Three-Dimensional Holographic Refractive-Index Measurement of Continuously Flowing Cells in a Microfluidic Channel

Recent Technologies on 2D and 3D Imaging Flow Cytometry

Imaging flow cytometry is a technology that performs microscopy image analysis of cells within flow cytometry and allows high-throughput, high-content cell analysis based on their intracellular molecular distribution and/or cellular morphology. While the technology has been available for a couple of decades, it has recently gained significant attention as technical limitations for higher throughput, sorting capability, and additional imaging dimensions have been overcome with various approaches. These evolutions have enabled imaging flow cytometry to offer a variety of solutions for life science and medicine that are not possible with conventional flow cytometry or microscopy-based screening. It is anticipated that the extent of applications will expand in the upcoming years as the technology becomes more accessible through dissemination. In this review, we will cover the technical advances that have led to this new generation of imaging flow cytometry, focusing on the advantages and limitations of each technique.

Towards ultrafast quantitative phase imaging via differentiable microscopy [Invited]

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the pixel-rate of the image sensors. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form so that more information can be transferred beyond the existing hardware bottleneck of the image sensor. To this end, we present a numerical simulation of a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy (∂-QPM) first uses learnable optical processors as image compressors. The intensity representations produced by these optical processors are then captured by the imaging sensor. Finally, a reconstruction network running on a computer decompresses the QPM images post aquisition. In numerical experiments, the proposed system achieves compression of × 64 while maintaining the SSIM of ∼0.90 and PSNR of ∼30 dB on cells. The results demonstrated by our experiments open up a new pathway to QPM systems that may provide unprecedented throughput improvements.

Recent Advances and Current Trends in Transmission Tomographic Diffraction Microscopy

Optical microscopy techniques are among the most used methods in biomedical sample characterization. In their more advanced realization, optical microscopes demonstrate resolution down to the nanometric scale. These methods rely on the use of fluorescent sample labeling in order to break the diffraction limit. However, fluorescent molecules' phototoxicity or photobleaching is not always compatible with the investigated samples. To overcome this limitation, quantitative phase imaging techniques have been proposed. Among these, holographic imaging has demonstrated its ability to image living microscopic samples without staining. However, for a 3D assessment of samples, tomographic acquisitions are needed. Tomographic Diffraction Microscopy (TDM) combines holographic acquisitions with tomographic reconstructions. Relying on a 3D synthetic aperture process, TDM allows for 3D quantitative measurements of the complex refractive index of the investigated sample. Since its initial proposition by Emil Wolf in 1969, the concept of TDM has found a lot of applications and has become one of the hot topics in biomedical imaging. This review focuses on recent achievements in TDM development. Current trends and perspectives of the technique are also discussed.

Utilizing 3D Models to Unravel the Dynamics of Myeloma Plasma Cells' Escape from the Bone Marrow Microenvironment

Recent therapeutic advancements have markedly increased the survival rates of individuals with multiple myeloma (MM), doubling survival compared to pre-2000 estimates. This progress, driven by highly effective novel agents, suggests a growing population of MM survivors exceeding the 10-year mark post-diagnosis. However, contemporary clinical observations indicate potential trends toward more aggressive relapse phenotypes, characterized by extramedullary disease and dominant proliferative clones, despite these highly effective treatments. To build upon these advances, it is crucial to develop models of MM evolution, particularly focusing on understanding the biological mechanisms behind its development outside the bone marrow. This comprehensive understanding is essential to devising innovative treatment strategies. This review emphasizes the role of 3D models, specifically addressing the bone marrow microenvironment and development of extramedullary sites. It explores the current state-of-the-art in MM modelling, highlighting challenges in replicating the disease's complexity. Recognizing the unique demand for accurate models, the discussion underscores the potential impact of these advanced 3D models on understanding and combating this heterogeneous and still incurable disease.

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy.

Dual-wavelength off-axis digital holography in ImageJ: toward real-time phase retrieval using CUDA streams

An ImageJ plug-in is developed to realize automatic real-time phase reconstruction for dual-wavelength digital holography (DH). This plug-in assembles the algorithms, including automatic phase reconstruction based on the division algorithm and post-processing. These algorithms are implemented and analyzed using a CPU and GPU, respectively. To hide the CPU-to-GPU data transfer latency, an optimization scheme using Compute Unified Device Architecture (CUDA) streams is proposed in ImageJ. Experimental results show that the proposed plug-in can perform faster reconstruction for dual-wavelength DH, resulting in frame rates up to 48 fps even for one-megapixel digital holograms on a normal PC. In other words, the proposed plug-in can realize real-time phase reconstruction for dual-wavelength digital holographic videos.

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.

Bond-selective intensity diffraction tomography

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 s per volume, with a lateral and axial resolution of ~350 nm and ~1.1 µm, respectively. BS-IDT's application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 µm x 100 µm).

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration.

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer

This article demonstrates a high-throughput technique to map cell images to cell positions. The technology uses a three-dimensional (3D) imaging flow cytometer to record multiparameter 3D cell images at a throughput of 1,000 cells/s and a cell placement robot to place the exiting cells from the imaging system on a filter plate in a first-in–first-out manner so the cells on the plate have the same order as the cells that are imaged. Innovative algorithms were developed to match the cell sequences from the imaging and placement modules to detect and eliminate errors to ensure high accuracy. The technology forms an unprecedented bridge between single-cell molecular analysis and single-cell image analysis to connect phenotype and genotype analysis with single-cell resolution.

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in–first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Physical Cytometry: Detecting Mass-Related Properties of Single Cells

The physical properties of a single cell, such as mass, volume, and density, are important indications of the cell's metabolic characteristics and homeostasis. Precise measurement of a single cell's mass has long been a challenge due to its minute size. It is only in the past 10 years that a variety of instruments for measuring living cellular mass have emerged with the development of MEMS, microfluidics, and optics technologies. In this review, we discuss the current developments of physical cytometry for quantifying mass-related physical properties of single cells, highlighting the working principle, applications, and unique merits. The review mainly covers these measurement methods: single-cell mass cytometry, levitation image cytometry, suspended microchannel resonator, phase-shifting interferometry, and opto-electrokinetics cell manipulation. Comparisons are made between these methods in terms of throughput, content, invasiveness, compatibility, and precision. Some typical applications of these methods in pathological diagnosis, drug efficacy evaluation, disease treatment, and other related fields are also discussed in this work.

Review: tomographic imaging flow cytometry

Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.

Snapshot three-dimensional absorption imaging of microscopic specimens

Snapshot projection optical tomography (SPOT) uses a micro-lens array (MLA) to simultaneously capture the projection images of a three-dimensional (3D) specimen corresponding to different viewing directions. Compared to other light-field imaging techniques using an MLA, SPOT is dual telecentric and can block high-angle stray rays without sacrificing the light collection efficiency. Using SPOT, we recently demonstrated snapshot 3D fluorescence imaging. Here we demonstrate snapshot 3D absorption imaging of microscopic specimens. For the illumination, we focus the incoherent light from a light-emitting diode onto a pinhole, which is placed at a conjugate plane to the sample plane. SPOT allows us to capture the ray bundles passing through the specimen along different directions. The images recorded by an array of lenslets can be related to the projections of 3D absorption coefficient along the viewing directions of lenslets. Using a tomographic reconstruction algorithm, we obtain the 3D map of absorption coefficient. We apply the developed system to different types of samples, which demonstrates the optical sectioning capability. The transverse and axial resolutions measured with gold nanoparticles are 1.3 μm and 2.3 μm, respectively.

Refractive index of biological tissues: Review, measurement techniques, and applications

Refractive index (RI) is a characteristic optical variable that controls the propagation of light in the medium (e.g., biological tissues). Basic research with the aim to investigate the RI of biological tissues is of paramount importance for biomedical optics and associated applications. Herein, we reviewed and summarized the RI data of biological tissues and the associated insights. Different techniques for the measurement of RI of biological tissues are also discussed. Moreover, several examples of the RI applications from basic research, clinics and optics industry are outlined. This study may provide a comprehensive reference for RI data of biological tissues for the biomedical research and beyond.

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.

Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint

This personal hybrid review piece, written in light of my recipience of the UIPAB 2020 young investigator award, contains a mixture of my scientific biography and work so far. This paper is not intended to be a comprehensive review, but only to highlight my contributions to computation-related aspects of super-resolution microscopy, as well as their origins and future directions.

Cell refractive index: Models, insights, applications and future perspectives

Cell refractive index (RI) is an intrinsic optical parameter that governs the propagation of light (i.e., scattering and absorption) in the cell matrix. The RI of cell is sensitively correlated with its mass distribution and thereby has the capability to provide important insights for diverse biological models. Herein, we review the cell refractive index and the fundamental models for measurement of cell RI, summarize the published RI data of cell and cell organelles and discuss the associated insights. Illustrative applications of cell RI in cell biology are also outlined. Finally, future research trends and applications of cell RI, including novel imaging techniques, reshaping flow cytometry and microfluidic platforms for single cell manipulation are discussed. The rapid technological advances in optical imaging integrated with microfluidic regime seems to enable deeper understanding of subcellular dynamics with high spatio-temporal resolution in real time.

Label-free discrimination and selection of cancer cells from blood during flow using holography-induced dielectrophoresis

We present a method for label-free imaging and sorting of cancer cells in blood, which is based on a dielectrophoretic microfluidic chip and label-free interferometric phase microscopy. The chip used for imaging has been embedded with dielectrophoretic electrodes, and therefore it can be used to sort the cells based on the decisions obtained during the cell flow by the label-free quantitative imaging method. Hence, we obtained a real-time, automatic, label-free imaging flow cytometry with the ability to sort the cells during flow. To validate our model, we combined into the label-free imaging interferometer a fluorescence imaging channel that indicated the correctness of the label-free sorting. We have achieved above 98% classification success and 69% sorting accuracy at flow rates of 4 to 7 μL hr^-1 . In the future, this method is expected to help in label-free sorting of circulating tumor cells in blood following an initial state-of-the-art cell enrichment.

Color lens-free imaging using multi-wavelength illumination based phase retrieval

Accurate image reconstruction in color lens-free imaging has proven challenging. The color image reconstruction of a sample is impacted not only by how strongly the illumination intensity is absorbed at a given spectral range, but also by the lack of phase information recorded on the image sensor. We present a compact and cost-effective approach of addressing the need for phase retrieval to enable robust color image reconstruction in lens-free imaging. The amplitude images obtained at transparent wavelength bands are used to estimate the phase in highly absorbed wavelength bands. The accurate phase information, obtained through our iterative algorithm, removes the color artefacts due to twin-image noise in the reconstructed image and improves image reconstruction quality to allow accurate color reconstruction. This could enable the technique to be applied for imaging of stained pathology slides, an important tool in medical diagnostics.

Lab-on-a-Chip Technologies for the Single Cell Level: Separation, Analysis, and Diagnostics

In the last three decades, microfluidics and its applications have been on an exponential rise, including approaches to isolate rare cells and diagnose diseases on the single-cell level. The techniques mentioned herein have already had significant impacts in our lives, from in-the-field diagnosis of disease and parasitic infections, through home fertility tests, to uncovering the interactions between SARS-CoV-2 and their host cells. This review gives an overview of the field in general and the most notable developments of the last five years, in three parts: 1. What can we detect? 2. Which detection technologies are used in which setting? 3. How do these techniques work? Finally, this review discusses potentials, shortfalls, and an outlook on future developments, especially in respect to the funding landscape and the field-application of these chips.

Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing

We propose a line-field quantitative phase-imaging flow cytometer for analyzing large populations of label-free cells. Hydrodynamical focusing brings cells into the focus plane of an optical system while diluting the cell suspension, resulting in decreased throughput rate. To overcome the trade-off between throughput rate and in-focus imaging, our cytometer involves digitally extending the depth-of-focus on loosely hydrodynamically focusing cell suspensions. The cells outside the depth-of-focus range in the 70-µm diameter of the core flow were automatically digitally refocused after image acquisition. We verified that refocusing was successful with our cytometer through statistical analysis of image quality before and after digital refocusing.

Reconstruction of bovine spermatozoa substances distribution and morphological differences between Holstein and Korean native cattle using three-dimensional refractive index tomography

Measurements of the three-dimensional (3D) structure of spermatozoon are crucial for the study of developmental biology and for the evaluation of in vitro fertilization. Here, we present 3D label-free imaging of individual spermatozoon and perform quantitative analysis of bovine, porcine, and mouse spermatozoa morphologies using refractive index tomography. Various morphological and biophysical properties were determined, including the internal structure, volume, surface area, concentration, and dry matter mass of individual spermatozoon. Furthermore, Holstein cows and Korean native cattle spermatozoa were systematically analyzed and revealed significant differences in spermatozoa head length, head width, midpiece length, and tail length between the two breeds. This label-free imaging approach provides a new technique for understanding the physiology of spermatozoa.

Wavelet compression of off-axis digital holograms using real/imaginary and amplitude/phase parts

Compression of digital holograms allows one to store, transmit, and reconstruct large sets of holographic data. There are many digital image compression methods, and usually wavelets are used for this task. However, many significant specialties exist for compression of digital holograms. As a result, it is preferential to use a set of methods that includes filtering, scalar and vector quantization, wavelet processing, etc. These methods in conjunction allow one to achieve an acceptable quality of reconstructed images and significant compression ratios. In this paper, wavelet compression of amplitude/phase and real/imaginary parts of the Fourier spectrum of filtered off-axis digital holograms is compared. The combination of frequency filtering, compression of the obtained spectral components, and extra compression of the wavelet decomposition coefficients by threshold processing and quantization is analyzed. Computer-generated and experimentally recorded digital holograms are compressed. The quality of the obtained reconstructed images is estimated. The results demonstrate the possibility of compression ratios of 380 using real/imaginary parts. Amplitude/phase compression allows ratios that are a factor of 2-4 lower for obtaining similar quality of reconstructed objects.

Low-coherent optical diffraction tomography by angle-scanning illumination

Temporally low-coherent optical diffraction tomography (ODT) is proposed and demonstrated based on angle-scanning Mach-Zehnder interferometry. Using a digital micromirror device based on diffractive tilting, the full-field interference of incoherent light is successfully maintained during every angle-scanning sequences. Further, current ODT reconstruction principles for temporally incoherent illuminations are thoroughly reviewed and developed. Several limitations of incoherent illumination are also discussed, such as the nondispersive assumption, optical sectioning capacity and illumination angle limitation. Using the proposed setup and reconstruction algorithms, low-coherent ODT imaging of plastic microspheres, human red blood cells and rat pheochromocytoma cells is experimentally demonstrated.

Direct Three-Dimensional Measurement of Refractive Index via Dual Photon-Phonon Scattering

We developed a microscopy technique that can measure the local refractive index without sampling the optical phase delay of the electromagnetic radiation. To do this, we designed and experimentally demonstrated a setup with two colocalized Brillouin scattering interactions that couple to a common acoustic phonon axis; in this scenario, the ratio of Brillouin frequency shifts depends on the refractive index, but not on any other mechanical and/or optical properties of the sample. Integrating the spectral measurement within a confocal microscope, the refractive index is mapped at micron-scale three-dimensional resolution. As the refractive index is probed in epidetection and without assumptions on the geometrical dimensions of the sample, this method may prove useful to characterize biological cells and tissues.

Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains

Holographic microscopy presents challenges for color reproduction due to the usage of narrow-band illumination sources, which especially impacts the imaging of stained pathology slides for clinical diagnoses. Here, an accurate color holographic microscopy framework using absorbance spectrum estimation is presented. This method uses multispectral holographic images acquired and reconstructed at a small number (e.g., three to six) of wavelengths, estimates the absorbance spectrum of the sample, and projects it onto a color tristimulus. Using this method, the wavelength selection is optimized to holographically image 25 pathology slide samples with different tissue and stain combinations to significantly reduce color errors in the final reconstructed images. The results can be used as a practical guide for various imaging applications and, in particular, to correct color distortions in holographic imaging of pathology samples spanning different dyes and tissue types.

Enhancement of optical resolution in three-dimensional refractive-index tomograms of biological samples by employing micromirror-embedded coverslips

Optical diffraction tomography (ODT) enables the reconstruction of the three-dimensional (3D) refractive-index (RI) distribution of a biological cell, which provides invaluable information for cellular and subcellular structures in a non-invasive manner. However, ODT suffers from an inferior axial resolution, due to the limited accessible angles imposed by the numerical aperture of the objective lens. In this study, we propose and experimentally demonstrate an approach to enhance the 3D reconstruction performance in ODT. By employing trapezoidal micromirrors, side scattered signals from the sample are measured for various side plane-wave-illumination angles. By combining the side scattered fields with the forward scattered fields, the axial resolution and 3D image quality of ODT are improved, without changing optical instruments. The feasibility and applicability of the proposed method are demonstrated by reconstructing the 3D RI distribution of a red blood cell and HeLa cells in hydrogel. We also present systematic analyses of the improved 3D imaging performance using numerical simulations and experimental measurements for the 3D transfer function, a point object, and a microsphere. The analyses demonstrate an improved axial resolution of 0.31 μm, 4.8 times smaller than that of the conventional method. The proposed method enables the non-invasive and accurate 3D imaging of 3D cultured cells, which is crucial for cell biology studies.

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.

Combining Three-Dimensional Quantitative Phase Imaging and Fluorescence Microscopy for the Study of Cell Pathophysiology

Quantitative phase imaging (QPI) has emerged as one of the powerful imaging tools for the study of live cells in a non-invasive manner. In particular, multimodal approaches combining QPI and fluorescence microscopic techniques have been recently developed for superior spatiotemporal resolution as well as high molecular specificity. In this review, we briefly summarize recent advances in three-dimensional QPI combined with fluorescence techniques for the correlative study of cell pathophysiology. Through this review, biologists and clinicians can be provided with insights on this rapidly growing field of research and may find broader applications to investigate unrevealed nature in cell physiology and related diseases.

Iterative optimization in tomographic deconvolution phase microscopy

Tomographic deconvolution phase microscopy (TDPM) is a three-dimensional (3D) quantitative phase imaging (QPI) method using partially coherent light that can be implemented on a commercial microscope platform. However, the measurement procedure is relatively time-consuming because it requires many illumination angles. In the present work, an edge-preserving iterative optimization algorithm is presented and applied to TDPM, so that the required number of illumination angles is reduced from 15 to 3, while the measurement accuracy remains high. In addition, the iterative algorithm does not require matrix representation of operators, so the memory requirement is correspondingly reduced.

Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography

Cells alter the path of light, a fact that leads to well-known aberrations in single cell or tissue imaging. Optical diffraction tomography (ODT) measures the biophysical property that causes these aberrations, the refractive index (RI). ODT is complementary to fluorescence imaging and does not require any markers. The present study introduces RI and fluorescence tomography with optofluidic rotation (RAFTOR) of suspended cells, facilitating the segmentation of the 3D-correlated RI and fluorescence data for a quantitative interpretation of the nuclear RI. The technique is validated with cell phantoms and used to confirm a lower nuclear RI for HL60 cells. Furthermore, the nuclear inversion of adult mouse photoreceptor cells is observed in the RI distribution. The applications shown confirm predictions of previous studies and illustrate the potential of RAFTOR to improve our understanding of cells and tissues.

Tomographic phase microscopy: principles and applications in bioimaging [Invited]

Tomographic phase microscopy (TPM) is an emerging optical microscopic technique for bioimaging. TPM uses digital holographic measurements of complex scattered fields to reconstruct three-dimensional refractive index (RI) maps of cells with diffraction-limited resolution by solving inverse scattering problems. In this paper, we review the developments of TPM from the fundamental physics to its applications in bioimaging. We first provide a comprehensive description of the tomographic reconstruction physical models used in TPM. The RI map reconstruction algorithms and various regularization methods are discussed. Selected TPM applications for cellular imaging, particularly in hematology, are reviewed. Finally, we examine the limitations of current TPM systems, propose future solutions, and envision promising directions in biomedical research.

Phase distribution analysis of tissues based on the off-axis digital holographic hybrid reconstruction algorithm

Off-axis digital holography (DH) has great potential in histopathology for its high efficiency and precision. Phase distribution, usually extracted by the angular spectrum (AS) algorithm from a digital hologram, reflects important structural information of biological tissues. However, the complex structure of tissues introduces spectrum aliasing of the hologram, making the AS algorithm hard to realize and accurate phase analysis difficult to conduct. Here, we present a hybrid reconstruction algorithm, combining Fresnel reconstruction in spatial domain with the AS algorithm in frequency domain, to solve aliasing by spatial filtering. Through simulation, we demonstrate the feasibility and superiority of the hybrid algorithm and verified the precision (10^-3 rad) of the hybrid algorithm with spectrum aliasing. We extract phase distributions from normal urothelial and bladder cancer tissues by the hybrid algorithm and make quantitative analysis through histogram and standard deviation. The result shows the hybrid algorithm in off-axis DH has great advantage for the high-precision phase extraction of tissues and supplies significant information for cancer diagnosis.

Fast label-free microscopy technique for 3D dynamic quantitative imaging of living cells

The refractive index (RI) is an important optical characteristic that is often exploited in label-free microscopy for analysis of biological objects. A technique for 3D RI reconstruction of living cells has to be fast enough to capture the cell dynamics and preferably needs to be compatible with standard wide-field microscopes. To solve this challenging problem, we present a technique that provides fast measurement and processing of data required for real-time 3D visualization of the object RI. Specifically, the 3D RI is reconstructed from the measurement of bright-field intensity images, axially scanned by a high-speed focus tunable lens mounted in front of a sCMOS camera, by using a direct deconvolution approach designed for partially coherent light microscopy in the non-paraxial regime. Both the measurement system and the partially coherent illumination, that provides optical sectioning and speckle-noise suppression, enable compatibility with wide-field microscopes resulting in a competitive and affordable alternative to the current holographic laser microscopes. Our experimental demonstrations show video-rate 3D RI visualization of living bacteria both freely swimming and optically manipulated by using freestyle laser traps allowing for their trapping and transport along 3D trajectories. These results prove that is possible to conduct simultaneous 4D label-free quantitative imaging and optical manipulation of living cells, which is promising for the study of the cell biophysics and biology.

Correlative three-dimensional fluorescence and refractive index tomography: bridging the gap between molecular specificity and quantitative bioimaging

Optical diffraction tomography (ODT) provides label-free three-dimensional (3D) refractive index (RI) measurement of biological samples. However, due to the nature of the RI values of biological specimens, ODT has limited access to molecular specific information. Here, we present an optical setup combining ODT with three-channel 3D fluorescence microscopy, to enhance the molecular specificity of the 3D RI measurement. The 3D RI distribution and 3D deconvoluted fluorescence images of HeLa cells and NIH-3T3 cells are measured, and the cross-correlative analysis between RI and fluorescence of live cells are presented.

Label-free, high-throughput holographic screening and enumeration of tumor cells in blood

We introduce inline digital holographic microscopy (in-line DHM) as a label-free technique for detecting tumor cells in blood. The optimized DHM platform fingerprints every cell flowing through a microchannel at 10 000 cells per second, based on three features - size, maximum intensity and mean intensity. To identify tumor cells in a background of blood cells, we developed robust gating criteria using machine-learning approaches. We established classifiers from the features extracted from 100 000-cell training sets consisting of red blood cells, peripheral blood mononuclear cells and tumor cell lines. The optimized classifier was then applied to targeted features of a single cell in a mixed cell population to make quantitative cell-type predictions. We tested our classification system with tumor cells spiked at different levels into a background of lysed blood that contained predominantly peripheral blood mononuclear cells. Results show that our holographic screening method can readily detect as few as 10 tumor cells per mL, and can identify tumor cells at a false positive rate of at most 0.001%. This purely optical approach obviates the need for antibody labeling and allows large volumes of sample to be quickly processed. Moreover, our in-line DHM approach can be combined with existing circulation tumor cell enrichment strategies, making it a promising tool for label-free analysis of liquid-biopsy samples.

3D imaging of optically cleared tissue using a simplified CLARITY method and on-chip microscopy

High-throughput sectioning and optical imaging of tissue samples using traditional immunohistochemical techniques can be costly and inaccessible in resource-limited areas. We demonstrate three-dimensional (3D) imaging and phenotyping in optically transparent tissue using lens-free holographic on-chip microscopy as a low-cost, simple, and high-throughput alternative to conventional approaches. The tissue sample is passively cleared using a simplified CLARITY method and stained using 3,3'-diaminobenzidine to target cells of interest, enabling bright-field optical imaging and 3D sectioning of thick samples. The lens-free computational microscope uses pixel super-resolution and multi-height phase recovery algorithms to digitally refocus throughout the cleared tissue and obtain a 3D stack of complex-valued images of the sample, containing both phase and amplitude information. We optimized the tissue-clearing and imaging system by finding the optimal illumination wavelength, tissue thickness, sample preparation parameters, and the number of heights of the lens-free image acquisition and implemented a sparsity-based denoising algorithm to maximize the imaging volume and minimize the amount of the acquired data while also preserving the contrast-to-noise ratio of the reconstructed images. As a proof of concept, we achieved 3D imaging of neurons in a 200-μm-thick cleared mouse brain tissue over a wide field of view of 20.5 mm2. The lens-free microscope also achieved more than an order-of-magnitude reduction in raw data compared to a conventional scanning optical microscope imaging the same sample volume. Being low cost, simple, high-throughput, and data-efficient, we believe that this CLARITY-enabled computational tissue imaging technique could find numerous applications in biomedical diagnosis and research in low-resource settings.

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.

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

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

Large population cell characterization using quantitative phase cytometer

A major challenge in cellular analysis is the phenotypic characterization of large cell populations within a short period of time. Among various parameters for cell characterization, the cell dry mass is often used to describe cell size but is difficult to be measured directly with traditional techniques. Here, we propose an interferometric approach based on line-focused beam illumination for high-content precision dry mass measurements of adherent cells in a non-invasive fashion-we call it quantitative phase cytometry (QPC). Besides dry mass, abundant cellular morphological features such as projected area, sphericity, and phase skewness can be readily extracted from the QPC interferometric data. To validate the utility of our technique, we demonstrate characterizing a large population of ∼10⁴ HeLa cells. Our reported QPC system is envisioned as a promising quantitative tool for label-free characterization of a large cell count at single cell resolution. © 2017 International Society for Advancement of Cytometry.

Tomographic flow cytometry by digital holography

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

Phase Uncertainty in Digital Holographic Microscopy Measurements in the Presence of Solution Flow Conditions

Digital holographic microscopy (DHM) is a surface topography measurement technique with reported sub-nanometer vertical resolution. Although it has been made commercially available recently, few studies have evaluated the uncertainty or noise in the phase measurement by the DHM. As current research is using the DHM to monitor surface topography changes of dissolving materials under flowing water conditions, it is necessary to evaluate the effect of water and flow rate on the uncertainty in the measurement. Uncertainty in this study was concerned with the temporal standard deviation per pixel of the reconstructed phase. Considering the effects of solution flow rate, magnification, objective lens type (air or immersion), and experimental configuration, measurements under static conditions in air and in water with an immersion lens yielded the smallest amount of uncertainty (mean of ≤ 0.5 nm up to 40× magnification). Increasing the water flow rate resulted in an increase in mean uncertainty to ≤ 0.6 nm up to 40× with an immersion lens. Observations of a sample through a glass window at 20× magnification in flowing water also yielded increasing uncertainty, with mean values of ≤ 0.5 nm, ≤ 0.8 nm, and ≤ 1.1 nm for flow rates of 0 mL min^-1, 15 mL min^-1, and 33 mL min^-1. Different hologram acquisition rates (12.5 s^-1 and 25 s^-1) did not significantly impact the uncertainty in the phase. Collecting holograms in single-wavelength versus dual-wavelength modes did impact the uncertainty, with the mean uncertainty at 10× magnification for the same wavelength being ≤ 0.5 nm from the single-wavelength mode compared to ≤ 1.5 nm from the dual-wavelength mode. When the quantified uncertainty was applied to simulated dissolution data, lower limits of measured dissolution rates were found below which the measured data may not be distinguishable from the uncertainty in the measurement. The limiting surface-normal dissolution velocity is -10^-11.7 m s^-1 for experiments with an immersion lens in flowing water conditions and -10^-11.7 m s^-1, -10^-11.4 m s^-1, and -10^-11.0 m s^-1 for static (0 mL min^-1), slow (≤ 15 mL min^-1), and fast (≤ 109 mL min^-1) flowing water conditions in experiments with a glass window, respectively. The data presented by this study will allow for better experimental design and methodology for future dissolution or precipitation studies using DHM and will provide confidence in the data produced in postprocessing.

Measuring sickle cell morphology during blood flow

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

Rapid 3D Refractive-Index Imaging of Live Cells in Suspension without Labeling Using Dielectrophoretic Cell Rotation

A major challenge in the field of optical imaging of live cells is achieving rapid, 3D, and noninvasive imaging of isolated cells without labeling. If successful, many clinical procedures involving analysis and sorting of cells drawn from body fluids, including blood, can be significantly improved. A new label-free tomographic interferometry approach is presented. This approach provides rapid capturing of the 3D refractive-index distribution of single cells in suspension. The cells flow in a microfluidic channel, are trapped, and then rapidly rotated by dielectrophoretic forces in a noninvasive and precise manner. Interferometric projections of the rotated cell are acquired and processed into the cellular 3D refractive-index map. Uniquely, this approach provides full (360°) coverage of the rotation angular range around any axis, and knowledge on the viewing angle. The experimental demonstrations presented include 3D, label-free imaging of cancer cells and three types of white blood cells. This approach is expected to be useful for label-free cell sorting, as well as for detection and monitoring of pathological conditions resulting in cellular morphology changes or occurrence of specific cell types in blood or other body fluids.