Real time blood testing using quantitative phase imaging

Changes to the shape, orientation and packing of red cells as a function of retinal capillary size

The free diameter of a red blood cell exceeds the lumen diameter of capillaries in the central nervous system, requiring significant deformation of cells. However the deformations undertaken in vivo are not well established due to the difficulty in observing cellular capillary flow in living human tissue. Here, we used high resolution adaptive optics imaging to non-invasively track 17,842 red blood cells in transit through 121 unique capillary segments of diameter 8 µm or less in the retina of 3 healthy human subjects. Within each vessel, a 2D en face profile was generated for the "average cell", whose shape was then inferred in 3D based on the key assumption of a circular capillary cross-section. From this we estimated the average volume, surface area, orientation, and separation between red cells within each capillary tube. Our results showed a network filtration effect, whereby narrower vessels were more likely to contain smaller cells (defined by surface area, which is thought not to vary during a cell's passage through the vascular system). A bivariate linear model showed that for larger cells in narrower vessels: cells re-orient themselves to align with the flow axis, their shape becomes more elongated, there are longer gaps between successive cells, and remarkably, that cell volume is less which implies the ejection of water from cells to facilitate capillary transit. Taken together, these findings suggest that red cells pass through retinal capillaries with some reluctance. A biphasic distribution for cell orientation and separation was evident, indicating a "tipping point" for vessels narrower than approx. 5 µm. This corresponds closely to the typical capillary lumen diameter, and may maximize sensitivity of cellular flow to small changes in diameter. We suggest that the minimization of unnecessary oxygen exchange, and hence of damage via reactive oxygen pathways, may have provided evolutionary pressure to ensure that capillary lumens are generally narrower than red blood cells.

Automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks

Whole blood cell analysis is widely used in medical applications since its results are indicators for diagnosing a series of diseases. In this work, we report automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks. First, a commercial microscope equipped with our developed Phase Real-time Microscope Camera (PhaseRMiC) obtains both bright-field and quantitative phase images. Then, these images are automatically processed by our designed blood smear recognition networks (BSRNet) that recognize erythrocytes, leukocytes and platelets. Finally, blood cell parameters such as counts, shapes and volumes can be extracted according to both quantitative phase images and automatic recognition results. The proposed whole blood cell analysis technique provides high-quality blood cell images and supports accurate blood cell recognition and analysis. Moreover, this approach requires rather simple and cost-effective setups as well as easy and rapid sample preparations. Therefore, this proposed method has great potential application in blood testing aiming at disease diagnostics.

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

Morphological Characteristics, Hemoglobin Content, and Membrane Mechanical Properties of Red Blood Cell Delivery Systems

Red blood cell (RBC)-based systems are under extensive development as platforms for the delivery of various biomedical agents. While the importance of the membrane biochemical characteristics in relation to circulation kinetics of RBC delivery systems has been recognized, the membrane mechanical properties of such carriers have not been extensively studied. Using optical methods in conjunction with image analysis and mechanical modeling, we have quantified the morphological and membrane mechanical characteristics of RBC-derived microparticles containing the near-infrared cargo indocyanine green (ICG). We find that these particles have a significantly lower surface area, volume, and deformability as compared to normal RBCs. The residual hemoglobin has a spatially distorted distribution in the particles. The membrane bending modulus of the particles is about twofold higher as compared to normal RBCs and exhibits greater resistance to flow. The induced increase in the viscous characteristics of the membrane is dominant over the elastic and entropic effects of ICG. Our results suggest that changes to the membrane mechanical properties are a result of impaired membrane-cytoskeleton attachment in these particles. We provide a mechanistic explanation to suggest that the compromised membrane-cytoskeleton attachment and altered membrane compositional and structural asymmetry induce curvature changes to the membrane, resulting in mechanical remodeling of the membrane. These findings highlight the importance of membrane mechanical properties as an important criterion in the design and engineering of future generations of RBC-based delivery systems to achieve prolonged circulation.

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.

A Light-Sheet-Based Imaaging Spectrometer to Characterize Acridine Orange Fluorescence within Leukocytes

Low-cost imaging systems that utilize exogenous fluorescent dyes, such as acridine orange (AO), have recently been developed for use as point-of-care (POC) blood analyzers. AO-based fluorescence imaging exploits variations in emission wavelength within different cell types to enumerate and classify leukocyte subpopulations from whole blood specimens. This approach to leukocyte classification relies on accurate and reproducible colorimetric features, which have previously been demonstrated to be highly dependent on the cell staining protocols (such as specific AO concentration, timing, and pH). We have developed a light-sheet-based fluorescence imaging spectrometer, featuring a spectral resolution of 9 nm, with an automated spectral extraction algorithm as an investigative tool to study the spectral features from AO-stained leukocytes. Whole blood specimens were collected from human subjects, stained with AO using POC methods, and leukocyte spectra were acquired on a cell-by-cell basis. The post-processing method involves three steps: image segmentation to isolate individual cells in each spectral image; image quality control to exclude cells with low emission intensity, out-of-focus cells, and cellular debris; and the extraction of spectra for each cell. An increase in AO concentration was determined to contribute to the red-shift in AO-fluorescence, while varied pH values did not cause a change in fluorescence. In relation to the spectra of AO-stained leukocytes, there were corresponding red-shift trends associated with dye accumulation within acidic vesicles and at increasing incubation periods. The system presented here could guide future development of POC systems reliant on AO fluorescence and colorimetric features to identify leukocyte subpopulations in whole blood specimens.

Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling

Connected Components Labeling is an essential step of many Image Processing and Computer Vision tasks. Since the first proposal of a labeling algorithm, which dates back to the sixties, many approaches have optimized the computational load needed to label an image. In particular, the use of decision forests and state prediction have recently appeared as valuable strategies to improve performance. However, due to the overhead of the manual construction of prediction states and the size of the resulting machine code, the application of these strategies has been restricted to small masks, thus ignoring the benefit of using a block-based approach. In this paper, we combine a block-based mask with state prediction and code compression: the resulting algorithm is modeled as a Directed Rooted Acyclic Graph with multiple entry points, which is automatically generated without manual intervention. When tested on synthetic and real datasets, in comparison with optimized implementations of state-of-the-art algorithms, the proposed approach shows superior performance, surpassing the results obtained by all compared approaches in all settings.

Simple adaptive mobile phone screen illumination for dual phone differential phase contrast (DPDPC) microscopy

Phase contrast imaging is widely employed in the physical, biological, and medical sciences. However, typical implementations involve complex imaging systems that amount to in-line interferometers. We adapt differential phase contrast (DPC) to a dual-phone illumination-imaging system to obtain phase contrast images on a portable mobile phone platform. In this dual phone differential phase contrast (dpDPC) microscope, semicircles are projected sequentially on the display of one phone, and images are captured using a low-cost, short focal length lens attached to the second phone. By numerically combining images obtained using these semicircle patterns, high quality DPC images with ≈ 2 micrometer resolution can be easily acquired with no specialized hardware, circuitry, or instrument control programs.

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion.

A Wearable Fiberless Optical Sensor for Continuous Monitoring of Cerebral Blood Flow in Mice

Continuous and longitudinal monitoring of cerebral blood flow (CBF) in animal models provides information for studying the mechanisms and interventions of various cerebral diseases. Since anesthesia may affect brain hemodynamics, researchers have been seeking wearable devices for use in conscious animals. We present a wearable diffuse speckle contrast flowmeter (DSCF) probe for monitoring CBF variations in mice. The DSCF probe consists of a small low-power near-infrared laser diode as a point source and an ultra-small low-power CMOS camera as a 2D detector array, which can be affixed on a mouse head. The movement of red blood cells in brain cortex (i.e., CBF) produces spatial fluctuations of laser speckles, which are captured by the camera. The DSCF system was calibrated using tissue phantoms and validated in a human forearm and mouse brains for continuous monitoring of blood flow increases and decreases against the established technologies. Significant correlations were observed among these measurements (R² ≥ 0.80, p < 10^-5). This small fiberless probe has the potential to be worn by a freely moving conscious mouse. Moreover, the flexible source-detector configuration allows for varied probing depths up to ~8 mm, which is sufficient for transcranially detecting CBF in the cortices of rodents and newborn infants.

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.

3D surface morphology imaging of opaque microstructures via light-field microscopy

Observing dynamic micro-scale phenomena occurring at millisecond time scales, such as organism activity, micron particle flows, or any opaque object observation, requires volumetric microscopy techniques able to achieve high data acquisition rates while maintaining contrast so that measurement of fine micro-scale features is possible. In realizing this purpose, the light-field (LF) technique has already been used on three-dimensional (3D) scene capturing and even for microscopic visualizations. In studying the ability and feasibility of 3D surface morphology reconstruction via LF microscopy, we adopted a lab-made LF microscope and integrated a four-dimensional Fourier slice algorithm and a Markov random field propagation algorithm. Furthermore, for numerical comparison and quantized analysis, the Tenengrad function was utilized to calculate the average contrast of the region of interest. Reflective US Air Force targets and 3D photolithography-made micro-scaffolds coated with 50 nm nickel thin films were adopted for system alignment and calibration. The experimental results demonstrate that the developed LF microscope with the signal processing algorithms can observe the 3D surface morphology of opaque microstructures with one snapshot, and has been preliminary applied to Brownian motion observation with 30 Hz volumetric image rate.

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.

Automated Cell Segmentation for Quantitative Phase Microscopy

Automated cell segmentation and tracking is essential for dynamic studies of cellular morphology, movement, and interactions as well as other cellular behaviors. However, accurate, automated, and easy-to-use cell segmentation remains a challenge, especially in cases of high cell densities, where discrete boundaries are not easily discernable. Here, we present a fully automated segmentation algorithm that iteratively segments cells based on the observed distribution of optical cell volumes measured by quantitative phase microscopy. By fitting these distributions to known probability density functions, we are able to converge on volumetric thresholds that enable valid segmentation cuts. Since each threshold is determined from the observed data itself, virtually no input is needed from the user. We demonstrate the effectiveness of this approach over time using six cell types that display a range of morphologies, and evaluate these cultures over a range of confluencies. Facile dynamic measures of cell mobility and function revealed unique cellular behaviors that relate to tissue origins, state of differentiation, and real-time signaling. These will improve our understanding of multicellular communication and organization.

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

The acquisition of the angiogenic phenotype is an essential component of the escape from tumor dormancy. Although several classic in vitro assays (e.g., proliferation, migration, and others) and in vivo models have been developed to investigate and characterize angiogenic and non-angiogenic cell phenotypes, these methods are time and labor intensive, and often require expensive reagents and instruments, as well as significant expertise. In a recent study, we used a novel quantitative phase imaging (QPI) technique to conduct time-lapse and labeling-free characterizations of angiogenic and non-angiogenic human osteosarcoma KHOS cells. A panel of cellular parameters, including cell morphology, proliferation, and motility, were quantitatively measured and analyzed using QPI. This novel and quantitative approach provides the opportunity to continuously and non-invasively study relevant cellular processes, behaviors, and characteristics of cancer cells and other cell types in a simple and integrated manner. This report describes our experimental protocol, including cell preparation, QPI acquisition, and data analysis.

Real-time halo correction in phase contrast imaging

As a label-free, nondestructive method, phase contrast is by far the most popular microscopy technique for routine inspection of cell cultures. However, features of interest such as extensions near cell bodies are often obscured by a glow, which came to be known as the halo. Advances in modeling image formation have shown that this artifact is due to the limited spatial coherence of the illumination. Nevertheless, the same incoherent illumination is responsible for superior sensitivity to fine details in the phase contrast geometry. Thus, there exists a trade-off between high-detail (incoherent) and low-detail (coherent) imaging systems. In this work, we propose a method to break this dichotomy, by carefully mixing corrected low-frequency and high-frequency data in a way that eliminates the edge effect. Specifically, our technique is able to remove halo artifacts at video rates, requiring no manual interaction or a priori point spread function measurements. To validate our approach, we imaged standard spherical beads, sperm cells, tissue slices, and red blood cells. We demonstrate real-time operation with a time evolution study of adherent neuron cultures whose neurites are revealed by our halo correction. We show that with our novel technique, we can quantify cell growth in large populations, without the need for thresholds and system variant calibration.

A new red cell index and portable RBC analyzer for screening of iron deficiency and Thalassemia minor in a Chinese population

Anemia is a widespread public health problem with 1/4 ~1/3 of the world's population being affected. In Southeast Asia, Thalassemia trait (TT) and iron deficiency anemia (IDA) are the two most common anemia types and can have a serious impact on quality of life. IDA patients can be treated with iron supplementation, yet TT patients have diminished capacity to process iron. Therefore, distinguishing between types of anemia is essential for effective diagnosis and treatment. Here, we present two advances towards low-cost screening for anemia. First: a new red-cell-based index, Joint Indicator A, to discriminate between IDA, TT, and healthy children in a Chinese population. We collected retrospective data from 384 Chinese children and used discriminant function analysis to determine the best analytic function to separate healthy and diseased groups, achieving 94% sensitivity and 90% specificity, significantly higher than reported indices. This result is achieved using only three red cell parameters: mean cell volume (MCV), red cell distribution width (RDW) and mean cell hemoglobin concentration (MCHC). Our second advance: the development of a low cost, portable red cell analyzer to measure these parameters. Taken together, these two results may help pave the way for widespread screening for nutritional and genetic anemias.

Melittin-induced alterations in morphology and deformability of human red blood cells using quantitative phase imaging techniques

Here, the actions of melittin, the active molecule of apitoxin or bee venom, were investigated on human red blood cells (RBCs) using quantitative phase imaging techniques. High-resolution real-time 3-D refractive index (RI) measurements and dynamic 2-D phase images of individual melittin-bound RBCs enabled in-depth examination of melittin-induced biophysical alterations of the cells. From the measurements, morphological, intracellular, and mechanical alterations of the RBCs were analyzed quantitatively. Furthermore, leakage of haemoglobin (Hb) inside the RBCs at high melittin concentration was also investigated.

Large population cell characterization using quantitative phase cytometer

A major challenge in cellular analysis is the phenotypic characterization of large cell populations within a short period of time. Among various parameters for cell characterization, the cell dry mass is often used to describe cell size but is difficult to be measured directly with traditional techniques. Here, we propose an interferometric approach based on line-focused beam illumination for high-content precision dry mass measurements of adherent cells in a non-invasive fashion-we call it quantitative phase cytometry (QPC). Besides dry mass, abundant cellular morphological features such as projected area, sphericity, and phase skewness can be readily extracted from the QPC interferometric data. To validate the utility of our technique, we demonstrate characterizing a large population of ∼10⁴ HeLa cells. Our reported QPC system is envisioned as a promising quantitative tool for label-free characterization of a large cell count at single cell resolution. © 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.

Tomographic flow cytometry by digital holography

High-throughput single-cell analysis is a challenging task. Label-free tomographic phase microscopy is an excellent candidate to perform this task. However, in-line tomography is very difficult to implement in practice because it requires a complex set-up for rotating the sample and examining the cell along several directions. We demonstrate that by exploiting the random rolling of cells while they are flowing along a microfluidic channel, it is possible to obtain in-line phase-contrast tomography, if smart strategies for wavefront analysis are adopted. In fact, surprisingly, a priori knowledge of the three-dimensional position and orientation of rotating cells is no longer needed because this information can be completely retrieved through digital holography wavefront numerical analysis. This approach makes continuous-flow cytotomography suitable for practical operation in real-world, single-cell analysis and with a substantial simplification of the optical system; that is, no mechanical scanning or multi-direction probing is required. A demonstration is given for two completely different classes of biosamples: red blood cells and diatom algae. An accurate characterization of both types of cells is reported, despite their very different nature and material content, thus showing that the proposed method can be extended by adopting two alternate strategies of wavefront analysis to many classes of cells.

Characterization of dormant and active human cancer cells by quantitative phase imaging

The switch of tumor cells from a dormant, non-angiogenic phenotype to an active, angiogenic phenotype is a critical step in early cancer progression. To date, relatively little is known about the cellular behaviors of angiogenic and non-angiogenic tumor cell phenotypes. In this study, holographic imaging cytometry, a quantitative phase imaging (QPI) technique was used to continuously and non-invasively analyze, quantify, and compare a panel of fundamental cellular behaviors of angiogenic and non-angiogenic human osteosarcoma cells (KHOS) in a simple and economical way. Results revealed that angiogenic KHOS cells (KHOS-A) have significantly higher cell motility speeds than their non-angiogenic counterpart (KHOS-N) while no difference in their cell proliferation rates and cell cycle lengths were observed. KHOS-A cells were also found to have significantly smaller cell areas and greater cell optical thicknesses when compared with the non-angiogenic KHOS-N cells. No difference in average cell volumes was observed. These studies demonstrate that the morphology and behavior of angiogenic and non-angiogenic cells can be continuously, efficiently, and non-invasively monitored using a simple, quantitative, and economical system that does not require tedious and time-consuming assays to provide useful information about tumor dormancy. © 2017 International Society for Advancement of Cytometry.

Considerations for point-of-care diagnostics: evaluation of acridine orange staining and postprocessing methods for a three-part leukocyte differential test

There exists a broad range of techniques that can be used to classify and count white blood cells in a point-of-care (POC) three-part leukocyte differential test. Improvements in lenses, light sources, and cameras for image-based POC systems have renewed interest in acridine orange (AO) as a contrast agent, whereby subpopulations of leukocytes can be differentiated by colorimetric analysis of AO fluorescence emission. We evaluated the effect on test accuracy using different AO staining and postprocessing methods in the context of an image-based POC colorimetric cell classification scheme. Thirty blood specimens were measured for percent cell counts using our POC system and a conventional hematology analyzer for comparison. Controlling the AO concentration used during whole-blood staining, the incubation time with AO, and the colorimetric ratios among the three population of leukocytes yielded a percent deviation of 0.706%, ? 1.534 % , and ? 0.645 % for the lymphocytes, monocytes, and granulocytes, respectively. Overall, we demonstrated that a redshift in AO fluorescence was observed at elevated AO concentrations, which lead to reproducible inaccuracy of cell counts. This study demonstrates there is a need for a strict control of the AO staining and postprocessing methods to improve test accuracy in these POC systems.

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).].

White-light diffraction phase microscopy at doubled space-bandwidth product

White light diffraction microscopy (wDPM) is a quantitative phase imaging method that benefits from both temporal and spatial phase sensitivity, granted, respectively, by the common-path geometry and white light illumination. However, like all off-axis quantitative phase imaging methods, wDPM is characterized by a reduced space-bandwidth product compared to phase shifting approaches. This happens essentially because the ultimate resolution of the image is governed by the period of the interferogram and not just the diffraction limit. As a result, off-axis techniques generates single-shot, i.e., high time-bandwidth, phase measurements, at the expense of either spatial resolution or field of view. Here, we show that combining phase-shifting and off-axis, the original space-bandwidth is preserved. Specifically, we developed phase-shifting diffraction phase microscopy with white light, in which we measure and combine two phase shifted interferograms. Due to the white light illumination, the phase images are characterized by low spatial noise, i.e., <1nm pathlength. We illustrate the operation of the instrument with test samples, blood cells, and unlabeled prostate tissue biopsy.

Investigation on dynamics of red blood cells through their behavior as biophotonic lenses

The possibility to adopt biological matter as photonic optical elements can open scenarios in biophotonics research. Recently, it has been demonstrated that a red blood cell (RBC) can be seen as an optofluidic microlens by showing its imaging capability as well as its focal tunability. Moreover, correlation between an RBC’s morphology and its behavior as a refractive optical element has been established and its exploitation for biomedical diagnostic purposes has been foreseen. In fact, any deviation from the healthy RBC morphology can be seen as additional aberration in the optical wavefront passing through the cell. By this concept, accurate localization of focal spots of RBCs can become very useful in the blood disorders identification. We investigate the three-dimensional positioning of such focal spots over time for samples with two different osmolarity conditions, i.e., when they assume discocyte and spherical shapes, respectively. We also demonstrate that a temporal variation of an RBC’s focal points along the optical axis is correlated to the temporal fluctuations in the RBC’s thickness maps. Furthermore, we show a sort of synchronization of the whole erythrocytes ensemble.

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.

Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Accuracy of image-plane holographic tomography with filtered backprojection: random and systematic errors

This paper explores the concept of image-plane holographic tomography applied to the measurements of laser-induced thermal gradients in an aqueous solution of a photosensitizer with respect to the reconstruction accuracy of three-dimensional variations of the refractive index. It uses the least-squares estimation algorithm to reconstruct refractive index variations in each holographic projection. Along with the bitelecentric optical system, transferring focused projection to the sensor plane, it facilitates the elimination of diffraction artifacts and noise suppression. This work estimates the influence of typical random and systematic errors in experiments and concludes that random errors such as accidental measurement errors or noise presence can be significantly suppressed by increasing the number of recorded digital holograms. On the contrary, even comparatively small systematic errors such as a displacement of the rotation axis projection in the course of a reconstruction procedure can significantly distort the results.

Quantitative phase microscopy via optimized inversion of the phase optical transfer function

Although the field of quantitative phase imaging (QPI) has wide-ranging biomedical applicability, many QPI methods are not well-suited for such applications due to their reliance on coherent illumination and specialized hardware. By contrast, methods utilizing partially coherent illumination have the potential to promote the widespread adoption of QPI due to their compatibility with microscopy, which is ubiquitous in the biomedical community. Described herein is a new defocus-based reconstruction method that utilizes a small number of efficiently sampled micrographs to optimally invert the partially coherent phase optical transfer function under assumptions of weak absorption and slowly varying phase. Simulation results are provided that compare the performance of this method with similar algorithms and demonstrate compatibility with large phase objects. The accuracy of the method is validated experimentally using a microlens array as a test phase object. Lastly, time-lapse images of live adherent cells are obtained with an off-the-shelf microscope, thus demonstrating the new method's potential for extending QPI capability widely in the biomedical community.

Deciphering the internal complexity of living cells with quantitative phase microscopy: a multiscale approach

The distribution of refractive indices (RIs) of a living cell contributes in a nonintuitive manner to its optical phase image and quite rarely can be inverted to recover its internal structure. The interpretation of the quantitative phase images of living cells remains a difficult task because (1) we still have very little knowledge on the impact of its internal macromolecular complexes on the local RI and (2) phase changes produced by light propagation through the sample are mixed with diffraction effects by the internal cell bodies. We propose to implement a two-dimensional wavelet-based contour chain detection method to distinguish internal boundaries based on their greatest optical path difference gradients. These contour chains correspond to the highest image phase contrast and follow the local RI inhomogeneities linked to the intracellular structural intricacy. Their statistics and spatial distribution are the morphological indicators suited for comparing cells of different origins and/or to follow their transformation in pathologic situations. We use this method to compare nonadherent blood cells from primary and laboratory culture origins and to assess the internal transformation of hematopoietic stem cells by the transduction of the BCR-ABL oncogene responsible for the chronic myelogenous leukemia.

Three-dimensional refractive index tomograms and deformability of individual human red blood cells from cord blood of newborn infants and maternal blood

Red blood cells (RBCs) from the cord blood of newborn infants have distinctive functions in fetal and infant development. To systematically investigate the biophysical characteristics of individual cord RBCs in newborn infants, a comparative study was performed on RBCs from the cord blood of newborn infants and from adult mothers or nonpregnant women using optical holographic microtomography. Optical measurements of the distributions of the three-dimensional refractive indices and the dynamic membrane fluctuations of individual RBCs were used to investigate the morphological, biochemical, and mechanical properties of cord, maternal, and adult RBCs at the individual cell level. The volume and surface area of the cord RBCs were significantly larger than those of the RBCs from nonpregnant women, and the cord RBCs had more flattened shapes than that of the RBCs in adults. In addition, the hemoglobin (Hb) content in the cord RBCs from newborns was significantly higher. The Hb concentration in the cord RBCs was higher than that in the nonpregnant women or maternal RBCs, but they were within the physiological range of adults. Interestingly, the amplitudes of the dynamic membrane fluctuations in cord RBCs were comparable to those in nonpregnant women and maternal RBCs, suggesting that the deformability of cord RBCs is similar to that of healthy RBCs in adults.

Profiling individual human red blood cells using common-path diffraction optical tomography

Due to its strong correlation with the pathophysiology of many diseases, information about human red blood cells (RBCs) has a crucial function in hematology. Therefore, measuring and understanding the morphological, chemical, and mechanical properties of individual RBCs is a key to understanding the pathophysiology of a number of diseases in hematology, as well as to opening up new possibilities for diagnosing diseases in their early stages. In this study, we present the simultaneous and quantitative measurement of the morphological, chemical, and mechanical parameters of individual RBCs employing optical holographic microtomography. In addition, it is demonstrated that the correlation analyses of these RBC parameters provide unique information for distinguishing and understanding diseases.

Quantitative phase imaging with partially coherent illumination

In this Letter, we formulate a mathematical model for predicting experimental outcomes in quantitative phase imaging (QPI) when the illumination field is partially spatially coherent. We derive formulae that apply to QPI and discuss expected results for two classes of QPI experiments: common path and traditional interferometry, under varying degrees of spatial coherence. In particular, our results describe the physical relationship between the spatial coherence of the illuminating field and the halo effect, which is well known in phase-contrast microscopy. We performed experiments relevant to this common situation and found that our theory is in excellent agreement with the data. With this new understanding of the effects of spatial coherence, our formulae offer an avenue for removing halo artifacts from phase images.

Optical assay of erythrocyte function in banked blood

Stored red blood cells undergo numerous biochemical, structural, and functional changes, commonly referred to as storage lesion. How much these changes impede the ability of erythrocytes to perform their function and, as result, impact clinical outcomes in transfusion patients is unknown. In this study we investigate the effect of the storage on the erythrocyte membrane deformability and morphology. Using optical interferometry we imaged red blood cell (RBC) topography with nanometer sensitivity. Our time-lapse imaging quantifies membrane fluctuations at the nanometer scale, which in turn report on cell stiffness. This property directly impacts the cell's ability to transport oxygen in microvasculature. Interestingly, we found that cells which apparently maintain their normal shape (discocyte) throughout the storage period, stiffen progressively with storage time. By contrast, static parameters, such as mean cell hemoglobin content and morphology do not change during the same period. We propose that our method can be used as an effective assay for monitoring erythrocyte functionality during storage time.

Effects of spatial coherence in diffraction phase microscopy

Quantitative phase imaging systems using white light illumination can exhibit lower noise figures than laser-based systems. However, they can also suffer from object-dependent artifacts, such as halos, which prevent accurate reconstruction of the surface topography. In this work, we show that white light diffraction phase microscopy using a standard halogen lamp can produce accurate height maps of even the most challenging structures provided that there is proper spatial filtering at: 1) the condenser to ensure adequate spatial coherence and 2) the output Fourier plane to produce a uniform reference beam. We explain that these object-dependent artifacts are a high-pass filtering phenomenon, establish design guidelines to reduce the artifacts, and then apply these guidelines to eliminate the halo effect. Since a spatially incoherent source requires significant spatial filtering, the irradiance is lower and proportionally longer exposure times are needed. To circumvent this tradeoff, we demonstrate that a supercontinuum laser, due to its high radiance, can provide accurate measurements with reduced exposure times, allowing for fast dynamic measurements.

Network signatures of nuclear and cytoplasmic density alterations in a model of pre and postmetastatic colorectal cancer

Cells contributing to the pathogenesis of cancer possess cytoplasmic and nuclear structural alterations that accompany their aberrant genetic, epigenetic, and molecular perturbations. Although it is known that architectural changes in primary and metastatic tumor cells can be quantified through variations in cellular density at the nanometer and micrometer spatial scales, the interdependent relationships among nuclear and cytoplasmic density as a function of tumorigenic potential has not been thoroughly investigated. We present a combined optical approach utilizing quantitative phase microscopy and partial wave spectroscopic microscopy to perform parallel structural characterizations of cellular architecture. Using the isogenic SW480 and SW620 cell lines as a model of pre and postmetastatic transition in colorectal cancer, we demonstrate that nuclear and cytoplasmic nanoscale disorder, micron-scale dry mass content, mean dry mass density, and shape metrics of the dry mass density histogram are uniquely correlated within and across different cellular compartments for a given cell type. The correlations of these physical parameters can be interpreted as networks whose nodal importance and level of connection independence differ according to disease stage. This work demonstrates how optically derived biophysical parameters are linked within and across different cellular compartments during the architectural orchestration of the metastatic phenotype.

Quantitative phase imaging via Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a recently developed imaging modality that uses angularly varying illumination to extend a system's performance beyond the limit defined by its optical components. The FPM technique applies a novel phase-retrieval procedure to achieve resolution enhancement and complex image recovery. In this Letter, we compare FPM data to theoretical prediction and phase-shifting digital holography measurement to show that its acquired phase maps are quantitative and artifact-free. We additionally explore the relationship between the achievable spatial and optical thickness resolution offered by a reconstructed FPM phase image. We conclude by demonstrating enhanced visualization and the collection of otherwise unobservable sample information using FPM's quantitative phase.