Hemoglobin consumption by P. falciparum in individual erythrocytes imaged via quantitative phase spectroscopy

Multimodal segmentation of dynamic subcellular features using quantitative phase imaging and FRET-based sensors [Invited]

Understanding cellular responses to mechanical environmental stimuli is important for cellular mechanotransduction studies. While fluorescence microscopy has been used for aiding mechanotransduction research due to its molecular sensitivity, the ability of quantitative phase imaging (QPI) to visualize morphology has yet to be widely applied, perhaps due to its limited specificity. Here, we seek to expand on previous work which combined quantitative phase imaging with a molecularly sensitive Förster resonance energy transfer (FRET) construct by developing additional analysis techniques. This work seeks to characterize the response of individual cells to mechanical stimulus through a novel, to the best of our knowledge, QPI-guided cellular segmentation algorithm. The multimodal imaging instrument and analysis techniques are employed to examine cellular responses to hypo-osmotic stimulus by observing the calcium ion flux using a FRET-based sensor coupled with a mapping of intracellular mass reorganization using QPI. The combined imaging modality enables a discrimination of cell response by localized region, revealing distinct behavior between regions and relative to a control group. Our novel analysis techniques can be used to identify cell expansion and cell region specific responses in both modalities due to the stimulus. With the broad array of FRET sensors under development, the complementary addition of QPI offers new avenues for studying cell responses to a range of environmental stimuli.

Two-dimensional Graphene/MoS2 vertical heterostructure for detection of hemoglobin concentration in blood samples

This study demonstrates the use of computational methods to simulate the molecular dynamics involved in hemoglobin concentration sensing, utilizing Material Studio and the TCAD Silvaco device simulator. A non-invasive and flexible Graphene/MoS2 heterostructure has been proposed for sensing hemoglobin concentration in blood samples. The findings reveal a notable shift in the wavelength-dependent refractive index and extinction coefficient, as well as significant changes in the absorption coefficient and reflectivity of the Graphene/MoS2 heterostructure in response to different hemoglobin concentrations, specifically within an approximate range of 0.3 μm to 1 μm. Moreover, the spectral response of the heterostructure demonstrates that at a particular wavelength of approximately 600 nm, a maximum response is obtained. This wavelength can be considered optimal for detecting various levels of hemoglobin using this heterostructure. The anticipated outcome is a comprehensive understanding of the fundamental principles, ultimately resulting in the development of an exceptionally sensitive platform for detecting hemoglobin concentration.

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques-optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)-were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model's accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin's RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

Label-free hematology analysis method based on defocusing phase-contrast imaging under illumination of 415 nm light

Label-free imaging technology is a trending way to simplify and improve conventional hematology analysis by bypassing lengthy and laborious staining procedures. However, the existing methods do not well balance system complexity, data acquisition efficiency, and data analysis accuracy, which severely impedes their clinical translation. Here, we propose defocusing phase-contrast imaging under the illumination of 415 nm light to realize label-free hematology analysis. We have verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast. It is demonstrated that the defocusing phase-contrast images for the five leucocyte subtypes can be automatically discriminated by a trained deep-learning program with high accuracy (the mean F1 score: 0.986 and mean average precision: 0.980). Since this technique is based on a regular microscope, it simultaneously realizes low system complexity and high data acquisition efficiency with remarkable quantitative analysis ability. It supplies a label-free, reliable, easy-to-use, fast approach to simplifying and reforming the conventional way of hematology analysis.

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.

Single-shot multiwavelength digital holography using Bragg diffraction of light by several ultrasound waves [Invited]

We report on wavelength-multiplexed digital holographic imaging based on simultaneous Bragg diffraction of wideband light by several ultrasound waves of different frequencies in crystalline media. This technique is easy to implement, avoids spectral scanning, and is applicable in various digital holography schemes. It also enables single-shot acquisition of a few spectral fringe patterns by a single monochrome sensor and wavelength demultiplexing of the resulting interferogram. We have assembled a Mach-Zehnder interferometer with an acousto-optical tunable filter operating in the multifrequency mode and have validated the proposed technique by dual-, three-, and four-wavelength quantitative phase imaging of the test samples.

Plasmepsin-like Aspartyl Proteases in Babesia

Apicomplexan genomes encode multiple pepsin-family aspartyl proteases (APs) that phylogenetically cluster to six independent clades (A to F). Such diversification has been powered by the function-driven evolution of the ancestral apicomplexan AP gene and is associated with the adaptation of various apicomplexan species to different strategies of host infection and transmission through various invertebrate vectors. To estimate the potential roles of Babesia APs, we performed qRT-PCR-based expressional profiling of Babesia microti APs (BmASP2, 3, 5, 6), which revealed the dynamically changing mRNA levels and indicated the specific roles of individual BmASP isoenzymes throughout the life cycle of this parasite. To expand on the current knowledge on piroplasmid APs, we searched the EuPathDB and NCBI GenBank databases to identify and phylogenetically analyse the complete sets of APs encoded by the genomes of selected Babesia and Theileria species. Our results clearly determine the potential roles of identified APs by their phylogenetic relation to their homologues of known function—Plasmodium falciparum plasmepsins (PfPM I–X) and Toxoplasma gondii aspartyl proteases (TgASP1–7). Due to the analogies with plasmodial plasmepsins, piroplasmid APs represent valuable enzymatic targets that are druggable by small molecule inhibitors—candidate molecules for the yet-missing specific therapy for babesiosis.

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individual red blood cells during storage under regular blood bank conditions. Samples from five blood donors were examined, over 100,000 cells examined for each, at three time points. The approach allows high-throughput phase imaging of a large number of cells, greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology images can provide measurements of multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine-learning algorithms. For the study here, logistic regression was used to classify the cells according to the storage time points. The analysis showed that at least 5000 cells are needed to ensure accuracy of the classifiers. Overall, results showed the potential of holographic cytometry as a diagnostic tool.

Multiparametric biophysical profiling of red blood cells in malaria infection

Biophysical separation promises label-free, less-invasive methods to manipulate the diverse properties of live cells, such as density, magnetic susceptibility, and morphological characteristics. However, some cellular changes are so minute that they are undetectable by current methods. We developed a multiparametric cell-separation approach to profile cells with simultaneously changing density and magnetic susceptibility. We demonstrated this approach with the natural biophysical phenomenon of Plasmodium falciparum infection, which modifies its host erythrocyte by simultaneously decreasing density and increasing magnetic susceptibility. Current approaches have used these properties separately to isolate later-stage infected cells, but not in combination. We present biophysical separation of infected erythrocytes by balancing gravitational and magnetic forces to differentiate infected cell stages, including early stages for the first time, using magnetic levitation. We quantified height distributions of erythrocyte populations-27 ring-stage synchronized samples and 35 uninfected controls-and quantified their unique biophysical signatures. This platform can thus enable multidimensional biophysical measurements on unique cell types.

Acoustofluidic phase microscopy in a tilted segmentation-free configuration

A low-cost device for registration-free quantitative phase microscopy (QPM) based on the transport of intensity equation of cells in continuous flow is presented. The method uses acoustic focusing to align cells into a single plane where all cells move at a constant speed. The acoustic focusing plane is tilted with respect to the microscope's focal plane in order to obtain cell images at multiple focal positions. As the cells are displaced at constant speed, phase maps can be generated without the need to segment and register individual objects. The proposed inclined geometry allows for the acquisition of a vertical stack without the need for any moving part, and it enables a cost-effective and robust implementation of QPM. The suitability of the solution for biological imaging is tested on blood samples, demonstrating the ability to recover the phase map of single red blood cells flowing through the microchip.

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.

Direct Quantification of Single Red Blood Cell Hemoglobin Concentration with Multiphoton Microscopy

Blood disorders, diseases, and infections often affect the shape, number, and content of red blood cells (RBCs) dramatically. To combat these pathologies, many therapies target RBCs and their contents directly. Mean corpuscular hemoglobin concentration (MCHC) is an important pathological metric in both identification and treatment. However, current methods for RBC analysis and MCHC quantification rely on bulk measurements. Single RBC measurements could provide necessary insight into the heterogeneity of RBC health and improve therapeutic efficacy. In this study, we present a novel multimodal multiphoton approach for quantifying hemoglobin concentration at single RBC resolution. We achieve this by collecting two images simultaneously that allows us to excite water with stimulated Raman scattering and hemoglobin with transient absorption. This multimodal imaging is enabled by a newly designed orthogonal modulation theme for dual-channel lock-in detection. By leveraging water as an internal standard, we quantify MCHC of healthy RBCs and RBCs infected with Plasmodium yoelii, a commonly studied rodent parasite model.

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.

Optical excitation and detection of neuronal activity

Optogenetics has emerged as an exciting tool for manipulating neural activity, which in turn, can modulate behavior in live organisms. However, detecting the response to the optical stimulation requires electrophysiology with physical contact or fluorescent imaging at target locations, which is often limited by photobleaching and phototoxicity. In this paper, we show that phase imaging can report the intracellular transport induced by optogenetic stimulation. We developed a multimodal instrument that can both stimulate cells with subcellular spatial resolution and detect optical pathlength (OPL) changes with nanometer scale sensitivity. We found that OPL fluctuations following stimulation are consistent with active organelle transport. Furthermore, the results indicate a broadening in the transport velocity distribution, which is significantly higher in stimulated cells compared to optogenetically inactive cells. It is likely that this label-free, contactless measurement of optogenetic response will provide an enabling approach to neuroscience.

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.

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.

Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model

BACKGROUND AND OBJECTIVES

The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge due to strong absorption of the hemoglobin especially at relevant high physiological concentrations. So far, only a few studies on direct measurement of refractive index have been reported and there are no firm agreements on the reported values of refractive index of hemoglobin due to measurement artifacts. In addition, it is time consuming, laborious and expensive to perform several experiments to obtain the refractive index of hemoglobin. In this work, we proposed a very rapid and accurate computational intelligent approach using Genetic Algorithm/Support Vector Regression models to estimate the real part of the refractive index for oxygenated and deoxygenated hemoglobin samples.

METHODS

These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR).

RESULTS

The developed methodology showed high accuracy as indicated by the low root mean square error values of 4.65 × 10^-4 and 4.62 × 10^-4 for oxygenated and deoxygenated hemoglobin, respectively. In addition, the models exhibited 99.85 and 99.84% correlation coefficients (r) for the oxygenated and deoxygenated hemoglobin, thus, validating the strong agreement between the predicted and the experimental results CONCLUSIONS: Due to the accuracy and relative simplicity of the proposed models, we envisage that these models would serve as important references for future studies on optical properties of blood.

Label-free, high-throughput detection of P. falciparum infection in sphered erythrocytes with digital holographic microscopy

Effective malaria treatment requires rapid and accurate diagnosis of infecting species and actual parasitemia. Despite the recent success of rapid tests, the analysis of thick and thin blood smears remains the gold standard for routine malaria diagnosis in endemic areas. For non-endemic regions, sample preparation and analysis of blood smears are an issue due to low microscopy expertise and few cases of imported malaria. Automation of microscopy results could be beneficial to quickly confirm suspected infections in such conditions. Here, we present a label-free, high-throughput method for early malaria detection with the potential to reduce inter-observer variation by reducing sample preparation and analysis effort. We used differential digital holographic microscopy in combination with two-dimensional hydrodynamic focusing for the label-free detection of P. falciparum infection in sphered erythrocytes, with a parasitemia detection limit of 0.01%. Moreover, the achieved differentiation of P. falciparum ring-, trophozoite- and schizont life cycle stages in synchronized cultures demonstrates the potential for future discrimination of even malaria species.

Skeletal muscle dispersion (400-1000 nm) and kinetics at optical clearing

Skeletal muscle dispersion and optical clearing (OC) kinetics were studied experimentally to prove the existence of the refractive index (RI) matching mechanism of OC. Sample thickness and collimated transmittance spectra were measured during treatments with glucose (40%) and ethylene glycol (EG; 99%) solutions and used to obtain the time dependence of the RI of tissue fluids based on the proposed theoretical model. Calculated results demonstrated an increase of RI of tissue fluids and consequently proved the occurrence of the RI matching mechanism. The RI increase was observed for the wavelength range between 400 and 1000 nm and for the 2 probing molecules explored. We found that for 30 min treatment with 40% glucose and 99% EG, RI of sarcoplasm plus interstitial fluid was increased at 800 nm from 1.328 to 1.348 and from 1.328 to 1.369, respectively.

Unraveling heme detoxification in the malaria parasite by in situ correlative X-ray fluorescence microscopy and soft X-ray tomography

A key drug target for malaria has been the detoxification pathway of the iron-containing molecule heme, which is the toxic byproduct of hemoglobin digestion. The cornerstone of heme detoxification is its sequestration into hemozoin crystals, but how this occurs remains uncertain. We report new results of in vivo rate of heme crystallization in the malaria parasite, based on a new technique to measure element-specific concentrations at defined locations in cell ultrastructure. Specifically, a high resolution correlative combination of cryo soft X-ray tomography has been developed to obtain 3D parasite ultrastructure with cryo X-ray fluorescence microscopy to measure heme concentrations. Our results are consistent with a model for crystallization via the heme detoxification protein. Our measurements also demonstrate the presence of considerable amounts of non-crystalline heme in the digestive vacuole, which we show is most likely contained in hemoglobin. These results suggest a tight coupling between hemoglobin digestion and heme crystallization, highlighting a new link in the crystallization pathway for drug development.

Speckle reduction in quantitative phase imaging by generating spatially incoherent laser field at electroactive optical diffusers

We studied quantitative phase imaging (QPI) using coherent laser illumination coupled with static and moving optical diffusers. The spatial coherence of a continuous-wave laser was controlled by tuning the particle size and the diffusion angle of optical diffusers for speckle-reduced 3D phase imaging of transparent objects. We used a common-path QPI configuration to investigate the coherent phase mapping of polystyrene micro-beads and breast cancer cells (MCF-7) under different degrees of coherent speckles. The proposed speckle reduction method could provide an avenue for enhancing lateral resolution and suppressing coherent artifacts of the phase images from QPI.

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.

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).].

Versatile spectral modulation of a broadband source for digital holographic microscopy

We demonstrate the potential of spatial light modulators for the spectral control of a broadband source in digital holographic microscopy. Used in a 'pulse-shaping' geometry, the spatial light modulator provides a versatile control over the bandwidth and wavelength of the light source. The control of these properties enables adaptation to various experimental conditions. As a first application, we show that the source bandwidth can be adapted to the off-axis geometry to provide quantitative phase imaging over the whole field of view. As a second application, we generate sequences of appropriate wavelengths for a hierarchical optical phase unwrapping algorithm, which enables the measurement of the topography of high-aspect ratio structures without phase ambiguity. Examples are given with step heights up to 50 µm.