3D Quantitative Phase Imaging Research Articles

GAN-based quantitative oblique back-illumination microscopy enables computationally efficient epi-mode refractive index tomography.

Quantitative oblique back-illumination microscopy (qOBM) is a novel imaging technology that enables epi-mode 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. The technology uses four oblique back illumination images captured at the same focal plane and a fast 2D deconvolution reconstruction algorithm to reconstruct 2D phase cross-sections of thick samples. Alternatively, a through-focus z-stack of oblique back illumination images can be used to recover 3D RI tomograms with improved RI quantitative fidelity at the cost of a more computationally expensive reconstruction algorithm. Here, we report on a generative adversarial network (GAN) assisted approach to reconstruct 3D RI tomograms with qOBM that achieves high fidelity and greatly reduces processing time. The proposed approach achieves high-fidelity 3D RI tomography using differential phase contrast images from three adjacent z-planes. A ∼9-fold improvement in volumetric reconstruction time is achieved. We further show that this technique provides high SNR RI tomograms with high quantitative fidelity, reduces motion artifacts, and generalizes to different tissue types. This work can lead to real-time, high-fidelity RI tomographic imaging for in-vivo pre-clinical and clinical applications.

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part II.

Quantitative phase imaging (QPI) is a non-invasive, label-free technique that provides intrinsic information about the sample under study. Such information includes the structure, function, and dynamics of the sample. QPI overcomes the limitations of conventional fluorescence microscopy in terms of phototoxicity to the sample and photobleaching of the fluorophore. As such, the application of QPI in estimating the three-dimensional (3D) structure and dynamics is well-suited for a range of samples from intracellular organelles to highly scattering multicellular samples while allowing for longer observation windows. We aim to provide a comprehensive review of 3D QPI and related phase-based measurement techniques along with a discussion of methods for the estimation of sample dynamics. We present information collected from 106 publications that cover the theoretical description of 3D light scattering and the implementation of related measurement techniques for the study of the structure and dynamics of the sample. We conclude with a discussion of the applications of the reviewed techniques in the biomedical field. QPI has been successfully applied to 3D sample imaging. The scattering-based contrast provides measurements of intrinsic quantities of the sample that are indicative of disease state, stage of growth, or overall dynamics. We reviewed state-of-the-art QPI techniques for 3D imaging and dynamics estimation of biological samples. Both theoretical and experimental aspects of various techniques were discussed. We also presented the applications of the discussed techniques as applied to biomedicine and biology research.

Perspective on quantitative phase imaging to improve precision cancer medicine.

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states. This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection. We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging. QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research. QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

Metrology for 3D Quantitative Phase Imaging Instrumentation

Three-dimensional quantitative phase imaging (3D QPI) delivers volumetric information about the refractive index (RI) distribution within microobjects and offers new solutions to outstanding challenges in cell biology and pathology [1-3]. Such imaging and measurement capabilities are of great interest to study the biophysical properties of biological microstructures such as single cells, cell aggregates, tissues or even small organisms, especially as they are complemented by high spatial resolution and label free sample preparation. Therefore, along with the development of multiplicity of research 3D QPI systems, the commercial devices using either spatial light interference (Phi Optics, Inc) or holographic tomography (TomoCube, Inc and NanoLive, Ltd) entered the market, readily tailored for microbiological applications. Despite of commercialization of these 3D QPI systems, their performance have not been fully quantified experimentally.

Label-free functional analysis of root-associated microbes with dynamic quantitative oblique back-illumination microscopy

The increasing global demand for food, coupled with concerns about the environmental impact of synthetic fertilizers, underscores the urgency of developing sustainable agricultural practices. Nitrogen-fixing bacteria, known as diazotrophs, offer a potential solution by converting atmospheric nitrogen into bioavailable forms, reducing the reliance on synthetic fertilizers. However, a deeper understanding of their interactions with plants and other microbes is needed. In this study, we introduce a recently developed label-free 3D quantitative phase imaging technology called dynamic quantitative oblique back-illumination microscopy (DqOBM) to assess the functional dynamic activity of diazotrophs in vitro and in situ. Our experiments involved three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media with amendments of carbon and nitrogen sources. Over 5 days, we observed increased dynamics in nutrient-amended media. These results suggest that the observed bacterial dynamics correlate with their metabolic activity. Furthermore, we applied qOBM to visualize microbial dynamics within the root cap and elongation zone of Arabidopsis thaliana primary roots. This allowed us to identify distinct areas of microbial infiltration in plant roots without the need for fluorescent markers. Our findings demonstrate that DqOBM can effectively characterize microbial dynamics and provide insights into plant-microbe interactions in situ, offering a valuable tool for advancing our understanding of sustainable agriculture.

Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual hematoxylin and eosin staining

Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the typical formalin-fixation, paraffin-embedding (FFPE) process is laborious and time consuming, often limiting its usage in time-sensitive applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep-learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.

Analysis of structural effects of sickle cell disease on brain vasculature of mice using three-dimensional quantitative phase imaging.

Although the molecular origins of sickle cell disease (SCD) have been extensively studied, the effects of SCD on the vasculature-which can influence blood clotting mechanisms, pain crises, and strokes-are not well understood. Improving this understanding can yield insight into the mechanisms and wide-ranging effects of this devastating disease. We aim to demonstrate the ability of a label-free 3D quantitative phase imaging technology, called quantitative oblique back-illumination microscopy (qOBM), to provide insight into the effects of SCD on brain vasculature. Using qOBM, we quantitatively analyze the vasculature of freshly excised, but otherwise unaltered, whole mouse brains. We use Townes sickle transgenic mice, which closely recapitulate the pathophysiology of human SCD, and sickle cell trait mice as controls. Two developmental time points are studied: 6-week-old mice and 20-week-old mice. Quantitative structural and biophysical parameters of the vessels (including the refractive index (RI), which is linearly proportional to dry mass) are extracted from the high-resolution images and analyzed. qOBM reveals structural differences in the brain blood vessel thickness (thinner for SCD in particular brain regions) and the RI of the vessel wall (higher and containing a larger variation throughout the brain for SCD). These changes were only significant in 20-week-old mice. Further, vessel breakages are observed in SCD mice at both time points. The vessel wall RI distribution near these breaks, up to away from the breaking point, shows an erratic behavior characterized by wide RI variations. Vessel diameter, tortuosity, texture within the vessel, and structural fractal patterns are found to not be statistically different. As with vessel breaks, we also observe blood vessel blockages only in mice brains with SCD. qOBM provides insight into the biophysical and structural composition of brain blood vessels in mice with SCD. Data suggest that the RI may be an indirect indicator of vessel rigidity, vessel strength, and/or tensions, which change with SCD. Future ex vivo and in vivo studies with qOBM could improve our understanding of SCD.

Hybrid machine-learning framework for volumetric segmentation and quantification of vacuoles in individual yeast cells using holotomography.

The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of cell data. We applied this method to live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.

Volumetric Refractive Index Measurement and Quantitative Density Analysis of Mouse Brain Tissue with Sub‐Micrometer Spatial Resolution

High‐resolution structural imaging of brain tissue is important for neuroscience research. However, conventional approaches have several limitations, such as the need for exogenous staining, limited accessibility to volumetric information, and qualitative analysis. Herein, high‐resolution label‐free volumetric imaging and analysis of mouse brain tissue are presented using 3D quantitative phase imaging. Measurement of the refractive index distribution of tissue enables direct imaging of the cellular and subcellular structures. Quantification of subcellular organelles is performed in the anatomical regions of the somatosensory cortex, corpus callosum, caudoputamen, and thalamus regions.

Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.

Functional imaging with dynamic quantitative oblique back-illumination microscopy.

.Significance: Quantitative oblique back-illumination microscopy (qOBM) is a recently developed label-free imaging technique that enables 3D quantitative phase imaging of thick scattering samples with epi-illumination. Here, we propose dynamic qOBM to achieve functional imaging based on subcellular dynamics, potentially indicative of metabolic activity. We show the potential utility of this novel technique by imaging adherent mesenchymal stromal cells (MSCs) grown in bioreactors, which can help address important unmet needs in cell manufacturing for therapeutics.Aim: We aim to develop dynamic qOBM and demonstrate its potential for functional imaging based on cellular and subcellular dynamics.Approach: To obtain functional images with dynamic qOBM, a sample is imaged over a period of time and its temporal signals are analyzed. The dynamic signals display an exponential frequency response that can be analyzed with phasor analysis. Functional images of the dynamic signatures are obtained by mapping the frequency dynamic response to phasor space and color-coding clustered signals.Results: Functional imaging with dynamic qOBM provides unique information related to subcellular activity. The functional qOBM images of MSCs not only improve conspicuity of cells in complex environments (e.g., porous micro-carriers) but also reveal two distinct cell populations with different dynamic behavior.Conclusions: In this work we present a label-free, fast, and scalable functional imaging approach to study and intuitively display cellular and subcellular dynamics. We further show the potential utility of this novel technique to help monitor adherent MSCs grown in bioreactors, which can help achieve quality-by-design of cell products, a significant unmet need in the field of cell therapeutics. This approach also has great potential for dynamic studies of other thick samples, such as organoids.

Roadmap on Digital Holography-Based Quantitative Phase Imaging.

Quantitative Phase Imaging (QPI) provides unique means for the imaging of biological or technical microstructures, merging beneficial features identified with microscopy, interferometry, holography, and numerical computations. This roadmap article reviews several digital holography-based QPI approaches developed by prominent research groups. It also briefly discusses the present and future perspectives of 2D and 3D QPI research based on digital holographic microscopy, holographic tomography, and their applications.

Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

Histopathological examination of blood cells plays a crucial role in the diagnosis of various diseases. However, it involves time-consuming and laborious staining procedures required for microscopic review by medical experts and is not directly applicable for point-of-care diagnosis in resource-limited locations. This study reports a dilution-, actuation- and label-free method for the analysis of individual red blood cells (RBCs) using a capillary microfluidic device and quantitative phase imaging. Blood, without any sample treatment, is directly loaded into a micrometer-thick channel such that it forms a quasi-monolayer inside the channel. The morphological and biochemical properties of RBCs, including hemoglobin concentration, hemoglobin content, and corpuscular volume, were retrieved using the refractive index tomograms of individual RBCs measured using 3D quantitative phase imaging. The deformability of individual RBCs was also obtained by measuring the dynamic membrane fluctuations. The proposed framework applies to other imaging modalities and biomedical applications, facilitating rapid and cost-effective diagnosis and prognosis of diseases.

Label‐Free Quantitative Analysis of Coacervates via 3D Phase Imaging

AbstractCoacervation is considered as a ubiquitous mechanism for assembling biomolecular materials outside cells and organizing membraneless organelles inside cells. Despite the importance of mapping binodals to understand the driving forces and thermodynamics of coacervate, quantifying protein concentration within a droplet is significantly challenging owing to its dynamic and viscous nature. A direct imaging‐based method is presented to quantify coacervate using real‐time 3D quantitative phase imaging. The proposed method utilizes the measurements of the refractive index tomograms of individual coacervates and retrieves the protein concentration and volume of individual protein droplets exploiting light‐scattering analysis. The retrievals of accurate protein concentrations are demonstrated in droplets, whereas conventional fluorescence‐based techniques present underestimations. With its simple, direct, real‐time, and quantitative analysis capability, the present method can be utilized in various protein analyses and quantifications for the study of coacervation both in vitro and in vivo.

Calibration-free quantitative phase imaging using data-driven aberration modeling.

We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity and stability of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells and microbeads, benchmarking against the conventional method using background subtractions.

Quantitative Optical Diffraction Tomography Imaging of Mouse Platelets.

Platelets are specialized anucleate cells that play a major role in hemostasis following vessel injury. More recently, platelets have also been implicated in innate immunity and inflammation by directly interacting with immune cells and releasing proinflammatory signals. It is likely therefore that in certain pathologies, such as chronic parasitic infections and myeloid malignancies, platelets can act as mediators for hemostatic and proinflammatory responses. Fortunately, murine platelet function ex vivo is highly analogous to human, providing a robust model for functional comparison. However, traditional methods of studying platelet phenotype, function and activation status often rely on using large numbers of whole isolated platelet populations, which severely limits the number and type of assays that can be performed with mouse blood. Here, using cutting edge 3D quantitative phase imaging, holotomography, that uses optical diffraction tomography (ODT), we were able to identify and quantify differences in single unlabeled, live platelets with minimal experimental interference. We analyzed platelets directly isolated from whole blood of mice with either a JAK2V617F-positive myeloproliferative neoplasm (MPN) or Leishmania donovani infection. Image analysis of the platelets indicates previously uncharacterized differences in platelet morphology, including altered cell volume and sphericity, as well as changes in biophysical parameters such as refractive index (RI) and dry mass. Together, these data indicate that, by using holotomography, we were able to identify clear disparities in activation status and potential functional ability in disease states compared to control at the level of single platelets.

High-speed in vitro intensity diffraction tomography

We demonstrate a label-free, scan-free {\it intensity} diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume 3D refractive index distributions in vitro. By optimally matching the illumination geometry to the microscope pupil, our technique reduces the data requirement by 60$\times$ to achieve high-speed 10 Hz volume rates. Using 8 intensity images, we recover $\sim350\times100\times20\mu$m$^3$ volumes with near diffraction-limited lateral resolution of 487 nm and axial resolution of 3.4 $\mu$m. Our technique's large volume rate and high resolution enables 3D quantitative phase imaging of complex living biological samples across multiple length scales. We demonstrate aIDT's capabilities on unicellular diatom microalgae, epithelial buccal cell clusters with native bacteria, and live \emph{Caenorhabditis elegans} specimens. Within these samples, we recover macro-scale cellular structures, subcellular organelles, and dynamic micro-organism tissues with minimal motion artifacts. Quantifying such features has significant utility in oncology, immunology, and cellular pathophysiology, where these morphological features are evaluated for changes in the presence of disease, parasites, and new drug treatments. Finally, we simulate our aIDT system to highlight the accuracy and sensitivity of our technique. aIDT shows promise as a powerful high-speed, label-free computational microscopy technique applications where natural imaging is required to evaluate environmental effects on a sample in real-time. We provide example datasets and an open source implementation of aIDT at \href{https://github.com/bu-cisl/IDT-using-Annular-Illumination}{https://github.com/bu-cisl/IDT-using-Annular-Illumination}.

3D-printed biological cell phantom for testing 3D quantitative phase imaging systems

As the 3D quantitative phase imaging (QPI) methods mature, their further development calls for reliable tools and methods to characterize and compare their metrological parameters. We use refractive index engineering during two-photon laser photolithography to fabricate a life-scale phantom of a biological cell with internal structures that mimic optical and structural properties of mammalian cells. After verification with a number of reference techniques, the phantom is used to characterize the performance of a limited-angle holographic tomography microscope.

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

Three-dimensional tomographic microscopy technique with multi-frequency combination with partially coherent illuminations.

We demonstrate a three-dimensional (3D) optical diffraction tomographic technique with multi-frequency combination (MFC-ODT) for the 3D quantitative phase imaging of unlabeled specimens. Three sets of through-focus intensity images are captured under an annular aperture and two circular apertures with different coherence parameters. The 3D phase optical transfer functions (POTF) corresponding to different illumination apertures are combined to obtain a synthesized frequency response, achieving high-quality, low-noise 3D reconstructions with imaging resolution up to the incoherent diffraction limit. Besides, the expression of 3D POTF for arbitrary illumination pupils is derived and analyzed, and the 3D imaging performance of annular illumination is explored. It is shown that the phase-contrast washout effect in high-NA circular apertures can be effectively addressed by introducing a complementary annular aperture, which strongly boosts the phase contrast and improves the imaging resolution. By incorporating high-NA illumination as well as high-NA detection, MFC-ODT can achieve a theoretical transverse resolution up to 200 nm and an axial resolution of 645 nm. To test the feasibility of the proposed MFC-ODT technique, the 3D refractive index reconstruction results are based on a simulated 3D resolution target and experimental investigations of micro polystyrene bead and unstained biological samples are presented. Due to its capability for high-resolution 3D phase imaging as well as the compatibility with a widely available commercial microscope, the MFC-ODT is expected to find versatile applications in biological and biomedical research.