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The functional properties of biofilms are intimately related to their spatial architecture. Structural data are therefore of prime importance to dissect the complex social and survival strategies of biofilms and ultimately to improve their control. Confocal laser scanning microscopy (CLSM) is the most widespread microscopic tool to decipher biofilm structure, enabling noninvasive three-dimensional investigation of their dynamics down to the single-cell scale. The emergence of fully automated high content screening (HCS) systems, associated with large-scale image analysis, has radically amplified the flow of available biofilm structural data. In this contribution, we present a HCS-CLSM protocol used to analyze biofilm four-dimensional structural dynamics at high throughput. Meta-analysis of the quantitative variables extracted from HCS-CLSM will contribute to a better biological understanding of biofilm traits.

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Resistance to anti-microbial agents is a world-wide health threat. Thus there is an urgent need for new treatments. An alternative approach to disarm pathogens consists in developing drugs targeting epigenetic modifiers. Bacterial pathogens can manipulate epigenetic regulatory systems of the host to bypass defences to proliferate and survive. One example is Legionella pneumophila, a Gram-negative intracellular pathogen that targets host chromatin with a specific, secreted bacterial SET-domain methyltransferase named RomA. This histone methyltransferase specifically methylates H3K14 during infection and is responsible for changing the host epigenetic landscape upon L. pneumophila infection. To inhibit RomA activity during infection, we developed a reliable high-content imaging screening assay, which we used to screen an in-house chemical library developed to inhibit DNA and histone methyltransferases. This assay was optimised using monocytic leukemic THP-1 cells differentiated into macrophages infected with L. pneumophila in a 96- or 384-well plate format using the Opera Phenix® (Perkin Elmer) confocal microscope, combined with Columbus™ software for automated image acquisition and analysis. H3K14 methylation was followed in infected, single cells and cytotoxicity was assessed in parallel. A first pilot screening of 477 compounds identified a potential starting point for inhibitors of H3K14 methylation.

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Plasmodium parasite resistance to antimalarial drugs is a serious threat to public health in malaria-endemic areas. Compounds that target core cellular processes like translation are highly desirable, as they should be capable of killing parasites in their liver and blood stage forms, regardless of molecular target or mechanism. Assays that can identify these compounds are thus needed. Recently, specific quantification of native Plasmodium berghei liver stage protein synthesis, as well as that of the hepatoma cells supporting parasite growth, was achieved via automated confocal feedback microscopy of the o-propargyl puromycin (OPP)-labeled nascent proteome, but this imaging modality is limited in throughput. Here, we developed and validated a miniaturized high content imaging (HCI) version of the OPP assay that increases throughput, before deploying this approach to screen the Pathogen Box. We identified only two hits; both of which are parasite-specific quinoline-4-carboxamides, and analogs of the clinical candidate and known inhibitor of blood and liver stage protein synthesis, DDD107498/cabamiquine. We further show that these compounds have strikingly distinct relationships between their antiplasmodial and translation inhibition efficacies. These results demonstrate the utility and reliability of the P. berghei liver stage OPP HCI assay for the specific, single-well quantification of Plasmodium and human protein synthesis in the native cellular context, allowing the identification of selective Plasmodium translation inhibitors with the highest potential for multistage activity.

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Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.

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The use of organoid models in biomedical research has grown substantially since their inception. As they gain popularity among scientists seeking more complex and biologically relevant systems, there is a direct need to expand and clarify potential uses of such systems in diverse experimental contexts. Herein we outline a high-content screening (HCS) platform that allows researchers to screen drugs or other compounds against three-dimensional (3D) cell culture systems in a multi-well format (384-well). Furthermore, we compare the quality of robotic liquid handling with manual pipetting and characterize and contrast the phenotypic effects detected by confocal imaging and biochemical assays in response to drug treatment. We show that robotic liquid handling is more consistent and amendable to high throughput experimental designs when compared to manual pipetting due to improved precision and automated randomization capabilities. We also show that image-based techniques are more sensitive to detecting phenotypic changes within organoid cultures than traditional biochemical assays that evaluate cell viability, supporting their integration into organoid screening workflows. Finally, we highlight the enhanced capabilities of confocal imaging in this organoid screening platform as they relate to discerning organoid drug responses in single-well co-cultures of organoids derived from primary human biopsies and patient-derived xenograft (PDX) models. Altogether, this platform enables automated, imaging-based HCS of 3D cellular models in a non-destructive manner, opening the path to complementary analysis through integrated downstream methods.

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INTRODUCTION: Bone morphogenetic proteins (BMPs) and transforming growth factors (TGF-ß) are members of the TGF-ß superfamily, known for their roles in several physiological and pathological processes. These factors are known to bind in vivo to BMP and TGF-ß receptors, respectively, which induces the phosphorylation of Smad (pSmad) transcription factors. This pathway is generally studied with Western blot and luciferase bioluminescence assay, which presents some limitations. PURPOSE: In this work, we developed and optimized a high-throughput assay to study pSmad pathways using immunofluorescence (IF) as an alternative to Western blot. We aimed to overcome the technical challenges usually faced in the classical IF assay in image acquisition, analysis, and quantification. METHODS: We used C2C12 cells as a cellular model. The cells were stimulated with BMP-2 and TGF-ß1 that were delivered either in solution (soluble) or via a biomaterial presenting the growth factor (GF), that is in a "matrix-bound" manner. Image acquisition parameters, analysis methods, and quantification of pSmads using IF were optimized for cells cultured on two types of supports: on bare glass and on a biomimetic coating made by self-assembly of the biopolymers hyaluronic acid and poly(l-lysine), which was crosslinked and then loaded with the GFs. RESULTS: We performed high-content kinetic studies of pSmad expression for cells cultured in 96-well microplates in response to soluble and matrix-bound BMP-2 and TGF-ß1. The detection limit of the IF-based assay was found to be similar to Western blot. Additionally, we provide a proof-of-concept for drug testing using inhibitors of BMP and TGF-ß receptors, under conditions where specific signaling pathways are engaged via the ligand/receptor interactions. Altogether, our findings offer perspectives for future mechanistic studies on cell signaling and for studies at the single cell level using imaging methods.

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In the rapidly evolving field of bioimaging, the integration and orchestration of findable, accessible, interoperable, and reusable (FAIR) image analysis workflows remains a challenge. We introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform; FAIR workflows; and high-performance computing (HPC) environments. BIOMERO facilitates seamless execution of FAIR workflows, particularly for large datasets from high-content or high-throughput screening. BIOMERO empowers researchers by eliminating the need for specialized knowledge, enabling scalable image processing directly from OMERO. BIOMERO notably supports the sharing and utilization of FAIR workflows between OMERO, Cytomine/BIAFLOWS, and other bioimaging communities. BIOMERO will promote the widespread adoption of FAIR workflows, emphasizing reusability, across the realm of bioimaging research. Its user-friendly interface will empower users, including those without technical expertise, to seamlessly apply these workflows to their datasets, democratizing the utilization of AI by the broader research community.

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Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

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[This corrects the article DOI: 10.3389/ftox.2024.1376118.].

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Hepatocellular carcinoma (HCC) is the major cause of cancer-associated mortality worldwide. Despite great advances have been made on the treatment of HCC, the survival rate of patients remains poor. Spindle apparatus coiled-coil protein 1 (SPDL1) is involved in the development of various cancers in humans. However, the role of SPDL1 in HCC remains unclear. In this study, we found high expression of SPDL1 in HCC tissues as compared to normal samples. In vitro, silencing of SPDL1 induced HCC cell apoptosis, and suppressed HCC cell propagation and migration. In vivo, knockdown of SPDL1 inhibited the tumor growth of HCC cells. These findings indicated the tumor-promoting role of SPDL1 in HCC. Mechanistically, we identified farnesyltransferase-beta (FNTB) as the downstream target protein of SPDL1 based on immunoprecipitation and mass spectrometry, which were confirmed by western blotting. Rescue assay determined that FNTB played a tumor promoting role in SPDL1-trigger HCC cell growth. Overexpression of FNTB recovered HCC cell viability and migration in SPDL1 knockdown cells. We also found that silencing of SPDL1 increased the sensitivity of Huh7 cells to sorafenib and lenvatinib, suggesting that SPDL1 is a new therapeutic target in HCC. Collectivity, the present study identified a new axis SPDL1/FNTB involved in the progression of HCC. Hence, SPDL1/FNTB is a potential target for the treatment of HCC.

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Cardiac development requires precise gene expression programs at each developmental stage guided by multiple signaling pathways and transcription factors (TFs). MESP1 is transiently expressed in mesoderm, and is essential for subsequent cardiac development, while the precise mechanism regulating its own transcription and mesoderm cell fate is not fully understood. Therefore, we developed a high content screen assay to identify regulators of MESP1 expression in mesodermal cells differentiated from human pluripotent stem cells (hPSCs). The screen identified CYT387, a JAK1/JAK2 kinase inhibitor, as a potent activator of MESP1 expression, which was also found to promote cardiomyocyte differentiation in vitro. Mechanistic studies found that JAK inhibition promotes MESP1 expression by reducing cytoplasmic calcium concentration and subsequently activating canonical WNT signaling. Our study identified a role of JAK signaling in early mesodermal cells, and sheds light on the connection between the JAK-STAT pathway and transcriptional regulation of MESP1, which expands our understanding of mesoderm and cardiac development.

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Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional genetic and environmental factors. Previously we revealed that HD primary fibroblasts exhibit unique features, including distinct nuclear morphology and perturbed actin cap, resembling characteristics seen in Hutchinson-Gilford Progeria Syndrome (HGPS). This study establishes a link between actin cap deficiency and cell motility in HD, which correlates with the HD patient disease severity. Here, we examined single-cell motility imaging features in HD primary fibroblasts to explore in depth the relationship between cell migration patterns and their respective HD patients' clinical severity status (premanifest, mild and severe). The single-cell analysis revealed a decline in overall cell motility in correlation with HD severity, being most prominent in severe HD subgroup and HGPS. Moreover, we identified seven distinct spatial clusters of cell migration in all groups, which their proportion varies within each group becoming a significant HD severity classifier between HD subgroups. Next, we investigated the relationship between Lamin B1 expression, serving as nuclear envelope morphology marker, and cell motility finding that changes in Lamin B1 levels are associated with specific motility patterns within HD subgroups. Based on these data we present an accurate machine learning classifier offering comprehensive exploration of cellular migration patterns and disease severity markers for future accurate drug evaluation opening new opportunities for personalized treatment approaches in this challenging disorder.

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This study demonstrated the strengths of in vivo molecular staining coupled with automated imaging analysis in Daphnia magna. A multiwell plate protocol was developed to assess mitochondrial membrane potential using the JC-1 dye. The suitability of five common anesthetics was initially tested, and 5% ethanol performed best in terms of anesthetic effects and healthy recovery. The staining conditions were optimized to 30 min staining with 2 µM JC-1 for best J-aggregate formation. The protocol was validated with the model compound carbonyl cyanide 3-chlorophenylhydrazone (CCCP) and used to measure the effect of four environmental contaminants, 2,4-dinitrophenol, triclosan, n-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), and ibuprofen, on mitochondrial health. Test organisms were imaged using an automated confocal microscope, and fluorescence intensities were automatically quantified. The effect concentrations for CCCP were lower by a factor of 30 compared with the traditional OECD 202 acute toxicity test. Mitochondrial effects were also detected at lower concentrations for all tested environmental contaminants compared to the OCED 202 test. For 2,4-dinitrophenol, mitochondria effects were detectable after 2 h exposure to environmentally relevant concentrations and predicted organism death was observed after 24 h. The high sensitivity and time efficiency of this novel automated imaging method make it a valuable tool for advancing ecotoxicological testing.

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Convalescent sera, rich in pathogen-specific antibodies, offers passive immunity to patients with infectious diseases. Screening assays using convalescent sera are crucial for evaluating therapeutic efficacy, selecting suitable serum donors, and standardizing assays. They measure antibody levels, neutralizing potential, and specificity against viruses like SARS-CoV-2, ensuring therapeutic serum contains potent antibodies. Standardized procedures enable reliable results and wider adoption of serum therapy for COVID-19. We have developed a high-content image-based assay for screening convalescent sera against SARS-CoV-2 variants. Using various cell lines, we identified optimal candidates, employed immunofluorescence to visualize infected cells, and assessed neutralizing antibody efficacy. Screening convalescent sera for therapeutic potential identified neutralizing activity against SARS-CoV-2 variants. Dose-response analysis showed variable neutralizing activity, with some sera exhibiting broad neutralization. Additionally, we explored the synergy between neutralizing sera and ß-d-N4-hydroxycytidine (NHC), an initial metabolite of molnupiravir. These assays enhance serum therapy's benefits for COVID-19 treatment and aid in understanding neutralizing activity against SARS-CoV-2 variants, addressing viral challenges.

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Genotoxic substances widely exist in the environment and the food supply, posing serious health risks due to their potential to induce DNA damage and cancer. Traditional genotoxicity assays, while valuable, are limited by insufficient sensitivity, specificity, and efficiency, particularly when applied to complex food matrices. This study introduces a multiparametric high-content analysis (HCA) for the detection of genotoxic substances in complex food matrices. The developed assay measures three genotoxic biomarkers, including γ-H2AX, p-H3, and RAD51, which enhances the sensitivity and accuracy of genotoxicity screening. Moreover, the assay effectively distinguishes genotoxic compounds with different modes of action, which not only offers a more comprehensive assessment of DNA damage and the cellular response to genotoxic stress but also provides new insights into the exploration of genotoxicity mechanisms. Notably, the five tested food matrices, including coffee, tea, pak choi, spinach, and tomato, were found not to interfere with the detection of these biomarkers under proper dilution ratios, validating the robustness and reliability of the assay for the screening of genotoxic compounds in the food industry. The integration of multiple biomarkers with HCA provides an efficient method for detecting and assessing genotoxic substances in the food supply, with potential applications in toxicology research and food safety.

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Autophagy is important for many physiological processes; and disordered autophagy can contribute to the pathogenesis of a broad range of systemic disorders. C. elegans is a useful model organism for studying the genetics of autophagy, however, current methods for studying autophagy are labor-intensive and not readily amenable to high-throughput procedures. Here we describe a fluorescent reporter, GFP::LGG-1::mKate2, which is useful for monitoring autophagic flux in live animals. In the intestine, the fusion protein is processed by endogenous ATG-4 to generate GFP::LGG-1 and mKate2 proteins. We provide data indicating that the GFP:mKate ratio is a suitable readout for measuring cellular autophagic flux. Using this reporter, we measured autophagic flux in L1 larvae to day 7 adult animals. We show that basal autophagic flux is relatively low during larval development but increases markedly in reproductive adults before decreasing with age. Furthermore, we show that wild-type, eat-2, and daf-2 mutant animals have distinct autophagic flux profiles through post-embryonic development. Finally, we demonstrate the utility of this reporter by performing a high-content small molecule screen to identify compounds that alter autophagic flux in C. elegans.

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Gnetum montanum Markgr. (Gnetaceae) is a commonly used traditional herbal medicine among the Yao ethnic group, with potential effects in preventing and treating tumors. However, the substance basis of its anti-tumor properties remains unclear. This study utilized ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to identify the chemical components of G. montanum extract (GME) and its absorbed prototypes in cynomolgus monkey plasma after oral administration. A total of 57 compounds were detected in the GME, with 14 compounds in positive ion mode and 43 compounds in negative ion mode. In the cynomolgus monkey plasma, 17 compounds were identified, with 3 compounds in positive ion mode and 14 compounds in negative ion mode. Subsequently, we utilized high content screening technology to investigate the anti-tumor effects of GME on colon cancer, lung cancer, breast cancer, gastric cancer, liver cancer, and esophageal cancer. We found that the GME exhibited significant proliferation inhibition on colon cancer cells SW480, with an IC50 value of 50.77 µg/mL. Further research using component separation and pharmacological tracking revealed that the F2 component of the GME demonstrated notable anti-tumor effects. Through UPLC-MS identification, the chemical components in the F2 fraction were identified as pinoresinol diglucoside, (+)-pinoresinol-4-O-beta-D-glucopyranoside, ursolic acid, and gnetol. In conclusion, this study contributes to elucidating the anti-tumor pharmacological basis of GME and provides robust support for future drug design and development.

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Antiretroviral therapy have significantly improved the treatment of viral infections and reduced the associated mortality and morbidity rates. However, highly effective antiretroviral therapy (HAART) may lead to an increased risk of cardiovascular diseases, which could be related to endothelial toxicity. Here, seven antiviral drugs (remdesivir, PF-00835231, ritonavir, lopinavir, efavirenz, zidovudine and abacavir) were characterized against aortic (HAEC) and pulmonary (hLMVEC) endothelial cells, using high-content microscopy. The colourimetric study (MTS test) revealed similar toxicity profiles of all antiviral drugs tested in the concentration range of 1 nM-50 µM in aortic and pulmonary endothelial cells. Conversely, the drugs' effects on morphological parameters were more pronounced in HAECs as compared with hLMVECs. Based on the antiviral drugs' effects on the cytoplasmic and nuclei architecture (analyzed by multiple pre-defined parameters including SER texture and STAR morphology), the studied compounds were classified into five distinct morphological subgroups, each linked to a specific cellular response profile. In relation to morphological subgroup classification, antiviral drugs induced a loss of mitochondrial membrane potential, elevated ROS, changed lipid droplets/lysosomal content, decreased von Willebrand factor expression and micronuclei formation or dysregulated cellular autophagy. In conclusion, based on specific changes in endothelial cytoplasm, nuclei and subcellular morphology, the distinct endothelial response was identified for remdesivir, ritonavir, lopinavir, efavirenz, zidovudine and abacavir treatments. The effects detected in aortic endothelial cells were not detected in pulmonary endothelial cells. Taken together, high-content microscopy has proven to be a robust and informative method for endothelial drug profiling that may prove useful in predicting the organ-specific endothelial toxicity of various drugs.

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Traditional Chinese medicine with rich resources in China and definite therapeutic effects on complex diseases demonstrates great development potential. However, the complex composition, the unclear pharmacodynamic substances and mechanisms of action, and the lack of reasonable methods for evaluating clinical safety and efficacy have limited the research and development of innovative drugs based on traditional Chinese medicine. The progress in cutting-edge disciplines such as artificial intelligence and biomimetics, especially the emergence of cell painting and organ-on-a-chip, helps to identify and characterize the active ingredients in traditional Chinese medicine based on the changes in model characteristics, thus providing more accurate guidance for the development and application of traditional Chinese medicine. The application of phenotypic drug discovery in the research and development of innovative drugs based on traditional Chinese medicine is gaining increasing attention. In recent years, the technology for phenotypic drug discovery keeps advancing, which improves the early discovery rate of new drugs and the success rate of drug research and development. Accordingly, phenotypic drug discovery gradually becomes a key tool for the research on new drugs. This paper discusses the enormous potential of traditional Chinese medicine in the discovery and development of innovative drugs and illustrates how the application of phenotypic drug discovery, supported by cutting-edge technologies such as cell painting, deep learning, and organ-on-a-chip, propels traditional Chinese medicine into a new stage of development.

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Disturbed flow is one of the pathological initiators of endothelial dysfunction in intimal hyperplasia (IH) which is commonly seen in vascular bypass grafts, and arteriovenous fistulas. Various in vitro disease models have been designed to simulate the hemodynamic conditions found in the vasculature. Nonetheless, prior investigations have encountered challenges in establishing a robust disturbed flow model, primarily attributed to the complex bifurcated geometries and distinctive flow dynamics. In the present study, we aim to address this gap by introducing an in vitro bypass flow model capable of inducing disturbed flow and other hemodynamics patterns through a pulsatile flow in the same model. To assess the model's validity, we employed computational fluid dynamics (CFD) to simulate hemodynamics and compared the morphology and functions of human umbilical venous endothelial cells (HUVECs) under disturbed flow conditions to those in physiological flow or stagnant conditions. CFD analysis revealed the generation of disturbed flow within the model, pinpointing the specific location in the channel where the effects of disturbed flow were observed. High-content screening, a single-cell morphological profile assessment, demonstrated that HUVECs in the disturbed flow area exhibited random orientation, and morphological features were significantly distinct compared to cells in the physiological flow or stagnant condition after a two days of flow exposure. Furthermore, HUVECs exposed to disturbed flow underwent extensive remodeling of the adherens junctions and expressed higher levels of endothelial cell activation markers compared to other hemodynamic conditions. In conclusion, our in vitro bypass flow model provides a robust platform for investigating the associations between disturbed flow pattern and vascular diseases.