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

Interaction of Human Respiratory Syncytial Virus (HRSV) Matrix Protein with Resveratrol Shows Antiviral Effect

The respiratory syncytial virus (RSV) matrix protein plays key roles in the virus life cycle and is essential for budding, as it stimulates the optimal membrane curvature necessary for the emergence of viral particles. Resveratrol, a polyphenol (3,4',5-trihydroxy-trans-stilbene) produced by plants, exhibits pharmacological effects, including anti-inflammatory and antiviral activities. In this study, resveratrol was tested in HEp-2 (Epidermoid carcinoma of the larynx cell) cells for its post-infection effects, and recombinant M protein was produced to characterize the biophysical mechanisms underlying this interaction. The CC50 (Cytotoxic concentration 50%) value for resveratrol was determined to be 297 μM over 48 h, and the results from the HEp-2 cell cultures demonstrated a viral inhibition of 42.7% in the presence of resveratrol, with an EC50 (Half maximal effective concentration) of 44.26 μM. This mechanism may occur through interaction with the M protein responsible for the budding of mature viral particles. Biophysical assays enabled us to characterize the interaction of the M/resveratrol complex as an entropically driven bond, guided by hydrophobic interactions at the dimerization interface of the M protein, which is essential for the stabilization and formation of the oligomers necessary for viral budding. These findings suggest that one of the targets for resveratrol binding is the M protein, indicating a potential site for blocking the progression of the infection.

Expediting adenovirus titer assays via an algorithmic live-cell imaging technique

Interest in virus-based therapeutics for the treatment of genetic and oncolytic diseases has created a demand for high-yield, low-cost virus-manufacturing processes. However, traditional analytical methods of assessing infectious virus titer require multiple processing steps and manual counting, limiting sample throughput, and increasing human error. This bottleneck severely limits the development of new manufacturing unit operations to drive down costs. In this work, we utilize an Incucyte Live-Cell Analysis System to develop a high-throughput infectious titer assay for adenovirus expressing a GFP-transgene. Although previous studies have demonstrated live-cell imaging's potential for use with other viruses, they provide little guidance regarding the selection of the viewing and analysis parameters. To fill this gap, we develop an algorithmic approach to identify the optimum viewing and analysis parameters and create a statistical workflow for quantifying infectious adenovirus in a sample dilution series in a standard 24-well microplate. The developed assay is comparable to Hexon staining, the gold-standard for adenovirus infectious titer, with a Pearson correlation coefficient of 0.9. Finally, the developed algorithmic approach and statistical workflow were applied to create an assay for adenovirus titer using a 96-well microplate, allowing five times more samples to be quantified compared to the standard 24-well plate. While this assay uses a GFP-insert that precludes its use in a clinical environment, the key learnings surrounding the careful use of viewing and analysis parameters, and the statistical workflow are widely applicable to implementing life-cell imaging for dilution-series-based assays. Moreover, this method directly enables the fast and accurate evaluation of virus samples in a preclinical environment.

Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 μL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of <7% and a very large dynamic range, covering cTnI concentrations over 6 orders of magnitude, up to 100 ng/mL, satisfying clinical requirements. In blinded testing, this computational hs-VFA platform accurately quantified cTnI levels in patient samples and showed a strong correlation with the ground truth values obtained by a benchtop clinical analyzer. This nanoparticle amplification-based computational hs-VFA platform can democratize access to high-sensitivity point-of-care diagnostics and provide a cost-effective alternative to laboratory-based biomarker testing.

Tutorial: design, production and testing of oncolytic viruses for cancer immunotherapy

Oncolytic viruses (OVs) represent a novel class of cancer immunotherapy agents that preferentially infect and kill cancer cells and promote protective antitumor immunity. Furthermore, OVs can be used in combination with established or upcoming immunotherapeutic agents, especially immune checkpoint inhibitors, to efficiently target a wide range of malignancies. The development of OV-based therapy involves three major steps before clinical evaluation: design, production and preclinical testing. OVs can be designed as natural or engineered strains and subsequently selected for their ability to kill a broad spectrum of cancer cells rather than normal, healthy cells. OV selection is further influenced by multiple factors, such as the availability of a specific viral platform, cancer cell permissivity, the need for genetic engineering to render the virus non-pathogenic and/or more effective and logistical considerations around the use of OVs within the laboratory or clinical setting. Selected OVs are then produced and tested for their anticancer potential by using syngeneic, xenograft or humanized preclinical models wherein immunocompromised and immunocompetent setups are used to elucidate their direct oncolytic ability as well as indirect immunotherapeutic potential in vivo. Finally, OVs demonstrating the desired anticancer potential progress toward translation in patients with cancer. This tutorial provides guidelines for the design, production and preclinical testing of OVs, emphasizing considerations specific to OV technology that determine their clinical utility as cancer immunotherapy agents.

Roadmap on computational methods in optical imaging and holography [invited]

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00340-024-08280-3.

Cryopreserved Kidney Epithelial (Vero) Cell Monolayers for Rapid Viral Quantification, Enabled by a Combination of Macromolecular Cryoprotectants

Plaque assays quantify the amount of active, replicating virus to study and detect infectious diseases by application of samples to monolayers of cultured cells. Due to the time taken in thawing, propagating, plating, counting, and then conducting the assay, the process can take over a week to gather data. Here, we introduce assay-ready cryopreserved Vero monolayers in multiwell plates, which can be used directly from the freezer with no cell culture to accelerate the process of plaque determination. Standard dimethyl sulfoxide cryopreservation resulted in just 25% recovery, but addition of polyampholytes (macromolecular cryoprotectants) increased post-thaw recovery and viability in 12- and 24-well plate formats. Variability between individual wells was reduced by chemically induced ice nucleation to prevent supercooling. Cryopreserved cells were used to determine influenza viral plaques in just 24 h, matching results from nonfrozen controls. This innovation may accelerate viral detection and quantification and facilitate automation by eliminating extensive cell culturing.

Periodontal disease and emerging point-of-care technologies for its diagnosis

Periodontal disease (PD), a chronic inflammatory disorder that damages the tooth and its supporting components, is a common global oral health problem. Understanding the intricacies of these disorders, from gingivitis to severe PD, is critical for efficient treatment, diagnosis, and prevention in dental care. Periodontal biosensors and biomarkers are critical in improving oral health diagnostic skills. Clinicians may accomplish early identification, tailored therapy, and efficient tracking of periodontal diseases by using these technologies, ushering in a new age of accurate oral healthcare. Traditional periodontitis diagnostic methods frequently rely on physical probing and visual examinations, necessitating the development of point-of-care (POC) devices. As periodontal disorders necessitate more precise and rapid diagnosis, incorporating novel innovations in biosensors and biomarkers becomes increasingly crucial. These innovations improve our capacity to diagnose, monitor, and adapt periodontal therapies, bringing in the next phase of customized and effective dental healthcare. The review discusses the characteristics and stages of PD, clinical treatment techniques, prominent biomarkers and infection-associated factors that may be employed to determine PD, biomedical sensing, and POC appliances that have been created so far to diagnose stages of PD and its progression profile, as well as predicting future developments in this field.

Single-shot inline holography using a physics-aware diffusion model

Among holographic imaging configurations, inline holography excels in its compact design and portability, making it the preferred choice for on-site or field applications with unique imaging requirements. However, effectively holographic reconstruction from a single-shot measurement remains a challenge. While several approaches have been proposed, our novel unsupervised algorithm, the physics-aware diffusion model for digital holographic reconstruction (PadDH), offers distinct advantages. By seamlessly integrating physical information with a pre-trained diffusion model, PadDH overcomes the need for a holographic training dataset and significantly reduces the number of parameters involved. Through comprehensive experiments using both synthetic and experimental data, we validate the capabilities of PadDH in reducing twin-image contamination and generating high-quality reconstructions. Our work represents significant advancements in unsupervised holographic imaging by harnessing the full potential of the pre-trained diffusion prior.

General phase-difference imaging of incoherent digital holography

The hologram formed by incoherent holography based on self-interference should preserve the phase difference information of the object, such as the phase difference between the mutually orthogonal polarizations of anisotropic object. How to decode this phase difference from this incoherent hologram, i.e., phase-difference imaging, is of great significance for studying the properties of the measured object. However, there is no general phase-difference imaging theory due to both diverse incoherent holography systems and the complicated reconstruction process from holograms based on the diffraction theory. To realize phase-difference image in incoherent holography, the relationship between the phase difference of the object and the image reconstructed by holograms is derived using a general physical model of incoherent holographic systems, and then the additional phase that will distort this relationship in actual holographic systems is analyzed and eliminated. Finally, the phase-difference imaging that is suitable for the most incoherent holographic systems is realized and the general theory is experimentally verified. This technology can be applied to phase-difference imaging of anisotropic objects, and has potential applications in materials science, biomedicine, polarized optics and other fields.

Rapid deep learning-assisted predictive diagnostics for point-of-care testing

Prominent techniques such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and rapid kits are currently being explored to both enhance sensitivity and reduce assay time for diagnostic tests. Existing commercial molecular methods typically take several hours, while immunoassays can range from several hours to tens of minutes. Rapid diagnostics are crucial in Point-of-Care Testing (POCT). We propose an approach that integrates a time-series deep learning architecture and AI-based verification, for the enhanced result analysis of lateral flow assays. This approach is applicable to both infectious diseases and non-infectious biomarkers. In blind tests using clinical samples, our method achieved diagnostic times as short as 2 minutes, exceeding the accuracy of human analysis at 15 minutes. Furthermore, our technique significantly reduces assay time to just 1-2 minutes in the POCT setting. This advancement has the potential to greatly enhance POCT diagnostics, enabling both healthcare professionals and non-experts to make rapid, accurate decisions.

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.

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.

Harnessing the power of optical microscopy for visualization and analysis of histopathological images

Histopathology is the foundation and gold standard for identifying diseases, and precise quantification of histopathological images can provide the pathologist with objective clues to make a more convincing diagnosis. Optical microscopy (OM), an important branch of optical imaging technology that provides high-resolution images of tissue cytology and structural morphology, has been used in the diagnosis of histopathology and evolved into a new disciplinary direction of optical microscopic histopathology (OMH). There are a number of ex-vivo studies providing applicability of different OMH approaches, and a transfer of these techniques toward in vivo diagnosis is currently in progress. Furthermore, combined with advanced artificial intelligence algorithms, OMH allows for improved diagnostic reliability and convenience due to the complementarity of retrieval information. In this review, we cover recent advances in OMH, including the exploration of new techniques in OMH as well as their applications, and look ahead to new challenges in OMH. These typical application examples well demonstrate the application potential and clinical value of OMH techniques in histopathological diagnosis.