The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death

Perspective on quantitative phase imaging to improve precision cancer medicine

Significance

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states.

Aim

This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection.

Approach

We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging.

Results

QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research.

Conclusions

QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

Deep-learning based 3D birefringence image generation using 2D multi-view holographic images

Refractive index stands as an inherent characteristic of a material, allowing non-invasive exploration of the three-dimensional (3D) interior of the material. Certain materials with different refractive indices produce a birefringence phenomenon in which incident light is split into two polarization components when it passes through the materials. Representative birefringent materials appear in calcite crystals, liquid crystals (LCs), biological tissues, silk fibers, polymer films, etc. If the internal 3D shape of these materials can be visually expressed through a non-invasive method, it can greatly contribute to the semiconductor, display industry, optical components and devices, and biomedical diagnosis. This paper introduces a novel approach employing deep learning to generate 3D birefringence images using multi-viewed holographic interference images. First, we acquired a set of multi-viewed holographic interference pattern images and a 3D volume image of birefringence directly from a polarizing DTT (dielectric tensor tomography)-based microscope system about each LC droplet sample. The proposed model was trained to generate the 3D volume images of birefringence using the two-dimensional (2D) interference pattern image set. Performance evaluations were conducted against the ground truth images obtained directly from the DTT microscopy. Visualization techniques were applied to describe the refractive index distribution in the generated 3D images of birefringence. The results show the proposed method's efficiency in generating the 3D refractive index distribution from multi-viewed holographic interference images, presenting a novel data-driven alternative to traditional methods from the DTT devices.

Engineering water exchange is a safe and effective method for magnetic resonance imaging in diverse cell types

Aquaporin-1 (Aqp1), a water channel, has garnered significant interest for cell-based medicine and in vivo synthetic biology due to its ability to be genetically encoded to produce magnetic resonance signals by increasing the rate of water diffusion in cells. However, concerns regarding the effects of Aqp1 overexpression and increased membrane diffusivity on cell physiology have limited its widespread use as a deep-tissue reporter. In this study, we present evidence that Aqp1 generates strong diffusion-based magnetic resonance signals without adversely affecting cell viability or morphology in diverse cell lines derived from mice and humans. Our findings indicate that Aqp1 overexpression does not induce ER stress, which is frequently associated with heterologous expression of membrane proteins. Furthermore, we observed that Aqp1 expression had no detrimental effects on native biological activities, such as phagocytosis, immune response, insulin secretion, and tumor cell migration in the analyzed cell lines. These findings should serve to alleviate any lingering safety concerns regarding the utilization of Aqp1 as a genetic reporter and should foster its broader application as a noninvasive reporter for in vivo studies.

Exploring contribution of phytoplankton cell death to settleable particulate organic carbon in the East China Sea in spring

Phytoplankton's death contributes to marine settleable particulate organic matter (POM). In this study, we used laboratory cultivation of different algal species to identify a positive correlation between the cumulative number of dead algal cells and POC>75 (carbon content of the settleable POM). The contribution coefficient of cell death to POC>75 varied among different algal species. Additionally, the field survey and incubation experiment were conducted in the East China Sea (ECS) to explore the spatial-temporal correlation between phytoplankton death and POC>75. The results concluded that phytoplankton death was the main factor controlling POC>75. In the ECS, the relationship between the surface cumulative mass of POC>75 and the cumulative number of dead cells followed: Cumulative mass of POC>75(mg) = 0.487 × Cumulative number of dead cells (/104) + 0.069. This study provided a methodology to quantitatively explain the relationship between phytoplankton death and settleable POM.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

Engineering water exchange is a safe and effective method for magnetic resonance imaging in diverse cell types

Aquaporin-1 (Aqp1), a water channel, has garnered significant interest for cell-based medicine and in vivo synthetic biology due to its ability to be genetically encoded to produce magnetic resonance signals by increasing the rate of water diffusion in cells. However, concerns regarding the effects of Aqp1 overexpression and increased membrane diffusivity on cell physiology have limited its widespread use as a deep-tissue reporter. In this study, we present evidence that Aqp1 generates strong diffusion-based magnetic resonance signals without adversely affecting cell viability or morphology in diverse cell lines derived from mice and humans. Our findings indicate that Aqp1 overexpression does not induce ER stress, which is frequently associated with heterologous expression of membrane proteins. Furthermore, we observed that Aqp1 expression had no detrimental effects on native biological activities, such as phagocytosis, immune response, insulin secretion, and tumor cell migration in the analyzed cell lines. These findings should serve to alleviate any lingering safety concerns regarding the utilization of Aqp1 as a genetic reporter and should foster its broader application as a noninvasive reporter for in vivo studies.

Strategies for Cartilage Repair in Osteoarthritis Based on Diverse Mesenchymal Stem Cells-Derived Extracellular Vesicles

Osteoarthritis (OA) causes disability and significant economic and social burden. Cartilage injury is one of the main pathological features of OA, and is often manifested by excessive chondrocyte death, inflammatory response, abnormal bone metabolism, imbalance of extracellular matrix (ECM) metabolism, and abnormal vascular or nerve growth. Regrettably, due to the avascular nature of cartilage, its capacity to repair is notably limited. Mesenchymal stem cells-derived extracellular vesicles (MSCs-EVs) play a pivotal role in intercellular communication, presenting promising potential not only as early diagnostic biomarkers in OA but also as efficacious therapeutic strategy. MSCs-EVs were confirmed to play a therapeutic role in the pathological process of cartilage injury mentioned above. This paper comprehensively provides the functions and mechanisms of MSCs-EVs in cartilage repair.

Current trends in luminescence-based assessment of apoptosis

Apoptosis, the most extensively studied type of cell death, is known to play a crucial role in numerous processes such as elimination of unwanted cells or cellular debris, growth, control of the immune system, and prevention of malignancies. Defective regulation of apoptosis can trigger various diseases and disorders including cancer, neurological conditions, autoimmune diseases and developmental disorders. Knowing the nuances of the cell death type induced by a compound can help decipher which therapy is more effective for specific diseases. The detection of apoptotic cells using classic methods has brought significant contribution over the years, but innovative methods are quickly emerging and allow more in-depth understanding of the mechanisms, aside from a simple quantification. Due to increased sensitivity, time efficiency, pathway specificity and negligible cytotoxicity, these innovative approaches have great potential for both in vitro and in vivo studies. This review aims to shed light on the importance of developing and using novel nanoscale methods as an alternative to the classic apoptosis detection techniques.

The intricate relationship between autoimmunity disease and neutrophils death patterns: a love-hate story

Autoimmune diseases are pathological conditions that result from the misidentification of self-antigens in immune system, leading to host tissue damage and destruction. These diseases can affect different organs and systems, including the blood, joints, skin, and muscles. Despite the significant progress made in comprehending the underlying pathogenesis, the complete mechanism of autoimmune disease is still not entirely understood. In autoimmune diseases, the innate immunocytes are not functioning properly: they are either abnormally activated or physically disabled. As a vital member of innate immunocyte, neutrophils and their modes of death are influenced by the microenvironment of different autoimmune diseases due to their short lifespan and diverse death modes. Related to neutrophil death pathways, apoptosis is the most frequent cell death form of neutrophil non-lytic morphology, delayed or aberrant apoptosis may contribute to the development anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV). In addition, NETosis, necroptosis and pyroptosis which are parts of lytic morphology exacerbate disease progression through various mechanisms in autoimmune diseases. This review aims to summarize recent advancements in understanding neutrophil death modes in various autoimmune diseases and provide insights into the development of novel therapeutic approaches for autoimmune diseases.

Red-Light-Activatable AND-Gated Antitumor Immunosuppressant

Immunosuppressants are emerging as promising candidates for cancer therapy with lower cytotoxicity compared to traditional chemotherapy drugs; yet, the intrinsic side effects such as immunosuppression remain a critical concern. Herein, we introduce a photoactivatable antitumor immunosuppressant called dmBODIPY-FTY720 (BF) that shows no cytotoxicity but can be temporally and locally activated by deep-red light illumination to induce tumor cell apoptosis. To further reduce potential side effects, we integrate BF with another classic photosensitizer called methylene blue (MB) that is activated under the same wavelength of deep-red light (>650 nm) and successfully establish a red-light-activatable AND Boolean logic gate through a mechanism that we found to be synergetic apoptotic induction. At further decreased dosages, deep-red light illumination does not induce cell death in the presence of either BF or MB, but significant cancer cell death is triggered in the presence of both drugs. Therefore, the dosage of BF is further reduced, which will be highly beneficial to minimize any potential side effects of BF. This AND-gated strategy has been successfully applied in vivo for effective suppression of hepatocarcinoma tumors in living mice.

Algal cell viability assessment: The role of environmental factors in phytoplankton population dynamics

The viability of algal cells is one of the most fundamental issues in marine ecological research. In this work, a method was designed to identify algal cell viability based on digital holography and deep learning, which divided algal cells into three categories: active, weak, and dead cells. This method was applied to measure algal cells in surface waters of the East China Sea in spring, revealing about 4.34 %-23.29 % weak cells and 3.98 %-19.47 % dead cells. Levels of nitrate and chlorophyll a were the main factors affecting the viability of algal cells. Furthermore, algal viability changes during the heating and cooling were observed in laboratory experiments: high temperatures led to an increase in weak algal cells. This may provide an explanation for why most harmful algal blooms occur in warming months. This study provided a novel insight into how to identify the viability of algal cells and understand their significance in the ocean.

Brightfield vs Fluorescent Staining Dataset-A Test Bed Image Set for Machine Learning based Virtual Staining

Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.

Exosomes treating osteoarthritis: hope with challenge

Osteoarthritis (OA) has been proven as the second primary cause of pain and disability in the elderly population, impact patients both physically and mentally, as well as imposing a heavy burden on the global healthcare system. Current treatment methods, whether conservative or surgical, that aim at relieving symptoms can not delay or reverse the degenerative process in the structure. Scientists and clinicians are facing a revolution in OA treatment strategies. The emergence of exosomes brings hope for OA treatment based on pathology, which is attributed to its full potential in protecting chondrocytes from excessive death, alleviating inflammation, maintaining cartilage matrix metabolism, and regulating angiogenesis and subchondral bone remodeling. Therefore, we summarized the recent studies of exosomes in OA, aiming to comprehensively understand the functions and mechanisms of exosomes in OA treatment, which may provide direction and theoretical support for formulating therapeutic strategies in the future.

Ghost messages: cell death signals spread

Cell death is a mystery in various forms. Whichever type of cell death, this is always accompanied by active or passive molecules release. The recent years marked the renaissance of the study of these molecules showing they can signal to and communicate with recipient cells and regulate physio- or pathological events. This review summarizes the defined forms of messages cells could spread while dying, the effects of these signals on the target tissue/cells, and how these types of communications regulate physio- or pathological processes. By doing so, this review hopes to identify major unresolved questions in the field, formulate new hypothesis worthy of further investigation, and when possible, provide references for the search of novel diagnostic/therapeutics agents. Video abstract.

Is image-to-image translation the panacea for multimodal image registration? A comparative study

Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is still often performed manually by specialists. The recent success of image-to-image (I2I) translation in computer vision applications and its growing use in biomedical areas provide a tempting possibility of transforming the multimodal registration problem into a, potentially easier, monomodal one. We conduct an empirical study of the applicability of modern I2I translation methods for the task of rigid registration of multimodal biomedical and medical 2D and 3D images. We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration. We evaluate these method combinations on four publicly available multimodal (2D and 3D) datasets and compare with the performance of registration achieved by several well-known approaches acting directly on multimodal image data. Our results suggest that, although I2I translation may be helpful when the modalities to register are clearly correlated, registration of modalities which express distinctly different properties of the sample are not well handled by the I2I translation approach. The evaluated representation learning method, which aims to find abstract image-like representations of the information shared between the modalities, manages better, and so does the Mutual Information maximisation approach, acting directly on the original multimodal images. We share our complete experimental setup as open-source (https://github.com/MIDA-group/MultiRegEval), including method implementations, evaluation code, and all datasets, for further reproducing and benchmarking.

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases. Code is available at: https://github.com/MIRCen/NeuronInstanceSeg.

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

Chemotherapy drug potency assessment method of ovarian cancer cells by digital holography microscopy

Drug potency assessment plays a crucial role in cancer chemotherapy. The selection of appropriate chemotherapy drugs can reduce the impact on the patient's physical condition and achieve a better therapeutic effect. Various methods have been used to achieve in vitro drug susceptibility assays, but there are few studies on calculating morphology and texture parameters quantitatively based on phase imaging for drug potency assessment. In this study, digital holography microscopy was used to get phase imaging of ovarian cancer cells after adding three different drugs, namely, Cisplatin, Adriamycin, and 5-fluorouracil. Based on the reconstructed phase imaging, four parameters of ovarian cancer cells changed with time, such as the average height, projected area, cluster shade, and entropy, were calculated. And the half-inhibitory concentration of cells under the effect of different drugs was calculated according to these four parameters. The half-inhibitory concentration, which can directly reflect the drug potency, is associated with the morphological and texture features extracted from phase images by numerical fitting. So, a new method for calculating the half-inhibitory concentration was proposed. The result shows that the morphological and texture feature parameters can be used to evaluate the sensitivity of ovarian cancer cells to different drugs by fitting the half-inhibitory concentration numerically. And the result provides a new idea for drug potency assessment methods before chemotherapy for ovarian cancer.

mRNA Subtype of Cancer-Associated Fibroblasts Significantly Affects Key Characteristics of Head and Neck Cancer Cells

Head and neck squamous cell carcinomas (HNSCC) belong among severe and highly complex malignant diseases showing a high level of heterogeneity and consequently also a variance in therapeutic response, regardless of clinical stage. Our study implies that the progression of HNSCC may be supported by cancer-associated fibroblasts (CAFs) in the tumour microenvironment (TME) and the heterogeneity of this disease may lie in the level of cooperation between CAFs and epithelial cancer cells, as communication between CAFs and epithelial cancer cells seems to be a key factor for the sustained growth of the tumour mass. In this study, we investigated how CAFs derived from tumours of different mRNA subtypes influence the proliferation of cancer cells and their metabolic and biomechanical reprogramming. We also investigated the clinicopathological significance of the expression of these metabolism-related genes in tissue samples of HNSCC patients to identify a possible gene signature typical for HNSCC progression. We found that the right kind of cooperation between cancer cells and CAFs is needed for tumour growth and progression, and only specific mRNA subtypes can support the growth of primary cancer cells or metastases. Specifically, during coculture, cancer cell colony supporting effect and effect of CAFs on cell stiffness of cancer cells are driven by the mRNA subtype of the tumour from which the CAFs are derived. The degree of colony-forming support is reflected in cancer cell glycolysis levels and lactate shuttle-related transporters.

Characterization and identification of cell death dynamics by quantitative phase imaging

SIGNIFICANCE

Investigating cell death dynamics at the single-cell level plays an essential role in biological research. Quantitative phase imaging (QPI), a label-free method without adverse effects of exogenous labels, has been widely used to image many types of cells under various conditions. However, the dynamics of QPI features during cell death have not been thoroughly characterized.

AIM

We aim to develop a label-free technique to quantitatively characterize single-cell dynamics of cellular morphology and intracellular mass distribution of cells undergoing apoptosis and necrosis.

APPROACH

QPI was used to capture time-lapse phase images of apoptotic, necrotic, and normal cells. The dynamics of morphological and QPI features during cell death were fitted by a sigmoid function to quantify both the extent and rate of changes.

RESULTS

The two types of cell death mainly differed from normal cells in the lower phase of the central region and differed from each other in the sharp nuclear boundary shown in apoptotic cells.

CONCLUSIONS

The proposed method characterizes the dynamics of cellular morphology and intracellular mass distributions, which could be applied to studying cells undergoing state transition such as drug response.

Automated detection of apoptotic versus nonapoptotic cell death using label-free computational microscopy

Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high-throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning-based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging-based screening tools.

Live-dead assay on unlabeled cells using phase imaging with computational specificity

Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, the step of exogenous staining makes these methods undesirable for rapid, nondestructive, and long-term investigation. Here, we present an instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity. This concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and nucleus area for the labeled and unlabeled populations reveal that the chemical reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments.

Common methods for characterising cell viability involve cell staining with chemical reagents. Here the authors report a method for cell viability assessment that does not require labelling; this uses quantitative phase imaging combined with deep learning.

Pump-free microfluidic magnetic levitation approach for density-based cell characterization

Magnetic levitation (MagLev) provides a simple but promising method for density-based analysis and detection down to the individual cell level. However, each existing MagLev configuration for the single-cell density measurement, mainly consisting of a capillary (∼50 mm) placed between two magnets, yields a fairly low sample utilization because of no knowledge about the sample cells in the regions other than the limited microscope vision. Moreover, the quantitative analysis may be affected due to the unclearly defined measurement area, which is specifically associated with the uneven magnetization of magnets, cell size, degree of aggregation. In this work, we explore a pump-free microfluidic magnetic levitation approach for density-based cell characterization, enabling sensitive and effective cellular density measurement on small sample volumes. The microfluidic MagLev comprises a pump-free microfluidic chip placed between two ring magnets with like poles facing. With no external pumps, connectors or control facility, much smaller amounts of fluids (∼4 μL) could be driven automatically in the entire microchannel in 16 s. Based on the pump-free mechanism, unique density signatures of cells from different lineages (ARPE-19, HCT116, HeLa, HT1080, Huh7) are characterized by monitoring the levitation profiles. Furthermore, variation in density of A549 lung cancer cells subjected to a drug treatment are observed in our platform, allowing evaluation of the efficacy of the drug treatment at the individual cell level. Thereby, the proposed pump-free microfluidic MagLev platform, a low-cost, fully automatic and portable design for label-free density-based cell characterization, provides a universal detection tool that operates efficiently within small-volume environments.

Addressing cancer invasion and cell motility with quantitative light microscopy

The incidence of death caused by cancer has been increasing worldwide. The growth of cancer cells is not the main problem. The majority of deaths are due to invasion and metastasis, where cancer cells actively spread from primary tumors. Our inbred rat model of spontaneous metastasis revealed dynamic phenotype changes in vitro correlating with the metastatic potential in vivo and led to a discovery of a metastasis suppressor, protein 4.1B, which affects their 2D motility on flat substrates. Subsequently, others confirmed 4.1B as metastasis suppressor using knock-out mice and patient data suggesting mechanism involving apoptosis. There is evidence that 2D motility may be differentially controlled to the 3D situation. Here we show that 4.1B affects cell motility in an invasion assay similarly to the 2D system, further supporting our original hypothesis that the role of 4.1B as metastasis suppressor is primarily mediated by its effect on motility. This is encouraging for the validity of the 2D analysis, and we propose Quantitative Phase Imaging with incoherent light source for rapid and accurate testing of cancer cell motility and growth to be of interest for personalized cancer treatment as illustrated in experiments measuring responses of human adenocarcinoma cells to selected chemotherapeutic drugs.

FDA approved L-type channel blocker Nifedipine reduces cell death in hypoxic A549 cells through modulation of mitochondrial calcium and superoxide generation

As hypoxia is a major driver for the pathophysiology of COVID-19, it is crucial to characterize the hypoxic response at the cellular and molecular levels. In order to augment drug repurposing with the identification of appropriate molecular targets, investigations on therapeutics preventing hypoxic cell damage is required. In this work, we propose a hypoxia model based on alveolar lung epithelial cells line using chemical inducer, CoCl₂ that can be used for testing calcium channel blockers (CCBs). Since recent studies suggested that CCBs may reduce the infectivity of SARS-Cov-2, we specifically select FDA approved calcium channel blocker, nifedipine for the study. First, we examined hypoxia-induced cell morphology and found a significant increase in cytosolic calcium levels, mitochondrial calcium overload as well as ROS production in hypoxic A549 cells. Secondly, we demonstrate the protective behaviour of nifedipine for cells that are already subjected to hypoxia through measurement of cell viability as well as 4D imaging of cellular morphology and nuclear condensation. Thirdly, we show that the protective effect of nifedipine is achieved through the reduction of cytosolic calcium, mitochondrial calcium, and ROS generation. Overall, we outline a framework for quantitative analysis of mitochondrial calcium and ROS using 3D imaging in laser scanning confocal microscopy and the open-source image analysis platform ImageJ. The proposed pipeline was used to visualize mitochondrial calcium and ROS level in individual cells that provide an understanding of molecular targets. Our findings suggest that the therapeutic value of nifedipine may potentially be evaluated in the context of COVID-19 therapeutic trials.

Self-supervised pretraining for transferable quantitative phase image cell segmentation

In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning

Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

SAP130 released by damaged tubule drives necroinflammation via miRNA-219c/Mincle signaling in acute kidney injury

Tubules injury and immune cell activation are the common pathogenic mechanisms in acute kidney injury (AKI). However, the exact modes of immune cell activation following tubule damage are not fully understood. Here we uncovered that the release of cytoplasmic spliceosome associated protein 130 (SAP130) from the damaged tubular cells mediated necroinflammation by triggering macrophage activation via miRNA-219c(miR-219c)/Mincle-dependent mechanism in unilateral ureteral obstruction (UUO) and cisplatin-induced AKI mouse models, and in patients with acute tubule necrosis (ATN). In the AKI kidneys, we found that Mincle expression was tightly correlated to the necrotic tubular epithelial cells (TECs) with higher expression of SAP130, a damaged associated molecule pattern (DAMP), suggesting that SAP130 released from damaged tubular cells may trigger macrophage activation and necroinflammation. This was confirmed in vivo in which administration of SAP130-rich supernatant from dead TECs or recombinant SAP130 promoted Mincle expression and macrophage accumulation which became worsen with profound tubulointerstitial inflammation in LPS-primed Mincle WT mice but not in Mincle deficient mice. Further studies identified that Mincle was negatively regulated via miR-219c-3p in macrophages as miR-219c-3p bound Mincle 3′-UTR to inhibit Mincle translation. Besides, lentivirus-mediated renal miR-219c-3p overexpression blunted Mincle and proinflammatory cytokine expression as well as macrophage infiltration in the inflamed kidney of UUO mice. In conclusion, SAP130 is released by damaged tubules which elicit Mincle activation on macrophages and renal necroinflammation via the miR-219c-3p-dependent mechanism. Results from this study suggest that targeting miR-219c-3p/Mincle signaling may represent a novel therapy for AKI.

Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis

Regulated cell death modalities such as apoptosis and necroptosis play an important role in regulating different cellular processes. Currently, regulated cell death is identified using the golden standard techniques such as fluorescence microscopy and flow cytometry. However, they require fluorescent labels, which are potentially phototoxic. Therefore, there is a need for the development of new label-free methods. In this work, we apply Digital Holographic Microscopy (DHM) coupled with a deep learning algorithm to distinguish between alive, apoptotic and necroptotic cells in murine cancer cells. This method is solely based on label-free quantitative phase images, where the phase delay of light by cells is quantified and is used to calculate their topography. We show that a combination of label-free DHM in a high-throughput set-up (~10,000 cells per condition) can discriminate between apoptosis, necroptosis and alive cells in the L929sAhFas cell line with a precision of over 85%. To the best of our knowledge, this is the first time deep learning in the form of convolutional neural networks is applied to distinguish-with a high accuracy-apoptosis and necroptosis and alive cancer cells from each other in a label-free manner. It is expected that the approach described here will have a profound impact on research in regulated cell death, biomedicine and the field of (cancer) cell biology in general.

Quantitative Confocal Microscopy for Grouping of Dose-Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine

Fluorescent calcium (Ca²⁺) imaging is one of the preferred methods to record cellular activity during in vitro preclinical studies, high-content drug screening, and toxicity analysis. Visualization and analysis for dose-response data obtained using high-resolution imaging remain challenging, due to the inherent heterogeneity present in the Ca²⁺ spiking. To address this challenge, we propose measurement of cytosolic Ca²⁺ ions using spinning-disk confocal microscopy and machine learning-based analytics that is scalable. First, we implemented uniform manifold approximation and projection (UMAP) for visualizing the multivariate time-series dataset in the two-dimensional (2D) plane using Python. The dataset was obtained through live imaging experiments with norepinephrine-induced Ca²⁺ oscillation in HeLa cells for a large range of doses. Second, we demonstrate that the proposed framework can be used to depict the grouping of the spiking pattern for lower and higher drug doses. To the best of our knowledge, this is the first attempt at UMAP visualization of the time-series dose response and identification of the Ca²⁺ signature during lytic death. Such quantitative microscopy can be used as a component of a high-throughput data analysis workflow for toxicity analysis.

Label-Free Single Cell Viability Assay Using Laser Interference Microscopy

Laser interference microscopy (LIM) is a promising label-free method for single-cell research applicable to cell viability assessment in the studies of mammalian cells. This paper describes the development of a sensitive and reproducible method for assessing cell viability using LIM. The method, based on associated signal processing techniques, has been developed as a result of real-time investigation in phase thickness fluctuations of viable and non-viable MCF-7 cells, reflecting the presence and absence of their metabolic activity. As evinced by the values of the variable vc, this variable determines the viability of a cell only in the attached state (vc exceeds 20 nm2 for viable attached cells). The critical value of the power spectrum slope βc of the phase thickness fluctuations equals 1.00 for attached MCF-7 cells and 0.71 for suspended cells. The slope of the phase fluctuations' power spectrum for MCF-7 cells was determined to exceed the threshold value of βc for a living cell, otherwise the cell is dead. The results evince the power spectrum slope as the most appropriate indicator of cell viability, while the integrated evaluation criterion (vc and βc values) can be used to assay the viability of attached cells.

Elevation of Circulating LIGHT (TNFSF14) and Interleukin-18 Levels in Sepsis-Induced Multi-Organ Injuries

Objective

The cytokines, LIGHT (TNFSF14) and Interleukin-18 (IL-18), are two important therapeutic targets due to their central roles in the function of activated T cells and inflammatory injury. LIGHT was recently shown to play a major role in COVID19 induced acute respiratory distress syndrome (ARDS), reducing mortality and hospital stay. This study aims to investigate the associations of LIGHT and IL-18 with non-COVID19 related ARDS, acute hypoxic respiratory failure (AHRF) or acute kidney injury (AKI), secondary to viral or bacterial sepsis.

Research Design and Methods

A cohort of 280 subjects diagnosed with sepsis, including 91 cases with sepsis triggered by viral infections, were investigated in this study and compared to healthy controls. Serum LIGHT, IL-18, and 59 other biomarkers (cytokines, chemokines and acute-phase reactants) were measured and associated with symptom severity.

Results

ARDS was observed in 36% of the patients, with 29% of the total patient cohort developing multi-organ failure (failure of two or more organs). We observed significantly increased LIGHT level (>2SD above mean of healthy subjects) in both bacterial sepsis patients (P=1.80E-05) and patients with sepsis from viral infections (P=1.78E-03). In bacterial sepsis, increased LIGHT level associated with ARDS, AKI and higher Apache III scores, findings also supported by correlations of LIGHT with other biomarkers of organ failures, suggesting LIGHT may be an inflammatory driver. IL-18 levels were highly variable across individuals, and consistently correlated with Apache III scores, mortality, and AKI, in both bacterial and viral sepsis.

Conclusions

For the first time, we demonstrate independent effects of LIGHT and IL-18 in septic organ failures. LIGHT levels are significantly elevated in non-COVID19 sepsis patients with ARDS and/or multi-organ failures suggesting that anti-LIGHT therapy may be effective therapy in a subset of patients with sepsis. Given the large variance of plasma IL-18 among septic subjects, targeting this pathway raises opportunities that require a precision application.

Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images

Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions. In this article, we introduce and reliably evaluate existing machine learning algorithms using a data set of microscopy images of macaque brain. This data set highlights various anatomical regions (e.g., cortex, caudate, thalamus, claustrum, putamen, hippocampus, subiculum, lateral geniculate, globus pallidus, etc.), poor contrast, and staining intensity differences of neurons. The evaluation was performed using 10 architectures of six classic machine learning algorithms in terms of typical Recall, Precision, F-score, aggregated Jaccard index (AJI), as well as a performance ranking of algorithms. F-score of most of the algorithms is superior to 0.7. Deep learning algorithms facilitate generally higher F-scores. U-net with suitable layer depth has been evaluated to be excellent classifiers with F-score of 0.846 and 0.837 when performing cross validation. The evaluation and analysis indicate the performance gap among algorithms in various anatomical regions and the strengths and limitations of each algorithm. The comparative result highlights at the same time the importance and difficulty of neuron segmentation and provides clues for future improvement. To the best of our knowledge, this work is the first comprehensive study for neuron segmentation in such large-scale anatomical regions.

Broad-Spectrum Antiviral Activity of 3'-Deoxy-3'-Fluoroadenosine against Emerging Flaviviruses

Emerging flaviviruses are causative agents of severe and life-threatening diseases, against which no approved therapies are available. Among the nucleoside analogues, which represent a promising group of potentially therapeutic compounds, fluorine-substituted nucleosides are characterized by unique structural and functional properties. Despite having first been synthesized almost 5 decades ago, they still offer new therapeutic opportunities as inhibitors of essential viral or cellular enzymes active in nucleic acid replication/transcription or nucleoside/nucleotide metabolism. Here, we report evaluation of the antiflaviviral activity of 28 nucleoside analogues, each modified with a fluoro substituent at different positions of the ribose ring and/or heterocyclic nucleobase. Our antiviral screening revealed that 3'-deoxy-3'-fluoroadenosine exerted a low-micromolar antiviral effect against tick-borne encephalitis virus (TBEV), Zika virus, and West Nile virus (WNV) (EC₅₀ values from 1.1 ± 0.1 μM to 4.7 ± 1.5 μM), which was manifested in host cell lines of neural and extraneural origin. The compound did not display any measurable cytotoxicity up to concentrations of 25 μM but had an observable cytostatic effect, resulting in suppression of cell proliferation at concentrations of >12.5 μM. Novel approaches based on quantitative phase imaging using holographic microscopy were developed for advanced characterization of antiviral and cytotoxic profiles of 3'-deoxy-3'-fluoroadenosine in vitro In addition to its antiviral activity in cell cultures, 3'-deoxy-3'-fluoroadenosine was active in vivo in mouse models of TBEV and WNV infection. Our results demonstrate that fluoro-modified nucleosides represent a group of bioactive molecules with excellent potential to serve as prospective broad-spectrum antivirals in antiviral research and drug development.

Development of assembled microchannel resonator as an alternative fabrication method of a microchannel resonator for mass sensing in flowing liquid

We propose an alternate fabrication technique of microchannel resonators based on an assembly method of three separate parts to form a microchannel resonator on a chip. The capability of the assembled microchannel resonator to detect mass is confirmed by injecting two liquids with different densities. The experimental and theoretical values of the resonator frequency shift are in agreement with each other, which confirms the consistency of the device. The noise level of the device is estimated from the Allan variance plot, so the minimum detectable mass of 230 fg after 16 s of operation is expected. By considering the time of the practical application of 1 ms, it is found that a detectable mass of around 8.51 pg is estimated, which is applicable for detecting flowing microparticles. The sub-pico to a few picogram levels of detection will be applicable for the mass analysis of flowing microparticles such as single cells and will be greatly beneficial for many fields such as chemistry, medicine, biology, and single-cell analysis.

Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy

We demonstrate a successful deep learning strategy for cell identification and disease diagnosis using spatio-temporal cell information recorded by a digital holographic microscopy system. Shearing digital holographic microscopy is employed using a low-cost, compact, field-portable and 3D-printed microscopy system to record video-rate data of live biological cells with nanometer sensitivity in terms of axial membrane fluctuations, then features are extracted from the reconstructed phase profiles of segmented cells at each time instance for classification. The time-varying data of each extracted feature is input into a recurrent bi-directional long short-term memory (Bi-LSTM) network which learns to classify cells based on their time-varying behavior. Our approach is presented for cell identification between the morphologically similar cases of cow and horse red blood cells. Furthermore, the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset of human red blood cells from healthy individuals and those with sickle cell disease. The results are presented at both the cell and patient levels. To the best of our knowledge, this is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system.

Quantitative Phase Dynamics of Cancer Cell Populations Affected by Blue Light

Increased exposition to blue light may induce many changes in cell behavior and significantly affect the critical characteristics of cells. Here we show that multimodal holographic microscopy (MHM) within advanced image analysis is capable of correctly distinguishing between changes in cell motility, cell dry mass, cell density, and cell death induced by blue light. We focused on the effect of blue light with a wavelength of 485 nm on morphological and dynamical parameters of four cell lines, malignant PC-3, A2780, G361 cell lines, and the benign PNT1A cell line. We used MHM with blue light doses 24 mJ/cm2, 208 mJ/cm2 and two kinds of expositions (500 and 1000 ms) to acquire real-time quantitative phase information about cellular parameters. It has been shown that specific doses of the blue light significantly influence cell motility, cell dry mass and cell density. These changes were often specific for the malignant status of tested cells. Blue light dose 208 mJ/cm2 × 1000 ms affected malignant cell motility but did not change the motility of benign cell line PNT1A. This light dose also significantly decreased proliferation activity in all tested cell lines but was not so deleterious for benign cell line PNT1A as for malignant cells. Light dose 208 mJ/cm2 × 1000 ms oppositely affected cell mass in A2780 and PC-3 cells and induced different types of cell death in A2780 and G361 cell lines. Cells obtained the least damage on lower doses of light with shorter time of exposition.