Active illumination using a digital micromirror device for quantitative phase imaging

Membrane potential as master regulator of cellular mechano-transduction

Membrane potential is a property of all living cells1. Nevertheless, its physiological role in non-excitable cells is poorly understood. Resting membrane potential is typically considered fixed and under tight homeostatic control2. Contrary to this paradigm, we find that membrane potential is a dynamic property that directly reflects mechanical forces acting on the cell and that cells use membrane potential to assess their biomechanical state. We show that several important mechano-sensitive signal transduction pathways, like MAPK and Hippo3-9, are directly controlled by membrane potential and this signaling is mediated by upstream membrane-bound receptors, including FAT1. We further show that mechano-transduction via membrane potential plays a critical role in the homeostasis of epithelial tissues, setting cellular biomass density and cell number density by controlling proliferation and cell elimination. In epithelial scratch wound assays, as well as Xenopus tadpole tail regeneration, we observe a wave of depolarization caused by a drop in cellular biomass density due to mechanical stretch and we show that this depolarization wave is critical for wound closure. Together, these data are explained by a first-principles biophysical model, which demonstrates that membrane potential is physically coupled to mechanical pressure and cellular biomass density. Membrane potential thereby provides a quasi-instantaneous, global readout of the biophysical state of the cell and in turn regulates cell growth, resulting in homeostatic feedback control of biomass density and cell number density in tissues. This interplay may be an ancient mechanism for growth control in multi-cellular organisms and its misregulation may play an important role in tumorigenesis.

Quantitative phase imaging based on holography: trends and new perspectives

In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering solution to a quantitative description of the optical wavefront. After 75 years of development, holographic imaging has become a powerful tool for optical wavefront measurement and quantitative phase imaging. The emergence of this technology has given fresh energy to physics, biology, and materials science. Digital holography (DH) possesses the quantitative advantages of wide-field, non-contact, precise, and dynamic measurement capability for complex-waves. DH has unique capabilities for the propagation of optical fields by measuring light scattering with phase information. It offers quantitative visualization of the refractive index and thickness distribution of weak absorption samples, which plays a vital role in the pathophysiology of various diseases and the characterization of various materials. It provides a possibility to bridge the gap between the imaging and scattering disciplines. The propagation of wavefront is described by the complex amplitude. The complex-value in the complex-domain is reconstructed from the intensity-value measurement by camera in the real-domain. Here, we regard the process of holographic recording and reconstruction as a transformation between complex-domain and real-domain, and discuss the mathematics and physical principles of reconstruction. We review the DH in underlying principles, technical approaches, and the breadth of applications. We conclude with emerging challenges and opportunities based on combining holographic imaging with other methodologies that expand the scope and utility of holographic imaging even further. The multidisciplinary nature brings technology and application experts together in label-free cell biology, analytical chemistry, clinical sciences, wavefront sensing, and semiconductor production.

Single-exposure multi-wavelength optical diffraction tomography based on space-angle dual multiplexing holography

We present a space-angle dual multiplexing holographic recording system for realizing single-exposure multi-wavelength optical diffraction tomographic (ODT) imaging. This system is achieved by combining the principle of single-exposure multi-wavelength holographic imaging technique based on angle-division multiplexing with the principle of single-exposure ODT imaging technique based on microlens array multi-angle illuminations and space-division multiplexing. Compared with the existing multi-wavelength ODT imaging methods, it enables the holographic recording of all the diffraction tomography information of a measured specimen at multiple illumination wavelengths in a single camera exposure without any scan mechanism. Using our proposed data processing method, the multi-wavelength three-dimensional (3D) refractive index tomograms of a specimen can be eventually reconstructed from single recorded multiplexing hologram. Experimental results of a static polystyrene bead and a living C. elegans worm demonstrate the feasibility of this system.

Cell-TIMP: Cellular Trajectory Inference based on Morphological Parameter

Cellular morphology, shaped by various genetic and environmental influences, is pivotal to studying experimental cell biology, necessitating precise measurement and analysis techniques. Traditional approaches, which rely on geometric metrics derived from stained images, encounter obstacles stemming from both the imaging and analytical domains. Staining processes can disrupt the cell's natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Our approach revealed a clear pattern of morphological evolution tied to the diseases' advancement, highlighting the efficacy of our method in identifying functionally significant shape changes where conventional techniques falter. This advancement offers a fresh perspective on analyzing cellular morphology and holds significant potential for the broader research community, enabling a deeper understanding of complex biological dynamics.

Monitoring the Interaction Between Solid Lipid Nanoparticles and Alveolar Macrophages Via the Label-Free Technique

Macrophages are employed as targets for delivering genes, drugs, or lipid nanoparticles into tumors or other specific sites. Studying the interaction between solid lipid nanoparticles (SLNs) and macrophages is essential for assessing nanotoxicity and advancing the development of nanomedicines. However, limited data are currently available on the membrane microstructure and biochemical changes that occur when macrophages interact with SLNs. We conducted a label-free morphological and biochemical investigation of NR8383 macrophages using optical diffraction tomography (ODT), which validated the efficiency of the SLNs as a drug delivery system. ODT provided intracellular holotomography to characterize the macrophages and fluorescence imaging to analyze delivery efficiency. ODT analysis revealed the responses of phagocytic macrophages. Additionally, a quantitative analysis of lipid droplets using refractive indices revealed that, compared with incubation with normal cells, incubation with SLNs significantly increased the lipid droplet volume and surface area. The uptake of SLNs into macrophages resulted in increased cell volume, surface area, and concentration, which indicated greater morphological and biochemical variability in the treated cells than in the control cells. The results suggest that ODT imaging is promising for understanding the intracellular distribution of SLNs and useful for validating the efficacy of delivery of SLNs to macrophages.

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.

Optical proximity correction of hot-spot patterns with subwavelength size in DMD maskless projection lithography

When the critical dimension (CD) of resist patterns nears the resolution limit of the digital micromirror device (DMD) maskless projection lithography (DMD-MPL), significant distortion can emerge in the silicon wafer due to the optical proximity effect (OPE). The significant distortion (breakpoints, line-end scaling, corner rounding, etc.) between resist patterns and target patterns results in reduced lithographic quality. To address this issue, we have proposed a pixel-based optical proximity correction (PB-OPC) method used for the hot-spot patterns with subwavelength sizes specifically designed for DMD-MPL. Employing an end-to-end learning neural network, the PB-OPC algorithm is both straightforward and efficient. A well-trained U-net framework facilitates the mapping from unoptimized masks to optimized masks. Experimental exposure trials have demonstrated that this method not only corrects OPC in general patterns but also effectively rectifies hot-spot patterns. The pattern error (PE) value can be reduced by about 30% in the design layouts. We believe this approach holds the potential to enhance the resolution and fidelity of resist patterns in DMD maskless lithography.

High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy

A critical requirement for studying cell mechanics is three-dimensional assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), which simultaneously quantifies the volumetric morphology and traction force of cells using a high-speed illumination scheme with 0.5-Hz temporal resolution. Without labelling, our method enables quantitative analyses of dry-mass distributions and shear (in-plane) and normal (out-of-plane) tractions of single cells on the extracellular matrix. When combined with a constrained total variation-based deconvolution algorithm, it provides 0.55-Pa shear and 1.59-Pa normal traction sensitivity for a 1-kPa hydrogel substrate. We demonstrate its utility by assessing the effects of compromised intracellular stress and capturing the rapid dynamics of cellular junction formation in the spatiotemporal changes in non-planar traction components.

Multi-harmonic structured illumination-based optical diffraction tomography

Imaging speed and spatial resolution are key factors in optical diffraction tomography (ODT), while they are mutually exclusive in 3D refractive index imaging. This paper presents a multi-harmonic structured illumination-based optical diffraction tomography (MHSI-ODT) to acquire 3D refractive index (RI) maps of transparent samples. MHSI-ODT utilizes a digital micromirror device (DMD) to generate structured illumination containing multiple harmonics. For each structured illumination orientation, four spherical spectral crowns are solved from five phase-shifted holograms, meaning that the acquisition of each spectral crown costs 1.25 raw images. Compared to conventional SI-ODT, which retrieves two spectral crowns from three phase-shifted raw images, MHSI-ODT enhances the imaging speed by 16.7% in 3D RI imaging. Meanwhile, MHSI-ODT exploits both the 1st-order and the 2nd-order harmonics; therefore, it has a better intensity utilization of structured illumination. We demonstrated the performance of MHSI-ODT by rendering the 3D RI distributions of 5 µm polystyrene (PS) microspheres and biological samples.

Loss of p53 function promotes DNA damage-induced formation of nuclear actin filaments

Tumor suppressor p53 plays a central role in response to DNA damage. DNA-damaging agents modulate nuclear actin dynamics, influencing cell behaviors; however, whether p53 affects the formation of nuclear actin filaments remains unclear. In this study, we found that p53 depletion promoted the formation of nuclear actin filaments in response to DNA-damaging agents, such as doxorubicin (DOXO) and etoposide (VP16). Even though the genetic probes used for the detection of nuclear actin filaments exerted a promotive effect on actin polymerization, the detected formation of nuclear actin filaments was highly dependent on both p53 depletion and DNA damage. Whilst active p53 is known to promote caspase-1 expression, the overexpression of caspase-1 reduced DNA damage-induced formation of nuclear actin filaments in p53-depleted cells. In contrast, co-treatment with DOXO and the pan-caspase inhibitor Q-VD-OPh or the caspase-1 inhibitor Z-YVAD-FMK induced the formation of nuclear actin filament formation even in cells bearing wild-type p53. These results suggest that the p53-caspase-1 axis suppresses DNA damage-induced formation of nuclear actin filaments. In addition, we found that the expression of nLifeact-GFP, the filamentous-actin-binding peptide Lifeact fused with the nuclear localization signal (NLS) and GFP, modulated the structure of nuclear actin filaments to be phalloidin-stainable in p53-depleted cells treated with the DNA-damaging agent, altering the chromatin structure and reducing the transcriptional activity. The level of phosphorylated H2AX (γH2AX), a marker of DNA damage, in these cells also reduced upon nLifeact-GFP expression, whilst details of the functional relationship between the formation of nLifeact-GFP-decorated nuclear actin filaments and DNA repair remained to be elucidated. Considering that the loss of p53 is associated with cancer progression, the results of this study raise a possibility that the artificial reinforcement of nuclear actin filaments by nLifeact-GFP may enhance the cytotoxic effect of DNA-damaging agents in aggressive cancer cells through a reduction in gene transcription.

Compressive video via IR-pulsed illumination

We propose and demonstrate a compressive temporal imaging system based on pulsed illumination to encode temporal dynamics into the signal received by the imaging sensor during exposure time. Our approach enables >10x increase in effective frame rate without increasing camera complexity. To mitigate the complexity of the inverse problem during reconstruction, we introduce two keyframes: one before and one after the coded frame. We also craft what we believe to be a novel deep learning architecture for improved reconstruction of the high-speed scenes, combining specialized convolutional and transformer architectures. Simulation and experimental results clearly demonstrate the reconstruction of high-quality, high-speed videos from the compressed data.

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.

Three-dimensional refractive index estimation based on deep-inverse non-interferometric optical diffraction tomography (ODT-Deep)

Optical diffraction tomography (ODT) solves an inverse scattering problem to obtain label-free, 3D refractive index (RI) estimation of biological specimens. This work demonstrates 3D RI retrieval methods suitable for partially-coherent ODT systems supported by intensity-only measurements consisting of axial and angular illumination scanning. This framework allows for access to 3D quantitative RI contrast using a simplified non-interferometric technique. We consider a traditional iterative tomographic solver based on a multiple in-plane representation of the optical scattering process and gradient descent optimization adapted for focus-scanning systems, as well as an approach that relies solely on 3D convolutional neural networks (CNNs) to invert the scattering process. The approaches are validated using simulations of the 3D scattering potential for weak phase 3D biological samples.

CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

Quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. In particular, three-dimensional (3D) tomographic imaging of live cells has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw 3D tomograms is not well-developed. This work focuses on a critical, yet often underappreciated, step of the analysis pipeline, that of 3D cell segmentation from the acquired tomograms. The current method employed for such tasks is the Otsu-based 3D watershed algorithm, which works well for isolated cells; however, it is very challenging to draw boundaries when the cells are clumped. This process is also memory intensive since the processing requires computation on a 3D stack of images. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the segmentation of QPI images, which outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 seconds per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused segmentation tools. We envision our work will lead to the broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Kudoa septempunctata Spores Cause Acute Gastroenteric Symptoms in Mouse and Musk Shrew Models as Evidenced In Vitro in Human Colon Cells

Kudoa septempunctata is a myxosporean parasite that infects the trunk muscles of olive flounder (Paralichthys olivaceus) and has been reported to cause foodborne illnesses in humans. However, the molecular mechanisms underlying K. septempunctata spore toxicity remain largely unknown. In this study, the gastroenteropathy of K. septempunctata was examined in human colon adenocarcinoma cells as well as experimental mice inoculated with spores. We found that K. septempunctata decreased transepithelial resistance and disrupted epithelial tight junctions by deleting ZO-1 in Caco-2 monolayers. Additionally, serotonin (5-HT), an emetic neurotransmitter, was increased in K. septempunctata-inoculated cells. In vivo, K. septempunctata spores induced diarrhea in suckling mice (80% in ddY and 70% in ICR mice), with a minimum provocative dose of 2 × 10⁵ K. septempunctata spores. In house musk shrews, K. septempunctata induced emesis within 1 h and induced serotonin secretion in the intestinal epithelium. In conclusion, K. septempunctata may induce diarrhea and emesis by increasing intestinal permeability and serotonin secretion.

RNA-mediated demixing transition of low-density condensates

Biomolecular condensates play a key role in organizing cellular reactions by concentrating a specific set of biomolecules. However, whether condensate formation is accompanied by an increase in the total mass concentration within condensates or by the demixing of already highly crowded intracellular components remains elusive. Here, using refractive index imaging, we quantify the mass density of several condensates, including nucleoli, heterochromatin, nuclear speckles, and stress granules. Surprisingly, the latter two condensates exhibit low densities with a total mass concentration similar to the surrounding cyto- or nucleoplasm. Low-density condensates display higher permeability to cellular protein probes. We find that RNA tunes the biomolecular density of condensates. Moreover, intracellular structures such as mitochondria heavily influence the way phase separation proceeds, impacting the localization, morphology, and growth of condensates. These findings favor a model where segregative phase separation driven by non-associative or repulsive molecular interactions together with RNA-mediated selective association of specific components can give rise to low-density condensates in the crowded cellular environment.

Parallel synthetic aperture transport-of-intensity diffraction tomography with annular illumination

Transport-of-intensity diffraction tomography (TIDT) is a recently developed label-free computational microscopy technique that retrieves high-resolution three-dimensional (3D) refractive index (RI) distribution of biological specimens from 3D intensity-only measurements. However, the non-interferometric synthetic aperture in TIDT is generally achieved sequentially through the acquisition of a large number of through-focus intensity stacks captured at different illumination angles, resulting in a very cumbersome and redundant data acquisition process. To this end, we present a parallel implementation of a synthetic aperture in TIDT (PSA-TIDT) with annular illumination. We found that the matched annular illumination provides a mirror-symmetric 3D optical transfer function, indicating the analyticity in the upper half-plane of the complex phase function, which allows for recovery of the 3D RI from a single intensity stack. We experimentally validated PSA-TIDT by conducting high-resolution tomographic imaging of various unlabeled biological samples, including human breast cancer cell lines (MCF-7), human hepatocyte carcinoma cell lines (HepG2), Henrietta Lacks (HeLa) cells, and red blood cells (RBCs).

Regularization of dielectric tensor tomography

Dielectric tensor tomography reconstructs the three-dimensional dielectric tensors of microscopic objects and provides information about the crystalline structure orientations and principal refractive indices. Because dielectric tensor tomography is based on transmission measurement, it suffers from the missing cone problem, which causes poor axial resolution, underestimation of the refractive index, and halo artifacts. In this study, we study the application of total variation and positive semi-definiteness regularization to three-dimensional tensor distributions. In particular, we demonstrate the reduction of artifacts when applied to dielectric tensor tomography.

Using an acousto-optic modulator as a fast spatial light modulator

High-speed spatial light modulators (SLM) are crucial components for free-space communication and structured illumination imaging. Current approaches for dynamical spatial mode generation, such as liquid crystal SLMs or digital micromirror devices, are limited to a maximum pattern refresh rate of 10 kHz and have a low damage threshold. We demonstrate that arbitrary spatial profiles in a laser pulse can be generated by mapping the temporal radio-frequency (RF) waveform sent to an acousto-optic modulator (AOM) onto the optical field. We find that the fidelity of the SLM performance can be improved through numerical optimization of the RF waveform to overcome the nonlinear effect of AOM. An AOM can thus be used as a 1-dimensional SLM, a technique we call acousto-optic spatial light modulator (AO-SLM), which has 50 µm pixel pitch, over 1 MHz update rate, and high damage threshold. We simulate the application of AO-SLM to single-pixel imaging, which can reconstruct a 32×32 pixel complex object at a rate of 11.6 kHz with 98% fidelity.

Standardizing image assessment in optical diffraction tomography

Optical diffraction tomography (ODT) has gradually become a popular label-free imaging technique that offers diffraction-limited resolution by mapping an object's three-dimensional (3D) refractive index (RI) distribution. However, there is a lack of comprehensive quantitative image assessment metrics in ODT for studying how various experimental conditions influence image quality, and subsequently optimizing the experimental conditions. In this Letter, we propose to standardize the image assessment in ODT by proposing a set of metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and structural distinguishability (SD). To test the feasibility of the metrics, we performed experiments on angle-scanning ODT by varying the number of illumination angles, RI contrast of samples, sample feature sizes, and sample types (e.g., standard polystyrene beads and 3D printed structures) and evaluated the RI tomograms with SNR, CNR, and SD. We further quantitatively studied how image quality can be improved, and tested the image assessment metrics on subcellular structures of living cells. We envision the proposed image assessment metrics may greatly benefit end-users for assessing the RI tomograms, as well as experimentalists for optimizing ODT instruments.

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.

Spatially multiplexed dielectric tensor tomography

Dielectric tensor tomography (DTT) enables the reconstruction of three-dimensional (3D) dielectric tensors, which provides a physical measure of 3D optical anisotropy. Herein, we present a cost-effective and robust method of DTT using spatial multiplexing. Exploiting two orthogonally polarized reference beams with different angles in an off-axis interferometer, two polarization-sensitive interferograms were multiplexed and recorded using a single camera. Then, the two interferograms were demultiplexed in the Fourier domain. By measuring the polarization-sensitive fields for various illumination angles, 3D dielectric tensor tomograms were reconstructed. The proposed method was experimentally demonstrated by reconstructing the 3D dielectric tensors of various liquid-crystal (LC) particles with radial and bipolar orientational configurations.

High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching

Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, the effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

Label-Free Morpho-Molecular Imaging for Studying the Differential Interaction of Black Phosphorus with Tumor Cells

Black phosphorus nanosheets (2D BP) are emerging as very promising, highly selective chemotherapeutic agents due to their fast degradation in the intracellular matrix of cancer cells. Here, optical diffraction tomography (ODT) and Raman spectroscopy were exploited as a powerful label-free approach to achieve integrated insights into the processes accompanying the administration of exfoliated 2D BP flakes in human prostatic adenocarcinoma and normal human prostate epithelial cells. Our ODT experiments provided unambiguous visualization of the 2D BP internalization in cancer cells and the morphological modifications of those cells in the apoptotic phase. The cellular internalization and damaging occurred, respectively, 18 h and 36–48 h after the 2D BP administration. Changes in the chemical properties of the internalized 2D BP flakes were monitored by Raman spectroscopy. Interestingly, a fast oxidation process of the 2D BP flakes was activated in the intracellular matrix of the cancer cells after 24 h of incubation. This was in sharp contrast to the low 2D BP uptake and minimal chemical changes observed in the normal cells. Along with the understanding of the 2D BP fate in the cancer cells, the proposed label-free morpho-molecular approach offers a powerful, rapid tool to study the pharmacokinetic properties of engineered nanomaterials in preclinical research.

Fast and pure phase-shifting off-axis holographic microscopy with a digital micromirror device

We present a phase-shifting digital holographic microscopy technique, where a digital micromirror device enables to perform a precise phase-only shift of the reference wave. By coupling the beam into a monomode fiber, we obtain a laser mode with a constant phase shift, equally acting on all pixels of the hologram. This method has the advantage of being relatively simple and compatible with high frame rate cameras, which makes it of great interest for the observation of fast phenomena. We demonstrate the validity of the technique in an off-axis configuration by imaging living paramecia caudata.

Tomographic measurement of dielectric tensors at optical frequency

The dielectric tensor is a physical descriptor of fundamental light-matter interactions, characterizing anisotropic materials with principal refractive indices and optic axes. Despite its importance in scientific and industrial applications ranging from material science to soft matter physics, the direct measurement of the three-dimensional dielectric tensor has been limited by the vectorial and inhomogeneous nature of light scattering from anisotropic materials. Here, we present a dielectric tensor tomographic approach to directly measure dielectric tensors of anisotropic structures including the spatial variations of principal refractive indices and directors. The anisotropic structure is illuminated with a polarized plane wave with various angles and polarization states. Then, the scattered fields are holographically measured and converted into vectorial diffracted field components. Finally, by inversely solving a vectorial wave equation, the three-dimensional dielectric tensor is reconstructed. Using this approach, we demonstrate quantitative tomographic measurements of various nematic liquid-crystal structures and their fast three-dimensional non-equilibrium dynamics.

Accelerated Fourier ptychographic diffraction tomography with sparse annular LED illuminations

Fourier ptychographic diffraction tomography (FPDT) is a recently developed label-free computational microscopy technique that retrieves high-resolution and large-field three-dimensional (3D) tomograms by synthesizing a set of low-resolution intensity images obtained with a low numerical aperture (NA) objective. However, in order to ensure sufficient overlap of Ewald spheres in 3D Fourier space, conventional FPDT requires thousands of intensity measurements and consumes a significant amount of time for stable convergence of the iterative algorithm. Herein, we present accelerated Fourier ptychographic diffraction tomography (aFPDT), which combines sparse annular light-emitting diode (LED) illuminations and multiplexing illumination to significantly decrease data amount and achieve computational acceleration of 3D refractive index (RI) tomography. Compared with existing FPDT technique, the equivalent high-resolution 3D RI results are obtained using aFPDT with reducing data requirement by more than 40 times. The validity of the proposed method is experimentally demonstrated on control samples and various biological cells, including polystyrene beads, unicellular algae and clustered HeLa cells in a large field of view. With the capability of high-resolution and high-throughput 3D imaging using small amounts of data, aFPDT has the potential to further advance its widespread applications in biomedicine.

Interactions of Nanoparticles with Macrophages and Feasibility of Drug Delivery for Asthma

Understanding the interaction between nanoparticles and immune cells is essential for the evaluation of nanotoxicity and development of nanomedicines. However, to date, there is little data on the membrane microstructure and biochemical changes in nanoparticle-loaded immune cells. In this study, we observed the microstructure of nanoparticle-loaded macrophages and changes in lipid droplets using holotomography analysis. Quantitatively analyzing the refractive index distribution of nanoparticle-loaded macrophages, we identified the interactions between nanoparticles and macrophages. The results showed that, when nanoparticles were phagocytized by macrophages, the number of lipid droplets and cell volume increased. The volume and mass of the lipid droplets slightly increased, owing to the absorption of nanoparticles. Meanwhile, the number of lipid droplets increased more conspicuously than the other factors. Furthermore, alveolar macrophages are involved in the development and progression of asthma. Studies have shown that macrophages play an essential role in the maintenance of asthma-related inflammation and tissue damage, suggesting that macrophage cells may be applied to asthma target delivery strategies. Therefore, we investigated the target delivery efficiency of gold nanoparticle-loaded macrophages at the biodistribution level, using an ovalbumin-induced asthma mouse model. Normal and severe asthma models were selected to determine the difference in the level of inflammation in the lung. Consequently, macrophages had increased mobility in models of severe asthma, compared to those of normal asthma disease. In this regard, the detection of observable differences in nanoparticle-loaded macrophages may be of primary interest, as an essential endpoint analysis for investigating nanomedical applications and immunotheragnostic strategies.

Adaptive Digital Hologram Binarization Method Based on Local Thresholding, Block Division and Error Diffusion

High-speed optical reconstruction of 3D-scenes can be achieved using digital holography with binary digital micromirror devices (DMD) or a ferroelectric spatial light modulator (fSLM). There are many algorithms for binarizing digital holograms. The most common are methods based on global and local thresholding and error diffusion techniques. In addition, hologram binarization is used in optical encryption, data compression, beam shaping, 3D-displays, nanofabrication, materials characterization, etc. This paper proposes an adaptive binarization method based on a combination of local threshold processing, hologram division into blocks, and error diffusion procedure (the LDE method). The method is applied for binarization of optically recorded and computer-generated digital holograms of flat objects and three-dimensional scenes. The quality of reconstructed images was compared with different methods of error diffusion and thresholding. Image reconstruction quality was up to 22% higher by various metrics than that one for standard binarization methods. The optical hologram reconstruction using DMD confirms the results of the numerical simulations.

Optical Diffraction Tomography Using Nearly In-Line Holography with a Broadband LED Source

We present optical tomography methods for a 3D refractive index reconstruction of weakly scattering objects using LED light sources. We are able to record holograms by minimizing the optical path difference between the signal and reference beams while separating the scattered field from its twin image. We recorded multiple holograms by illuminating the LEDs sequentially and reconstructed the 3D refractive index reconstruction of the sample. The reconstructions show high signal-to-noise ratio in which the effect of speckle artifacts is highly minimized due to the partially incoherent illumination of the LEDs. Results from combining different illumination wavelengths are also described demonstrating higher acquisition speed.

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.

Label-free monitoring of 3D cortical neuronal growth in vitro using optical diffraction tomography

The highly complex central nervous systems of mammals are often studied using three-dimensional (3D) in vitro primary neuronal cultures. A coupled confocal microscopy and immunofluorescence labeling are widely utilized for visualizing the 3D structures of neurons. However, this requires fixation of the neurons and is not suitable for monitoring an identical sample at multiple time points. Thus, we propose a label-free monitoring method for 3D neuronal growth based on refractive index tomograms obtained by optical diffraction tomography. The 3D morphology of the neurons was clearly visualized, and the developmental processes of neurite outgrowth in 3D spaces were analyzed for individual neurons.

High-speed Fourier ptychographic microscopy for quantitative phase imaging

Fourier ptychographic microscopy (FPM), as an emerging computational imaging method, has been applied to quantitative phase imaging with resolution bypassing the physical limit of the detection objective. Due to the weak illumination intensity and long image acquisition time, the achieved imaging speed in current FPM methods is still low, making them unsuitable for real-time imaging applications. We propose and demonstrate a high-speed FPM method based on using laser illumination and digital micro-mirror devices for illumination angle scanning. In this new, to the best of our knowledge, FPM method, we realized quantitative phase imaging and intensity imaging at over 42 frames per second (fps) with around 1 µm lateral resolution. The quantitative phase images have revealed membrane height fluctuations of red blood cells with nanometer-scale sensitivity, while the intensity images have resolved subcellular features in stained cancer tissue slices.

High speed, complex wavefront shaping using the digital micro-mirror device

Digital micro-mirror devices (DMDs) have been deployed in many optical applications. As compared to spatial light modulators (SLMs), they are characterized by their much faster refresh rates (full-frame refresh rates up to 32 kHz for binary patterns) compared to 120 Hz for most liquid crystal SLMs. DMDs however can only display binary, unipolar patterns and utilize temporal modulation to represent with excellent accuracy multiple gray-levels in display applications. We used the built-in time domain dynamic range representation of the DMD to project 8-bit complex-fields. With this method, we demonstrated 8-bit complex field modulation with a frame time of 38.4 ms (around 0.15 s for the entire complex-field). We performed phase conjugation by compensating the distortions incurred due to propagation through free-space and a scattering medium. For faster modulation speed, an electro-optic modulator was used in synchronization with the DMD in an amplitude modulation mode to create grayscale patterns with frame rate ~ 833 Hz with display time of only 1.2 ms instead of 38.4 ms for time multiplexing gaining a speed up by a factor of 32.

Isotropically resolved label-free tomographic imaging based on tomographic moulds for optical trapping

A major challenge in three-dimensional (3D) microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states. In conventional 3D microscopy, axial resolution is inferior to spatial resolution due to the inaccessibility to side scattering signals. In this study, we demonstrate the isotropic microtomography of free-floating samples by optically rotating a sample. Contrary to previous approaches using optical tweezers with multiple foci which are only applicable to simple shapes, we exploited 3D structured light traps that can stably rotate freestanding complex-shaped microscopic specimens, and side scattering information is measured at various sample orientations to achieve isotropic resolution. The proposed method yields an isotropic resolution of 230 nm and captures structural details of colloidal multimers and live red blood cells, which are inaccessible using conventional tomographic microscopy. We envision that the proposed approach can be deployed for solving diverse imaging problems that are beyond the examples shown here.

Detection of intracellular monosodium urate crystals in gout synovial fluid using optical diffraction tomography

Optical diffraction tomography (ODT) enables imaging of unlabeled intracellular components by measuring the three-dimensional (3D) refractive index (RI). We aimed to detect intracellular monosodium urate (MSU) crystals in synovial leukocytes derived from gout patients using ODT. The 3D RI values of the synthetic MSU crystals, measured by ODT, ranged between 1.383 and 1.440. After adding synthetic MSU crystals to a macrophage, RI tomograms were reconstructed using ODT, and the reconstructed RI tomograms discerned intracellular and extracellular MSU crystals. We observed unlabeled synthetic MSU crystal entry into the cytoplasm of a macrophage through time-lapse imaging. Furthermore, using gout patient-derived synovial leukocytes, we successfully obtained RI tomogram images of intracellular MSU crystals. The 3D RI identification of MSU crystals was verified with birefringence through polarization-sensitive ODT measurements. Together, our results provide evidence that this novel ODT can identify birefringent MSU crystals in synovial leukocytes of patients with gout.

Holographic tomography: techniques and biomedical applications [Invited]

Holographic tomography (HT) is an advanced label-free optical microscopic imaging method used for biological studies. HT uses digital holographic microscopy to record the complex amplitudes of a biological sample as digital holograms and then numerically reconstruct the sample's refractive index (RI) distribution in three dimensions. The RI values are a key parameter for label-free bio-examination, which correlate with metabolic activities and spatiotemporal distribution of biophysical parameters of cells and their internal organelles, tissues, and small-scale biological objects. This article provides insight on this rapidly growing HT field of research and its applications in biology. We present a review summary of the HT principle and highlight recent technical advancement in HT and its applications.

Refractive Index Changes of Cells and Cellular Compartments Upon Paraformaldehyde Fixation Acquired by Tomographic Phase Microscopy

Three-dimensional quantitative phase imaging is an emerging method, which provides the 3D distribution of the refractive index (RI) and the dry mass in live and fixed cells as well as in tissues. However, an insufficiently answered question is the influence of chemical cell fixation procedures on the results of RI reconstructions. Therefore, this work is devoted to systematic investigations on the RI in cellular organelles of live and fixed cells including nucleus, nucleolus, nucleoplasm, and cytoplasm. The research was carried out on four different cell lines using a common paraformaldehyde (PFA)-based fixation protocol. The selected cell types represent the diversity of mammalian cells and therefore the results presented provide a picture of fixation caused RI changes in a broader context. A commercial Tomocube HT-1S device was used for 3D RI acquisition. The changes in the RI values after the fixation process are detected in the reconstructed phase distributions and amount to the order of 10-3 . The RI values decrease and the observed RI changes are found to be different between various cell lines; however, all of them show the most significant loss in the nucleolus. In conclusion, our study demonstrates the evident need for standardized preparation procedures in phase tomographic measurements. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.

Correlation of dynamic membrane fluctuations in red blood cells with diabetes mellitus and cardiovascular risks

The rheological and physiological properties of red blood cells (RBCs) are affected by many factors in the vascular environment. Among them, membrane fluctuations (MFs), particularly dynamic fluctuations in RBC cell membrane thickness (RBC-MFs), are likely to be altered by the level of glycation of haemoglobin in patients with diabetes mellitus (DM). We investigated the associations of RBC-MFs with physiological variables associated with DM and cardiovascular diseases (CVDs). Forty-one healthy control subjects and 59 patients with DM were enrolled. Five-microliter samples of blood were collected and diluted 400 times. To measure the RBC-MFs, holotomography was used, which non-invasively and precisely analyses the optical characteristics of RBCs. Associations between the RBC-MFs and biochemical parameters related to glucose homeostasis and lipid profiles were investigated. Independent associations of the RBC-MFs with the presence of CVDs were also analysed. RBC-MFs were lower in patients with DM than in healthy participants (61.64 ± 7.49 nm vs 70.65 ± 6.65 nm, P = 1.4 × 10⁻⁸). RBC-MFs correlated modestly with glycated haemoglobin level (ρ = − 0.47) and weakly with age (ρ = − 0.36), duration of diabetes (ρ = − 0.36), fasting plasma glucose level (ρ = − 0.37), and the 10-year Framingham risk score (ρ = − 0.38) (all P < 0.05). Low RBC-MFs were independently associated with the presence of CVDs after adjusting for CVD risk factors. The weak but significant associations of RBC-MFs with cardiometabolic risk factors and CVDs suggest that such deformity of circulating RBCs may be a useful marker of vascular complications of DM.

Optimizing illumination in three-dimensional deconvolution microscopy for accurate refractive index tomography

In light transmission microscopy, axial scanning does not directly provide tomographic reconstruction of specimen. Phase deconvolution microscopy can convert a raw intensity image stack into a refractive index tomogram, the intrinsic sample contrast which can be exploited for quantitative morphological analysis. However, this technique is limited by reconstruction artifacts due to unoptimized optical conditions, which leads to a sparse and non-uniform optical transfer function. Here, we propose an optimization method based on simulated annealing to systematically obtain optimal illumination schemes that enable artifact-free deconvolution. The proposed method showed precise tomographic reconstruction of unlabeled biological samples.

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

High-speed programmable lithium niobate thin film spatial light modulator

High-speed spatial modulation of light is the key technology in various applications, such as optical communications, imaging through scattering media, video projection, pulse shaping, and beam steering, in which spatial light modulators (SLMs) are the underpinning devices. Conventional SLMs, such as liquid crystal (LC), digital micromirror device (DMD), and micro-electro-mechanical system (MEMS) ones, operate at a typical speed on the order of several kilohertz as limited by the slow response of the pixels. Achieving high-speed spatial modulation is still challenging and highly desired. Here, we demonstrate a one-dimensional (1D) high-speed programmable spatial light modulator based on the electro-optic effect in lithium niobate thin film, which achieves a low driving voltage of 10 V and an overall high-speed modulation speed of 5 MHz. Furthermore, we transfer an image by using parallel data transmission based on the proposed lithium niobate SLM as a proof-of-principle demonstration. Our device exhibits improved performance over traditional SLMs and opens new avenues for future high-speed and real-time applications, such as light detection and ranging (LiDAR), pulse shaping, and beam steering.

Label-free three-dimensional observations and quantitative characterisation of on-chip vasculogenesis using optical diffraction tomography

Label-free, three-dimensional (3D) quantitative observations of on-chip vasculogenesis were achieved using optical diffraction tomography. Exploiting 3D refractive index maps as an intrinsic imaging contrast, the vascular structures, multicellular activities, and subcellular organelles of endothelial cells were imaged and analysed throughout vasculogenesis to characterise mature vascular networks without exogenous labelling.

3D reconstruction of weakly scattering objects from 2D intensity-only measurements using the Wolf transform

A new approach to optical diffraction tomography (ODT) based on intensity measurements is presented. By applying the Wolf transform directly to intensity measurements, we observed unexpected behavior in the 3D reconstruction of the sample. Such a reconstruction does not explicitly represent a quantitative measure of the refractive index of the sample; however, it contains interesting qualitative information. This 3D reconstruction exhibits edge enhancement and contrast enhancement for nanostructures compared with the conventional 3D refractive index reconstruction and thus could be used to localize nanoparticles such as lipids inside a biological sample.

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.

Iterative optical diffraction tomography for illumination scanning configuration

Optical diffraction tomography (ODT) is used to reconstruct refractive-index distributions from multiple measurements in the object rotating configuration (ORC) or the illumination scanning configuration (ISC). Because of its fast data acquisition and stability, ISC-based ODT has been widely used for biological imaging. ODT typically fails to reconstruct multiply-scattering samples. The previously developed iterative ODT (iODT) was for the multiply-scattering objects in ORC, and could not be directly applied to ISC. To resolve this mismatch, we developed an ISC update and numerically demonstrated its accuracy. With the same prior knowledge, iODT-ISC outperforms conventional ODT in resolving the missing-angle problem.

Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells

The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.

Three-dimensional label-free imaging and quantification of migrating cells during wound healing

The wound-healing assay is a simple but effective tool for studying collective cell migration (CCM) that is widely used in biophysical studies and high-throughput screening. However, conventional imaging and analysis methods only address two-dimensional (2D) properties in a wound healing assay, such as gap closure rate. This is unfortunate because biological cells are complex 3D structures, and their dynamics provide significant information about cell physiology. Here, we presented 3D label-free imaging for wound healing assays and investigated the 3D dynamics of CCM using optical diffraction tomography. High-resolution subcellular structures as well as their collective dynamics were imaged and analyzed quantitatively.

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.