Due to the heterogeneity of viruses and their hosts, a comprehensive view of viral infection is best achieved by analyzing large populations of infected cells. However, information regarding variation in infected cell populations is lost in bulk measurements. Motivated by an interest in the temporal progression of events in virally infected cells, we used image flow cytometry (IFC) to monitor changes in Acanthamoeba polyphaga cells infected with Mimivirus. This first use of IFC to study viral infection required the development of methods to preserve morphological features of adherent amoeba cells prior to detachment and analysis in suspension. It also required the identification of IFC parameters that best report on key events in the Mimivirus infection cycle. The optimized IFC protocol enabled the simultaneous monitoring of diverse processes including generation of viral factories, transport, and fusion of replication centers within the cell, accumulation of viral progeny, and changes in cell morphology for tens of thousands of cells. After obtaining the time windows for these processes, we used IFC to evaluate the effects of perturbations such as oxidative stress and cytoskeletal disruptors on viral infection. Accurate dose-response curves could be generated, and we found that mild oxidative stress delayed multiple stages of virus production, but eventually infection processes occurred with approximately the same amplitudes. We also found that functional actin cytoskeleton is required for fusion of viral replication centers and later for the production of viral progeny. Through this report, we demonstrate that IFC offers a quantitative, high-throughput, and highly robust approach to study viral infection cycles and virus-host interactions. © The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Feline coronaviruses (FCoV) are members of the alphacoronavirus genus that are further characterized by serotype (types I and II) based on the antigenicity of the spike (S) protein and by pathotype based on the associated clinical conditions. Feline enteric coronaviruses (FECV) are associated with the vast majority of infections and are typically asymptomatic. Within individual animals, FECV can mutate and cause a severe and usually fatal disease called feline infectious peritonitis (FIP), the leading infectious cause of death in domestic cat populations. There are no approved antiviral drugs or recommended vaccines to treat or prevent FCoV infection. The plaque reduction neutralization test (PRNT) traditionally employed to assess immune responses and to screen therapeutic and vaccine candidates is time-consuming, low-throughput, and typically requires 2-3 days for the formation and manual counting of cytolytic plaques. Host cells are capable of carrying heavy viral burden in the absence of visible cytolytic effects, thereby reducing the sensitivity of the assay. In addition, operator-to-operator variation can generate uncertainty in the results and digital records are not automatically created. To address these challenges we developed a novel high-throughput viral microneutralization assay, with quantification of virus-infected cells performed in a plate-based image cytometer. Host cell seeding density, microplate surface coating, virus concentration and incubation time, wash buffer and fluorescent labeling were optimized. Subsequently, this FCoV viral neutralization assay was used to explore immune correlates of protection using plasma from naturally FECV-infected cats. We demonstrate that the high-throughput viral neutralization assay using the Celigo Image Cytometer provides a robust and efficient method for the rapid screening of therapeutic antibodies, antiviral compounds, and vaccines. This method can be applied to various viral infectious diseases to accelerate vaccine and antiviral drug discovery and development.

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

Natural Killer (NK) cells are lymphocytes that are the first line of defense against malignantly transformed cells, virally infected cells and other stressed cell types. To study the cytolytic function of NK cells in vitro, a cytotoxicity assay is normally conducted against a target cancerous cell line. Current assay methods are typically performed in mixed 2D cocultures with destructive endpoints and low throughput, thereby limiting the scale, time-resolution, and relevance of the assay to in vivo conditions. Here, we evaluated a novel, non-invasive, quantitative image-based cytometry (qIBC) assay for detection of NK-mediated killing of target cells in 2D and 3D environments in vitro and compared its performance to two common flow cytometry- and fluorescence-based cytotoxicity assays. Similar to the other methods evaluated, the qIBC assay allowed for reproducible detection of target cell killing across a range of effector-to-target ratios with reduced variability. The qIBC assay also allowed for detection of NK cytolysis in 3D spheroids, which enabled scalable measurements of cell cytotoxicity in 3D models. Our findings suggest that quantitative image-based cytometry would be suitable for rapid, high-throughput screening of NK cytolysis in vitro, including in quasi-3D structures that model tissue environments in vivo.

Image cytometry is the process of converting image data to flow cytometry-style plots, and it usually requires computer-aided surface creation to extract out statistics for cells or structures. One way of dealing with structures stained with multiple markers in three-dimensional images, is carrying out multiple rounds of channel co-localization and image masking before surface creation, which is cumbersome and laborious. We propose the application of the hue-saturation-brightness color space to streamline this process, which produces complete surfaces, and allows the user to have a global view of the data before flexibly defining cell subsets. Spectral compensation can also be performed after surface creation to accurately resolve different signals. We demonstrate the utility of this workflow in static and dynamic imaging datasets of a needlestick injury on the mouse ear, and we believe this scalable and intuitive approach will improve the ease of performing histocytometry on biological samples.

Natural killer (NK) cells belong to the innate arm of the immune system and though activated NK cells can modulate immune responses through the secretion of cytokines, their primary effector function is through target cell lysis. Accordingly, cytotoxicity assays are central to studying NK cell function. The 51Chromium release assay, is the "gold standard" for cytotoxicity assay, however, due to concerns over toxicity associated with the use and disposal of radioactive compounds there is a significant interest in non-radioactive methods. We have previously used the calcein release assay as a non-radioactive alternative for studying NK cell cytotoxicity. In this study, we show that the calcein release assay varies in its dynamic range for different tumor targets, and that the entrapped calcein could remain unreleased within apoptotic bodies of lysed tumor targets or incompletely released resulting in underestimation of percent specific lysis. To overcome these limitations, we developed a novel cytotoxicity assay using the Cellometer Vision Image Cytometer and compared this method to standard calcein release assay for measuring NK cell cytotoxicity. Using tumor lines K562, 721.221, and Jurkat, we demonstrate here that image cytometry shows significantly higher percent specific lysis of the target cells compared to the standard calcein release assay within the same experimental setup. Image cytometry is able to accurately analyze live target cells by excluding dimmer cells and smaller apoptotic bodies from viable target cell counts. The image cytometry-based cytotoxicity assay is a simple, direct and sensitive method and is an appealing option for routine cytotoxicity assay.

Immunohistochemistry (IHC) is a valuable tool in clinical and biological research for evaluating proteins and other antigens in spatially bound tissue. In neuroinflammatory pain research, primary afferent neurons of the dorsal root ganglion (DRG) are studied to understand molecular signaling mechanisms involved in nociception (pain) and inflammation. Measuring IHC (immunofluorescence) in DRG neurons requires manual hand tracing of nuclear and somatic boundaries, which is laborious, error-prone, and may require several weeks to collect the appropriate sample size with a mouse or pen-input display monitor. To overcome these limitations and increase standardization of sampling and measurement, we employed a reliable neuronal cytoplasmic reporter, exclusive to DRG neuronal soma, in a semi-automated algorithm-based approach of Image Cytometry in rat DRG (IC-DRG). The resulting output images are binary nuclear and somatic masks of DRG neurons, defining boundaries of measurement for CellProfiler and manually scored at 94% accurate. Herein, we successfully show a novel approach of automated image analysis for DRG neurons using a robust ImageJ/FIJI script, overcoming morphological variability and imaging artifacts native to imaging frozen tissue sections processed with immunofluorescence.

Organoids are three-dimensional (3D) structures that can be derived from stem cells or adult tissue progenitor cells and exhibit an extraordinary ability to autonomously organize and resemble the cellular composition and architectural integrity of specific tissue segments. This feature makes them a useful tool for analyzing therapeutical relevant aspects, including organ development, wound healing, immune disorders and drug discovery. Most organoid models do not contain cells that mimic the neighboring tissue’s microenvironment, which could potentially hinder deeper mechanistic studies. However, to use organoid models in mechanistic studies, which would enable us to better understand pathophysiological processes, it is necessary to emulate the in situ microenvironment. This can be accomplished by incorporating selected cells of interest from neighboring tissues into the organoid culture. Nevertheless, the detection and quantification of organoids in such co-cultures remains a major technical challenge. These imaging analysis approaches would require an accurate separation of organoids from the other cell types in the co-culture. To efficiently detect and analyze 3D organoids in co-cultures, we developed a high-throughput imaging analysis platform. This method integrates automated imaging techniques and advanced image processing tools such as grayscale conversion, contrast enhancement, membrane detection and structure separation. Based on machine learning algorithms, we were able to identify and classify 3D organoids within dense co-cultures of immune cells. This procedure allows a high-throughput analysis of organoid-associated parameters such as quantity, size, and shape. Therefore, the technology has significant potential to advance contextualized research using organoid co-cultures and their potential applications in translational medicine.

Modeling of cellular functions on the basis of experimental observation is increasingly common in the field of cellular signaling. However, such modeling requires a large amount of quantitative data of signaling events with high spatio-temporal resolution. A novel technique which allows us to obtain such data is needed for systems biology of cellular signaling.

The emergence of new pathogens, such as Middle East respiratory syndrome coronavirus (MERS-CoV), poses serious challenges to global public health and highlights the urgent need for methods to rapidly identify and characterize potential therapeutic or prevention options, such as neutralizing antibodies. Spike (S) proteins are present on the surface of MERS-CoV virions and mediate viral entry. S is the primary target for MERS-CoV vaccine and antibody development, and it has become increasingly important to understand MERS-CoV antibody binding specificity and function. Commonly used serological methods like ELISA, biolayer interferometry, and flow cytometry are informative, but limited. Here, we demonstrate a high-throughput protein binding inhibition assay using image cytometry. The image cytometry-based high-throughput screening method was developed by selecting a cell type with high DPP4 expression and defining optimal seeding density and protein binding conditions. The ability of monoclonal antibodies to inhibit MERS-CoV S binding was then tested. Binding inhibition results were comparable with those described in previous literature for MERS-CoV spike monomer and showed similar patterns as neutralization results. The coefficient of variation (CV) of our cell-based assay was <10%. The proposed image cytometry method provides an efficient approach for characterizing potential therapeutic antibodies for combating MERS-CoV that compares favorably with current methods. The ability to rapidly determine direct antibody binding to host cells in a high-throughput manner can be applied to study other pathogen-antibody interactions and thus can impact future research on viral pathogens.

Chemotaxis is an important aspect of immune cell behavior within the tumor microenvironment (TME). One prominent example of chemotaxis within the TME is the migration of regulatory T cells (Tregs) in response to the chemokine ligands CCL17 and CCL22. Tregs within the TME cause the suppression of anti-tumor immunity and inhibition of the effect of immunotherapeutic treatments. Therefore, the ability to screen for therapeutic antibodies that can inhibit or stimulate the chemotaxis of various immune cell types is crucial. Traditionally, chemotaxis is studied by determining the number of cells in the bottom reservoir of a Transwell microplate using flow cytometry; however, this method is time-consuming and thus not appropriate for high-throughput screening purposes. The Celigo Image Cytometer has been employed to perform high-throughput cell-based assays and was used to develop a new detection method for chemotaxis measurement. The image-based detection method was developed using chemokine ligands CCL17 and CCL22 to induce the migration of CCR4+ T cells and directly count them on the bottom of the Transwell plates. Finally, the method was applied to measure the inhibitory effects of commercially available anti-CCL17 and anti-CCL22 antibodies, which caused a dose-dependent decrease in the number of migrated T cells. The proposed image cytometry method allowed screening of multiple antibodies at various concentrations, simultaneously, which can improve the efficiency for discovering potential antibody candidates that can induce or inhibit recruitment of immune cells to the tumor microenvironment.

Natural killer (NK) cell-mediated cytotoxicity is traditionally measured using the chromium release assay, which measures the fraction of radioactive 51Cr released from dying target cells co-cultured with NK cells. However, the time frame of 51Cr release assays is limited to approximately 4 h due to spontaneous release of 51Cr. In the tumor microenvironment, interactions between NK cells and tumor cells occur over extended time periods, and NK cell-mediated cytotoxicity is modulated by cytokines produced by tumor cells and other immune cells. Here we demonstrate that the interaction of NK cells and tumor cells can be imaged and quantified over an extended period of time using a novel image cytometry method. Specifically, we imaged killing of human ZsGreen+ melanoma cells by primary human NK cells in the presence of an antibody targeting MICA and MICB on the tumor cell surface. The number of live ZsGreen+ A375 cells was counted in 96-well plates over a three day time frame, and the results were used to first calculate % specific killing at the 4 h time point to compare to 51Cr release assay. Analysis of data from the 4 h time point demonstrated that both 51Cr and image cytometry enable sensitive detection of NK cell-mediated killing of tumor cells. Image cytometry demonstrated that the combination of the MICA/B antibody and IL-2 induced near-complete eradication of A375 melanoma cells by NK cells at later time points. This novel image cytometry based approach will be suitable for the discovery of combination therapies that enhance the cytotoxic function of NK cells against tumor cells.

Neutrophil extracellular traps (NETs) formation has been implicated in an increasing number of infectious and non-infectious pathologies. NETosis is a tightly regulated process; the end-stage and read-out is the formation of DNA strands extruded from the nuclei, and traditionally assessed by fluorescence microscopy. Since NETosis has emerged as a possible biomarker of the inflammatory process, there is a need for less time-consuming, consistent, and quantitative approaches to improve its application in clinical assessment of pro-inflammatory conditions. Imaging Flow Cytometry (IFC) combines features of conventional flow cytometry with qualitative power of fluorescence microscopy and has an added advantage of the capability of assessing the early processes leading up to extrusion of the DNA-scaffolded strands. We explored the optimal imaging-based tools that can be used to measure citrullination of H4 in early NETosis. IFC identified and quantified histone 4 citrullination (H4cit3) induced with several known NETosis stimuli (Ionophore, PMA, LPS, Hemin, and IL-8) following treatment periods ranging from 2 to 60 min. Its relationship with other alterations at nuclear and cellular level, such as nuclear decondensation and super-condensation, multi-lobulated nuclei vs. 1-lobe nuclei and cell membrane damage, were also quantified. We show that the early progress of the H4cit3 response in NETosis depends on the stimulus. Our method identifies fast (Ionophore and Hemin), intermediate and slow (PMA) inducers and shows that H4cit3 appears to have a limited contribution to both early LPS- and IL-8-induced NETosis. While this method is rapid and of a higher throughput compared to fluorescence microscopy, detection and quantification is limited to H4cit3-mediated nuclear events and is likely to be stimulus- and signaling pathway dependent.

Here, we present a protocol for multivariate quantitative-image-based cytometry (QIBC) analysis by fluorescence microscopy of asynchronous adherent cells. We describe steps for the preparation, treatment, and fixation of cells, sample staining, and imaging for QIBC. We then detail image analysis with our open source Fiji script developed for QIBC and present multiparametric data visualization. Our QIBC Fiji script integrates modern artificial-intelligence-based tools, applying deep learning, for robust automated nuclei segmentation with minimal user adjustments, a major asset for efficient QIBC analysis. For complete details on the use and execution of this protocol, please refer to Besse et al. (2023).1.

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

Cellular behavior is strongly influenced by the architecture and pattern of its interfacing extracellular matrix (ECM). For an artificial culture system which could eventually benefit the translation of scientific findings into therapeutic development, the system should capture the key characteristics of a physiological microenvironment. At the same time, it should also enable standardized, high throughput data acquisition. Since an ECM is composed of different fibrous proteins, studying cellular interaction with individual fibrils will be of physiological relevance. In this study, we employ near-field electrospinning to create ordered patterns of collagenous fibrils of gelatin, based on an acetic acid and ethyl acetate aqueous co-solvent system. Tunable conformations of micro-fibrils were directly deposited onto soft polymeric substrates in a single step. We observe that global topographical features of straight lines, beads-on-strings, and curls are dictated by solution conductivity; whereas the finer details such as the fiber cross-sectional profile are tuned by solution viscosity. Using these fibril constructs as cellular assays, we study EA.hy926 endothelial cells' response to ROCK inhibition, because of ROCK's key role in the regulation of cell shape. The fibril array was shown to modulate the cellular morphology towards a pre-capillary cord-like phenotype, which was otherwise not observed on a flat 2-D substrate. Further facilitated by quantitative analysis of morphological parameters, the fibril platform also provides better dissection in the cells' response to a H1152 ROCK inhibitor. In conclusion, the near-field electrospun fibril constructs provide a more physiologically-relevant platform compared to a featureless 2-D surface, and simultaneously permit statistical single-cell image cytometry using conventional microscopy systems. The patterning approach described here is also expected to form the basics for depositing other protein fibrils, seen among potential applications as culture platforms for drug screening.

Since the FDA approval of two Chimeric Antigen Receptor (CAR) T cell therapies against CD19+ malignancies, there has been significant interest in adapting CAR technology to other diseases. As such, the ability to simultaneously monitor manufacturing criteria and functional characteristics of multiple CAR T cell products by a single instrument would likely accelerate the development of candidate therapies. Here, we demonstrate that image-based cytometry yields high-throughput measurements of CAR T cell proliferation and size, and captures the kinetics of in vitro antigen-specific CAR T cell-mediated killing. The data acquired and analyzed by the image cytometer are congruent with results derived from conventional technologies when tested contemporaneously. Moreover, the use of bright-field and fluorescence microscopy by the image cytometer provides kinetic measurements and rapid data acquisition, which are direct advantages over industry standard instruments. Together, image cytometry enables fast, reproducible measurements of CAR T cell manufacturing criteria and effector function, which can greatly facilitate the evaluation of novel CARs with therapeutic potential.

Liquid-based cytology is one of the most useful methods to diagnose a patient with serous effusion, especially when malignancy is suspected. As an alternative to the use of liquid-based cytology only, the serous effusion can be further processed using the technique of DNA image cytometry, which may augment diagnostic utility. The aim of this study was to compare the diagnostic yields of liquid-based cytology, DNA image cytometry, and both in combination, regardless of serous-effusion etiology.

Advances in automated image-based microscopy platforms coupled with high-throughput liquid workflows have facilitated the design of large-scale screens utilising multicellular model organisms such as Caenorhabditis elegans to identify genetic interactions, therapeutic drugs or disease modifiers. However, the analysis of essential genes has lagged behind because lethal or sterile mutations pose a bottleneck for high-throughput approaches, and a systematic way to analyse genetic interactions of essential genes in multicellular organisms has been lacking.

Medical artificial intelligence (AI) has been moving from the research phase to clinical implementation. However, most AI-based models are mainly built using high-quality images preprocessed in the laboratory, which is not representative of real-world settings. This dataset bias proves a major driver of AI system dysfunction. Inspired by the design of flow cytometry, DeepFundus, a deep-learning-based fundus image classifier, is developed to provide automated and multidimensional image sorting to address this data quality gap. DeepFundus achieves areas under the receiver operating characteristic curves (AUCs) over 0.9 in image classification concerning overall quality, clinical quality factors, and structural quality analysis on both the internal test and national validation datasets. Additionally, DeepFundus can be integrated into both model development and clinical application of AI diagnostics to significantly enhance model performance for detecting multiple retinopathies. DeepFundus can be used to construct a data-driven paradigm for improving the entire life cycle of medical AI practice.