.14 mmHg*min/ml after the third injection), and wave reflections (0.31 ± 0.02 at baseline to 0.43 ± 0.03 after the third injection). Chemically stiffened RBCs also decreased cardiac output, but did not change hematocrit, blood viscosity, pulmonary arterial compliance, or heart rate. The main finding of this study is that increased RBC stiffness alone affects pulmonary pulsatile hemodynamics, which suggests that RBC stiffness plays an important role in the development of PH in patients with SCD.

Measuring cell surface area and deformability of individual human red blood cells over blood storage using quantitative phase imaging

The functionality and viability of stored human red blood cells (RBCs) is an important clinical issue in transfusions. To systematically investigate changes in stored whole blood, the hematological properties of individual RBCs were quantified in blood samples stored for various periods with and without a preservation solution called citrate phosphate dextrose adenine-1 (CPDA-1). With 3-D quantitative phase imaging techniques, the optical measurements for 3-D refractive index (RI) distributions and membrane fluctuations were done at the individual cell level. From the optical measurements, the morphological (volume, surface area and sphericity), biochemical (hemoglobin content and concentration), and mechanical parameters (dynamic membrane fluctuation) were simultaneously quantified to investigate the functionalities and progressive alterations of stored RBCs. Our results show that stored RBCs without CPDA-1 had a dramatic morphological transformation from discocytes to spherocytes within two weeks which was accompanied by significant decreases in cell deformability and cell surface area, and increases in sphericity. However, the stored RBCs with CPDA-1 maintained their morphology and deformability for up to 6 weeks.

Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.

Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging

Sickle cell disease (SCD) is common across Sub-Saharan Africa. However, the investigation of SCD in this area has been significantly limited mainly due to the lack of research facilities and skilled personnel. Here, we present optical measurements of individual red blood cells from healthy individuals and individuals with SCD and sickle cell trait in Tanzania using the quantitative phase imaging technique. By employing a quantitative phase imaging unit, an existing microscope in a clinic is transformed into a powerful quantitative phase microscope providing measurements on the morphological, biochemical, and biomechanical properties of individual cells. The present approach will open up new opportunities for cost-effective investigation and diagnosis of several diseases in low resource environments.

Cellular normoxic biophysical markers of hydroxyurea treatment in sickle cell disease

Hydroxyurea (HU) has been used clinically to reduce the frequency of painful crisis and the need for blood transfusion in sickle cell disease (SCD) patients. However, the mechanisms underlying such beneficial effects of HU treatment are still not fully understood. Studies have indicated a weak correlation between clinical outcome and molecular markers, and the scientific quest to develop companion biophysical markers have mostly targeted studies of blood properties under hypoxia. Using a common-path interferometric technique, we measure biomechanical and morphological properties of individual red blood cells in SCD patients as a function of cell density, and investigate the correlation of these biophysical properties with drug intake as well as other clinically measured parameters. Our results show that patient-specific HU effects on the cellular biophysical properties are detectable at normoxia, and that these properties are strongly correlated with the clinically measured mean cellular volume rather than fetal hemoglobin level.

Single-Shot Smartphone-Based Quantitative Phase Imaging Using a Distorted Grating

Blood testing has been used as an essential tool to diagnose diseases for decades. Recently, there has been a rapid developing trend in using Quantitative Phase Imaging (QPI) methods for blood cell screening. Compared to traditional blood testing techniques, QPI has the advantage of avoiding dyeing or staining the specimen, which may cause damage to the cells. However, most existing systems are bulky and costly, requiring experienced personnel to operate. This work demonstrates the integration of one QPI method onto a smartphone platform and the application of imaging red blood cells. The adopted QPI method is based on solving the Intensity Transport Equation (ITE) from two de-focused pupil images taken in one shot by the smartphone camera. The device demonstrates a system resolution of about 1 μm, and is ready to be used for 3D morphological study of red blood cells.

Measurement of mean thickness of transparent samples using simultaneous phase shifting interferometry with four interferograms

In this research a novel interferometric system is reported, which allows the generation of four simultaneous interferograms with phase shifts of π/2. The system consists of three coupled interferometers: a rectangular Sagnac interferometer which generates a primary pattern with crossed circular polarizations, coupled to two Michelson interferometers which operate as a multiplexing system, and generating replicas of the primary pattern. The two coupled Michelson interferometers generate four patterns retaining their polarization properties, which allow independent phase shifts by placing a linear polarizer over each pattern, thereby, four interferograms with relative phase shifts of π/2 are obtained. The optical phase is calculated using the well-known four-step algorithm. With knowledge of the optical phase, different properties of the samples can be calculated or analyzed; in this case, by knowing the mean refractive index, we can calculate the mean thickness of test objects. The results obtained for static transparent samples are presented. The capability of the system to analyze dynamic events is shown when results for the calculation of a temperature field of a heat flow are presented.