Статті в журналах: "High-Content automated microscopy"

1

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.

2

Wang, Jun, Xiaobo Zhou, Pamela L. Bradley, Shih-Fu Chang, Norbert Perrimon, and Stephen T. C. Wong. "Cellular Phenotype Recognition for High-Content RNA Interference Genome-Wide Screening." Journal of Biomolecular Screening 13, no. 1 (November 26, 2007): 29–39. http://dx.doi.org/10.1177/1087057107311223.

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Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein RacV12. The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis. ( Journal of Biomolecular Screening 2008:29-39)

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Kraus, Oren Z., Ben T. Grys, Jimmy Ba, Yolanda Chong, Brendan J. Frey, Charles Boone, and Brenda J. Andrews. "Automated analysis of high‐content microscopy data with deep learning." Molecular Systems Biology 13, no. 4 (April 2017): 924. http://dx.doi.org/10.15252/msb.20177551.

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4

Nghi, Do Huu, and Le Mai Huong. "APPLICATION OF IMAGE-BASED HIGH CONTENT ANALYSIS FOR THE SCREENING OF BIOACTIVE NATURAL PRODUCTS." Vietnam Journal of Science and Technology 56, no. 4A (October 19, 2018): 1. http://dx.doi.org/10.15625/2525-2518/56/4a/13065.

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Each bioactive compound induces phenotypic changes in target cells that can be made visible by labelling selected molecules of the cells with fluorescent dyes and/or directly observed under the high-throughput microscope. A comparison of the cellular phenotype induced by a compound of interest with known cellular targets allows predicting its mode of action. Over the past 15 years, high-throughput microscopy has been one of the fastest growing fields in cell biology. When combined with automated multiparametric image and data analysis, it is referred to as high-content screening (HCS). Whilst HCS has been successfully applied to the bioactivity characterization of natural products, recent studies used automated microscopy and software to increase speed and to reduce subjective interpretation. In 2017, Institute of Natural Products Chemistry (INPC-VAST) has been equipped with a HCS platform (Olympus Scan^R) that designed for fully automated image acquisition and analysis of biological samples to visually inspect the cellular morphology induced by hit compounds as well as to discriminate from false positives. Accordingly, this short review covers the concepts of HCS and its application in screening of biologically active natural products whose molecular targets could be identified through such approaches.

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Gilbert, Daniel F., Till Meinhof, Rainer Pepperkok, and Heiko Runz. "DetecTiff©: A Novel Image Analysis Routine for High-Content Screening Microscopy." Journal of Biomolecular Screening 14, no. 8 (July 29, 2009): 944–55. http://dx.doi.org/10.1177/1087057109339523.

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In this article, the authors describe the image analysis software DetecTiff©, which allows fully automated object recognition and quantification from digital images. The core module of the LabView©-based routine is an algorithm for structure recognition that employs intensity thresholding and size-dependent particle filtering from microscopic images in an iterative manner. Detected structures are converted into templates, which are used for quantitative image analysis. DetecTiff © enables processing of multiple detection channels and provides functions for template organization and fast interpretation of acquired data. The authors demonstrate the applicability of DetecTiff© for automated analysis of cellular uptake of fluorescencelabeled low-density lipoproteins as well as diverse other image data sets from a variety of biomedical applications. Moreover, the performance of DetecTiff© is compared with preexisting image analysis tools. The results show that DetecTiff© can be applied with high consistency for automated quantitative analysis of image data (e.g., from large-scale functional RNAi screening projects). ( Journal of Biomolecular Screening 2009:944-955)

6

Moreau, Dimitri, and Jean Gruenberg. "Automated Microscopy and High Content Screens (Phenotypic Screens) in Academia Labs." CHIMIA International Journal for Chemistry 70, no. 12 (December 21, 2016): 878–82. http://dx.doi.org/10.2533/chimia.2016.878.

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7

Bray, Mark-Anthony, Adam N. Fraser, Thomas P. Hasaka, and Anne E. Carpenter. "Workflow and Metrics for Image Quality Control in Large-Scale High-Content Screens." Journal of Biomolecular Screening 17, no. 2 (September 28, 2011): 266–74. http://dx.doi.org/10.1177/1087057111420292.

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Automated microscopes have enabled the unprecedented collection of images at a rate that precludes visual inspection. Automated image analysis is required to identify interesting samples and extract quantitative information for high-content screening (HCS). However, researchers are impeded by the lack of metrics and software tools to identify image-based aberrations that pollute data, limiting experiment quality. The authors have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-content microscopy experiments. They have implemented these as a versatile, open-source toolbox of algorithms and metrics readily usable by biologists to improve data quality in a wide variety of biological experiments.

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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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Wen, Yuan, Kevin A. Murach, Ivan J. Vechetti, Christopher S. Fry, Chase Vickery, Charlotte A. Peterson, John J. McCarthy, and Kenneth S. Campbell. "MyoVision: software for automated high-content analysis of skeletal muscle immunohistochemistry." Journal of Applied Physiology 124, no. 1 (January 1, 2018): 40–51. http://dx.doi.org/10.1152/japplphysiol.00762.2017.

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Analysis of skeletal muscle cross sections is an important experimental technique in muscle biology. Many aspects of immunohistochemistry and fluorescence microscopy can now be automated, but most image quantification techniques still require extensive human input, slowing progress and introducing the possibility of user bias. MyoVision is a new software package that was developed to overcome these limitations. The software improves upon previously reported automatic techniques and analyzes images without requiring significant human input and correction. When compared with data derived by manual quantification, MyoVision achieves an accuracy of ≥94% for basic measurements such as fiber number, fiber type distribution, fiber cross-sectional area, and myonuclear number. Scientists can download the software free from www.MyoVision.org and use it to automate the analysis of their own experimental data. This will improve the efficiency and consistency of the analysis of muscle cross sections and help to reduce the burden of routine image quantification in muscle biology. NEW & NOTEWORTHY Scientists currently analyze images of immunofluorescently labeled skeletal muscle using time-consuming techniques that require sustained human supervision. As well as being inefficient, these techniques can increase variability in studies that quantify morphological adaptations of skeletal muscle at the cellular level. MyoVision is new software that overcomes these limitations by performing high-content analysis of muscle cross sections with minimal manual input. It is open source and freely available.

10

Preston, K. "High-resolution image analysis." Journal of Histochemistry & Cytochemistry 34, no. 1 (January 1986): 67–74. http://dx.doi.org/10.1177/34.1.3941268.

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In many departments of cytology, cytogenetics, hematology, and pathology, research projects using high-resolution computerized microscopy are now being mounted for computation of morphometric measurements on various structural components, as well as for determination of cellular DNA content. The majority of these measurements are made in a partially automated, computer-assisted mode, wherein there is strong interaction between the user and the computerized microscope. At the same time, full automation has been accomplished for both sample preparation and sample examination for clinical determination of the white blood cell differential count. At the time of writing, approximately 1,000 robot differential counting microscopes are in the field, analyzing images of human white blood cells, red blood cells, and platelets at the overall rate of about 100,000 slides per day. This mammoth through-put represents a major accomplishment in the application of machine vision to automated microscopy for hematology. In other areas of automated high-resolution microscopy, such as cytology and cytogenetics, no commercial instruments are available (although a few metaphase-finding machines are available and other new machines have been announced during the past year). This is a disappointing product, considering the nearly half century of research effort in these areas. This paper provides examples of the state of the art in automation of cell analysis for blood smears, cervical smears, and chromosome preparations. Also treated are new developments in multi-resolution automated microscopy, where images are now being generated and analyzed by a single machine over a range of 64:1 magnification and from 10,000 X 20,000 to 500 X 500 in total picture elements (pixels). Examples of images of human lymph node and liver tissue are presented. Semi-automated systems are not treated, although there is mention of recent research in the automation of tissue analysis.

11

Mata, Gadea, Miroslav Radojević, Carlos Fernandez-Lozano, Ihor Smal, Niels Werij, Miguel Morales, Erik Meijering, and Julio Rubio. "Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning." Neuroinformatics 17, no. 2 (September 13, 2018): 253–69. http://dx.doi.org/10.1007/s12021-018-9399-4.

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Gasparri, Fabio, Paolo Cappella, and Arturo Galvani. "Multiparametric Cell Cycle Analysis by Automated Microscopy." Journal of Biomolecular Screening 11, no. 6 (June 7, 2006): 586–98. http://dx.doi.org/10.1177/1087057106289406.

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Cell cycle analysis using flow cytometry (FC) to measure cellular DNA content is a common procedure in drug mechanism of action studies. Although this technique lends itself readily to cell lines that grow in suspension, adherent cell cultures must be resuspended in a cumbersome and potentially invasive procedure that normally involves trypsinization and mechanical agitation of monolayer cultures. High-content analysis (HCA), an automated microscopy-based technology, is well suited to analysis of monolayer cell cultures but provides intrinsically less accurate determination of cellular DNA content than does FC and thus is not the method of choice for cell cycle analysis. Using Cellomics’s ArrayScan™ reader, the authors have developed a 4-color multiparametric HCA approach for cell cycle analysis of adherent cells based on detection of DNA content (4,6-diamidino-2-phenylindole [DAPI] fluorescence), together with the known cell cycle markers bromo-2-deoxyuridine (BrdU) incorporation, cyclin B1 expression, and histone H3 (Ser28) phosphorylation within a single cell population. Considering all 4 markers together, a reliable and accurate quantification of cell cycle phases was possible, as compared with flow cytometric analysis. Using this assay, specific cell cycle blocks induced by treatment with thymidine, paclitaxel, or nocodazole as test drugs were easily monitored in adherent cultures of U-2 OS osteosarcoma cells.

13

Thomas, Nick. "Review Article: High-Content Screening: A Decade of Evolution." Journal of Biomolecular Screening 15, no. 1 (December 11, 2009): 1–9. http://dx.doi.org/10.1177/1087057109353790.

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In the past decade, high-content screening has become a highly developed approach to obtaining richly descriptive quantitative phenotypic data using automated microscopy. From early use in drug screening, the technique has evolved to embrace a diverse range of applications in both academic and industrial sectors and is now widely recognized as providing an efficient and effective approach to large-scale programs investigating cell biology in situ and in context.

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Ibáñez, Glorymar, Paul A. Calder, Constantin Radu, Bhavneet Bhinder, David Shum, Christophe Antczak, and Hakim Djaballah. "Evaluation of Compound Optical Interference in High-Content Screening." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 4 (May 3, 2017): 321–29. http://dx.doi.org/10.1177/2472555217707725.

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Compound optical interference remains an inherent problem in chemical screening and has been well documented for biochemical assays and less so for automated microscopy-based assays. It has also been the assumption that the latter should not suffer from such interference because of the washing steps involved in the process, thus eliminating the residual nonspecific compound effects. Instead, these compounds may have no relevance to the actual target, and as such, compound optical interference contributes to a number of false-positives, resulting in a high attrition rate during subsequent follow-up studies. In this report, we analyze the outcome of a high-content screen using enhanced green fluorescent protein as a reporter in a gain-of-function cell-based assay in search of modulators of the micro RNA (miRNA) biogenesis pathway. Using a previously validated image-based biosensor, we screened a diverse library collection of ~315,000 compounds covering natural and synthetic derivatives in which 1130 positives were identified to enhance green fluorescence expression. Lateral confirmation and dose-response studies revealed that all of these compounds were the result of optical interference and not specific inhibition of miRNA biogenesis. Here, we highlight the chemical classes that are susceptible to compound optical interference and discuss their implications in automated microscopy-based assays.

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Menduti, Giovanna, and Marina Boido. "Recent Advances in High-Content Imaging and Analysis in iPSC-Based Modelling of Neurodegenerative Diseases." International Journal of Molecular Sciences 24, no. 19 (September 28, 2023): 14689. http://dx.doi.org/10.3390/ijms241914689.

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In the field of neurodegenerative pathologies, the platforms for disease modelling based on patient-derived induced pluripotent stem cells (iPSCs) represent a valuable molecular diagnostic/prognostic tool. Indeed, they paved the way for the in vitro recapitulation of the pathological mechanisms underlying neurodegeneration and for characterizing the molecular heterogeneity of disease manifestations, also enabling drug screening approaches for new therapeutic candidates. A major challenge is related to the choice and optimization of the morpho-functional study designs in human iPSC-derived neurons to deeply detail the cell phenotypes as markers of neurodegeneration. In recent years, the specific combination of high-throughput screening with subcellular resolution microscopy for cell-based high-content imaging (HCI) screening allowed in-depth analyses of cell morphology and neurite trafficking in iPSC-derived neuronal cells by using specific cutting-edge microscopes and automated computational assays. The present work aims to describe the main recent protocols and advances achieved with the HCI analysis in iPSC-based modelling of neurodegenerative diseases, highlighting technical and bioinformatics tips and tricks for further uses and research. To this end, microscopy requirements and the latest computational pipelines to analyze imaging data will be explored, while also providing an overview of the available open-source high-throughput automated platforms.

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Simonen, Marjo, Yvonne Ibig-Rehm, Gabriele Hofmann, Johann Zimmermann, Genevieve Albrecht, Maxime Magnier, Valerie Heidinger, and Daniela Gabriel. "High-Content Assay to Study Protein Prenylation." Journal of Biomolecular Screening 13, no. 6 (July 2008): 456–67. http://dx.doi.org/10.1177/1087057108318757.

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The mevalonate pathway leads to synthesis of cholesterol and isoprenoid lipids. Prenyltransferases attach the isoprenoid lipids to the C-terminus of several small guanosine triphosphate—binding proteins. The prenyl groups are essential for the biological activity of these proteins. The prenyltransferases and other components of the mevalonate pathway are either present or potential drug targets for cancer, osteoporosis, restenosis, or high serum cholesterol level. Until recently, cellular assays to study protein prenylation have been tedious, low-throughput assays. The authors have developed a high-content imaging-based assay to study protein prenylation. The assay is based on a green fluorescent protein (GFP) reporter, which is tagged with the prenylation motif of human H-Ras. The C-terminus of H-Ras targets GFP to the plasma membrane. When protein prenylation is inhibited, the tagged GFP cannot be localized to plasma membrane but is soluble in the cells. The localization of the GFP reporter can be analyzed in the 96- or 384-well format using automated microscopy and automated image analysis. Information about cell number and nuclear intensity can be obtained from the same images. In compound screening, these readouts provide valuable information about the toxicity of the compounds. The authors have validated their assay using several inhibitors of the mevalonate pathway as well as siRNA against farnesyl pyrophosphate synthase, a critical enzyme in the synthesis of the isoprenoid lipids. ( Journal of Biomolecular Screening 2008:456-467)

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Ge, Y., D. Zhang, X. Zhou, and Z. Zhang. "High-content Analysis in Monastrol Suppressor Screens." Methods of Information in Medicine 50, no. 03 (2011): 265–72. http://dx.doi.org/10.3414/me09-01-0030.

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SummaryObjectives: High-content screening (HCS) via automated fluorescent microscopy is a powerful technology for the effective expression of cellular processes. However, HCS will generally produce tremendous image datasets, which leads to difficulties of handling and analyzing. We proposed an automatic classification approach for simultaneous feature extraction and cell phenotype recognition of monoaster and bipolar cells in HCS system.Methods: The proposed approach was composed of image segmentation, feature extraction, and classification. The image segmentation was based on the Laplacian of Gaussian (LoG) edge detection method. For the reduction of noise effect on cellular images, we employed an adaptive threshold in microtubule channel. The principal component analysis was used in the feature selection process. The classification was performed with a back-propagation neural network (BPNN). Using the current approach, the cell phases were distinguished from three-channel acquisitions of cellular images and the numbers of bipolar and monoaster cells were automatically counted.Results: The validity of this approach was examined by the application of screening the response of drug compounds in suppressing Monastrol. Our results indicate that the proposed algorithm could improve the recognition rates of monoaster and bipolar cells to 97.98% and 93.12%, respectively, compared with 97.02% and 86.96% obtained from the same samples by multi-phenotypic mitotic analysis (MMA).Conclusions: We have shown that BPNN is a valuable tool to classify cell phenotype. To further improve the classification performance, more test data, more optimized feature selection approaches, and advanced classifier may be required and will be investigated in future works.

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Li, Zhuyin, Yongping Yan, Elaine A. Powers, Xiaoyou Ying, Khurram Janjua, Tina Garyantes, and Bruce Baron. "Identification of Gap Junction Blockers Using Automated Fluorescence Microscopy Imaging." Journal of Biomolecular Screening 8, no. 5 (October 2003): 489–99. http://dx.doi.org/10.1177/1087057103257309.

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Gap junctions coordinate electrical signals and facilitate metabolic synchronization between cells. In this study, the authors have developed a novel assay for the identification of gap junction blockers using fluorescence microscopy imaging-based high-content screening technology. In the assay, the communication between neighboring cells through gap junctions was measured by following the redistribution of a fluorescent marker. The movement of calcein dye from dye-loaded donor cells to dye-free acceptor cells through gap junctions overexpressed on cell surface membranes was monitored using automated fluorescence microscopy imaging in a high-throughput compatible format. The fluorescence imaging technology consisted of automated focusing, image acquisition, image processing, and data mining. The authors have successfully performed a high-throughput screening of a 486,000- compound program with this assay, and they were able to identify false positives without additional experiments. Selective and pharmacologically interesting compounds were identified for further optimization. ( Journal of Biomolecular Screening 2003:489-499)

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Frölich, Sonja, Rebecca Robker, and Darryl Russell. "Development of Automated Microscopy‐Assisted High‐Content Multiparametric Assays for Cell Cycle Staging and Foci Quantitation." Cytometry Part A 97, no. 4 (February 21, 2020): 378–93. http://dx.doi.org/10.1002/cyto.a.23988.

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20

Krausz, Eberhard, Ronald de Hoogt, Emmanuel Gustin, Frans Cornelissen, Thierry Grand-Perret, Lut Janssen, Nele Vloemans, et al. "Translation of a Tumor Microenvironment Mimicking 3D Tumor Growth Co-culture Assay Platform to High-Content Screening." Journal of Biomolecular Screening 18, no. 1 (August 24, 2012): 54–66. http://dx.doi.org/10.1177/1087057112456874.

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For drug discovery, cell-based assays are becoming increasingly complex to mimic more realistically the nature of biological processes and their diversifications in diseases. Multicellular co-cultures embedded in a three-dimensional (3D) matrix have been explored in oncology to more closely approximate the physiology of the human tumor microenvironment. High-content analysis is the ideal technology to characterize these complex biological systems, although running such complex assays at higher throughput is a major endeavor. Here, we report on adapting a 3D tumor co-culture growth assay to automated microscopy, and we compare various imaging platforms (confocal vs. nonconfocal) with correlating automated image analysis solutions to identify optimal conditions and settings for future larger scaled screening campaigns. The optimized protocol has been validated in repeated runs where established anticancer drugs have been evaluated for performance in this innovative assay.

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Martinent, Rémi, Javier López-Andarias, Dimitri Moreau, Yangyang Cheng, Naomi Sakai, and Stefan Matile. "Automated high-content imaging for cellular uptake, from the Schmuck cation to the latest cyclic oligochalcogenides." Beilstein Journal of Organic Chemistry 16 (August 14, 2020): 2007–16. http://dx.doi.org/10.3762/bjoc.16.167.

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Recent progress with chemistry tools to deliver into living cells has seen a shift of attention from counterion-mediated uptake of cell-penetrating peptides (CPPs) and their mimics, particularly the Schmuck cation, toward thiol-mediated uptake with cell-penetrating poly(disulfide)s (CPDs) and cyclic oligochalcogenides (COCs), here exemplified by asparagusic acid. A persistent challenge in this evolution is the simultaneous and quantitative detection of cytosolic delivery and cytotoxicity in a high-throughput format. Here, we show that the combination of the HaloTag-based chloroalkane penetration assay (CAPA) with automated high-content (HC) microscopy can satisfy this need. The automated imaging of thousands of cells per condition in multiwell plates allows us to obtain quantitative data on not only the fluorescence intensity but also on the localization in a very short time. Quantitative and statistically relevant results can be obtained from dose–response curves of the targeted delivery to selected cells and the cytotoxicity in the same experiment, even with poorly optimized cellular systems.

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Li, Tong, Hadrien Mary, Marie Grosjean, Jonathan Fouchard, Simon Cabello, Céline Reyes, Sylvie Tournier, and Yannick Gachet. "MAARS: a novel high-content acquisition software for the analysis of mitotic defects in fission yeast." Molecular Biology of the Cell 28, no. 12 (June 15, 2017): 1601–11. http://dx.doi.org/10.1091/mbc.e16-10-0723.

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Faithful segregation of chromosomes during cell division relies on multiple processes such as chromosome attachment and correct spindle positioning. Yet mitotic progression is defined by multiple parameters, which need to be quantitatively evaluated. To study the spatiotemporal control of mitotic progression, we developed a high-content analysis (HCA) approach that combines automated fluorescence microscopy with real-time quantitative image analysis and allows the unbiased acquisition of multiparametric data at the single-cell level for hundreds of cells simultaneously. The Mitotic Analysis and Recording System (MAARS) provides automatic and quantitative single-cell analysis of mitotic progression on an open-source platform. It can be used to analyze specific characteristics such as cell shape, cell size, metaphase/anaphase delays, and mitotic abnormalities including spindle mispositioning, spindle elongation defects, and chromosome segregation defects. Using this HCA approach, we were able to visualize rare and unexpected events of error correction during anaphase in wild-type or mutant cells. Our study illustrates that such an expert system of mitotic progression is able to highlight the complexity of the mechanisms required to prevent chromosome loss during cell division.

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Fetz, V., H. Prochnow, M. Brönstrup, and F. Sasse. "Target identification by image analysis." Natural Product Reports 33, no. 5 (2016): 655–67. http://dx.doi.org/10.1039/c5np00113g.

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Biologically active compounds induce phenotypic changes in target cells, which can be used to predict their modes of action. Such changes were initially detected by a visual inspection of images, while recent studies are based on high content analysis (HCA) methods using automated microscopy and analysis software.

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Haasen, Dorothea, Susanne Merk, Peter Seither, Domnic Martyres, Silke Hobbie, and Ralf Heilker. "Pharmacological Profiling of Chemokine Receptor–Directed Compounds Using High-Content Screening." Journal of Biomolecular Screening 13, no. 1 (November 26, 2007): 40–53. http://dx.doi.org/10.1177/1087057107312128.

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High-content screening, typically defined as automated fluorescence microscopy combined with image analysis, is now well established as a means to study test compound effects in cellular disease-modeling systems. In this work, the authors establish several high-content screening assays in the 384-well format to measure the activation of the CC-type chemokine receptors 2B and 3 (CCR2B, CCR3). As a cellular model system, the authors use Chinese hamster ovary cells, stably transfected with 1 of the respective receptors. They characterize receptor stimulation by human monocyte chemoattractant protein-1 for CCR2B and by human eotaxin-1 for CCR3: Receptor internalization and receptor-induced phosphorylation of ERK1/2 (pERK) were quantified using fluorescence imaging and image analysis. The 4 assay formats were robust, displayed little day-to-day variability, and delivered good Z′ statistics for both CCRs. For each of the 2 receptors, the authors evaluated the potency of inhibitory compounds in the internalization format and the pERK assay and compared the results with those from other assays (ligand displacement binding, Ca2+ mobilization, guanosine triphosphate exchange, chemotaxis). Both physiological agonists and test compounds differed significantly with respect to potencies and efficacies in the various profiling assays. The diverse assay formats delivered partially overlapping and partially complementary information, enabling the authors to reduce the probability of test compound—related technology artifacts and to specify the mode of action for individual test compounds. Transfer of the high-content screening format to a fully automated medium-throughput screening platform for CCR3 enabled the profiling of large compound numbers with respect to G protein signaling and possible tolerance-inducing liabilities. ( Journal of Biomolecular Screening 2008:40-53)

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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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Whittaker, Ross, Patricia A. Loy, Eugene Sisman, Eigo Suyama, Pedro Aza-Blanc, Randall S. Ingermanson, Jeffrey H. Price, and Patrick M. MCdonough. "Identification of MicroRNAs That Control Lipid Droplet Formation and Growth in Hepatocytes via High-Content Screening." Journal of Biomolecular Screening 15, no. 7 (July 16, 2010): 798–805. http://dx.doi.org/10.1177/1087057110374991.

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Hepatic lipid droplets (LDs) are associated with metabolic syndrome, type 2 diabetes, hepatitis C, and both alcoholic and nonalcoholic fatty liver disease. MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the level of translation. Approximately 1000 different miRNA species are encoded within the human genome, and many are differentially expressed by healthy and diseased liver. However, few studies have investigated the role of miRNAs in regulating LD expression. Accordingly, a high-content assay (HCA) was performed in which human hepatocytes (Huh-7 cells) were transiently transfected with 327 unique human miRNAs; the cells were then fixed, labeled for nuclei and lipid droplets, and imaged with an automated digital microscopy workstation. LD expression was analyzed on a cell-by-cell basis, using automated image analysis. Eleven miRNAs were identified that altered LDs. MiR-181d was the most efficacious inhibitor, decreasing LDs by about 60%. miRNA-181d was also confirmed to reduce cellular triglycerides and cholesterol ester via biochemical assays. Furthermore, a series of proteins was identified via miRNA target analysis, and siRNAs directed against many of these proteins also modified LDs. Thus, HCA-based screening identified novel miRNA and protein regulators of LDs and cholesterol metabolism that may be relevant to hepatic diseases arising from obesity and alcohol abuse.

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McDonough, Patrick M., Ramses M. Agustin, Randall S. Ingermanson, Patricia A. Loy, Benjamin M. Buehrer, James B. Nicoll, Natalie L. Prigozhina, Ivana Mikic, and Jeffrey H. Price. "Quantification of Lipid Droplets and Associated Proteins in Cellular Models of Obesity via High-Content/High-Throughput Microscopy and Automated Image Analysis." ASSAY and Drug Development Technologies 7, no. 5 (October 2009): 440–60. http://dx.doi.org/10.1089/adt.2009.0196.

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Schneidereit, Dominik, Larissa Kraus, Jochen C. Meier, Oliver Friedrich, and Daniel F. Gilbert. "Step-by-step guide to building an inexpensive 3D printed motorized positioning stage for automated high-content screening microscopy." Biosensors and Bioelectronics 92 (June 2017): 472–81. http://dx.doi.org/10.1016/j.bios.2016.10.078.

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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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Rameseder, Jonathan, Konstantin Krismer, Yogesh Dayma, Tobias Ehrenberger, Mun Kyung Hwang, Edoardo M. Airoldi, Scott R. Floyd, and Michael B. Yaffe. "A Multivariate Computational Method to Analyze High-Content RNAi Screening Data." Journal of Biomolecular Screening 20, no. 8 (April 27, 2015): 985–97. http://dx.doi.org/10.1177/1087057115583037.

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High-content screening (HCS) using RNA interference (RNAi) in combination with automated microscopy is a powerful investigative tool to explore complex biological processes. However, despite the plethora of data generated from these screens, little progress has been made in analyzing HC data using multivariate methods that exploit the full richness of multidimensional data. We developed a novel multivariate method for HCS, multivariate robust analysis method (M-RAM), integrating image feature selection with ranking of perturbations for hit identification, and applied this method to an HC RNAi screen to discover novel components of the DNA damage response in an osteosarcoma cell line. M-RAM automatically selects the most informative phenotypic readouts and time points to facilitate the more efficient design of follow-up experiments and enhance biological understanding. Our method outperforms univariate hit identification and identifies relevant genes that these approaches would have missed. We found that statistical cell-to-cell variation in phenotypic responses is an important predictor of hits in RNAi-directed image-based screens. Genes that we identified as modulators of DNA damage signaling in U2OS cells include B-Raf, a cancer driver gene in multiple tumor types, whose role in DNA damage signaling we confirm experimentally, and multiple subunits of protein kinase A.

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Garner, Kathryn L. "High content imaging for monitoring signalling dynamics in single cells." Journal of Molecular Endocrinology 65, no. 4 (November 2020): R91—R100. http://dx.doi.org/10.1530/jme-20-0169.

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All living cells are sensors of their environment: they sense signals, hormones, cytokines, and growth factors, among others. Binding of these signals to cell surface receptors initiates the transmission of messages along intracellular signalling pathways through protein–protein interactions, enzymatic modifications and conformational changes. Typically, the activation of signalling pathways are monitored in whole populations of cells, giving population average measures, often using experimental methods that destroy and homogenise the cell population. High content imaging is an automated, high-throughput fluorescence microscopy method that enables measurements of signal transduction pathways to be taken from live cells. It can be used to measure signalling dynamics, how the abundance of particular proteins of interest change over time, or to record how particular proteins move and change their localisation in response to a signal from their environment. Using this, and other single cell methods, it is becoming increasingly clear that cells are in fact very variable in their response to a given stimulus and in the quantities of cellular components they express, even in clonal (isogenic) cell lines. This review will discuss how high content imaging has contributed to our growing understanding of cellular heterogeneity. It will discuss how data generated has been combined with information theoretic approaches to quantify the amount of information transferred through noisy signalling pathways. Lastly, the relevance of heterogeneity to our understanding and treatment of disease will be considered, highlighting the importance of single cell measurements.

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Okolo, Chidinma A., Thomas M. Fish, Kamal L. Nahas, Archana C. Jadhav, Nina Vyas, Adam Taylor, and Maria Harkiolaki. "A combination of soft X-ray and laser light sources offer 3D high content information on the native state of the cellular environment." Journal of Physics: Conference Series 2380, no. 1 (December 1, 2022): 012042. http://dx.doi.org/10.1088/1742-6596/2380/1/012042.

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Abstract Beamline B24 is a life sciences correlative cryo-imaging beamline at Diamond Light Source. B24 uses a combination of conventional and super-resolution visible-light fluorescence microscopy and soft X-ray tomography (cryoSXT) to provide 3D imaging of the cellular landscape at a resolution up to 25 nm in cryo-preserved biological samples up to 12 μm thick. B24 offers user-friendly, semi-automated 3D correlative cryo-imaging through an integrated platform of methods that encompass (a) sample preparation and evaluation, (b) data collection and processing and (c) data analysis and correlation. CryoSXT fills the current resolution gap between fluorescence and electron microscopy while cryo-structured illumination microscopy provides the additional dimension of chemical localization within the same cellular ultrastructure captured by cryoSXT. Beamline instruments can be accessed biannually by academics and industry globally through peer-reviewed standard and rapid access proposal processes. The B24 user base is primarily academic research groups studying cell function and cytopathology in biological systems ranging from viruses and algae to mammalian cells and proto-tissue complexes. Future work will consolidate development efforts and experiences gained thus far to enable high-throughput data collection. Special emphasis is placed on the delivery of other integrated advanced imaging methods such as X-ray absorption near-edge spectroscopy and phase contrast.

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Isherwood, Beverley J., Rebecca E. Walls, Mark E. Roberts, Thomas M. Houslay, Sandra R. Brave, Simon T. Barry, and Neil O. Carragher. "High-Content Analysis to Leverage a Robust Phenotypic Profiling Approach to Vascular Modulation." Journal of Biomolecular Screening 18, no. 10 (October 9, 2013): 1246–59. http://dx.doi.org/10.1177/1087057113499775.

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Phenotypic screening seeks to identify substances that modulate phenotypes in a desired manner with the aim of progressing first-in-class agents. Successful campaigns require physiological relevance, robust screening, and an ability to deconvolute perturbed pathways. High-content analysis (HCA) is increasingly used in cell biology and offers one approach to prosecution of phenotypic screens, but challenges exist in exploitation where data generated are high volume and complex. We combine development of an organotypic model with novel HCA tools to map phenotypic responses to pharmacological perturbations. We describe implementation for angiogenesis, a process that has long been a focus for therapeutic intervention but has lacked robust models that recapitulate more completely mechanisms involved. The study used human primary endothelial cells in co-culture with stromal fibroblasts to model multiple aspects of angiogenic signaling: cell interactions, proliferation, migration, and differentiation. Multiple quantitative descriptors were derived from automated microscopy using custom-designed algorithms. Data were extracted using a bespoke informatics platform that integrates processing, statistics, and feature display into a streamlined workflow for building and interrogating fingerprints. Ninety compounds were characterized, defining mode of action by phenotype. Our approach for assessing phenotypic outcomes in complex assay models is robust and capable of supporting a range of phenotypic screens at scale.

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Vianello, Caterina, Federica Dal Bello, Sang Hun Shin, Sara Schiavon, Camilla Bean, Ana Paula Magalhães Rebelo, Tomáš Knedlík, et al. "High-Throughput Microscopy Analysis of Mitochondrial Membrane Potential in 2D and 3D Models." Cells 12, no. 7 (April 5, 2023): 1089. http://dx.doi.org/10.3390/cells12071089.

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Recent proteomic, metabolomic, and transcriptomic studies have highlighted a connection between changes in mitochondria physiology and cellular pathophysiological mechanisms. Secondary assays to assess the function of these organelles appear fundamental to validate these -omics findings. Although mitochondrial membrane potential is widely recognized as an indicator of mitochondrial activity, high-content imaging-based approaches coupled to multiparametric to measure it have not been established yet. In this paper, we describe a methodology for the unbiased high-throughput quantification of mitochondrial membrane potential in vitro, which is suitable for 2D to 3D models. We successfully used our method to analyze mitochondrial membrane potential in monolayers of human fibroblasts, neural stem cells, spheroids, and isolated muscle fibers. Moreover, by combining automated image analysis and machine learning, we were able to discriminate melanoma cells from macrophages in co-culture and to analyze the subpopulations separately. Our data demonstrated that our method is a widely applicable strategy for large-scale profiling of mitochondrial activity.

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Moreno-Andrés, Daniel, Anuk Bhattacharyya, Anja Scheufen, and Johannes Stegmaier. "LiveCellMiner: A new tool to analyze mitotic progression." PLOS ONE 17, no. 7 (July 7, 2022): e0270923. http://dx.doi.org/10.1371/journal.pone.0270923.

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Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

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Nardou, Katya, Michael Nicolas, Fabien Kuttler, Katarina Cisarova, Elifnaz Celik, Mathieu Quinodoz, Nicolo Riggi, et al. "Identification of New Vulnerabilities in Conjunctival Melanoma Using Image-Based High Content Drug Screening." Cancers 14, no. 6 (March 19, 2022): 1575. http://dx.doi.org/10.3390/cancers14061575.

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Recent evidence suggests that numerous similarities exist between the genomic landscapes of both conjunctival and cutaneous melanoma. Since alterations of several components of the MAP kinases, PI3K/mTOR, and cell cycle pathways have been reported in conjunctival melanoma, we decided to assess the sensitivity of conjunctival melanoma to targeted inhibition mostly of kinase inhibitors. A high content drug screening assay based on automated fluorescence microscopy was performed in three conjunctival melanoma cell lines with different genomic backgrounds with 489 kinase inhibitors and 53 other inhibitors. IC50 and apoptosis induction were respectively assessed for 53 and 48 compounds. The genomic background influenced the response to MAK and PI3K/mTOR inhibition, more specifically cell lines with BRAF V600E mutations were more sensitive to BRAF/MEK inhibition, while CRMM2 bearing the NRASQ61L mutation was more sensitive to PI3k/mTOR inhibition. All cell lines demonstrated sensitivity to cell cycle inhibition, being more pronounced in CRMM2, especially with polo-like inhibitors. Our data also revealed new vulnerabilities to Hsp90 and Src inhibition. This study demonstrates that the genomic background partially influences the response to targeted therapy and uncovers a large panel of potential vulnerabilities in conjunctival melanoma that may expand available options for the management of this tumor.

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Laan, Sebastiaan N. J., Richard J. Dirven, Petra E. Bürgisser, Jeroen Eikenboom, and Ruben Bierings. "Automated segmentation and quantitative analysis of organelle morphology, localization and content using CellProfiler." PLOS ONE 18, no. 6 (June 14, 2023): e0278009. http://dx.doi.org/10.1371/journal.pone.0278009.

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One of the most used and versatile methods to study number, dimensions, content and localization of secretory organelles is confocal microscopy analysis. However, considerable heterogeneity exists in the number, size and shape of secretory organelles that can be present in the cell. One thus needs to analyze large numbers of organelles for valid quantification. Properly evaluating these parameters requires an automated, unbiased method to process and quantitatively analyze microscopy data. Here, we describe two pipelines, run by CellProfiler software, called OrganelleProfiler and OrganelleContentProfiler. These pipelines were used on confocal images of endothelial colony forming cells (ECFCs), which contain unique secretory organelles called Weibel-Palade bodies (WPBs), and on early endosomes in ECFCs and human embryonic kidney 293T (HEK293T) cells. Results show that the pipelines can quantify the cell count, size, organelle count, organelle size, shape, relation to cells and nuclei, and distance to these objects in both endothelial and HEK293T cells. Additionally, the pipelines were used to measure the reduction in WPB size after disruption of the Golgi and to quantify the perinuclear clustering of WPBs after triggering of cAMP-mediated signaling pathways in ECFCs. Furthermore, the pipeline is able to quantify secondary signals located in or on the organelle or in the cytoplasm, such as the small WPB GTPase Rab27A. Cell profiler measurements were checked for validity using Fiji. To conclude, these pipelines provide a powerful, high-processing quantitative tool for the characterization of multiple cell and organelle types. These pipelines are freely available and easily editable for use on different cell types or organelles.

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Lempereur, Sylvain, Arnim Jenett, Elodie Machado, Ignacio Arganda-Carreras, Matthieu Simion, Pierre Affaticati, Jean-Stéphane Joly, and Hugues Talbot. "Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains." Mathematical Morphology - Theory and Applications 4, no. 1 (July 27, 2020): 31–45. http://dx.doi.org/10.1515/mathm-2020-0100.

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AbstractTissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible.Here, we propose a sample-dependent method for contrast correction. It relies on segmenting the sample, and estimating sample depth isosurfaces that serve as reference for the correction. We segment the brain white matter of zebrafish larvae. We show that this correction allows a better stitching of opposite sides of each larva, in order to image the entire larva with a high signal-to-noise ratio throughout. We also show that our proposed contrast correction method makes it possible to better recognize the deep structures of the brain by comparing manual vs. automated segmentations. This is expected to improve image observations and analyses in high-content methods where signal loss in the samples is significant.

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Ferron, P. J., S. Huet, K. Hogeveen, V. Fessard, and L. Le Hegarat Anses. "Effects of food chemical contaminants in human HepaRG and Caco-2 cells using an automated microscopy and high content analysis based approach." Toxicology Letters 238, no. 2 (October 2015): S86—S87. http://dx.doi.org/10.1016/j.toxlet.2015.08.290.

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Yip, Kenneth W., Michael Cuddy, Clemencia Pinilla, Marc Giulanotti, Susanne Heynen-Genel, Shu-Ichi Matsuzawa, and John C. Reed. "A High-Content Screening (HCS) Assay for the Identification of Chemical Inducers of PML Oncogenic Domains (PODs)." Journal of Biomolecular Screening 16, no. 2 (January 13, 2011): 251–58. http://dx.doi.org/10.1177/1087057110394181.

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PML is a multi-functional protein with roles in tumor suppression and host defense against viruses. When active, PML localizes to subnuclear structures named PML oncogenic domains (PODs) or PML nuclear bodies (PML-NBs), whereas inactive PML is located diffusely throughout the nucleus of cells. The objective of the current study was to develop a high content screening (HCS) assay for the identification of chemical activators of PML. We describe methods for automated analysis of POD formation using high throughput microscopy (HTM) to localize PML immunofluorescence in conjunction with image analysis software for POD quantification. Using this HCS assay in 384 well format, we performed pilot screens of a small synthetic chemical library and mixture-based combinatorial libraries, demonstrating the robust performance of the assay. HCS counter-screening assays were also developed for hit characterization, based on immunofluorescence analyses of the subcellular location of phosphorylated H2AX or phosphorylated CHK1, which increase in a punctate nuclear pattern in response to DNA damage. Thus, the HCS assay devised here represents a high throughput screen that can be utilized to discover POD-inducing compounds that may restore the tumor suppressor activity of PML in cancers or possibly promote anti-viral states.

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George, Thaddeus, Anne Spurkland, Vibeke Sundvold-Gjerstadt, Brandon Burbach, Yoji Shimizu, Brian Hall, and Sherree Friend. "Quantitative analysis of immune synapse formation using imaging flow cytometry. (130.18)." Journal of Immunology 184, no. 1_Supplement (April 1, 2010): 130.18. http://dx.doi.org/10.4049/jimmunol.184.supp.130.18.

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Abstract Antigen-specific immune responses are initiated via direct cellular contact between an antigen presenting cell (APC) and an effector cell. Early events in the interaction between these two cells involve reorganization of the actin cytoskeleton and recruitment of adhesive and signaling molecules to the immunological synapse (IS), which ultimately results in effector cell activation. Because manual image acquisition and quantitative image analysis are time consuming processes, microscopic analyses of protein recruitment to the IS have remained either qualitative or statistically limited, despite the rich information content inherent in high resolution digital images. Here we describe an objective, statistically robust microscopy-based method for quantifying molecular recruitment to the IS using multispectral imaging flow cytometry, which enables quantitative image analysis of large populations of automatically collected images. The analysis technique uses morphology-based features to identify conjugates, followed by measurement of fluorescence specifically at the conjugate junction. Two models are described, including: 1) actin recruitment to the IS following T cell activation by artificial APC coated with anti-CD3; 2) antigen-specific recruitment of ADAP to the IS within TCR transgenic T cells. These data outline an objective and statistically robust method to rapidly quantify molecular recruitment to the immune synapse using automated high speed microscopy.

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Aggarwal, Sonam, Sheifali Gupta, Deepali Gupta, Yonis Gulzar, Sapna Juneja, Ali A. Alwan, and Ali Nauman. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images." Sustainability 15, no. 2 (January 16, 2023): 1695. http://dx.doi.org/10.3390/su15021695.

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Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.

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Ramm, Susanne, Robert Vary, Twishi Gulati, Jennii Luu, Karla J. Cowley, Michael S. Janes, Nicholas Radio, and Kaylene J. Simpson. "High-Throughput Live and Fixed Cell Imaging Method to Screen Matrigel-Embedded Organoids." Organoids 2, no. 1 (December 24, 2022): 1–19. http://dx.doi.org/10.3390/organoids2010001.

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Technical advances in microscopy and automation have enabled image-based phenotypic screening of spheroids and organoids to become increasingly high throughput and high content at the same time. In particular, matrix-embedded 3D structures can recapitulate many aspects of parent (e.g., patient) tissues. Live-cell imaging of growing structures allows tremendous insight into population heterogeneity during drug treatment. However, screening for targeted markers and more detailed morphological analyses typically require fixation of 3D structures, and standard formaldehyde (FA) incubation conditions can dissolve collagen-based extracellular matrices such as Matrigel. The dislocation and clumping of the spheroids make image-based segmentation very difficult and the tracking of structures from the live cell stage to their fixed cell location virtually impossible. In this method, we present a fixation and staining protocol that is gentle enough to maintain 3D structures exactly in their live-cell location and does not alter their morphology. This opens up analytical strategies that connect the spheroid’s growth kinetics and heterogeneity of treatment responses with the more targeted fixed cell stains. Furthermore, we optimized the automated seeding and imaging of spheroids so that screening and phenotypic characterization can be performed in high-throughput at either low or high magnification and yield the same result, independent of the microscope used.

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Pandey, Gunjan, Jens Westhoff, Franz Schaefer, and Jochen Gehrig. "A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model." International Journal of Molecular Sciences 20, no. 6 (March 14, 2019): 1290. http://dx.doi.org/10.3390/ijms20061290.

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The zebrafish is being increasingly used in biomedical research and drug discovery to conduct large-scale compound screening. However, there is a lack of accessible methodologies to enable automated imaging and scoring of tissue-specific phenotypes at enhanced resolution. Here, we present the development of an automated imaging pipeline to identify chemical modifiers of glomerular cyst formation in a zebrafish model for human cystic kidney disease. Morpholino-mediated knockdown of intraflagellar transport protein Ift172 in Tg(wt1b:EGFP) embryos was used to induce large glomerular cysts representing a robustly scorable phenotypic readout. Compound-treated embryos were consistently aligned within the cavities of agarose-filled microplates. By interfacing feature detection algorithms with automated microscopy, a smart imaging workflow for detection, centring and zooming in on regions of interests was established, which enabled the automated capturing of standardised higher resolution datasets of pronephric areas. High-content screening datasets were processed and analysed using custom-developed heuristic algorithms implemented in common open-source image analysis software. The workflow enables highly efficient profiling of entire compound libraries and scoring of kidney-specific morphological phenotypes in thousands of zebrafish embryos. The demonstrated toolset covers all the aspects of a complex whole organism screening assay and can be adapted to other organs, specimens or applications.

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Pelicci, Simone, Laura Furia, Pier Giuseppe Pelicci, and Mario Faretta. "From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction." International Journal of Molecular Sciences 25, no. 9 (April 25, 2024): 4672. http://dx.doi.org/10.3390/ijms25094672.

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Surpassing the diffraction barrier revolutionized modern fluorescence microscopy. However, intrinsic limitations in statistical sampling, the number of simultaneously analyzable channels, hardware requirements, and sample preparation procedures still represent an obstacle to its widespread diffusion in applicative biomedical research. Here, we present a novel pipeline based on automated multimodal microscopy and super-resolution techniques employing easily available materials and instruments and completed with open-source image-analysis software developed in our laboratory. The results show the potential impact of single-molecule localization microscopy (SMLM) on the study of biomolecules’ interactions and the localization of macromolecular complexes. As a demonstrative application, we explored the basis of p53-53BP1 interactions, showing the formation of a putative macromolecular complex between the two proteins and the basal transcription machinery in situ, thus providing visual proof of the direct role of 53BP1 in sustaining p53 transactivation function. Moreover, high-content SMLM provided evidence of the presence of a 53BP1 complex on the cell cytoskeleton and in the mitochondrial space, thus suggesting the existence of novel alternative 53BP1 functions to support p53 activity.

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Furia, Laura, Simone Pelicci, Mirco Scanarini, Pier Giuseppe Pelicci, and Mario Faretta. "From Double-Strand Break Recognition to Cell-Cycle Checkpoint Activation: High Content and Resolution Image Cytometry Unmasks 53BP1 Multiple Roles in DNA Damage Response and p53 Action." International Journal of Molecular Sciences 23, no. 17 (September 5, 2022): 10193. http://dx.doi.org/10.3390/ijms231710193.

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53BP1 protein has been isolated in-vitro as a putative p53 interactor. From the discovery of its engagement in the DNA-Damage Response (DDR), its role in sustaining the activity of the p53-regulated transcriptional program has been frequently under-evaluated, even in the case of a specific response to numerous DNA Double-Strand Breaks (DSBs), i.e., exposure to ionizing radiation. The localization of 53BP1 protein constitutes a key to decipher the network of activities exerted in response to stress. We present here an automated-microscopy for image cytometry protocol to analyze the evolution of the DDR, and to demonstrate how 53BP1 moved from damaged sites, where the well-known interaction with the DSB marker γH2A.X takes place, to nucleoplasm, interacting with p53, and enhancing the transcriptional regulation of the guardian of the genome protein. Molecular interactions have been quantitatively described and spatiotemporally localized at the highest spatial resolution by a simultaneous analysis of the impairment of the cell-cycle progression. Thanks to the high statistical sampling of the presented protocol, we provide a detailed quantitative description of the molecular events following the DSBs formation. Single-Molecule Localization Microscopy (SMLM) Analysis finally confirmed the p53–53BP1 interaction on the tens of nanometers scale during the distinct phases of the response.

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Jayamani, Elamparithi, Rajmohan Rajamuthiah, Jonah Larkins-Ford, Beth Burgwyn Fuchs, Annie L. Conery, Andreas Vilcinskas, Frederick M. Ausubel, and Eleftherios Mylonakis. "Insect-Derived Cecropins Display Activity against Acinetobacter baumannii in a Whole-Animal High-Throughput Caenorhabditis elegans Model." Antimicrobial Agents and Chemotherapy 59, no. 3 (January 12, 2015): 1728–37. http://dx.doi.org/10.1128/aac.04198-14.

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ABSTRACTThe rise of multidrug-resistantAcinetobacter baumanniiand a concomitant decrease in antibiotic treatment options warrants a search for new classes of antibacterial agents. We have found thatA. baumanniiis pathogenic and lethal to the model host organismCaenorhabditis elegansand have exploited this phenomenon to develop an automated, high-throughput, high-content screening assay in liquid culture that can be used to identify novel antibiotics effective againstA. baumannii. The screening assay involves coincubatingC. eleganswithA. baumanniiin 384-well plates containing potential antibacterial compounds. At the end of the incubation period, worms are stained with a dye that stains only dead animals, and images are acquired using automated microscopy and then analyzed using an automated image analysis program. This robust assay yields a Z′ factor consistently greater than 0.7. In a pilot experiment to test the efficacy of the assay, we screened a small custom library of synthetic antimicrobial peptides (AMPs) that were synthesized using publicly available sequence data and/or transcriptomic data from immune-challenged insects. We identified cecropin A and 14 other cecropin or cecropin-like peptides that were able to enhanceC. eleganssurvival in the presence ofA. baumannii. Interestingly, one particular hit, BR003-cecropin A, a cationic peptide synthesized by the mosquitoAedes aegypti, showed antibiotic activity against a panel of Gram-negative bacteria and exhibited a low MIC (5 μg/ml) againstA. baumannii. BR003-cecropin A causes membrane permeability inA. baumannii, which could be the underlying mechanism of its lethality.

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Jorge-Oliva, Marta, Jan R. T. van Weering, and Wiep Scheper. "Structurally and Morphologically Distinct Pathological Tau Assemblies Differentially Affect GVB Accumulation." International Journal of Molecular Sciences 24, no. 13 (June 29, 2023): 10865. http://dx.doi.org/10.3390/ijms241310865.

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Tau aggregation is central to the pathogenesis of a large group of neurodegenerative diseases termed tauopathies, but it is still unclear in which way neurons respond to tau pathology and how tau accumulation leads to neurodegeneration. A striking neuron-specific response to tau pathology is presented by granulovacuolar degeneration bodies (GVBs), lysosomal structures that accumulate specific cargo in a dense core. Here we employed different tau aggregation models in primary neurons to investigate which properties of pathological tau assemblies affect GVB accumulation using a combination of confocal microscopy, transmission electron microscopy, and quantitative automated high-content microscopy. Employing GFP-tagged and untagged tau variants that spontaneously form intraneuronal aggregates, we induced pathological tau assemblies with a distinct subcellular localization, morphology, and ultrastructure depending on the presence or absence of the GFP tag. The quantification of the GVB load in the different models showed that an increased GVB accumulation is associated with the untagged tau aggregation model, characterized by shorter and more randomly distributed tau filaments in the neuronal soma. Our data indicate that tau aggregate structure and/or subcellular localization may be key determinants of GVB accumulation.

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Mergenthaler, Philipp, Santosh Hariharan, James M. Pemberton, Corey Lourenco, Linda Z. Penn, and David W. Andrews. "Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning." PLOS Computational Biology 17, no. 2 (February 22, 2021): e1008630. http://dx.doi.org/10.1371/journal.pcbi.1008630.

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Phenotypic profiling of large three-dimensional microscopy data sets has not been widely adopted due to the challenges posed by cell segmentation and feature selection. The computational demands of automated processing further limit analysis of hard-to-segment images such as of neurons and organoids. Here we describe a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and advanced data visualization. We demonstrate the analysis potential on complex 3D images by investigating the phenotypic alterations of: neurons in response to apoptosis-inducing treatments and morphogenesis for oncogene-expressing human mammary gland acinar organoids. Our novel implementation of image analysis algorithms called Phindr3D allowed rapid implementation of data-driven voxel-based feature learning into 3D high content analysis (HCA) operations and constitutes a major practical advance as the computed assignments represent the biology while preserving the heterogeneity of the underlying data. Phindr3D is provided as Matlab code and as a stand-alone program (https://github.com/DWALab/Phindr3D).

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Kraus, Annalena, Victoria Rose, René Krüger, George Sarau, Lasse Kling, Mario Schiffer, Silke Christiansen, and Janina Müller-Deile. "Characterizing Intraindividual Podocyte Morphology In Vitro with Different Innovative Microscopic and Spectroscopic Techniques." Cells 12, no. 9 (April 25, 2023): 1245. http://dx.doi.org/10.3390/cells12091245.

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Podocytes are critical components of the glomerular filtration barrier, sitting on the outside of the glomerular basement membrane. Primary and secondary foot processes are characteristic for podocytes, but cell processes that develop in culture were not studied much in the past. Moreover, protocols for diverse visualization methods mostly can only be used for one technique, due to differences in fixation, drying and handling. However, we detected by single-cell RNA sequencing (scRNAseq) analysis that cells reveal high variability in genes involved in cell type-specific morphology, even within one cell culture dish, highlighting the need for a compatible protocol that allows measuring the same cell with different methods. Here, we developed a new serial and correlative approach by using a combination of a wide variety of microscopic and spectroscopic techniques in the same cell for a better understanding of podocyte morphology. In detail, the protocol allowed for the sequential analysis of identical cells with light microscopy (LM), Raman spectroscopy, scanning electron microscopy (SEM) and atomic force microscopy (AFM). Skipping the fixation and drying process, the protocol was also compatible with scanning ion-conductance microscopy (SICM), allowing the determination of podocyte surface topography of nanometer-range in living cells. With the help of nanoGPS Oxyo®, tracking concordant regions of interest of untreated podocytes and podocytes stressed with TGF-β were analyzed with LM, SEM, Raman spectroscopy, AFM and SICM, and revealed significant morphological alterations, including retraction of podocyte process, changes in cell surface morphology and loss of cell-cell contacts, as well as variations in lipid and protein content in TGF-β treated cells. The combination of these consecutive techniques on the same cells provides a comprehensive understanding of podocyte morphology. Additionally, the results can also be used to train automated intelligence networks to predict various outcomes related to podocyte injury in the future.

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