Journal articles: 'High Content Imaging Analysis'

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McGee, J. "High-Content Screening: Imaging, Analysis, and Application." Journal of Biomolecular Screening 16, no. 5 (June 2011): 557–59. http://dx.doi.org/10.1177/1087057111409683.

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Hart, Charles. "High-Content Screening, Imaging, Analysis, and Application." Journal of Biomolecular Screening 17, no. 7 (July 11, 2012): 999–1001. http://dx.doi.org/10.1177/1087057112451013.

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Mattheakis, Larry. "High-Content Screening: Imaging, Analysis, and Applications." Journal of Biomolecular Screening 18, no. 7 (July 23, 2013): 845–47. http://dx.doi.org/10.1177/1087057113491048.

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Hart, Charles. "High-Content Screening, Imaging, Analysis, and Applications." Journal of Biomolecular Screening 19, no. 2 (January 16, 2014): 331–32. http://dx.doi.org/10.1177/1087057113514939.

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Jiang, Xiaoqi, Steven Wink, Bob van de Water, and Annette Kopp-Schneider. "Functional analysis of high-content high-throughput imaging data." Journal of Applied Statistics 44, no. 11 (September 30, 2016): 1903–19. http://dx.doi.org/10.1080/02664763.2016.1238048.

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Truong, Thai V., and Willy Supatto. "Toward high-content/high-throughput imaging and analysis of embryonic morphogenesis." genesis 49, no. 7 (June 24, 2011): 555–69. http://dx.doi.org/10.1002/dvg.20760.

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Shariff, Aabid, Joshua Kangas, Luis Pedro Coelho, Shannon Quinn, and Robert F. Murphy. "Automated Image Analysis for High-Content Screening and Analysis." Journal of Biomolecular Screening 15, no. 7 (May 20, 2010): 726–34. http://dx.doi.org/10.1177/1087057110370894.

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The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

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Huang, Shuguang. "Statistical Issues in Subpopulation Analysis of High Content Imaging Data." Journal of Computational Biology 17, no. 7 (July 2010): 879–94. http://dx.doi.org/10.1089/cmb.2009.0071.

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Micalessi, Maria Isabel, Gaëlle Boulet, Isabel Pintelon, Peter Verstraelen, Frans Nauwelaers, Martin Ryser, and Johannes Bogers. "High-Content Imaging in Cervical Cancer Screening." Journal of Biomolecular Screening 18, no. 1 (September 12, 2012): 135–42. http://dx.doi.org/10.1177/1087057112459748.

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A shift from conventional cytology to a molecular approach could improve cervical cancer screening. This proof-of-concept study aims to develop a high-content imaging platform for the simultaneous detection of multiple biomarkers for cervical disease. Liquid-based cytology (LBC) samples were used to optimize a dual ProExC/Ki-67 immunofluorescence staining protocol for SurePath-fixed cells. The simultaneous and automated detection of these biomarkers was performed using the BD Pathway 435 system. The ability of high-content imaging to detect dysplastic cervical cells was assessed using keratinocytes spiked with immunopositive SiHa cells and a high-grade squamous intraepithelial lesion (HSIL) LBC sample. The percentages of Ki-67- and ProExC-immunopositive objects correlated significantly with the percentages of spiked SiHa cells. The dysplastic cells of the HSIL sample could be detected using high-content cell analysis. In conclusion, high-content imaging allows the simultaneous and automated detection of Ki-67- and ProExC-immunopositive dysplastic cells in LBC specimens.

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Harris, Georgina, Taina Palosaari, Zuzana Magdolenova, Milena Mennecozzi, Jean Michel Gineste, Luis Saavedra, Anne Milcamps, et al. "Iron oxide nanoparticle toxicity testing using high-throughput analysis and high-content imaging." Nanotoxicology 9, sup1 (July 17, 2013): 87–94. http://dx.doi.org/10.3109/17435390.2013.816797.

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Conrad, Christian, and Daniel W. Gerlich. "Automated microscopy for high-content RNAi screening." Journal of Cell Biology 188, no. 4 (February 22, 2010): 453–61. http://dx.doi.org/10.1083/jcb.200910105.

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Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs.

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Talbot, Clifford B., James McGinty, David M. Grant, Ewan J. McGhee, Dylan M. Owen, Wei Zhang, Tom D. Bunney, et al. "High speed unsupervised fluorescence lifetime imaging confocal multiwell plate reader for high content analysis." Journal of Biophotonics 1, no. 6 (December 2008): 514–21. http://dx.doi.org/10.1002/jbio.200810054.

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Neal, Christopher A., Jacob G. Hodge, David S. Zamierowski, and Adam J. Mellott. "Customizable Automated High Content Image Acquisition and Analysis for Tissue Biopsies." Microscopy Today 30, no. 2 (March 2022): 30–38. http://dx.doi.org/10.1017/s155192952200044x.

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Abstract:Advancements in high content image analysis have led to an increase in the adoption of these techniques in basic science and clinical research. High-throughput approaches to imaging and image analysis require minimal user interventions, circumventing the often prohibitively time-consuming and unreliable standard manual analysis. In this study, we demonstrate how high content imaging (HCI) techniques, in combination with high content analysis (HCA), can be paired with more traditional manual analysis to quantify both micro- and macro-level features of biopsied tissue sections. High-resolution, full-color images of stained tissue were acquired and stitched together to reconstruct the entire tissue section, which enabled analyses that required accurate identification of a given region's location within the tissue section. A custom region of interest grid was generated that followed the curvature of the tissue. The composite images were used in two separate analyses: tissue layer thickness as a macro-level approach, and nuclei density as a micro-level approach. Ultimately, the flexibility of the HCI and HCA methodologies used in this study allowed for complex analysis of tissue that would not have been otherwise feasible.

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Boyd, Justin D., Ann F. Hoffman, and David Gebhard. "Society of Biomolecular Imaging and Informatics High-Content Screening/High-Content Analysis Preclinical Translational Imaging Colloquium: How Emerging Technologies in Disease Models, High-Content Imaging, and Data Analytics Are Changing Our Approach to Drug Discovery." ASSAY and Drug Development Technologies 17, no. 1 (January 2019): 3–7. http://dx.doi.org/10.1089/adt.2018.29085.col.

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Tanaka, T., T. Yoshino, Y. Maeda, T. Saeki, R. Negishi, R. Iwata, A. Kogiso, H. Dobashi, and T. Matsunaga. "High-Content Analysis of Single Cells Using a Wide-Field Imaging Sensor." ECS Transactions 75, no. 16 (September 23, 2016): 139–46. http://dx.doi.org/10.1149/07516.0139ecst.

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Chen, Dandan, Lei Yang, Xinjian Chen, Xihui Zhang, Yongming Liu, Zhengqing Guo, and Leshuai W. Zhang. "Automated contour analysis of multi-cellular spheroids spreading through high content imaging." Physical Biology 15, no. 2 (January 24, 2018): 026006. http://dx.doi.org/10.1088/1478-3975/aaa27b.

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Gelles, Jesse D., and Jerry Edward Chipuk. "Robust high-throughput kinetic analysis of apoptosis with real-time high-content live-cell imaging." Cell Death & Disease 7, no. 12 (December 2016): e2493-e2493. http://dx.doi.org/10.1038/cddis.2016.332.

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Abstract Quantitative and kinetic analyses of apoptotic cell death are integral components of exploring cell biology, measuring cellular stress responses, and performing high-throughput genomic/RNAi/drug screens. Here, we present a detailed method that integrates robust kinetic real-time high-content imaging with Annexin V labelling to provide a highly sensitive, accurate, simple and zero-handling approach to quantify extrinsic and intrinsic inducers of apoptosis. The sensitivity of this non-toxic method outperforms previous high-throughput methodologies using viability dyes or caspase-activation reporters. This method also incorporates a multiplex adaptation to integrate variability in cell number due to treatment-induced proliferation changes and the detachment of dying cells. Compared to Annexin V detection by flow cytometry, this method is 10-fold more sensitive, eliminates extensive sample handling and processing, and provides real-time kinetics of apoptosis at both single-cell and population-level resolutions.

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Zhang, Xian, Marjo Götte, Yvonne Ibig-Rehm, Ansgar Schuffenhauer, Marion Kamke, Dan Beisner, Danilo Guerini, et al. "Identification of SPPL2a Inhibitors by Multiparametric Analysis of a High-Content Ultra-High-Throughput Screen." SLAS DISCOVERY: Advancing the Science of Drug Discovery 22, no. 9 (July 21, 2017): 1106–19. http://dx.doi.org/10.1177/2472555217719834.

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The intramembrane protease signal peptide peptidase-like 2a (SPPL2a) is a potential drug target for the treatment of autoimmune diseases due to an essential role in B cells and dendritic cells. To screen a library of 1.4 million compounds for inhibitors of SPPL2a, we developed an imaging assay detecting nuclear translocation of the proteolytically released cytosolic substrate fragment. The state-of-the-art hit calling approach based on nuclear translocation resulted in numerous false-positive hits, mainly interrupting intracellular protein trafficking. To filter the false positives, we extracted 340 image-based readouts and developed a novel multiparametric analysis method that successfully triaged the primary hit list. The identified scaffolds were validated by demonstrating activity on endogenous SPPL2a and substrate CD74/p8 in B cells. The multiparametric analysis discovered diverse cellular phenotypes and provided profiles for the whole library. The principle of the presented imaging assay, the screening strategy, and multiparametric analysis are potentially applicable in future screening campaigns.

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Hoffman, Ann F., John Nolan, David F. Gebhard, Debra Nickischer, Wienand Omta, Sam Cooper, Sharon Presnell, Judi Wardwell-Swanson, and Myles Fennell. "Society of Biomolecular Imaging and Informatics High-Content Screening/High-Content Analysis Emerging Technologies in Biological Models, When and Why?" ASSAY and Drug Development Technologies 16, no. 1 (January 2018): 1–6. http://dx.doi.org/10.1089/adt.2017.29070.afh.

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Allison, Karen. "The First Automated High Content Screening System." JALA: Journal of the Association for Laboratory Automation 8, no. 3 (June 2003): 27–29. http://dx.doi.org/10.1016/s1535-5535-04-00266-7.

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With biological discovery and technology advancing in parallel, AstraZeneca (AZ) announced that it would soon take delivery of what is thought to be the first fully automated high-throughput high-content screening system. This custom designed assay platform from RTS Life Science International (RTS) automates the IN Cell Analyzer 3000 sub cellular analysis system from Amersham Biosciences. RTS has integrated its advanced scheduling system with the imaging tool to enable AZ to evaluate the effect of drug compounds on cellular processes.

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Zhou, Xiaobo, Xinhua Cao, Zach Perlman, and Stephen T. C. Wong. "A computerized cellular imaging system for high content analysis in Monastrol suppressor screens." Journal of Biomedical Informatics 39, no. 2 (April 2006): 115–25. http://dx.doi.org/10.1016/j.jbi.2005.05.008.

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Gelles, Jesse D., and Jerry Edward Chipuk. "Erratum: Robust high-throughput kinetic analysis of apoptosis with real-time high-content live-cell imaging." Cell Death & Disease 8, no. 5 (May 2017): e2758-e2758. http://dx.doi.org/10.1038/cddis.2017.156.

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Yang, Samuel J., Scott L. Lipnick, Nina R. Makhortova, Subhashini Venugopalan, Minjie Fan, Zan Armstrong, Thorsten M. Schlaeger, et al. "Applying Deep Neural Network Analysis to High-Content Image-Based Assays." SLAS DISCOVERY: Advancing the Science of Drug Discovery 24, no. 8 (July 8, 2019): 829–41. http://dx.doi.org/10.1177/2472555219857715.

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The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable features from patient-derived materials. These datasets, when combined with machine learning, potentially hold the power to elucidate the subtle patterns that stratify patients by shared pathology. In this study, we interrogated whether high-content imaging of primary skin fibroblasts, using the Cell Painting method, could reveal disease-relevant information among patients. First, we showed that technical features such as batch/plate type, plate, and location within a plate lead to detectable nuisance signals, as revealed by a pre-trained deep neural network and analysis with deep image embeddings. Using a plate design and image acquisition strategy that accounts for these variables, we performed a pilot study with 12 healthy controls and 12 subjects affected by the severe genetic neurological disorder spinal muscular atrophy (SMA), and evaluated whether a convolutional neural network (CNN) generated using a subset of the cells could distinguish disease states on cells from the remaining unseen control–SMA pair. Our results indicate that these two populations could effectively be differentiated from one another and that model selectivity is insensitive to batch/plate type. One caveat is that the samples were also largely separated by source. These findings lay a foundation for how to conduct future studies exploring diseases with more complex genetic contributions and unknown subtypes.

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Wang, Zhiyong, Peter A. McPherson, Brianne S. Raccor, Raghavan Balachandran, Guangyu Zhu, Billy W. Day, Andreas Vogt, and Peter Wipf. "Structure?activity and High-content Imaging Analyses of Novel Tubulysins." Chemical Biology & Drug Design 70, no. 2 (August 2007): 75–86. http://dx.doi.org/10.1111/j.1747-0285.2007.00541.x.

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Booij, Tijmen H., Leo S. Price, and Erik H. J. Danen. "3D Cell-Based Assays for Drug Screens: Challenges in Imaging, Image Analysis, and High-Content Analysis." SLAS DISCOVERY: Advancing the Science of Drug Discovery 24, no. 6 (February 28, 2019): 615–27. http://dx.doi.org/10.1177/2472555219830087.

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The introduction of more relevant cell models in early preclinical drug discovery, combined with high-content imaging and automated analysis, is expected to increase the quality of compounds progressing to preclinical stages in the drug development pipeline. In this review we discuss the current switch to more relevant 3D cell culture models and associated challenges for high-throughput screening and high-content analysis. We propose that overcoming these challenges will enable front-loading the drug discovery pipeline with better biology, extracting the most from that biology, and, in general, improving translation between in vitro and in vivo models. This is expected to reduce the proportion of compounds that fail in vivo testing due to a lack of efficacy or to toxicity.

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Kredel, Simone, Michael Wolff, Silke Hobbie, Michael Bieler, Peter Gierschik, and Ralf Heilker. "High-Content Analysis of CCR2 Antagonists on Human Primary Monocytes." Journal of Biomolecular Screening 16, no. 7 (May 3, 2011): 683–93. http://dx.doi.org/10.1177/1087057111406884.

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The monocyte chemoattractant protein 1 (MCP-1)–driven activation of CC-type chemokine receptor 2 (CCR2) is one of the early key events to induce monocyte migration toward centers of inflammation. In this work, the authors analyzed MCP-1 internalization into primary human monocytes using partially automated liquid handling, automated fluorescence microscopic imaging, and a specific image analysis algorithm. A fluorophore-conjugated form of MCP-1 was rapidly endocytosed and retained by the monocytes. The CCR2 dependency of the MCP-1 internalization was demonstrated by the use of BMS CCR2 22, a CCR2-specific antagonist. The apparent inhibitory potencies of a series of small-molecule CCR2 antagonists were determined and compared in five assay formats, including the high-content analysis assay described in this work. Interestingly, some but not all antagonists showed markedly different inhibitory behaviors in the five readout systems, with an up to more than 100-fold difference between the highest and the lowest apparent inhibitory potencies. These findings raise the distinct possibility that some CCR2 antagonists are capable of discriminating between different functional states of the CCR2 receptor(s) and suggest strategies for the identification of functionally selective CCR2 antagonists with increased therapeutic advantage over nonselective antagonists.

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Kobayashi, Kumiko, Hiroyuki Suzuki, Kayo Sumida, and Koichi Saito. "Neurite outgrowth assay using high content imaging system and analysis of neurotoxicity marker genes." Toxicology Letters 229 (September 2014): S44—S45. http://dx.doi.org/10.1016/j.toxlet.2014.06.195.

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Low, Jonathan, Shuguang Huang, Michele Dowless, Wayne Blosser, Thomas Vincent, Scott Davis, Jeff Hodson, et al. "High-Content Imaging Analysis of the Knockdown Effects of Validated siRNAs and Antisense Oligonucleotides." Journal of Biomolecular Screening 12, no. 6 (May 21, 2007): 775–88. http://dx.doi.org/10.1177/1087057107302675.

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High-content imaging (HCI) provides researchers with a powerful tool for understanding cellular processes. Although phenotypic analysis generated through HCI is a potent technique to determine the overall cellular effects of a given treatment, it frequently produces complex data sets requiring extensive interpretation. The authors developed statistical analyses to decrease the time spent to determine the outcome of each HCI assay and to better understand complex phenotypic changes. To test these tools, the authors performed a comparison experiment between 2 types of oligonucleotide-mediated gene silencing (OMGS), antisense oligonucleotides (ASOs), and short, double-stranded RNAs (siRNAs). Although similar in chemical structure, these 2 methods differ in cellular mechanism of action and off-target effects. Using a library of 50 validated ASOs and siRNAs to the same targets, the authors characterized the differential effects of these 2 technologies using a HeLa cell G2-M cell cycle assay. Although knockdown of a variety of targets by ASOs or siRNAs affected the cell cycle profile, few of those targets were affected by both ASOs and siRNAs. Distribution analysis of population changes induced through target knockdown led to the identification of targets that, when inhibited, could affect the G2-M transition in the cell cycle in a statistically significant manner. The distinctly different mechanisms of action of these 2 forms of gene silencing may help define the use of these treatments in both clinical and research environments. ( Journal of Biomolecular Screening 2007:775-788)

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Masinas, Myra Paz David, Mojca Mattiazzi Usaj, Matej Usaj, Charles Boone, and Brenda J. Andrews. "TheCellVision.org: A Database for Visualizing and Mining High-Content Cell Imaging Projects." G3: Genes|Genomes|Genetics 10, no. 11 (September 15, 2020): 3969–76. http://dx.doi.org/10.1534/g3.120.401570.

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Advances in genome engineering and high throughput imaging technologies have enabled genome-scale screens of single cells for a variety of phenotypes, including subcellular morphology and protein localization. We constructed TheCellVision.org, a freely available and web-accessible image visualization and data browsing tool that serves as a central repository for fluorescence microscopy images and associated quantitative data produced by high-content screening experiments. Currently, TheCellVision.org hosts ∼575,590 images and associated analysis results from two published high-content screening (HCS) projects focused on the budding yeast Saccharomyces cerevisiae. TheCellVision.org allows users to access, visualize and explore fluorescence microscopy images, and to search, compare, and extract data related to subcellular compartment morphology, protein abundance, and localization. Each dataset can be queried independently or as part of a search across multiple datasets using the advanced search option. The website also hosts computational tools associated with the available datasets, which can be applied to other projects and cell systems, a feature we demonstrate using published images of mammalian cells. Providing access to HCS data through websites such as TheCellVision.org enables new discovery and independent re-analyses of imaging data.

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Ma, Yanli, Huini Liang, and Yingying Jin. "Data Analysis of Nursing Effects in Pediatric Gastroenterology Department under High Content Image Analysis Technology." Contrast Media & Molecular Imaging 2022 (July 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/4302331.

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Objective. Based on the nursing analysis of children aged 0∼3 in the Department of Gastroenterology, from the perspective of nursing methods and nutrition, bird’s nest nursing and high-quality nursing methods were used to intervene children to help them improve their gastrointestinal conditions and sleep quality (Su-Jin, 2021). Methods. A total of 2465 healthy children under 3 years were selected, with normal action and intelligence and accompanied by their parents. Among them, there were 635 children in the observation group (O group) and 355 children in the reference group (R group) in the bird’s nest nursing group, 572 children in the O group and 603 children in the R group in the other high-quality nursing group. The observation group received routine nursing, while the control group received nest nursing and quality nursing. The height growth rate, weight growth rate, brain weight growth rate, blood cell routine, and serum biochemical examination results of infants aged 6 months and 3 years in the O group, image analysis technology is used to analyze and discuss the gastrointestinal imaging examination of children, so as to conduct more detailed and accurate research. Results. Through the analysis and comparison of the physiological index data of 6-month-old and 3-year-old infants in the observation group and the reference group, and the satisfaction of parents, it was found that the children’s physiological indexes, gastrointestinal digestion, weight gain, and brain weight gain rate through bird’s nest care and high-quality care were slightly better than those in the observation group, and the parents’ satisfaction was also slightly higher. Conclusion. Wei et al. (2020). By creating a comfortable environment and simulating the children’s growth environment under the mother’s care, it is found that the bird’s nest care and high-quality care programs can calm children’s emotions, increase children’s appetite and digestion, so as to promote children’s gastrointestinal digestion and achieve the goal of healthy growth (Wei et al, 2020). Finally, the conclusion is drawn that starting from the needs of children, scientific and correct gastrointestinal care and dietary nutrition should be used to promote the healthy growth of children.

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Panchal, Rekha G., Krishna P. Kota, Kevin B. Spurgers, Gordon Ruthel, Julie P. Tran, Robert C. “Dutch” Boltz, and Sina Bavari. "Development of High-Content Imaging Assays for Lethal Viral Pathogens." Journal of Biomolecular Screening 15, no. 7 (July 16, 2010): 755–65. http://dx.doi.org/10.1177/1087057110374357.

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Filoviruses such as Ebola (EBOV) and Marburg (MARV) are single-stranded negative sense RNA viruses that cause acute hemorrhagic fever with high mortality rates. Currently, there are no licensed vaccines or therapeutics to counter filovirus infections in humans. The development of higher throughput/high-content primary screening assays followed by validation using the low-throughput traditional plaque or real-time PCR assays will greatly aid efforts toward the discovery of novel antiviral therapeutics. Specifically, high-content imaging technology is increasingly being applied for primary drug screening. In this study, the authors describe the challenges encountered when optimizing bioassays based on image acquisition and analyses for the highly pathogenic filoviruses Ebola and Marburg. A number of biological and imaging-related variables such as plating density, multiplicity of infection, the number of fields scanned per well, fluorescence intensity, and the cell number analyzed were evaluated during the development of these assays. Furthermore, the authors demonstrate the benefits related to the statistical analyses of single-cell data to account for heterogeneity in the subcellular localization and whole-cell integrated intensity of the viral antigen staining pattern. In conclusion, they show that image-based methods represent powerful screening tools for identifying antiviral compounds for highly pathogenic viruses.

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Wardwell-Swanson, Judith, Mahomi Suzuki, Karen G. Dowell, Manuela Bieri, Eva C. Thoma, Irina Agarkova, Francesca Chiovaro, et al. "A Framework for Optimizing High-Content Imaging of 3D Models for Drug Discovery." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 7 (June 2, 2020): 709–22. http://dx.doi.org/10.1177/2472555220929291.

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Three-dimensional (3D) spheroid models are rapidly gaining favor for drug discovery applications due to their improved morphological characteristics, cellular complexity, long lifespan in culture, and higher physiological relevance relative to two-dimensional (2D) cell culture models. High-content imaging (HCI) of 3D spheroid models has the potential to provide valuable information to help researchers untangle disease pathophysiology and assess novel therapies more effectively. The transition from 2D monolayer models to dense 3D spheroids in HCI applications is not trivial, however, and requires 3D-optimized protocols, instrumentation, and resources. Here, we discuss considerations for moving from 2D to 3D models and present a framework for HCI and analysis of 3D spheroid models in a drug discovery setting. We combined scaffold-free, multicellular spheroid models with scalable, automation-compatible plate technology enabling image-based applications ranging from high-throughput screening to more complex, lower-throughput microphysiological systems of organ networks. We used this framework in three case studies: investigation of lipid droplet accumulation in a human liver nonalcoholic steatohepatitis (NASH) model, real-time immune cell interactions in a multicellular 3D lung cancer model, and a high-throughput screening application using a 3D co-culture model of gastric carcinoma to assess dose-dependent drug efficacy and specificity. The results of these proof-of-concept studies demonstrate the potential for high-resolution image-based analysis of 3D spheroid models for drug discovery applications, and confirm that cell-level and temporal-spatial analyses that fully exploit multicellular features of spheroid models are not only possible but soon will be routine practice in drug discovery workflows.

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Marescotti, Diego, David Bovard, Moran Morelli, Antonin Sandoz, Karsta Luettich, Stefan Frentzel, Manuel Peitsch, and Julia Hoeng. "In Vitro High-Content Imaging-Based Phenotypic Analysis of Bronchial 3D Organotypic Air–Liquid Interface Cultures." SLAS TECHNOLOGY: Translating Life Sciences Innovation 25, no. 3 (January 23, 2020): 247–52. http://dx.doi.org/10.1177/2472630319895473.

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High-content imaging (HCI) is a powerful method for quantifying biological effects in vitro. Historically, HCI has been applied to adherent cells growing in monolayers. With the advent of confocal versions of HCI devices, researchers now have the option of performing analyses on 3D cell cultures. However, some obstacles remain in integrating the third dimension, such as limited light penetration and less sophisticated image analysis. Here, we report the development of an HCI technique for imaging human bronchial 3D organotypic air–liquid interface (ALI) cultures (hBR-ALI). In this method, we monitored differentiation status through HCI evaluation markers representative of ciliated epithelial cells and goblet cells (Muc5AC [mucin 5AC]). As a second use case for demonstrating the utility of this technique, we induced goblet cell hyperplasia in hBR-ALI by using interleukin (IL)-13. Our results demonstrate the utility of the HCI technique for imaging hBR-ALI grown on Transwell inserts. This technique may be expanded to other cell culture systems, such as skin epithelia and 3D intestinal systems.

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Alworth, Samuel V., Hirotada Watanabe, and James S. J. Lee. "Teachable, High-Content Analytics for Live-Cell, Phase Contrast Movies." Journal of Biomolecular Screening 15, no. 8 (July 16, 2010): 968–77. http://dx.doi.org/10.1177/1087057110373546.

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CL-Quant is a new solution platform for broad, high-content, live-cell image analysis. Powered by novel machine learning technologies and teach-by-example interfaces, CL-Quant provides a platform for the rapid development and application of scalable, high-performance, and fully automated analytics for a broad range of live-cell microscopy imaging applications, including label-free phase contrast imaging. The authors used CL-Quant to teach off-the-shelf universal analytics, called standard recipes, for cell proliferation, wound healing, cell counting, and cell motility assays using phase contrast movies collected on the BioStation CT and BioStation IM platforms. Similar to application modules, standard recipes are intended to work robustly across a wide range of imaging conditions without requiring customization by the end user. The authors validated the performance of the standard recipes by comparing their performance with truth created manually, or by custom analytics optimized for each individual movie (and therefore yielding the best possible result for the image), and validated by independent review. The validation data show that the standard recipes’ performance is comparable with the validated truth with low variation. The data validate that the CL-Quant standard recipes can provide robust results without customization for live-cell assays in broad cell types and laboratory settings.

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Chin, Marcus Y., Jether Amos Espinosa, Grace Pohan, Sarine Markossian, and Michelle R. Arkin. "Reimagining dots and dashes: Visualizing structure and function of organelles for high-content imaging analysis." Cell Chemical Biology 28, no. 3 (March 2021): 320–37. http://dx.doi.org/10.1016/j.chembiol.2021.01.016.

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Masataka, Yanagawa. "Toward Next Generation High-Content Analysis with Automated Multicolor Single-Molecule Imaging in Living Cells." Proceedings for Annual Meeting of The Japanese Pharmacological Society 95 (2022): 3—S39–2. http://dx.doi.org/10.1254/jpssuppl.95.0_3-s39-2.

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Trask, O. Joseph, Amanda Moore, and Edward L. LeCluyse. "A Micropatterned Hepatocyte Coculture Model for Assessment of Liver Toxicity Using High-Content Imaging Analysis." ASSAY and Drug Development Technologies 12, no. 1 (January 2014): 16–27. http://dx.doi.org/10.1089/adt.2013.525.

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Ranade, Aarati R., Melinda S. Wilson, Amy M. McClanahan, and Andrew J. Ball. "High Content Imaging and Analysis Enable QuantitativeIn SituAssessment of CYP3A4 Using Cryopreserved Differentiated HepaRG Cells." Journal of Toxicology 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/291054.

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High-throughput imaging-based hepatotoxicity studies capable of analyzing individual cellsin situhold enormous promise for drug safety testing but are frequently limited by a lack of sufficient metabolically competent human cells. This study examined cryopreserved HepaRG cells, a human liver cell line which differentiates into both hepatocytes and biliary epithelial cells, to determine if these cells may represent a suitable metabolically competent cellular model for novel High Content Analysis (HCA) applications. Characterization studies showed that these cells retain many features characteristic of primary human hepatocytes and display significant CYP3A4 and CYP1A2 induction, unlike the HepG2 cell line commonly utilized for HCA studies. Furthermore, this study demonstrates that CYP3A4 induction can be quantified via a simple image analysis-based method, using HepaRG cells as a model system. Additionally, data demonstrate that the hepatocyte and biliary epithelial subpopulations characteristic of HepaRG cultures can be separated during analysis simply on the basis of nuclear size measurements. Proof of concept studies with fluorescent cell function reagents indicated that further multiparametric image-based assessment is achievable with HepaRG. In summary, image-based screening of metabolically competent human hepatocyte models cells such as HepaRG offers novel approaches for hepatotoxicity assessment and improved drug screening tools.

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Kelly, Douglas J., Sean C. Warren, Dominic Alibhai, Sunil Kumar, Yuriy Alexandrov, Ian Munro, Anca Margineanu, et al. "Automated multiwell fluorescence lifetime imaging for Förster resonance energy transfer assays and high content analysis." Analytical Methods 7, no. 10 (2015): 4071–89. http://dx.doi.org/10.1039/c5ay00244c.

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Hanamura, Kenji, Yuko Sekino, and Tomoaki Shirao. "High-content imaging analysis for detecting the status of synapse using drebrin in cultured neurons." Proceedings for Annual Meeting of The Japanese Pharmacological Society WCP2018 (2018): PO4–1–5. http://dx.doi.org/10.1254/jpssuppl.wcp2018.0_po4-1-5.

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Tanaka, Tsuyoshi, Tatsuya Saeki, Yoshihiko Sunaga, and Tadashi Matsunaga. "High-content analysis of single cells directly assembled on CMOS sensor based on color imaging." Biosensors and Bioelectronics 26, no. 4 (December 2010): 1460–65. http://dx.doi.org/10.1016/j.bios.2010.07.081.

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Bee, Weilin Tiger, Wensheng Xie, Maggie Truong, Matthew Will, Brandon Turunen, William J. Zuercher, Lynette McMillan, et al. "The Development of a High-Content Screening Binding Assay for the Smoothened Receptor." Journal of Biomolecular Screening 17, no. 7 (May 29, 2012): 900–911. http://dx.doi.org/10.1177/1087057112447872.

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In this study, the development of an image-based high-content screening (HCS) binding assay for the seven-transmembrane (7TM) receptor Smoothened (Smo) is described. Using BacMam-based gene delivery of Smo, BODIPY-cyclopamine as a fluorescent probe, and a confocal imaging system, a robust 384-well assay that could be used for high-throughput compound profiling activities was developed. The statistically robust HCS binding assay was developed through optimization of multiple parameters, including cell transduction conditions, Smo expression levels, the image analysis algorithm, and staining procedures. Evaluation of structurally diverse compounds, including functional Smo activators, inhibitors, and related analogs, demonstrated good compound potency correlations between high-content imaging binding, membrane fluorescence polarization binding, and gene reporter assays. Statistical analysis of data from a screening test set of compounds at a single 10-µM concentration suggested that the high-content imaging Smo binding assay is amenable for use in hit identification. The 384-well HCS assay was rapidly developed and met statistical assay performance targets, thus demonstrating its utility as a fluorescent whole-cell binding assay suitable for compound screening and profiling.

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Dorval, Thierry, Arnaud Ogier, Auguste Genovesio, Hye Kuyon Lim, Do Yoon Kwon, Joo-Hyun Lee, Howard J. Worman, William Dauer, and Regis Grailhe. "Contextual Automated 3D Analysis of Subcellular Organelles Adapted to High-Content Screening." Journal of Biomolecular Screening 15, no. 7 (July 16, 2010): 847–57. http://dx.doi.org/10.1177/1087057110374993.

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Advances in automated imaging microscopy allow fast acquisitions of multidimensional biological samples. Those microscopes open new possibilities for analyzing subcellular structures and spatial cellular arrangements. In this article, the authors describe a 3D image analysis framework adapted to medium-throughput screening. Upon adaptive and regularized segmentation, followed by precise 3D reconstruction, they achieve automatic quantification of numerous relevant 3D descriptors related to the shape, texture, and fluorescence intensity of multiple stained subcellular structures. A global analysis of the 3D reconstructed scene shows additional possibilities to quantify the relative position of organelles. Implementing this methodology, the authors analyzed the subcellular reorganization of the nucleus, the Golgi apparatus, and the centrioles occurring during the cell cycle. In addition, they quantified the effect of a genetic mutation associated with the early onset primary dystonia on the redistribution of torsinA from the bulk endoplasmic reticulum to the perinuclear space of the nuclear envelope. They show that their method enables the classification of various translocation levels of torsinA and opens the possibility for compound-based screening campaigns restoring the normal torsinA phenotype.

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Azegrouz, Hind, Gopal Karemore, Alberto Torres, Carlos M. Alaíz, Ana M. Gonzalez, Pedro Nevado, Alvaro Salmerón, et al. "Cell-Based Fuzzy Metrics Enhance High-Content Screening (HCS) Assay Robustness." Journal of Biomolecular Screening 18, no. 10 (September 17, 2013): 1270–83. http://dx.doi.org/10.1177/1087057113501554.

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High-content screening (HCS) allows the exploration of complex cellular phenotypes by automated microscopy and is increasingly being adopted for small interfering RNA genomic screening and phenotypic drug discovery. We introduce a series of cell-based evaluation metrics that have been implemented and validated in a mono-parametric HCS for regulators of the membrane trafficking protein caveolin 1 (CAV1) and have also proved useful for the development of a multiparametric phenotypic HCS for regulators of cytoskeletal reorganization. Imaging metrics evaluate imaging quality such as staining and focus, whereas cell biology metrics are fuzzy logic–based evaluators describing complex biological parameters such as sparseness, confluency, and spreading. The evaluation metrics were implemented in a data-mining pipeline, which first filters out cells that do not pass a quality criterion based on imaging metrics and then uses cell biology metrics to stratify cell samples to allow further analysis of homogeneous cell populations. Use of these metrics significantly improved the robustness of the monoparametric assay tested, as revealed by an increase in Z′ factor, Kolmogorov-Smirnov distance, and strict standard mean difference. Cell biology evaluation metrics were also implemented in a novel supervised learning classification method that combines them with phenotypic features in a statistical model that exceeded conventional classification methods, thus improving multiparametric phenotypic assay sensitivity.

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Andrews, S. B., N. B. Pivovarova, J. Hongpaisan, and R. D. Leapman. "High-Resolution Analysis of Rapidly Frozen Biological Specimens: Capabilities and Limitations." Microscopy and Microanalysis 5, S2 (August 1999): 412–13. http://dx.doi.org/10.1017/s1431927600015385.

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The past decade has seen major advances in the analytical capability and utility of both fixed beam and scanning beam electron microscopes. In particular, scanning transmission electron microscopy (STEM) and energy-filtering transmission microscopy (EFTEM) have benefited from the development of devices and techniques—including improved electron optics, sensitive solid-state detectors and new software for imaging and electron energy loss spectroscopy (EELS)—that optimize detection of weak spectroscopic signals arising from biological specimens while minimizing specimen damage. Here we discuss and illustrate some of these advances, especially in the context of structural imaging, detection limits and mapping techniques for the biologically important elements phosphorus and calcium. Analytical microscopy of biological tissues is absolutely dependent on cryotechniques. It is generally agreed that rapid freezing and subsequent low-temperature processing, e.g., cryosectioning or direct cryotransfer of frozen-hydrated specimens, is the most reliable way to preserve the native distribution and organization of biological structures. Equally important, however, as an adjunct to spectroscopic analysis is the use of established low-temperature, low-dose techniques for recording optimized images. By limiting beam exposure, low-dose methods greatly improve the quality of images from fragile, freeze-dried preparations. In this case, the quality and information content of, e.g., cryosections are virtually as good as conventional preparations (Fig 1).

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Joseph, Jeena, Mahendra Seervi, Praveen K. Sobhan, and Santhoshkumar Thankayyan Retnabai. "High Throughput Ratio Imaging to Profile Caspase Activity: Potential Application in Multiparameter High Content Apoptosis Analysis and Drug Screening." PLoS ONE 6, no. 5 (May 27, 2011): e20114. http://dx.doi.org/10.1371/journal.pone.0020114.

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Taylor, D. Lansing. "A Personal Perspective on High-Content Screening (HCS)." Journal of Biomolecular Screening 15, no. 7 (July 16, 2010): 720–25. http://dx.doi.org/10.1177/1087057110374995.

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High-content screening (HCS) was introduced in 1997 based on light microscope imaging technologies to address the need for an automated platform that could analyze large numbers of individual cells with subcellular resolution using standard microplates. Molecular specificity based on fluorescence was a central element of the platform taking advantage of the growing list of reagent classes and the ability to multiplex. In addition, image analysis coupled to data management, data mining, and data visualization created a tool that focused on biological information and knowledge to begin exploring the functions of genes identified in the genomics revolution. This overview looks at the development of HCS, the evolution of the technologies, and the market up to the present day. In addition, the options for adopting uniform definitions is suggested along with a perspective on what advances are needed to continue building the value of HCS in biomedical research, drug discovery, and development and diagnostics.

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Paluri, Sravanti, Mohammed Shavezipur, Dennis R. Heldman, and Fatemeh Maleky. "Analysis of moisture diffusion mechanism in structured lipids using magnetic resonance imaging." RSC Advances 5, no. 94 (2015): 76904–11. http://dx.doi.org/10.1039/c5ra13882e.

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Dalton, Lauren E., Björn D. M. Bean, Michael Davey, and Elizabeth Conibear. "Quantitative high-content imaging identifies novel regulators of Neo1 trafficking at endosomes." Molecular Biology of the Cell 28, no. 11 (June 2017): 1539–50. http://dx.doi.org/10.1091/mbc.e16-11-0772.

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P4-ATPases are a family of putative phospholipid flippases that regulate lipid membrane asymmetry, which is important for vesicle formation. Two yeast flippases, Drs2 and Neo1, have nonredundant functions in the recycling of the synaptobrevin-like v-SNARE Snc1 from early endosomes. Drs2 activity is needed to form vesicles and regulate its own trafficking, suggesting that flippase activity and localization are linked. However, the role of Neo1 in endosomal recycling is not well characterized. To identify novel regulators of Neo1 trafficking and activity at endosomes, we first identified mutants with impaired recycling of a Snc1-based reporter and subsequently used high-content microscopy to classify these mutants based on the localization of Neo1 or its binding partners, Mon2 and Dop1. This analysis identified a role for Arl1 in stabilizing the Mon2/Dop1 complex and uncovered a new function for Vps13 in early endosome recycling and Neo1 localization. We further showed that the cargo-selective sorting nexin Snx3 is required for Neo1 trafficking and identified an Snx3 sorting motif in the Neo1 N-terminus. Of importance, the Snx3-dependent sorting of Neo1 was required for the correct sorting of another Snx3 cargo protein, suggesting that the incorporation of Neo1 into recycling tubules may influence their formation.

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Wheeler, Nicolas J., Kendra J. Gallo, Elena J. G. Rehborg, Kaetlyn T. Ryan, John D. Chan, and Mostafa Zamanian. "wrmXpress: A modular package for high-throughput image analysis of parasitic and free-living worms." PLOS Neglected Tropical Diseases 16, no. 11 (November 18, 2022): e0010937. http://dx.doi.org/10.1371/journal.pntd.0010937.

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Advances in high-throughput and high-content imaging technologies require concomitant development of analytical software capable of handling large datasets and generating relevant phenotypic measurements. Several tools have been developed to analyze drug response phenotypes in parasitic and free-living worms, but these are siloed and often limited to specific instrumentation, worm species, and single phenotypes. No unified tool exists to analyze diverse high-content phenotypic imaging data of worms and provide a platform for future extensibility. We have developed wrmXpress, a unified framework for analyzing a variety of phenotypes matched to high-content experimental assays of free-living and parasitic nematodes and flatworms. We demonstrate its utility for analyzing a suite of phenotypes, including motility, development/size, fecundity, and feeding, and establish the package as a platform upon which to build future custom phenotypic modules. We show that wrmXpress can serve as an analytical workhorse for anthelmintic screening efforts across schistosomes, filarial nematodes, and free-living model nematodes and holds promise for enabling collaboration among investigators with diverse interests.

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Journal articles on the topic 'High Content Imaging Analysis'

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