Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling

Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifications of organelle cross-regulation that are triggered by infection and syncytia formation. Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides an Artificial Intelligence-enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.

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

Significance

Quantitative phase imaging (QPI) techniques offer intrinsic information about the sample of interest in a label-free, noninvasive manner and have an enormous potential for wide biomedical applications with negligible perturbations to the natural state of the sample in vitro.

Aim

We aim to present an in-depth review of the scattering formulation of light-matter interactions as applied to biological samples such as cells and tissues, discuss the relevant quantitative phase measurement techniques, and present a summary of various reported applications.

Approach

We start with scattering theory and scattering properties of biological samples followed by an exploration of various microscopy configurations for 2D QPI for measurement of structure and dynamics.

Results

We reviewed 157 publications and presented a range of QPI techniques and discussed suitable applications for each. We also presented the theoretical frameworks for phase reconstruction associated with the discussed techniques and highlighted their domains of validity.

Conclusions

We provide detailed theoretical as well as system-level information for a wide range of QPI techniques. Our study can serve as a guideline for new researchers looking for an exhaustive literature review of QPI methods and relevant applications.

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.

EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report EVATOM: a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates

OBJECTIVES

Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva.

METHODS

Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR).

RESULTS

SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered.

CONCLUSIONS

Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

Roadmap on Label-Free Super-Resolution Imaging

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.

MGCBFormer: The multiscale grid-prior and class-inter boundary-aware transformer for polyp segmentation

The polyp segmentation technology based on deep learning could better and faster help doctors diagnose the polyps in the intestinal wall, which are predecessors of colorectal cancer. Mainstream polyp segmentation methods are implemented under full supervision. For these methods, expensive and precious pixel-level labels couldn't be utilized sufficiently, and it's a deviation direction to strengthen the feature expression only using the more powerful backbone network instead of fully mining existing polyp target information. To address the situation, the multiscale grid-prior and class-inter boundary-aware transformer (MGCBFormer) is proposed. MGCBFormer is composed of highly interpretable components: 1) the multiscale grid-prior and nested channel attention block (MGNAB) for seeking the optimal feature expression, 2) the class-inter boundary-aware block (CBB) for focusing on the foreground boundary and fully inhibiting the background boundary by combining the boundary preprocessing strategy, 3) reasonable deep supervision branches and noise filters called the global double-axis association coupler (GDAC). Numerous persuasive experiments are conducted on five public polyp datasets (Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS-LaribPolypDB) comparing with twelve methods of polyp segmentation, and demonstrate the superior predictive performance and generalization ability of MGCBFormer over the state-of-the-art polyp segmentation methods.

Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity

BACKGROUND

The clinical spectrum of acute SARS-CoV-2 infection ranges from an asymptomatic to life-threatening disease. Considering the broad spectrum of severity, reliable biomarkers are required for early risk stratification and prediction of clinical outcomes. Despite numerous efforts, no COVID-19-specific biomarker has been established to guide further diagnostic or even therapeutic approaches, most likely due to insufficient validation, methodical complexity, or economic factors. COVID-19-associated coagulopathy is a hallmark of the disease and is mainly attributed to dysregulated immunothrombosis. This process describes an intricate interplay of platelets, innate immune cells, the coagulation cascade, and the vascular endothelium leading to both micro- and macrothrombotic complications. In this context, increased levels of immunothrombotic components, including platelet and platelet-leukocyte aggregates, have been described and linked to COVID-19 severity.

METHODS

Here, we describe a label-free quantitative phase imaging approach, allowing the identification of cell-aggregates and their components at single-cell resolution within 30 min, which prospectively qualifies the method as point-of-care (POC) testing.

RESULTS

We find a significant association between the severity of COVID-19 and the amount of platelet and platelet-leukocyte aggregates. Additionally, we observe a linkage between severity, aggregate composition, and size distribution of platelets in aggregates.

CONCLUSIONS

This study presents a POC-compatible method for rapid quantitative analysis of blood cell aggregates in patients with COVID-19.

Artificial intelligence-enabled quantitative phase imaging methods for life sciences

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

Quantitative phase imaging for characterization of single cell growth dynamics

Quantitative phase imaging (QPI) has emerged as an indispensable tool in the field of biomedicine, offering the ability to obtain quantitative maps of phase changes due to optical path length delays without the need for contrast agents. These maps provide valuable information about cellular morphology and dynamics, unperturbed by the introduction of exogenous substances. In this review, a summary of recent studies that have focused on elucidating the growth dynamics of individual cells using QPI is presented. Specifically, investigations into cellular changes occurring during mitosis, the differentiation of cellular organelles, the assessment of distinct cell death processes (i.e., apoptosis, necrosis, and oncosis) and the precise measurement of live cell temperature are explored. Furthermore, the captivating applications of QPI in theragnostics, where its potential for transformative impact is prominently showcased, are highlighted. Finally, the challenges that need to be overcome for its wider adoption and successful integration into biomedical research are outlined.

The molecular mechanism of non-covalent inhibitor WU-04 targeting SARS-CoV-2 3CLpro and computational evaluation of its effectiveness against mainstream coronaviruses

There is an urgent need for highly effective therapeutic agents to interrupt the continued spread of SARS-CoV-2. As a pivotal protease in the replication process of coronaviruses, the 3CLpro protein is considered as a potential target of drug development to stop the spread and infection of the virus. In this work, molecular dynamics (MD) simulations were used to elucidate the molecular mechanism of a novel and highly effective non-covalent inhibitor, WU-04, targeting the SARS-CoV-2 3CLpro protein. The difference in dynamic behavior between the apo-3CLpro and the holo-3CLpro systems suggests that the presence of WU-04 inhibits the motion amplitude of the 3CLpro protein relative to the apo-3CLpro system, thus maintaining a stable conformational binding state. The energy calculations and interaction analysis show that the hot-spot residues Q189, M165, M49, E166, and H41 and the warm-spot residues H163 and C145 have a strong binding capacity to WU-04 by forming multiple hydrogen bonds and hydrophobic interactions, which stabilizes the binding of the inhibitor. After that, the resistance of WU-04 to the six SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Lambda, and Omicron) and two other mainstream coronavirus (SARS-CoV and MERS-CoV) 3CLpro proteins was further investigated. Excitingly, the slight difference in energy values relative to the SARS-CoV-2 system indicates that WU-04 is still highly effective against the coronaviruses, which becomes crucial evidence that WU-04 is a pan-inhibitor of the 3CLpro protein in various SARS-CoV-2 variants and other mainstream coronaviruses. The study will hopefully provide theoretical insights for the future rational design and improvement of novel non-covalent inhibitors targeting the 3CLpro protein.

Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%-89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging.

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm2 and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis.

Digital staining facilitates biomedical microscopy

Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational "virtual" staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.

Label-free high-resolution white light quantitative phase nanoscopy system

We present a high-resolution white light quantitative phase nanoscopy (WLQPN) system that can be utilized to visualize nanoparticles and subcellular features of the biological specimens. The five-phase shifting technique, along with deconvolution, is adopted to obtain super-resolution in phase imaging. The phase shifting technique can provide full detector resolution, making it beneficial as compared to the well-known Fourier analysis method. The Fourier transform method requires minimum angle of sin - 1 3 f x λ , where f x is maximum achievable spatial frequency. It limits the highest achievable resolution to much below the actual diffraction limit of the system. Thus, to obtain a high-resolution phase map of the biological specimen, a two-step process is adopted. First, the phase map is recovered using the five-phase shifting algorithm, with full detector spatial resolution. Second, the complex field is obtained from the recovered phase map and further processed using the Richardson Lucy total variation deconvolution algorithm to obtain super-resolution phase images. The present technique was tested on 1951 USAF resolution chart, 200 nm polystyrene beads and Escherichia coli bacteria using a 50×, 0.55NA objective lens. The 200 nm polystyrene beads are visually resolvable and subcellular features of the E. coli bacteria are also observed, suggesting a significant improvement in the resolution.

Investigation on the interaction mechanism of different SARS-CoV-2 spike variants with hACE2: insights from molecular dynamics simulations

Since the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), SARS-CoV-2 has evolved by acquiring genomic mutations, resulting in the recent emergence of several SARS-CoV-2 variants with improved transmissibility and infectivity relative to the original strain. An underlying mechanism may be the increased ability of the mutants to bind the receptor proteins and infect the host cell. In this work, we implemented all-atom molecular dynamics (MD) simulations to study the binding and interaction of the receptor binding domain (RBD) of the SARS-CoV-2 spike protein singly (D614G), doubly (D614G + L452R and D614G + N501Y), triply (D614G + N501Y + E484K), and quadruply (D614G + N501Y + E484K + K417T) mutated variants with the human angiotensin-converting enzyme 2 (hACE2) receptor protein in the host cell. A combination of multiple analysis approaches elucidated the effects of mutations and the extent of molecular divergence from multiple perspectives, including the dynamic correlated motions, interaction patterns, dominant motions, free energy landscape, and charge distribution on the electrostatic potential surface between the hACE2 and all RBD variants. Moreover, free energy calculations using the MM/PBSA method evaluated the binding affinity between these RBD variants and hACE2. The results showed that the D614G + N501Y + E484K variant possessed the lowest free energy value (highest affinity) compared to the D614G + N501Y + E484K + K417T, D614G + L452R, D614G + N501Y, and D614G mutants. The residue-based energy decomposition also indicated that the energy contribution of residues at the mutation site to the total binding energy was highly variable. The interaction mechanisms between the different RBD variants and hACE2 elucidated in this study will provide some insights into the development of drugs targeting the new SARS-CoV-2 variants.

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.

Ultralow-level all-optical self-switching in a nanostructured moiré superlattice

We report an all-optical self-switching performed at an ultralow-level of intensity in a nanostructured moiré superlattice on a silicon platform. The moiré superlattice was formed by twisting two sets of triangular lattices in a silicon membrane in the same layer with a twist angle of 9.43°. The near flatband was formed, and the electric field was well confined in the center of the superlattice, which enabled all-optical switching under an ultralow intensity when the Kerr nonlinearity of silicon was considered. The intensity, which was reduced to 300 W/m² and even 20 W/m², can cause the transmittance of the nanostructure to change from 0 to 80% under x- and y-polarized pump light, respectively, and could be further decreased by optimizing the nanostructure or nonlinear materials. The results indicate that moiré superlattices fabricated from nonlinear materials are promising for integrated all-optical devices.

GANscan: continuous scanning microscopy using deep learning deblurring

Most whole slide imaging (WSI) systems today rely on the "stop-and-stare" approach, where, at each field of view, the scanning stage is brought to a complete stop before the camera snaps a picture. This procedure ensures that each image is free of motion blur, which comes at the expense of long acquisition times. In order to speed up the acquisition process, especially for large scanning areas, such as pathology slides, we developed an acquisition method in which the data is acquired continuously while the stage is moving at high speeds. Using generative adversarial networks (GANs), we demonstrate this ultra-fast imaging approach, referred to as GANscan, which restores sharp images from motion blurred videos. GANscan allows us to complete image acquisitions at 30x the throughput of stop-and-stare systems. This method is implemented on a Zeiss Axio Observer Z1 microscope, requires no specialized hardware, and accomplishes successful reconstructions at stage speeds of up to 5000 μm/s. We validate the proposed method by imaging H&E stained tissue sections. Our method not only retrieves crisp images from fast, continuous scans, but also adjusts for defocusing that occurs during scanning within +/- 5 μm. Using a consumer GPU, the inference runs at <20 ms/ image.

Computational Design of Miniprotein Inhibitors Targeting SARS-CoV-2 Spike Protein

The ongoing pandemic of COVID-19 caused by SARS-CoV-2 has become a global health problem. There is an urgent need to develop therapeutic drugs, effective therapies, and vaccines to prevent the spread of the virus. The virus first enters the host cell through the interaction between the receptor binding domain (RBD) of spike protein and the peptidase domain (PD) of the angiotensin-converting enzyme 2 (ACE2). Therefore, blocking the binding of RBD and ACE2 is a promising strategy to inhibit the invasion and infection of the virus in the host cell. In the study, we designed several miniprotein inhibitors against SARS-CoV-2 by single/double/triple-point mutant, based on the initial inhibitor LCB3. Molecular dynamics (MD) simulations and trajectory analysis were performed for an in-depth analysis of the structural stability, essential protein motions, and per-residue energy decomposition involved in the interaction of inhibitors with the RBD. The results showed that the inhibitors have adapted the protein RBD in the binding interface, thereby forming stable complexes. These inhibitors display low binding free energy in the MM/PBSA calculations, substantiating their strong interaction with RBD. Moreover, the binding affinity of the best miniprotein inhibitor, H6Y-M7L-L17F mutant, to RBD was ∼45 980 times (ΔG = RT ln Ki) higher than that of the initial inhibitor LCB3. Following H6Y-M7L-L17F mutant, the inhibitors with strong binding activity are successively H6Y-L17F, L17F, H6Y, and F30Y mutants. Our research proves that the miniprotein inhibitors can maintain their secondary structure and have a highly stable blocking (binding) effect on SARS-CoV-2. This study proposes novel miniprotein mutant inhibitors with enhanced binding to spike protein and provides potential guidance for the rational design of new SARS-CoV-2 spike protein inhibitors.

Imaging Techniques: Essential Tools for the Study of SARS-CoV-2 Infection

The world has seen the emergence of a new virus in 2019, SARS-CoV-2, causing the COVID-19 pandemic and millions of deaths worldwide. Microscopy can be much more informative than conventional detection methods such as RT-PCR. This review aims to present the up-to-date microscopy observations in patients, the in vitro studies of the virus and viral proteins and their interaction with their host, discuss the microscopy techniques for detection and study of SARS-CoV-2, and summarize the reagents used for SARS-CoV-2 detection. From basic fluorescence microscopy to high resolution techniques and combined technologies, this article shows the power and the potential of microscopy techniques, especially in the field of virology.

Atomic and structural modifications of two-dimensional transition metal dichalcogenides for various advanced applications

Two-dimensional (2D) transition metal dichalcogenides (TMDs) and their heterostructures have attracted significant interest in both academia and industry because of their unusual physical and chemical properties. They offer numerous applications, such as electronic, optoelectronic, and spintronic devices, in addition to energy storage and conversion. Atomic and structural modifications of van der Waals layered materials are required to achieve unique and versatile properties for advanced applications. This review presents a discussion on the atomic-scale and structural modifications of 2D TMDs and their heterostructures via post-treatment. Atomic-scale modifications such as vacancy generation, substitutional doping, functionalization and repair of 2D TMDs and structural modifications including phase transitions and construction of heterostructures are discussed. Such modifications on the physical and chemical properties of 2D TMDs enable the development of various advanced applications including electronic and optoelectronic devices, sensing, catalysis, nanogenerators, and memory and neuromorphic devices. Finally, the challenges and prospects of various post-treatment techniques and related future advanced applications are addressed.

Quantitative phase imaging through an ultra-thin lensless fiber endoscope

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

Small form factor flow virometer for SARS-CoV-2

Current diagnostics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection heavily rely on reverse transcription-polymerase chain reaction (RT-PCR) or on rapid antigen detection tests. The former suffers from long time-to-result and high cost while the latter from poor sensitivity. Therefore, it is crucial to develop rapid, sensitive, robust, and inexpensive methods for SARS-CoV-2 testing. Herein, we report a novel optofluidic technology, a flow-virometry reader (FVR), for fast and reliable SARS-CoV-2 detection in saliva samples. A small microfluidic chip together with a laser-pumped optical head detects the presence of viruses tagged with fluorescent antibodies directly from saliva samples. The technology has been validated using clinical samples with high sensitivity (91.2%) and specificity (90%). Thanks also to its short time-to-result (<30 min) and small size (25 × 30 × 13 cm), which can be further reduced in the future, it is a strong alternative to existing tests, especially for point-of-care (POC) and low resource settings.

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us to access high-resolution imaging with high-throughput, the huge amount of time-lapse holographic images to be processed (hundreds of digital holograms per cell) constitutes the actual bottleneck. This prevents the system from being suitable for lab-on-a-chip platforms in real-world applications, where fast analysis of measured data is mandatory. Here we demonstrate a significant speeding-up reconstruction of phase-contrast tomograms by introducing in the processing pipeline a multi-scale fully-convolutional context aggregation network. Although it was originally developed in the context of semantic image analysis, we demonstrate for the first time that it can be successfully adapted to a holographic lab-on-chip platform for achieving 3D tomograms through a faster computational process. We trained the network with input-output image pairs to reproduce the end-to-end holographic reconstruction process, i.e. recovering quantitative phase maps (QPMs) of single cells from their digital holograms. Then, the sequence of QPMs of the same rotating cell is used to perform the tomographic reconstruction. The proposed approach significantly reduces the computational time for retrieving tomograms, thus making them available in a few seconds instead of tens of minutes, while essentially preserving the high-content information of tomographic data. Moreover, we have accomplished a compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach by classifying between two types of cancer cell lines of different metastatic potential originating from the same patient. It is based on the fact that both the cancer-cell morphology and its mechanical properties, as indicated by the cell temporal and spatial fluctuations, change over the disease progression. We tested different fusion methods for inputting both the morphological optical thickness maps and the coinciding spatio-temporal fluctuation maps of the cells to the classifying network framework. We show that the proposed integrated triple-path deep-learning architecture improves over deep-learning classification that is based only on the cell morphological evaluation via its quantitative optical thickness map, demonstrating the benefit in the acquisition of the cells over time and in extracting their spatio-temporal fluctuation maps, to be used as an input to the classifying deep neural network.