Angle-resolved light scattering of individual rod-shaped bacteria based on Fourier transform light scattering

In silico full-angle high-dynamic range scattering of microscopic objects exploiting holotomography

Accurate optical characterization of microscopic objects is crucial in academic research, product development, and clinical diagnosis. We present a method for obtaining full and high-dynamic range, angle-resolved light scattering attributes of microparticles, enabling distinction of variations in both overall morphology and detailed internal structures. This method overcomes previous limitations in observable scattering angles and dynamic range of signals through computationally assisted three-dimensional holotomography. This advancement is significant for particles spanning tens of wavelengths, such as human erythrocytes, which have historically posed measurement challenges due to faint side-scattering signals indicative of their complex interiors. Our technique addresses three key challenges in optical side-scattering analysis: limited observational angular range, reliance on simplified computational models, and low signal-to-noise ratios in both experimental and computational evaluations. We incorporate three-dimensional tomographic complex refractive index data from Fourier-transform light scattering into a tailored finite-difference time-domain simulation space. This approach facilitates precise near-to-far-field transformations. The process yields complete full-angle scattering phase functions, crucial for particles like Plasmodium falciparum-parasitized erythrocytes, predominantly involved in forward scattering. The resultant scattering data exhibit an extreme dynamic range exceeding 100 dB at various incident angles of a He-Ne laser. These findings have the potential to develop point-of-care, cost-effective, and rapid malaria diagnostic tools, inspiring further clinical and research applications in microparticle scattering.

Three-dimensional angular scattering simulations inform analysis of scattering from single cells

Significance

Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens.

Aim

Angularly resolved elastic light scattering is known to be sensitive to changes in organelle size distribution. We developed a Mie theory-based simulation of angular scattering from single cells to quantify the effects of noise on scattering and size estimates.

Approach

We simulated randomly sampled organelle sizes (drawn from a log normal distribution), interference between different organelles' scattering, and detector noise. We quantified each noise source's effect upon the estimated mean and standard deviation of organelle size distributions.

Results

The results demonstrate that signal-to-noise ratio in the angular scattering increased with the number of scatterers, cell area, and exposure time and decreased with the size distribution width. The error in estimating the mean of the size distributions remained below 5% for nearly all experimental parameters tested, but the widest size distribution tested (standard deviation of 600 nm) reached 20%.

Conclusions

The simulator revealed that sparse sampling of a broad size distribution can dominate the mismatch between actual and predicted size parameters. Alternative estimation strategies could reduce the discrepancy.

A low-cost, label-free microfluidic scanning flow cytometer for high-accuracy quantification of size and refractive index of particles

Flow cytometers and fluorescence activated cells sorters (FCM/FACS) represent the gold standard for high-throughput single-cell analysis, but their usefulness for label-free applications is limited by the unreliability of forward and side scatter measurements. Scanning flow cytometers represent an appealing alternative, as they exploit measurements of the angle-resolved scattered light to provide accurate and quantitative estimates of cellular properties, but the requirements of current setups are unsuitable for integration with other lab-on-chip technologies or for point-of-care applications. Here we present the first microfluidic scanning flow cytometer (μSFC), able to achieve accurate angle-resolved scattering measurements within a standard polydimethylsiloxane microfluidic chip. The system exploits a low cost linearly variable optical density (OD) filter to reduce the dynamic range of the signal and to increase its signal-to-noise ratio. We present a performance comparison between the μSFC and commercial machines for the label free characterization of polymeric beads with different diameters and refractive indices. In contrast to FCM and FACS, the μSFC yields size estimates linearly correlated with nominal particle sizes (R² = 0.99) and quantitative estimates of particle refractive indices. The feasibility of using the μSFC for the characterization of biological samples is demonstrated by analyzing a population of monocytes identified based on the morphology of a peripheral blood mononuclear cells sample, which yields values in agreement with the literature. The proposed μSFC combines low setup requirements with high performance, and has great potential for integration within other lab-on-chip systems for multi-parametric cell analysis and for next-generation point-of-care diagnostic applications.

Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy

Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.

Molecular profiling of the intestinal mucosa and immune cells of the colon by multi-parametric histological techniques

The impact of the post-mortem interval (PMI) on the optical molecular characteristics of the colonic mucosa and the gut-associated lymphoid tissue (GALT) were examined by multi-parametric measurements techniques. Inflammatory cells were identified by immunohistochemical staining. Molecular parameters were estimated using the Raman spectroscopy (RS) and Fourier Transform Infrared (FTIR) spectroscopic imaging. The 3D refractive index (3D-RI) distributions of samples were determined using the digital holographic tomography. The distribution of immune cells between post-mortem (PM) and normal controls did show significant differences for CD4 (P = 0.0016) or CD8 (P < 0.0001), whose expression level was decreased in PM cases. No association was found between individual PMI values and inflammatory cell distribution. However, there was a tendency for a negative correlation between CD4⁺ cells and PMI (r = - 0.542, P = 0.032). The alterations ongoing in post-mortem tissue may suggest that PMI has a suppressive effect on the effector properties of the cell-mediated immunity. Moreover, it was confirmed that spectroscopic and digital holotomographic histology are also a useful technique for characterization of the differences in inflammation of varying intensity and in GALT imaging in a solid tissue. Anatomical location of immune cells and methods of tissue fixation determine the molecular and optical parameters of the examined cases.

In vitro optoacoustic flow cytometry with light scattering referencing

Morphological and functional optoacoustic imaging is enhanced by dedicated transgene reporters, in analogy to fluorescence methods. The development of optoacoustic reporters using protein engineering and directed evolution would be accelerated by high-throughput in-flow screening for intracellular, genetically encoded, optoacoustic contrast. However, accurate characterization of such contrast is impeded because the optoacoustic signals depend on the cell's size and position in the flow chamber. We report herein an optoacoustic flow cytometer (OA-FCM) capable of precise measurement of intracellular optoacoustic signals of genetically-encoded chromoproteins in flow. The novel system records light-scattering as a reference for the detected optoacoustic signals in order to account for cell size and position, as well as excitation light flux in the focal volume, which we use to reference the detected optoacoustic signals to enhance the system's precision. The OA-FCM was calibrated using micrometer-sized particles to showcase the ability to assess in-flow objects in the size range of single-cells. We demonstrate the capabilities of our OA-FCM to identify sub-populations in a mixture of two E. coli stocks expressing different reporter-proteins with a precision of over 90%. High-throughput screening of optoacoustic labels could pave the way for identifying genetically encoded optoacoustic reporters by transferring working concepts of the fluorescence field such as directed evolution and activated cell sorting.

Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging

Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight into how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

Phase-sensitive, angle-resolved light-scattering microscopy of single cells

We report what is to our knowledge the first use of Fourier phase microscopy (FPM) to estimate diameters of individual single-micrometer beads and to classify cells based upon changes in scatterer size distribution. FPM, a quantitative phase imaging (QPI) method, combines the planar illumination typically used in off-axis QPI (ideal for Mie theory analysis) with the common-path geometry typically used in on-axis QPI (ideal for optimizing angular scattering range). Low-spatial-frequency imaging artifacts inherent to FPM have negligible impact upon these angular-domain applications. The system is simple to align and stable, and requires no external reference beam. Angular scattering patterns obtained from single 1 µm polystyrene beads in glycerol (Δn=0.11) display unprecedented fidelity to Mie theory, produce diameter estimates consistent with the manufacturer's specifications, and offer precision on the scale of tens of nanometers. Measurements of macrophages at different stages of antibody-dependent cellular phagocytosis demonstrate the ability to detect changes in a cell's scattering caused by the presence of phagocytosed material within.

On the application of multi-parametric optical phenotyping of bacterial colonies for multipurpose microbiological diagnostics

The development of new diagnostics techniques and modalities is critical for early detection of microbial contamination. In this study, the novel integrated system for multi-parametric optical phenotyping and characterization of bacterial colonies, is presented. The system combines Mach-Zehnder interferometer with a spectral imaging system for capturing multispectral diffraction patterns and multispectral two-dimensional transmission maps of bacterial colonies, along with the simultaneous interferometric profilometry. The herein presented investigation was carried out on five representative bacteria species and nearly 3000 registered multispectral optical signatures. The interferograms were analyzed by four-step phase shift algorithm to reconstruct the colony profile to enable the obtaining of the comparable optical signatures. The dedicated image processing algorithms were used for extraction of quantitative features of these signatures. The random forest algorithm was applied for selection of the most predictive set of features, which were used in classification model based on Support-Vector Machine. Obtained results have shown that the use of multiple multispectral optical signatures provide a multi-parametric bacteria identification at an exceptionally high accuracy (99.4-100%), significantly better than in case of classification based on each of these signatures (multispectral diffraction patterns, two-dimensional transmission coefficient maps), separately. Obtained results revealed that analysis of multispectral signatures can also be applied for characterisation of physical, physicochemical and chemical properties of the bacterial colonies in the presence of the antimicrobial factors. Therefore, the proposed label-free, non-destructive optical technique has perspectives to be exploited in the multipurpose diagnostics and it can be used as a pre-screening tool in microbiological laboratories.

An electronic approach for the automation of angle-resolved spectroscopic measurements

Angle-resolved light scattering techniques are powerful tools to obtain structural and spectroscopic information on the investigated sample by means of the study of the pattern of the angular distribution of scattered light. In this paper, we show the details of a new electronic system conceived to automate a Raman coherent backscattering setup, in which it is crucial to acquire several spectra at different angles in a wide spectral acquisition range. In this frame, we used this electrical circuit to trigger the signal edges between the charged-coupled device and the motorized nanorotator stage in our setup, carrying out a considerable quantity of measurements only with an initial input given by the operator and minimizing the supervision of the experiment and, therefore, the time invested by the user in it. By means of this system that can be easily integrated in the setup, we can perform distinct type of measurements by using different configurations of the components that make up the experimental setup.

Three-dimensional label-free observation of individual bacteria upon antibiotic treatment using optical diffraction tomography

Measuring alterations in bacteria upon antibiotic application is important for basic studies in microbiology, drug discovery, clinical diagnosis, and disease treatment. However, imaging and 3D time-lapse response analysis of individual bacteria upon antibiotic application remain largely unexplored mainly due to limitations in imaging techniques. Here, we present a method to systematically investigate the alterations in individual bacteria in 3D and quantitatively analyze the effects of antibiotics. Using optical diffraction tomography, in-situ responses of Escherichia coli and Bacillus subtilis to various concentrations of ampicillin were investigated in a label-free and quantitative manner. The presented method reconstructs the dynamic changes in the 3D refractive-index distributions of living bacteria in response to antibiotics at sub-micrometer spatial resolution.

Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning

We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label-free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label-free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry.

Light scattering methods for tissue diagnosis

Light scattering has become a common biomedical research tool, enabling diagnostic sensitivity to myriad tissue alterations associated with disease. Light-tissue interactions are particularly attractive for diagnostics due to the variety of contrast mechanisms that can be used, including spectral, angle-resolved, and Fourier-domain detection. Photonic diagnostic tools offer further benefit in that they are non-ionizing, non-invasive, and give real-time feedback. In this review, we summarize recent innovations in light scattering technologies, with a focus on clinical achievements over the previous ten years.

Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology

Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two-dimensional light scattering pattern acquisition platform based on single-mode optical fiber illumination is designed to collect a certain number of two-dimensional light scattering patterns of ovarian cancer cells and normal ovarian cells. Then, the HOG (Histogram of Oriented Gradient) algorithm is used to extract shaving anisotropy feature of two-dimensional light scattering pattern. The results show that the accuracy of classification and identification of ovarian cancer cells by two-dimensional light scattering technology is 90.81%, which suggests that the specificity of cancer cells and normal cells can be characterized by two-dimensional light scattering technology.

Dispersion preparation, characterization, and dosimetric analysis of cellulose nano-fibrils and nano-crystals: Implications for cellular toxicological studies

The characterization of cellulose-based nanomaterial (CNM) suspensions in environmental and biological media is impaired because of their high carbon content and anisotropic shape, thus making it difficult to derive structure activity relationships (SAR) in toxicological studies. Here, a standardized method for the dispersion preparation and characterization of cellulose nanofibrils (CNF) and nanocrystals (CNC) in biological and environmental media was developed. Specifically, electron microscopy was utilized and allowed to specify optimum practices for efficiently suspending CNF and CNC in water and cell culture medium. Furthermore, a technique for measuring the in vitro particle kinetics of CNF and CNC suspended in cell culture medium utilizing fluorescently tagged materials was developed to assess the delivery rate of such CNM at the bottom of the well. Interestingly, CNF were shown to settle and create a loosely packed layer at the bottom of cell culture wells within a few hours. On the contrary, CNC settled gradually at a significantly slower rate, highlighting the discordance between administered and delivered mass dose. This work is both novel and urgent in the field of environmental health and safety as it introduces well-defined techniques for the dispersion and characterization of emerging, cellulose-based engineered nanomaterials. It also provides useful insights to the in vitro behavior of suspended anisotropic nanomaterials in general, which should enable dosimetry and comparison of toxicological data across laboratories as well as promote the safe and sustainable use of nanotechnology.

Angle-resolved light scattering of single human chromosomes: experiments and simulations

Angle-resolved light scattering measurements of human metaphase chromosomes were compared to the results of numerical light scattering simulations with geometrical models based on atomic force microscopy (AFM) measurements of the same chromosomes. The simulations were conducted using the discrete dipole approximation method (DDA), which solves Maxwell's equations for induced dipoles, positioned in a discrete lattice. A remarkable agreement between the light scattering simulations and measurements of all 6 studied chromosomes was found. Additionally, the influence of small changes in the orientation of a complex scatterer geometry on its angle-resolved scattering pattern is shown. A method is presented to approximate such variations in the scatterer's orientation by a linear shift of the angular scattering pattern. This method provides an initial guess on the scatterers orientation, reducing the amount of simulations needed considerably. It was validated on simulations of a cuboid and successfully applied in the evaluation of the chromosome measurements.

Measurements of polarization-dependent angle-resolved light scattering from individual microscopic samples using Fourier transform light scattering

We present a method to measure the vector-field light scattering of individual microscopic objects. The polarization-dependent optical field images are measured with quantitative phase imaging at the sample plane, and then numerically propagated to the far-field plane. This approach allows the two-dimensional polarization-dependent angle-resolved light scattered patterns from individual object to be obtained with high precision and sensitivity. Using this method, we present the measurements of the polarization-dependent light scattering of a liquid crystal droplet and individual silver nanowires over scattering angles of 50°. In addition, the spectroscopic extension of the polarization-dependent angle-resolved light scattering is demonstrated using wavelength-scanning illumination.

Measurements of complex refractive index change of photoactive yellow protein over a wide wavelength range using hyperspectral quantitative phase imaging

A novel optical holographic technique is presented to simultaneously measure both the real and imaginary components of the complex refractive index (CRI) of a protein solution over a wide visible wavelength range. Quantitative phase imaging was employed to precisely measure the optical field transmitted from a protein solution, from which the CRIs of the protein solution were retrieved using the Fourier light scattering technique. Using this method, we characterized the CRIs of the two dominant structural states of a photoactive yellow protein solution over a broad wavelength range (461-582 nm). The significant CRI deviation between the two structural states was quantified and analysed. The results of both states show the similar overall shape of the expected rRI obtained from the Kramers-Kronig relations.

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.

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

Super-resolution in diffractive imaging from hemispherical elastic light scattering data

Angle-resolved hemispherical elastic light scattering techniques have been used to reconstruct the surface profile of two-dimensional photonic crystals with submicron resolution and metrological precision. Iterative algorithms permit subsequent calculation of a surface autocorrelation function with additional numerical approximation of the power spectrum and then yield final reconstruction of the surface shape. The proposed method enables filtering out unwanted scattering background, precluding the convergence of phase-retrieval algorithms. The estimation of higher harmonics in the power spectrum provides the reconstruction of a realistic surface achieving subwavelength resolution.

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).].

Hybrid bright-field and hologram imaging of cell dynamics

Volumetric observation is essential for understanding the details of complex biological phenomena. In this study, a bright-field microscope, which provides information on a specific 2D plane, and a holographic microscope, which provides information spread over 3D volumes, are integrated to acquire two complementary images simultaneously. The developed system was successfully applied to capture distinct T-cell adhesion dynamics on inflamed endothelial layers, including capture, rolling, crawling, transendothelial migration, and subendothelial migration.

Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry

Optical imaging is arguably the most effective tool to visualize living cells with high spatiotemporal resolution and in a nearly noninvasive manner. Driven by this capability, state-of-the-art cellular assay techniques have increasingly been adopting optical imaging for classifying different cell types/stages, and thus dissecting the respective cellular functions. However, it is still a daunting task to image and characterize cell-to-cell variability within an enormous and heterogeneous population - an unmet need in single-cell analysis, which is now widely advocated in modern biology and clinical diagnostics. The challenge stems from the fact that current optical imaging technologies still lack the practical speed and sensitivity for measuring thousands to millions of cells down to the single-cell precision. Adopting the wisdom in high-speed fiber-optics communication, optical time-stretch imaging has emerged as a completely new optical imaging concept which is now proven for ultrahigh-throughput optofluidic single-cell imaging, at least 1-2 orders-of-magnitude higher (up to ∼100 000 cells per second) compared to the existing imaging flow cytometers. It also uniquely enables quantification of intrinsic biophysical markers of individual cells - a largely unexploited class of single-cell signatures that is known to be correlated with the overwhelmingly investigated biochemical markers. With the aim of reaching a wider spectrum of experts specializing in cellular assay developments and applications, this paper highlights the essential basics of optical time-stretch imaging, followed by reviewing the recent developments and applications of optofluidic time-stretch imaging. We will also discuss the current challenges of this technology, in terms of providing new insights in basic biology and enriching the clinical diagnostic toolsets.

Cell refractive index for cell biology and disease diagnosis: past, present and future

Cell refractive index is a key biophysical parameter, which has been extensively studied. It is correlated with other cell biophysical properties including mechanical, electrical and optical properties, and not only represents the intracellular mass and concentration of a cell, but also provides important insight for various biological models. Measurement techniques developed earlier only measure the effective refractive index of a cell or a cell suspension, providing only limited information on cell refractive index and hence hindering its in-depth analysis and correlation. Recently, the emergence of microfluidic, photonic and imaging technologies has enabled the manipulation of a single cell and the 3D refractive index of a single cell down to sub-micron resolution, providing powerful tools to study cells based on refractive index. In this review, we provide an overview of cell refractive index models and measurement techniques including microfluidic chip-based techniques for the last 50 years, present the applications and significance of cell refractive index in cell biology, hematology, and pathology, and discuss future research trends in the field, including 3D imaging methods, integration with microfluidics and potential applications in new and breakthrough research areas.

Active illumination using a digital micromirror device for quantitative phase imaging

We present a powerful and cost-effective method for active illumination using a digital micromirror device (DMD) for quantitative phase-imaging techniques. Displaying binary illumination patterns on a DMD with appropriate spatial filtering, plane waves with various illumination angles are generated and impinged onto a sample. Complex optical fields of the sample obtained with various incident angles are then measured via Mach-Zehnder interferometry, from which a high-resolution 2D synthetic aperture phase image and a 3D refractive index tomogram of the sample are reconstructed. We demonstrate the fast and stable illumination-control capability of the proposed method by imaging colloidal spheres and biological cells. The capability of high-speed optical diffraction tomography is also demonstrated by measuring 3D Brownian motion of colloidal particles with the tomogram acquisition rate of 100 Hz.

Label-free identification of individual bacteria using Fourier transform light scattering

Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

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.